5 (Afternoon Session - November 15, 1999)








13 Medicare Coverage Advisory Committee

14 Laboratory & Diagnostic Services Panel






20 November 15 and 16, 1999


22 Sheraton Inner Harbor Hotel

23 Baltimore, Maryland




1 Panelists

2 Chairperson

John H. Ferguson, M.D.



4 Robert L. Murray, M.D.

5 Voting Members

David N. Sundwall, M.D.

6 George G. Klee, M.D., Ph.D.

Paul D. Mintz, M.D.

7 Richard J. Hausner, M.D.

Mary E. Kass, M.D.

8 Cheryl J. Kraft, M.S.

Neysa R. Simmers, M.B.A.

9 John J.S. Brooks, M.D.

Paul M. Fischer, M.D.


Temporary Voting Member

11 Kathy Helzlsouer, M.D.

12 Consumer Representative

Kathryn A. Snow, M.H.A.


Industry Representative

14 James (Rod) Barnes, M.B.A.

15 Carrier Medical Director

Bryan Loy, M.D., M.B.A.


Director of Coverage, HCFA

17 Grant Bagley, M.D.

18 Executive Secretary

Katherine Tillman, R.N., M.S.











2 Welcome and Conflict of Interest Statement

Katherine Tillman, R.N., M.A. 5


Opening Remarks & Overview

4 Grant Bagley, M.D. 10

5 Chairman's Remarks

John H. Ferguson, M.D. 28


Brian E. Harvey, M.D., Ph.D. 30


Open Public Comments & Scheduled Commentaries

8 Frank J. Kiesner, J.D. 48

Larry Weisenthal, M.D. 57

9 Randy Stein 92

Richard H. Nalick, M.D. 99

10 William R. Grace, M.D. 108

John P. Fruehauf, M.D., Ph.D. 110

11 James Orr, M.D. 127

Robert M. Hoffman, Ph.D. 131

12 Andrew G. Bosanquet, Ph.D. 136

David Alberts, M.D. 142

13 Robert Nagourney, M.D. 147

David Kern, M.D. 159

14 Daniel F. Hayes, M.D. 168

Bryan Loy, M.D. 178






Open Public Comments & Scheduled Commentaries

18 Edward Sausville, M.D. 201

Harry Handelsman, D.O. 227

19 Harry Burke, M.D., Ph.D. 234

Mitchell I. Burken, M.D. 262


Open Committee Discussion 304


Day One Adjournment 330








3 Opening Remarks - Introduction 336

4 Open Committee Discussion 337

5 Motions, Discussions and

Recommendations 425


Adjournment 487






















2 (The meeting was called to order at

3 1:16 p.m., Monday, November 15, 1999.

4 DR. FERGUSON: Dr. Sausville?

5 DR. SAUSVILLE: Good afternoon, all.

6 And if I could have the first overhead, this says

7 who I am, and the general topic that I hope

8 you're interested in hearing about this

9 afternoon. Anyway, my task this afternoon is to

10 provide an overview, at least from the

11 perspective of the preclinical therapeutics

12 development program of NCI of antitumor drug

13 sensitivity testing. And I will approach this,

14 therefore, from the standpoint of one who uses

15 tests like this, and indeed, in some cases

16 actually tests that have been used for this

17 purpose for the preclinical selection of drugs

18 for more detailed evaluation, as well as from the

19 perspective of an oncologist who has occasionally

20 thought about using these tests in the treatment

21 of patients. Next.

22 So the basis for this issue in cancer

23 derives directly from the infectious diseases

24 experience, wherein a number of different disease

25 categories, such as tuberculosis, where it's well


1 established that one has to establish that a

2 particular patient's infected bacillus is

3 sensitive to the agents, and a number of

4 non-tuberculosis indications, which would

5 include, for example, pyelonephritis or

6 endocarditis, where it is well established from

7 the standpoint of standard medical practice that

8 such sensitivity tests are that valuable. Next.

9 The assays as applied to cancer ideally

10 would have 95 percent sensitivity and

11 specificity, and short of that goal, would

12 hopefully be better in predicting outcome than

13 the empirical choice of the physician. And the

14 essence of the question from an oncological

15 standpoint, therefore, is whether a particular

16 test conveys information over and above what is

17 implicit in the histologic diagnosis of a

18 patient's tumor. Ideally the test would be

19 biased in favor of detecting sensitivity rather

20 than resisting, for this reason, and ultimately,

21 these tests should be able to demonstrate an

22 impact on ultimate outcome, as opposed to simply

23 response, since in oncology, good outcome begins

24 with the response, it does not end with a

25 response. One ultimately has to have evidence of


1 tangible clinical benefit that changes outcome.

2 Next.

3 So among the specific assays that

4 through the years have been utilized include the

5 by now classical Hamburger Salmon clonogenic

6 assay, wherein tumors that were biopsied for

7 example, were disaggregated, plated in agarose or

8 other solid media after relatively brief

9 exposures to drug, and ultimately colonies

10 counted in 14 days. There have been

11 modifications to this, most notably the capillary

12 tube modification used by Von Hoff and

13 colleagues, and it seems to increase the number

14 of patients for which valuable data are

15 obtained. Modifications of this also include

16 radionuclide based assays, in which radioactive

17 thymidine is added after three days and thus,

18 although it is a soft agar base, one can obtain

19 information after shorter periods of time. And

20 there are also non-agar based assays assessing

21 radionuclide uptake in mass culture. Next.

22 Technical problems with clonogenic

23 assays include a number of artifacts intrinsic to

24 the practice of the assay, including clumping of

25 tumor cells, the potential of growth perturbation


1 from manipulation of potential clonogenic cells,

2 reduced nutrient uptake from nonclonogenic cells,

3 with increase in the size of colonies that grow

4 out in the treated cells. Counting evaluations

5 with a potential large coefficient of variation,

6 and poor cloning efficiencies. And a major

7 limitation in the widespread use of this

8 technique relates to the fact that in many

9 instances, the majority of the specimens are not

10 actually valuable, and there is the inability of

11 this type of assay to score small numbers of

12 resistant cells, which in a clinical scenario are

13 thought to translate new ultimately resistance to

14 therapy, of the sort that is manifest by the

15 subsequent relapse of a patient with drug

16 resistant tumor. Next.

17 In various reviews, actually extending

18 from the initial use of this technique into the

19 early '80s, the cumulative experience is that a

20 relatively small fraction of patients actually

21 have colony growth. And the data that is

22 tabulated here is contained in the references

23 that were indicated. But also, there is the

24 finding that the tests are clearly better at

25 predicting negative or resistant assays, than


1 sensitive assays, such that for example, if one

2 looks at those specimens that were sensitive in

3 vitro as opposed to sensitive in vivo, we have a

4 60 percent true positive, with a range of 47 to

5 71 percent. In contrast to those specimens that

6 were resistant in vitro and resistant in vivo,

7 where there was, as you can see, a 97 percent

8 true negative information. Next.

9 This led to a so-called perspective

10 evaluation of chemotherapy selection utilizing a

11 clonogenic assay, as opposed to the choice of a

12 clinician. And again, this was published by von

13 Hoff and colleagues in 1990 in the Journal of the

14 National Cancer Institute. And in the 133

15 patients randomized in a single agent therapy, of

16 those where the therapy was assigned by a

17 clinician, one had one partial response, and in

18 19 of 68 that were possible to have an assay

19 directed assignment, there were four partial

20 responses. Certainly there was no evidence that

21 this was statistically different and one

22 concluded, or this article concluded, that what

23 one might conceive of potentially a somewhat

24 improved response rate, did not translate into

25 any noticeable effect on survival. And again,


1 approximately a third of the tests could not be

2 evaluated, and there were clearly no evidence of

3 survival in patients either treated according to

4 that which was recommended by the physician, or

5 all patients that were compared versus the test

6 population. Next.

7 Other specific assays which have come

8 to the fore in an effort to meet some of the

9 clear difficulties in the widespread use of the

10 clonogenic assay include the so-called

11 differential standing cytoxicity assay, or DiSC

12 assay, pioneered by Weisenthal and colleagues.

13 And here, one is essentially assessing the effect

14 on whether or not cells remain alive after short

15 periods of culture after exposure to a drug.

16 Thus, either marrow, buffy coat or a tumor

17 suspension after disaggregation, can be treated

18 with drug for anywhere from one hour to four

19 days. Interestingly, the quantification was

20 aided by the addition of so-called duck red blood

21 cells, which are easily distinguishable

22 microscopically, a dye added, and then after a

23 cytospin, one can either assess the dead cells

24 per duck red blood cells, or live cells per duck

25 red blood cells, based on the differential


1 staining of live and dead cells with either

2 fast-green, which stains dead cells, or HD, which

3 stains live cells.

4 Over variants of this approach include

5 the so-called MTT assay, which is a dye that

6 depends for its coloration properties as to

7 whether or not it is reduced by living

8 mitochondria, or a fluorescein assay, where live

9 cells take up a dye, hydrolyze to it in a point

10 that is detected by a change in fluorescence.

11 But all of these techniques, again, don't then

12 depend on the growth out of clonogenic cells, but

13 rather allow a relatively short term exposure to

14 the drug to define whether there is an effect on

15 the viability of the cells. Next.

16 When this assay was, and again, this is

17 in reference to the DiSC assay, was applied

18 initially to hematologic neoplasms, there was

19 clear evidence that there was increased cell

20 survival, that is to say resistance in patients

21 who ultimately were not responsive to

22 chemotherapy that was assigned on the basis of a

23 knowledge of the tests. So in that respect, the

24 assay was certainly suggestive that it might

25 eventually correlate with clinical outcome. And


1 in addition, there was a fairly good

2 correspondence, again, with delineation of true

3 positives and true negatives by this assay.

4 Next.

5 When this assay was applied to the

6 somewhat more difficult clinical category of

7 patients with lung cancer, here in an initial

8 study with non-small cell assay, the DiSC assay

9 was performed assessing sensitivity to ten drugs,

10 treating with a regimen that ultimately

11 incorporated the three most sensitive agents. In

12 this series of 25 patients, there was a 36

13 percent partial response rate with a median

14 duration of 6.5 months and with the, if you want

15 to read, it looks to be responders and a median,

16 or I should say a median survival of about seven

17 months, with an overall of about 12 months.

18 There was clearly a threefold lower assay

19 survival. That is to say, people with greater

20 cell kill in responders versus non-responders.

21 However, these authors concluded that outcome as

22 measured by response rate and survival is within

23 the range reported by the literature, that is to

24 say, even though you can detect this difference,

25 the issue of whether or not it ultimately caused


1 a different outcome that might be afforded by

2 treating with drugs that would be available from

3 the literature and without knowing the patient's

4 histologic diagnosis was not apparent. In

5 addition, some drugs clearly had a much greater

6 discordance in the predictive value of the test.

7 Thus for example, 5 fluorouracil did

8 not seem to have any ultimate value in its

9 performance, and on the other hand, etoposide,

10 behavior to etoposide, was essentially predictive

11 of the behavior of all of the of the drugs. And

12 actually from a scientific perspective, we now

13 recognize that since many of these agents act by

14 inducing apoptosis, this actual result

15 retrospectively, is not that surprising.

16 Interestingly, this paper also

17 introduced the concept of so-called extreme drug

18 resistance. That is to say, you can define

19 patients who had greater than one or more

20 standard deviations resistance than the median in

21 the population, and these patients essentially

22 had zero percent response to any of the agents.

23 Next.

24 This assay was also applied in a study

25 that was recently published from the NIH, and


1 attempted to individualize chemotherapy for

2 patients with non-small cell lung cancer. And

3 from a population of 165 study patients, 21

4 received DiSC based regimens, and these had a 9

5 percent partial response rate. Whereas, 69

6 patients received empiric treatment with

7 etoposide and cisplatin; these had a 14 percent

8 partial response rate. And ultimately, the

9 survival of in vitro best regimen was comparable

10 to what one would have expected from the

11 empirically chosen chemotherapy.

12 Interestingly, this study also revealed

13 an issue that also has to come up in any test in

14 which there is a second or subsequent procedure

15 to obtain tissue, in that the survival of

16 patients who had any in vitro test was actually

17 worse than those without, and this implies

18 potentially that those people that had a

19 sufficient volume of tumor to have the tests had

20 an intrinsically less survival than those that

21 did not. Next.

22 And the last clinical study that I'll

23 touch on also emanated from the NCI and was

24 published in 1997. This attempted to use the

25 DiSC assay in limited stage small cell, and here


1 we turn the somewhat, and consider the use of the

2 test in what may be considered in its most

3 favorable scenario, because this disease which is

4 traditionally, and now actually standardly

5 treated with the combination of radiation therapy

6 and chemotherapy, would potentially treat

7 empirically with a regimen known to produce a

8 high level of response, and then come back after

9 finishing consolidation with radiation

10 sensitivity with either a chosen regimen based on

11 the in vitro sensitivity or a standard approach

12 using an additional three drugs that the patient

13 had not seen previously that would be regarded as

14 standard or part of the standard care of patients

15 with small cell lung cancer.

16 And in this study, there was actually a

17 trend towards somewhat improved survival in

18 patients who could actually receive the in vitro

19 best regimen, but it certainly was just a trend.

20 And most interestingly, of the 54 patients that

21 were entered, the minority of the patients could

22 actually be successfully biopsied in this very,

23 shall we say well coordinated, well resourced

24 clinical trials scenario. Next.

25 So in terms of summarizing what I list


1 here as my own disinterested perspective on

2 whether or not chemosensitivity testing is what

3 one would might consider to be ready to prime

4 time in widespread use, I would offer that from

5 my perspective, no method has emerged as a

6 quote-unquote gold standard, owing to

7 methodologic variation and the definition of what

8 constitutes resistance or sensitive tests. The

9 unfortunate fact that one cannot get reliable

10 data from most if not many patients. And in the

11 few completed prospective or randomized trials,

12 there is little assurance that ultimately there

13 is a difference effected by the test.

14 What we ultimately need if tests of

15 this nature are to be potentially useful, is

16 probably better drugs, because in point of fact,

17 since most of the drugs are unfortunately

18 inactive in many of the diseases in which these

19 tests would be used, knowing that they won't work

20 is not actually terribly valuable.

21 We need a method that is applicable to

22 all specimens obtained in real time with the

23 diagnostic specimen; that is to say, to require a

24 second test, or second procedure, in order to

25 obtain the specimen, inevitably indicating or


1 introduces potential biases in studies related to

2 those patients who could withstand or undergo

3 these procedures, as well as of course, making

4 the test, the performance of the test more costly

5 than one might potentially desire.

6 But on the other hand, I think the

7 future holds potentially with newer approaches,

8 including gene expression arrays, serial analysis

9 of gene expression, there may be better, and

10 hopefully more useful techniques to assess this

11 in the future. But whatever the test, be it some

12 permutation of a currently available test, or one

13 of the newer methodologies here, its ultimate

14 value should be established in prospective

15 randomized trials where one uses the

16 diagnostically guided as opposed to the empirical

17 treatment before assessing whether or not it is

18 openly valuable.

19 And I thank you for your attention.

20 DR. FERGUSON: Thank you, Dr.

21 Sausville. I think you've gone in shorter time

22 than even I asked for, and so I'll open for a

23 question or comment. Yes, Dr. Hoffman?

24 DR. HOFFMAN: Yes. I would like to ask

25 Dr. Sausville his opinion about the assays that


1 were discussed this morning, based on three

2 dimensional culture and other new third

3 generation techniques that address these problems

4 and have shown to be able to assess greater than

5 95 percent of the patients' specimens, have shown

6 survival benefit, have shown very high

7 correlation to response. I would like Dr.

8 Sausville's comments on this morning's talks.

9 DR. SAUSVILLE: Again, I wasn't here

10 this morning, and indeed, my brief was not to

11 comment on specific assays from this morning's

12 activities, but to offer an overview of problems

13 in the field in general. And I would certainly

14 say that if the tests that were proposed this

15 morning seem of interest, the real question is

16 have they been evaluated in prospective

17 randomized studies. Because unless they have

18 not, or I should say until they have, one, and

19 since as far as I'm aware, they have not, it

20 would be, I think premature to conclude that they

21 are, therefore, of widespread general use.

22 DR. FERGUSON: Dr. Weisenthal?

23 DR. WEISENTHAL: Now would be as good a

24 time as any to address the issue of the

25 requirement of prospective randomized trials for


1 acceptance of this technology. I think it's a

2 very important issue, several speakers have

3 raised it, and the issue is this: Should these

4 tests be used in clinical medicine until it has

5 been established in prospective randomized trials

6 that patients treated on the basis of assay

7 result have a superior therapeutic outcome to

8 patients treated without the assay result? The

9 cop-out way to answer this, which I'm not, this

10 is not my answer to it, but what I could say if I

11 wanted to cop out, and it's perfectly valid, is

12 that never has the bar been raised so high for

13 any diagnostic test in history.

14 Dr. Sausville began his talk by

15 pointing out bacterial cultures done in

16 sensitivity testing, including one of his

17 examples was serum bactericidal testing. Serum

18 bactericidal testing, for those of you who may

19 know it, is something that Medicare does

20 reimburse for. It's very controversial, it's

21 much more controversial actually than cell

22 culture drug resistance testing. The performance

23 characteristics are certainly inferior based on

24 sensitivity and specificity. And furthermore,

25 there has certainly never been a prospective


1 randomized trial showing survival advantage or

2 therapeutic outcome, you know, higher cure rate

3 or anything, whether you use serum bactericidal

4 testing or not, or any other form of antibiotic

5 sensitivity test.

6 We're talking about laboratory tests,

7 not a therapeutic agent, and I think that one

8 would be advised, at least first of all, to judge

9 them on the basis of the way that other

10 laboratory tests have been judged, and that is,

11 do they have acceptable accuracy, sensitivity and

12 specificity?

13 However, moving on to the question of

14 the prospective randomized trial, all of us, no

15 one more than those of us who have been working

16 in this field for 20 years, would love to see

17 prospective randomized trials, physician's choice

18 therapy versus assay directed therapy. This has

19 been the Holy Grail. I hope before I die, I will

20 be able to participate in such a trial. I

21 mentioned earlier, the fact is that there have

22 been a lot of energetic, very talented people,

23 that have devoted their careers to this, and the

24 best example is Dr. Dan von Hoff, who is the most

25 energetic. He and I were clinical oncology


1 trainees together at the National Cancer

2 Institute. We both started working in this field

3 in the same lab at the same time. And Dan had --

4 you know, my CV lists about 50 publications;

5 Dan's CV is probably closing in on 2,000. And

6 he's organized more prospective randomized trials

7 and things like that. He was unable to

8 successfully get a study initiated and patients

9 accrued, and completed. I have devoted enormous

10 amounts of effort to getting those trial done,

11 and for one reason or another, they didn't accrue

12 patients, and things like this.

13 I want to point out that in Medicare,

14 Medicare has a problem, and the problem is not in

15 the year 2000, the years between 2010 and 2015.

16 The budgetary crunch in Medicare is going to come

17 in 2010 and 2015 when those of use who are now 52

18 years old are going to be 60, 65, 75 years old,

19 and we're going to be getting cancer. What's

20 going to happen over the next ten years is that

21 there's going to be an ever increasing array of

22 partially effective and very expensive cancer

23 treatments. We're seeing that now. Drugs are

24 being approved at a very rapid pace. We don't

25 have a clue how to use them.


1 We brought up the idea about using the

2 test as a litmus test, like should you pay for

3 the therapy. Well, the only way that you're

4 going to be able to ever use the test as a litmus

5 test is if you do the prospective randomized.

6 And I would submit to you that the way to get the

7 prospective randomized trials done is as

8 follows: Look at the data that you heard about

9 this morning. Surely, you must be convinced that

10 there is a germ of truth in this. You know,

11 there is a consistent, overwhelming, and I think

12 that study after study is showing that these

13 tests do predict, they can identify the

14 difference between good treatments and bad

15 treatments. So it is not much of a leap of faith

16 to say that if only someone could do the trials,

17 then there's a good chance that they would turn

18 out to be positive, and if they do turn out to be

19 positive, by the year 2010, we will have a

20 wonderful tool to triage therapy, to triage

21 patients, right at the time when Medicare most

22 needs it, when the budgetary crunch comes, when

23 we've got all these expensive cancer therapies.

24 You know, I gave you the example of the five

25 patients treated with the bone marrow transplants


1 at $200,000 a patient, who did not benefit from

2 that, who then got an assay and had a great

3 result. What if they had gotten the assay? It

4 has the potential to be enormously cost

5 effective. But the only way that it will be used

6 in that way is if you do the trials, but the only

7 way -- it's a catch 22 -- the only trials will

8 ever get done -- I personally believe that if

9 Medicare approves this, it will be the shot heard

10 round the world, Swann, ECOG, CLGB, they will be

11 lining up to do trials. You guys, you know, come

12 back and maybe approve it conditionally, come

13 back in five years and see what's happening.

14 DR. BAGLEY: Well, you know, today --

15 it brings up an interesting point, and I think

16 you bring up the comment that, you know, never

17 has the bar been this high. Well, I would take

18 exception to that. I think the bar is not any

19 higher for this than for anything else that we

20 are currently looking at. And it is not

21 something that we are not used to hearing for

22 other things too, and that is, gee, we're paying

23 for things that were never subjected to any

24 scrutiny, so why should we subject it to

25 scrutiny. I mean, that's -- we hear it all the


1 time, and that's just not going to work in this

2 day of evidence based medicine. And I'll tell

3 you because, you know, how much it costs isn't an

4 issue that we're really here to talk about today.

5 Because, you know, two years HCFA reorganized

6 and changed the whole focus of coverage, moved

7 the coverage office away from that part of HCFA

8 that pays for things and looks at program

9 integrity, and moved it into the place at HCFA

10 that looks at quality and clinical standards.

11 And that's exactly the focus we ought

12 to be doing because, you know, what it boils down

13 to is, it's not just why not pay for it, it

14 doesn't cost that much, or it might save a little

15 money. But it's let's pay for it because it's

16 the right thing to do, and represents quality

17 medicine. And when that happens, you know, we

18 shouldn't just pay for it, we should pay for it,

19 we should promote it, and perhaps, if the

20 evidence is there, we ought to insist on it. I

21 don't think the clinical community or the

22 beneficiary community would tolerate us insisting

23 upon a pattern of behavior, or even promoting a

24 pattern of behavior, without evidence, and so why

25 should we pay for it without evidence? And


1 that's the change we're trying to make, that's

2 been the whole point of changing the coverage

3 process, putting together advisory committees

4 like this, is to say, let's look at evidence and

5 let's make decisions about what we pay for based

6 on quality, and once we know what quality is,

7 let's not just pay for it, let's not stop there,

8 but let's pay for things that we are willing to

9 promote and perhaps even insist upon. And so,

10 that is the reason for the focus on evidence, and

11 it's going to be there. And the fact that we may

12 not have subjected past technologies to the same

13 evidence, doesn't mean we can't go back and look

14 at them, time willing, but it doesn't mean we

15 should lower the bar for new technologies.

16 DR. FERGUSON: Do you want to respond a

17 minute, Dr. Sausville?

18 DR. SAUSVILLE: Yes, I do wish to

19 respond to that. And I want to thank you for

20 that perspective, because clearly, there is

21 nothing that ever, that doesn't lack for good

22 intentions. Clearly, the desire to convey useful

23 patient benefit goes without question. And the

24 efforts that were cited over the past two decades

25 have really been enormous efforts in that regard.


1 But one distinction that I must point out is

2 that when one considers the bacteriologic

3 analogy, the diagnostic specimen, that is to say

4 the bacteria growing in a bottle, equals the test

5 specimen. So that is one intrinsic difference.

6 In many cases, cancer related

7 sensitivity testing requires additional efforts

8 to get and process tissue different than the

9 routine. So it is a point where the analogy is

10 not exactly apt, I think. And you quoted the

11 endocarditis issue, and you're right. It is

12 controversial as to whether or not ultimately

13 sensitivity testing is beneficial, because among

14 the lethal consequences of endocarditis are a

15 series of almost anatomical problems, valve

16 problems, thrombi, et cetera, that are not in any

17 way predicted or dealt with by the sensitivity

18 testing. So again, it's -- I think that the two

19 are, recall each other, but have important

20 differences in thinking about the ultimate value

21 of the tests.

22 DR. FERGUSON: Dr. Sundwall?

23 DR. SUNDWALL: Just a quick question.

24 Dr. Sausville, I am a family physician, not an

25 oncologist, but I was very perplexed by your


1 statement. If I heard it correctly, you said,

2 knowing what drugs won't work is not all that

3 helpful. I don't understand that, given the

4 morbidity and the difficulties with

5 chemotherapeutic agents. I've had many patients

6 suffer terribly from chemotherapy, and how can

7 you say not knowing what won't work isn't that

8 helpful?

9 DR. SAUSVILLE: Because the context in

10 which -- and I respect your point, and I don't

11 certainly mean to in any way imply a lot of sole

12 searching on both the part of physicians and

13 patients that goes into the decision to entertain

14 therapy. But in oncology, frequently the

15 treatment is driven by the histologic diagnosis,

16 so if for example the initial diagnosis of small

17 cell lung cancer, if one could have a pattern of

18 drugs that have more or less a susceptibility, I

19 am not aware that such tests would be considered

20 definitive in saying, well, because you happen to

21 have a resistant small cell lung cancer, you

22 should not receive any therapy. So in that case

23 the therapy, or choice of therapy, is ultimately

24 driven by the histologic diagnosis that's

25 apparent. Consider the opposite point. Somebody


1 with a chemotherapy refractory neoplasm,

2 manifested, such as pancreatic or renal, which

3 are problems which as far as I'm aware, are not

4 considered responsive to any set of agents

5 routinely.

6 Again, the information of whether the

7 patient has that dire situation is implicit in

8 the histology. It's not clear that any tests

9 that can be done ultimately defines a drug that

10 can change the outcome that is at the present

11 time ordained by the histology. So I take your

12 point, that being able to reliably choose drugs

13 that convey a useful clinical benefit is very

14 worthwhile and a goal that should be pursued. I

15 am not sure that the current tests actually allow

16 the clear delineation of such agents.

17 And in that regard, you can tell a

18 patient who has the unfortunate diagnosis of

19 pancreatic cancer, that they are likely not going

20 to respond to a medicine chosen on that basis, or

21 chosen after having gone through an additional

22 test to obtain tissue and then tested for assay

23 resistance.

24 MR. KIESNER: I think Dr. Sundwall

25 asked a very interesting question, and I think


1 there are at least two clinical strategies for

2 using this type of information. I think on one

3 hand you can say, we're going to select a drug,

4 and another, there may be a different clinical

5 setting, and I will give you two examples. I

6 think this is very important.

7 Dr. Alberts spoke this morning about a

8 clinical situation where he would be referred a

9 patient from another hospital, and that patient

10 may not, may be unaffected by the primary care,

11 he has relapsed, the tumor is growing, and they

12 send him, they send the patient to him. Doctor,

13 what can you do to help me? In that situation,

14 there may be three or four or five different

15 drugs, single agents, none of which have been

16 determined to have a significant clinical benefit

17 over the other drug in that situation. If I am a

18 patient and if any physician can tell me of the

19 five drugs, Frank, two of those drugs you're

20 resistant to, what has he told me? He's said,

21 I'm not going to use those two drugs, I've saved

22 you from the possibility that you're going to get

23 those two drugs and not benefit from it. It's

24 very, very well documented that these tests are

25 able to identify resistance, and if I'm a patient


1 and if my physician in that setting can identify

2 the resistance, I believe he has done me a real

3 service.

4 The second situation is one which I

5 experienced personally. And I'm not mentioning

6 it because it's personal, I'm mentioning it

7 because it's exemplary of the position that a lot

8 of families can be in relation to elderly

9 Medicare patients. In -- I'm from Minneapolis.

10 My father was in St. Mary's Hospital. He was ten

11 years past Medicare age and was being treated for

12 cancer. We saw what the drug was doing to him.

13 If his physician could have come to me and said,

14 Frank, I have two or three other drugs, two or

15 three other choices I could try, and I have done

16 a test and I could see that they are all

17 resistant, I don't think we should go any

18 further. From the family situation, it was a

19 very difficult situation to make, do you go

20 further. We made the situation not to. But to

21 this date, if I would have had an assay that

22 would have told me the drugs that the physician

23 was considering will not work, I would feel I

24 would have been served, our family would have

25 been served, and my father would have been


1 served. Elimination of drugs, identification of

2 drugs in those types of clinical settings that

3 don't help, or help you stop therapy, I think is

4 something worth considering.

5 DR. FERGUSON: Thank you. Very

6 briefly.

7 DR. SAUSVILLE: So my response to that

8 is the essence of the issue, and it also pertains

9 to the question before, is whether or not one

10 could have reached the conclusion that drugs

11 would not have benefitted your relative by the

12 diagnosis itself, and not have ultimately had to

13 rely on a test. And here the performance

14 properties of the -- the unfortunate performance

15 properties that when a drug is predicted to be

16 sensitive by these tests, the outcome is

17 unfortunately not any different in many cases,

18 than when things -- and in fact, in all cases

19 that I'm aware -- of when drugs are seen as

20 resistant, is the essence of why we are in a

21 quandary about how to appropriately use this.

22 DR. FERGUSON: Thank you. Harry.

23 Dr. Handelsman?

24 DR. HANDELSMAN: I'm Harry Handelsman.

25 I'm at the Center for Practice and Technology


1 Assessment, Agency for Health Care Policy and

2 Research, and our office was asked by HCFA to

3 review the 1990 article by Kern and Weisenthal on

4 the use of suprapharmacologic drug exposures.

5 And I'm going to briefly synthesize what I think

6 was the essence of that article and then give my

7 personal critique. Unfortunately, some of this

8 is going to be repeating some of the data that

9 you heard earlier today, and that's unavoidable.

10 Bayes' theorem suggests that drug

11 sensitivity testing in vitro will be accurate in

12 predicting clinical drug resistance in tumors

13 with high overall response rates only if the

14 assays have a specificity of greater than 98

15 percent for drug resistance. A 1989 review of

16 the literature by the authors indicated that a 30

17 to 50 percent false positive rate, and a false

18 negative rate as high as 15 percent.

19 This reported assay, which was

20 developed by Kern, uses a soft agar culture with

21 products of concentration times time higher than

22 those which can be achieved clinically and used

23 drug exposures 100-fold higher than other

24 contemporaneous studies. Response assessments

25 were made by retrospective and blinded chart


1 reviews. The authors reviewed 450 correlations

2 between assay results and clinical response over

3 an eight-year period. The assay was calibrated

4 to produce extremely high specificity for drug

5 resistance. Two assay end points were used,

6 colony formation and thymidine incorporation.

7 Overall response rates were 28 percent

8 using the colony formation end point, and 34

9 percent using the thymidine incorporation end

10 point. At the assay lower cutoff value, the

11 assay was 99 percent specific in identifying

12 non-responders, fulfilling the Bayes prediction.

13 Patients with drug resistant tumors could be

14 accurately identified in otherwise highly

15 responsive patient cohorts. The demonstration

16 that the post-test response probabilities of

17 patients varied according to assay results in

18 pretest response probabilities allowed the

19 construction of a nomogram for predicting

20 probability of response.

21 In 1976, it appeared that no method of

22 predictive testing had gained general acceptance,

23 and during the subsequent decade, high false

24 positive and false negative rates continued to

25 plague the field of in vitro testing.


1 The clinical advantages of developing a

2 highly specific drug resistant assay include:

3 The avoidance of the use of inactive agents in

4 treating responsive tumors; the avoidance of drug

5 related morbidity of inactive agents; the

6 identification of drug resistant tumors for

7 timely consideration of alternative therapies;

8 and obviously, the cost savings of avoiding the

9 use of ineffective agents. Alternative assay

10 methods are available. However, the use of cell

11 culture assay had the advantage of measuring the

12 net effect of both known and unknown mechanisms

13 involved in drug resistance.

14 It is indeed possible to estimate the

15 post-test response probability for specific drugs

16 in specific tumors and patients. This can be

17 achieved through the determination of assay

18 results and the application of a constructed

19 nomogram for assay predicted probability of

20 response.

21 In general, efficacy studies in both in

22 vitro and in vivo tumor models provide an

23 opportunity to obtain data on both efficacy and

24 toxicity, and to refine dose and schedule

25 information for clinical trials. In vitro


1 testing has been extensively applied to determine

2 the potential efficacy of individual drugs and

3 remains an attractive alternative to testing

4 empiric regimens in phase I and phase II clinical

5 trials. In vitro testing can differentiate

6 active and inactive agents, but cannot serve as a

7 substitute for in vivo studies, despite providing

8 elements of both positive and negative predictive

9 reliability.

10 Combinations of agents, which are the

11 most widely applied treatment strategy, are best

12 evaluated using in vivo models, where both

13 toxicity and pharmacokinetics can be adequately

14 studied. Although in vitro assays can provide

15 primary drug resistance date, the most relevant

16 outcome from such assays is improved patient

17 survival, and there have been no clinical trials

18 demonstrating such a result. In addition, it

19 remains to be determined if in vitro testing will

20 be found to have direct clinical applications for

21 disease or patient specific therapies. There

22 have been encouraging reports of survival

23 advantage of patients treated with in vitro

24 directed therapies, but these require

25 confirmation from larger numbers of patients and


1 variety of tumors.

2 Both randomized and non-randomized

3 studies comparing tumor responses to chemotherapy

4 selected by in vitro testing with empirical

5 chemotherapy have produced conflicting results.

6 Response rates appear to be better with in vivo

7 selective agents. However, the impact on

8 survival has not been adequately addressed.

9 Ideally, in vitro assays should be correlated

10 with both response and survival data. The most

11 significant issue in the realm of cancer

12 chemotherapy is that of the resistant

13 mechanisms. The ability to identify ineffective

14 agents in these assays, albeit potentially

15 important, does little to elucidate the

16 mechanisms problem. The assay described in this

17 article can perform its intended task of

18 identifying resistant tumors, and determining a

19 probability of response, but its clinical utility

20 has not been established.

21 DR. FERGUSON: Thank you. I think we

22 have time for a few questions. Panel, or

23 others? Comments? Yes?

24 DR. FRUEHAUF: I think that was a nice

25 summary of the paper and I think the issue of


1 survival was addressed this morning.

2 DR. HANDELSMAN: Excuse me, if I can

3 interrupt. The issue of survival was predicted,

4 but it wasn't on a comparison with alternative

5 therapies.

6 DR. FRUEHAUF: That's true. It was

7 survival in a blinded prospective way, looking at

8 people just getting empirical therapy and asking

9 the question, if you're not going to respond,

10 will you have inferior survival? And we've

11 addressed the issue of, do you want to get a

12 drug, as a person who has cancer, that won't work

13 and won't benefit your survival? And I think

14 that's the important point that this paper is

15 establishing, the utility of knowing that a drug

16 will not be of benefit to a cancer patient.

17 And I have dealt with neuropathies that

18 are incapacitating to professional tennis

19 players. I have had to deal with all sorts of

20 toxicities, and many of these people progress

21 through therapy and die of their disease, and the

22 quality of their life during that progression was

23 significantly adversely affected by getting

24 ineffective therapy. So the clinical utility

25 question to me as a practicing physician, is to


1 not harm people with ineffective therapy that

2 will not, which has been demonstrated not to

3 benefit their survival. And I think most people

4 understand, if you don't respond to the therapy,

5 you're not going to live longer. And we're not

6 trying to say that the test will predict a drug

7 that will help people live longer, for drug

8 resistance assays. We are trying to say, and you

9 stated, and Dr. Sausville stated, that these

10 assays accurately predict drugs that will not be

11 effective. And then the question is, so what? I

12 think the answer to the question so what is, so I

13 don't want to give those drugs to my patient.

14 DR. FERGUSON: Thank you. Okay. I

15 guess we can go on. Harry, thank you very much.

16 Dr. Burke?

17 DR. BURKE: I think we're going to have

18 a lot of fun this afternoon. My name is Harry

19 Burke. I'm a consultant to HCFA. I'm an

20 internist. I'm a methodologist. I'm only here

21 for today.

22 The first couple slides that I am going

23 to present are not HCFA's position, they're my

24 personal review on the subject, and shouldn't be

25 considered HCFA's policy. I am going to address


1 three issues today. First, the levels of

2 evidence, which has been raised several times by

3 various speakers. I'm going to talk about test

4 accuracy. And then I am going to talk about the

5 Kern and Weisenthal article that Dr. Handelsman

6 just gave us an introduction to.

7 But before that, I would like to make a

8 couple comments. First, the extreme drug

9 resistance is really a therapy specific

10 prognostic factor. It really has to be looked at

11 in the context of other therapy specific

12 prognostic factors. Dan Hayes was right. It's

13 like ER and PR, and these other factors. And

14 there's a scientific rationale underlying therapy

15 specific prognostic factors that must be dealt

16 with. The utility of the test depends on the

17 characteristics of the test; that's clear. But

18 it also depends on the efficacy of the treatments

19 if it's a therapy specific prognostic factor,

20 because they're inextricably linked together, and

21 you can't separate the two. And it depends on

22 the prevalence of the disease or the resistance

23 in the population under study. So it's really

24 those three factors together that must be taken

25 into consideration when looking at something like


1 this.

2 Let me make another point, and that is,

3 when we talk about the utility of this test, we

4 can't be talking about the utility for individual

5 patients. We have to be talking about the

6 utility of the test for a population of

7 patients. So we can't switch back and forth

8 between the two, because we're really mixing

9 apples and oranges when we do that.

10 I'd like to make a couple comments

11 about what has been said earlier. Fruehauf,

12 Weisenthal and others have suggested consistent

13 findings across studies, and they've made a claim

14 that that proves something. And I would like to

15 suggest that, yes, consistent findings across

16 studies can be due to robustness of the

17 underlying phenomenon. But it can also be due to

18 consistent biases across the studies. And so if

19 you are going to make a claim for consistency of

20 35 studies, that all suggest the same thing, and

21 that's a robustness claim, you'd better be

22 prepared to tell me why it isn't due to biases in

23 the 35 studies themselves. So you really have to

24 look at each of the 35 studies and you have to

25 ask the question, are these really consistent?


1 You can't just wave a hand and point to 35

2 studies. You have to rule out the alternative

3 hypothesis.

4 Secondly, I'm a little confused.

5 Kiesner pointed out that we could use this task

6 at the bedside at the discretion of the patient,

7 or I mean of the physician, while Kern suggested

8 that it could be used to deny a particular drug.

9 And I need to know, which is it? Is it that the

10 evidence is so convincing that it can be used to

11 deny a particular therapy, or that it's not that

12 convincing, and it's just one of an armamentarium

13 of tests that are available to the physician.

14 First, let me just do a little

15 background. Comparative clinical benefit is what

16 I'm looking at. This is my gloss on reasonable

17 and necessary. It could be defined as the test

18 or treatment providing a measurable improvement

19 over all the current relevant tests and

20 treatments at a cost commensurate with the

21 measured improvement. I also suggest that FDA

22 approval is prima facie evidence of safety and

23 efficacy, but if that isn't there, I think safety

24 and efficacy must also be demonstrated. And a

25 comparative clinical benefit study of a


1 prospective, or of the test or treatment, must

2 compare itself to the other tests or treatments.

3 It doesn't stand alone. And so when you say

4 well, this test or treatment is really good, you

5 have to say what the other tests and treatments

6 are, what you're comparing it to.

7 Now I would -- I am not totally a

8 believer in randomized clinical trials for

9 everything. My suggestion is that there may be

10 three levels of evidence that can be adduced: A

11 strong evidence, which is either a large

12 prospective randomized clinical trial, or two

13 large retrospective studies where one study

14 independently replicates the other study. I

15 think that's good science as well. Or two medium

16 size randomized prospective trials. I think all

17 of those would be strong evidence. If I saw a

18 really large retrospective study that was

19 independently replicated by independent

20 investigators, independent institutions, I would

21 take that as fairly strong evidence.

22 Moderate strength are medium sized

23 prospective trials, a large well designed

24 retrospective study that hasn't been replicated,

25 or two medium randomized prospective clinical


1 trials, medium size.

2 Weak evidence. Small properly designed

3 and implemented prospective randomized trials, I

4 think are weak evidence, and I think are well

5 recognized as that, and I think Pito and others

6 have suggested meta-analysis to overcome the

7 weaknesses of small randomized clinical trials.

8 Alternatively, two medium sized retrospective

9 studies that were done by independent

10 investigators might be good evidence.

11 But insufficient evidence, small

12 systematic studies, I consider them really

13 exploratory rather than evidence. Case series, I

14 think are well considered as anecdotal. And any

15 study that's not properly designed, implemented

16 or analyzed must be considered fatally flawed.

17 Large is 500 patients, medium sized,

18 250, small is less than 250. You know.

19 Okay. Test accuracy. What is a

20 properly designed, implemented and analyzed

21 study? Well, test accuracy of course is an

22 association between each patient's predictions

23 based on the test, and each patient's true

24 outcome. That's test accuracy. The factors that

25 affect test accuracy include the study


1 population, were the patients who were selected

2 easy to predict. Because you can select patient

3 populations, and we'll get into that later, that

4 are very easy to predict by just about any test.

5 The test characteristics: Was the test assessed

6 in the clinical setting which it's intended to be

7 used for? The reproducibility: Does the

8 prediction variability increase across

9 laboratories and reagents? And finally, the

10 method of measuring the accuracy: Was the

11 correct method used?

12 And I'm going to focus on two of these,

13 the first and fourth, which are the most

14 problematic.

15 Okay. Sample size, or study sample

16 characteristics. The composition of the study,

17 the study population, makes a difference in the

18 observed accuracy. A sample with only extreme

19 cases, i.e., the predictors are extreme values of

20 their range, will be easier to predict than a

21 sample with many intermediate cases, the

22 predictions are mostly in the middle of their

23 range. For example, for women with breast cancer

24 who have many positive lymph nodes, their

25 outcomes are fairly easy to predict. Women with


1 metastatic disease, their outcomes are pretty

2 easy to predict. It isn't a hard task to do.

3 What is hard to do is to predict the women with

4 small tumors and with no lymph node involvement

5 or metastatic involvement, that's really tough to

6 do. So, if you just pick an extreme population,

7 it turns out those are pretty easy predictions to

8 make, but it turns out that most patients aren't

9 in the extreme, so it's relatively unuseful.

10 Okay? Thus, the sample must be representative of

11 the real world in which the test is to be used.

12 Measurement of test accuracy. There

13 are several ways to assess test accuracy. The

14 correctness of the accuracy assessment method

15 depends on, so when you select a method of test

16 accuracy, whether there is a preexisting

17 threshold, in other words, is there something out

18 there that says everybody above this should be

19 positive, everybody below should be negative,

20 does that already exist, or do you have to

21 construct it? The number of tests to be

22 assessed. And whether the assessment is

23 performed on one population or more than one

24 population.

25 And just very briefly, this is really


1 hard to read. I can't get the lines on tables to

2 work out for me, so this is lineless. But it

3 turns out that the sensitivity and specificity

4 pairs are really, have one threshold, they do one

5 test, and its one population. Okay? Positive

6 and negative predicted value, there is one

7 threshold, one test, and two or more populations,

8 because really, the positive and negative

9 predictive value we're talking about are

10 different prevalences, therefore, different

11 populations. And the area under the receiver

12 operating characteristic includes all thresholds,

13 two or more tests are assessed, and one

14 population. So in other words, we use the ROC as

15 a best unbiased measure of test accuracy.

16 In terms of the measures of accuracy

17 discussed above, without changing the test

18 itself, there are only two ways to change the

19 accuracy of the test. One way, of course, is to

20 change the threshold of the predictions, and then

21 your sensitivity and specificity would change.

22 And the other way is to change the prevalence of

23 the disease in the population, because then your

24 negative and predicted -- positive and negative

25 predicted values will change. Okay?


1 Prevalence's effect on accuracy. The

2 optimal prevalence for assessing the accuracy of

3 the test is to use a population composed of 50

4 percent disease, 50 percent unaffected. In this

5 situation, the prevalence itself provides no

6 advantage to the test. As the prevalence departs

7 from 50-50, the impact of predicting the

8 prevalence becomes more prominent. In other

9 words, if the test acted as a naive Bayesian

10 classifier, then for each patient it would always

11 predict the most frequent outcome, in other

12 words, it would predict the prevalence. So for

13 example, if there was a 90 percent prevalence in

14 a diseased population, then the naive Bayesian

15 classifier would say disease every single time

16 for every single patient, and you would be right

17 90 percent of the time. That's pretty good.

18 Okay? That's a pretty accurate approach. As the

19 proportion of patients with or without the event

20 moves, either toward a hundred percent or zero,

21 the naive Bayesian approach becomes more

22 effective, more efficient in its predictions. So

23 it's only at 50-50 for binary outcome, that you

24 neutralize the naive Bayesian classifier

25 approach. In other words, if the true prevalence


1 of the disease in a population is close to a

2 hundred percent, it's almost possible for a test

3 to add predictive information. Okay? That's

4 really an important idea. So, as you get towards

5 high prevalences, almost no test will be helpful

6 anymore. Okay?

7 Changes in the prevalence of the

8 disease in a population, as reflected by

9 corresponding changes in the test's positive and

10 negative predictive values. If one were allowed

11 to report the positive predictive value, or the

12 negative predictive value of tests just by

13 itself, then one might be tempted to create or

14 select a high prevalence population for

15 assessment of the test, because the test would

16 appear to possess a high predictive accuracy,

17 okay? Until of course, it was compared to the

18 naive Bayesian classifier, at which point it

19 would cease. Thus, both the positive and

20 negative predictive values of the test must be

21 assessed. Then, if the prevalence is not 50

22 percent, the test must be compared to the naive

23 Bayesian classifier. Further, both the

24 sensitivity and specificity of the test must be

25 assessed in terms of the cutoff that was


1 selected, and the prevalence, because it turns

2 out that although it's commonly thought that

3 prevalence doesn't affect sensitivity and

4 specificity, it certainly does, and there are a

5 number of papers that demonstrate that.

6 So, a better way to assess the accuracy

7 of the test is to use the ROC. This measure of

8 accuracy is impervious to changes in prevalence

9 and reflects the characteristics of the test

10 across all sensitivity and specificity pairs.

11 Well, okay. So now, I was asked to

12 take a peek at Kern and Weisenthal's paper as

13 well, and it turns out that they really are very

14 sophisticated in their use of data and results.

15 It's probably one of the most sophisticated

16 papers I've ever read, and I have read quite a

17 few. I'm going to talk about those areas of the

18 paper.

19 Overview of the study. Kern and

20 Weisenthal used two in vitro tests, which have

21 been mentioned, as surrogate outcomes for

22 response to chemotherapy in patients with

23 different types of cancer. If a patient's tumor

24 demonstrated drug resistance in a test, i.e.,

25 after the patient's tumor cells were exposed to


1 the drugs for a certain period of time, and the

2 cells did not achieve a threshold inhibition, the

3 test was interpreted as predicting that the

4 patient would not clinically benefit from

5 receiving the drug.

6 So, we go back to our levels of

7 evidence, and we can ask overall about this

8 study, where it would lie in our levels of

9 evidence? Well, the colony formation test is

10 really Level III, it's weak evidence. The

11 thymidine incorporation is really Level IV,

12 insufficient evidence.

13 But, not letting that bother us too

14 much, let's talk about the study itself. It's a

15 retrospective chart review, subject to several

16 biases, including therapy selection bias, who

17 received the therapy, and study selection bias,

18 which patients were included in the study. And

19 the study was not validated on an independent

20 population, but it was done on the same

21 population. It was done from 1980 to 1987 in the

22 United States.

23 The study characteristics. Initial

24 population was 5,059 patients. From that, they

25 winnowed it down to 450 patients that they


1 actually studied, about 9 percent of the initial

2 population. They looked at eight different types

3 of cancer. They had 332 colony formation

4 patients, 116 thymidine incorporation patients.

5 And the non-respondent prevalence was 71 percent

6 of the population.

7 One thing that the study wasn't very

8 clear about, it said that virtually all patients

9 were treated with standard chemotherapy, but then

10 later on it said, most of the patients whose

11 specimens were analyzed did not receive

12 chemotherapy because they underwent curative

13 surgical procedures. And I didn't understand

14 that distinction.

15 We'll assume that all 450 patients in

16 the study received chemotherapy. The percentage

17 of patients who receive chemotherapy today may

18 actually be much higher. The criteria to decide

19 which patients received chemotherapy is not

20 reliable. This is really not a very acceptable

21 approach to a study. If in fact you're going to

22 predict who's going to respond to chemotherapy, I

23 think you really have to say how chemotherapy was

24 selected, what the selection criteria were.

25 Also not provided were the patient


1 characteristics of the study population, and this

2 is really critical information. For example, if

3 the population was composed of patients who had

4 already received primary chemotherapy, had

5 incurable disease, and were undergoing salvage

6 treatment, then this study would not be

7 applicable today, and in addition, the results

8 would be biased. So we really need to know what

9 the chemotherapy selection criteria were, and

10 what the patient population characteristics were,

11 neither of which are provided to us. There is no

12 basis from which to understand the results that

13 we are seeing.

14 Now, the function of the test is to

15 predict clinical non-response to chemotherapy

16 using suprapharmacologic drug doses. Now, we're

17 interested in the non-response rate per drug per

18 cancer type per test type. That's what we're

19 interested in. So there were eight drugs. Now

20 I'm not going to talk about combination therapy

21 because that's a whole other subject. There are

22 eight drugs, eight cancer types, that means there

23 were 64 bins, okay? So that means per cancer,

24 per treatment, so there were 64 of those

25 combinations for each of the two tests, for a


1 total of 128 accuracy assessments. And excuse,

2 the lines aren't there, but you see 64 bins. And

3 for each bin, you would want to know

4 prospectively, hopefully randomized, you would

5 want to know, for disease one, treatment one,

6 what does the test say, okay, about this

7 population? In that one cell. And then you

8 would want to follow that population over time

9 and see what actually happened to those people.

10 So for breast cancer and a particular

11 chemotherapeutic agent, you would like to see,

12 did the test predict for that chemotherapeutic

13 agent for breast cancer, successfully. And you'd

14 want to do that for each of the 64 cells. And in

15 fact, you must do it for each of the 64 cells.

16 If there were the same number of

17 patients per cancer type, then the 118 patients

18 tested for thymidine incorporation would be at

19 1.8 patients per bin, for this study. And for

20 the 332 patients tested for colony formation,

21 there would be 5.2 patients per bin. These

22 frequencies would be too low to be meaningful.

23 Now, out of the eight drugs tested and

24 reported, the only drugs to use today, and are

25 they not used in combination, the efficacy, the


1 efficiency of these tests must be demonstrated

2 with each chemotherapeutic agent in use today,

3 and for each combination of agents, each type of

4 cancer.

5 Now, just a couple final points. It's

6 unclear why this study provided two sets of

7 thresholds instead of one. Further, although two

8 thresholds were tested for significance, three

9 were presented in the text, shown in the tables

10 and figures. The first threshold is 45 to 75,

11 and the second one was 15 to 40. In this study,

12 the thresholds that were selected to assess on

13 the same population that were used to determine

14 the optimal thresholds. This elementary mistake,

15 reporting the results from the population used to

16 create the threshold, rather than the results of

17 an independent population, always results in the

18 overestimation of test accuracy.

19 The outcome was standard response

20 criteria. We are never given a definition of

21 what standard response criteria are. We don't

22 know who got the chemo and why. We don't know

23 the study population. We don't even know the

24 outcome. We are never given definitions of any

25 of those three. It's absolutely critical that


1 the specific response criteria employed by the

2 investigators be revealed if that is their

3 outcome.

4 Now of course, Rich Simon, who many of

5 you know at the NCI, and others, have pointed out

6 that response is an unreliable outcome and should

7 be avoided if at all possible. So, okay. So

8 rather than the 64 sets of results that we were

9 looking for, two sets of results were presented,

10 one for each of the two thresholds. Each of the

11 results is across all eight cancers, all eight

12 therapies, and the tests, and the results are

13 there for the first threshold, 60 something

14 percent sensitivity, 87 specificity, 43 and 99.

15 Clearly, the sensitivity goes down as the

16 specificity goes up. Neither sensitivity or

17 specificity pair is very high. Combining all

18 results into one conglomeration provides no

19 information regarding the utility of the test for

20 each drug in terms of each cancer type. The

21 study should have reported the area of the ROC

22 curve, both tests, for the 64 sets of results.

23 Thank you.

24 DR. FERGUSON: Thank you very much.

25 We're actually at our time for a break.


1 Perhaps -- it is almost 2:30. If we take a

2 15-minute break, I think, yes, would you please

3 come back up, because there may be a couple of

4 questions for you.

5 (Recess from 2:25 p.m. to 2:45 p.m.)

6 DR. FERGUSON: I wonder if there are

7 any in the audience, or panel for that matter,

8 who would like to ask Dr. Burke some questions

9 related to his presentation? And also, Dr. Burke

10 has promised to give us the last few slides. Are

11 there questions for Dr. Burke from members of the

12 audience or from the panel?

13 Dr. Weisenthal, did you have a question

14 that you wanted to ask, or a comment?

15 DR. WEISENTHAL: I want to thank

16 Dr. Burke. He started off by paying me

17 compliment, and he said of Dr. Kern and I's

18 paper, that this is one of the most sophisticated

19 papers that he's ever read. I've also been

20 talking to critics of these technologies for 20

21 years, and that's the most sophisticated

22 criticism that I've ever had, so I want to

23 congratulate you on that.

24 There are several points that were

25 raised in your talk which should be addressed.


1 Just to begin with, the study by Kern and

2 Weisenthal that you spent the bulk of your time

3 reviewing, just to begin with that, you brought

4 up several methodologic criticisms and raised

5 questions about patient selection and so forth.

6 I want to remind everyone here that that was

7 published in the Journal of the National Cancer

8 Institute. I assure you it underwent rigorous

9 peer review. When we submitted our first draft

10 of the manuscript, the reviewers there had

11 certain problems with it and they had certain

12 things they wanted clarification of.

13 DR. BURKE: But that's an appeal to

14 authority.

15 DR. WEISENTHAL: No, no, no. Dr.

16 Burke, had you been one of the reviewers, no

17 doubt you would have raised those issues at the

18 time and we would have responded to those. And

19 I'd like to ask Dr. Kern now if he can respond,

20 so we're in consideration of that you were one of

21 the -- you know, you can't blame us because you

22 were not the reviewer of our paper. Had you been

23 there and helping us to get the essential

24 information out there, I'm sure it would have

25 been a better paper. But we'd like to address


1 those issues that you raised at this time, if

2 that's okay.

3 DR. BURKE: Absolutely.

4 DR. KERN: One of the points was the

5 selection bias. How could you end up with 450

6 correlations out of 5,000 patients in the study?

7 Well, the 5,000 patients was an overview of all

8 the tests that we had done in the laboratory. It

9 wasn't meant to imply that the clinical study was

10 based on 5,000 patients. And in fact, at the

11 Department of Surgery, UCLA, where I was, most of

12 the patients were treated with surgery or

13 radiation, not with chemotherapy.

14 Secondly, many of the patients that

15 received chemotherapy received adjuvant

16 chemotherapy. So the inclusion criteria of the

17 study to get to 400 patients included, first,

18 patients had to have advanced disease; second,

19 they all had to have objectively measurable

20 disease, either by CT scan, x-rays or so on.

21 Okay?

22 Now, as far as another comment that you

23 made about one study, but it's not been

24 independently validated, I think I may ask Dr.

25 Bosanquet to address that issue, because he


1 published an article in Lancet a couple of years

2 after our paper.

3 DR. BURKE: Did you want to address any

4 of the other issues that I brought up?

5 (Inaudible response from audience.)

6 DR. BURKE: I mean, this is not an

7 opportunity for us to get into whether the study

8 has been validated or not at this time. That was

9 just an issue that I raised, and perhaps at

10 another forum that can be addressed further. I

11 think we have time limitations.

12 DR. KERN: Well, I will try to answer.

13 DR. BURKE: So keep going. There were

14 a lot of issues.

15 DR. KERN: Bring up a couple of the

16 issues, remind me of them. Let me see what you

17 consider a serious objection.

18 DR. BURKE: Well, the selection, the

19 patient characteristics, the criteria for who got

20 what treatment.

21 DR. KERN: Let's go one at a time. Who

22 got what treatment was determined independently,

23 not by the assay, but by the disease type. The

24 patients went on standard protocols. Most of the

25 patients who ended up at being UCLA, an academic


1 center, were all on some sort of clinical trial,

2 randomized trial protocol.

3 DR. BURKE: What were the standard

4 protocols? What was the response criteria that

5 you used?

6 DR. KERN: The response criteria were

7 the ECROG criteria of partial response and

8 complete response.

9 DR. BURKE: And what percentage was

10 each in terms of your study?

11 DR. KERN: I'm sorry, I don't

12 understand.

13 DR. BURKE: In other words, in terms of

14 response, global response measured, and what

15 percentage of these patients were partial

16 responses, what were complete, and then at that

17 time, how were those defined in your study

18 population?

19 DR. KERN: Okay. The responses were,

20 again, just by objective measurements. It was

21 retrospective, but scans, x-rays. And the

22 complete response, obviously, complete

23 disappearance of the disease. Partial response

24 was by the criteria of two dimensions and the

25 shrinkage of at least half in two dimensions.


1 Standard criteria.

2 DR. BURKE: But this was a

3 retrospective study where you went back to the

4 charts. We all know about the paucity of

5 information and the error of information, and in

6 follow-up information not being in the charts.

7 How did you manage those issues in your

8 retrospective study?

9 DR. KERN: Well, the follow-up --

10 obviously, there are problems, and I'm not trying

11 to say there's not biases in it. We all know the

12 disadvantages of retrospective chart reviews.

13 The only thing I can tell you is what actually

14 was done, two oncologists reviewed the charts and

15 made their best decisions of what the responses

16 were, based on measurable criteria.

17 DR. BURKE: What did they do when they

18 disagreed? What did they do when information

19 wasn't there? What did they do make sure it was

20 accurate information? Do you want to continue

21 with this?

22 DR. KERN: No, I cannot say that I can

23 answer every question. I mean, I'm not an expert

24 in your field.

25 DR. FERGUSON: One brief, and then


1 we'll let Dr. Bosanquet speak.

2 DR. WEISENTHAL: This is really

3 important, okay? You know, you talked about an

4 eight-by-eight table, and we only have one

5 point. You know, a study like this is never

6 going to be done again in the history of the

7 world. Never again are you going to have 330

8 patients treated with single agents. The

9 important thing about it was that this was an

10 honest blinded study in the following fashion,

11 and that is that the clinical results were

12 determined independent of knowledge of assay

13 results. The clinical results were reported to

14 the Department of Biomathematics at UCLA; they

15 were like the stakeholder in this, they had the

16 clinical assessments. Likewise, they received

17 independently from the laboratory the laboratory

18 assessments, and then the correlations were made

19 as stated.

20 DR. BURKE: Let's just deal with that

21 issue for a moment, because Dr. Kern sat down and

22 you stood up. So we've got the 64 bin table,

23 right.


25 DR. BURKE: And the issue is, how do we


1 know the utility of this test for a chemotherapy

2 in a disease?

3 DR. WEISENTHAL: Okay. You're making

4 the same criticism as Maury Markman has made.

5 What Maury Markman says is as follows, and he

6 says that he notes that there have been no

7 prospective randomized trials.

8 DR. BURKE: That's not my question.

9 DR. WEISENTHAL: Wait a second. But

10 it's the same thing. He says that even if some

11 day there were to be a prospective randomized

12 study, that that would only apply to that one

13 particular situation, and it would not tell you

14 anything about all the other situations.

15 You know, the sort of information that

16 you're asking for in the real world will not be

17 available for 20 to 50 years, if ever.

18 DR. BURKE: No, no. I understand the

19 mitigating circumstances. But the question is,

20 if you want this test to predict a particular

21 chemotherapeutic regimen in a particular disease,

22 then I want that information, and I don't have it

23 in your study.

24 DR. FERGUSON: Okay. I am going to ask

25 for Dr. Bosanquet to give his response, and then


1 we are going to go ahead. We will try to have a

2 little more time at the end.

3 DR. WEISENTHAL: There's an extremely

4 important point. Basically he started out his

5 talk denigrating -- in other words, I made the

6 point that we have 35 studies consistently

7 showing the same thing, and he denigrated that,

8 and he said, oh, that's just due to consistent

9 bias. And I would like to prove to you that that

10 is not true.

11 DR. BURKE: Excuse me. I didn't. I

12 posed an alternative hypothesis. I said there

13 are two hypotheses for the 35 consistent studies.

14 Assuming that they are consistent, which we

15 have no evidence of, but assuming that they are

16 consistent, it could be due to two things. It

17 could be due to the fact that there is a

18 phenomenon there, or it could be due to

19 consistent study bias. And until you eliminate

20 the alternative hypothesis, you haven't done

21 science.

22 DR. WEISENTHAL: I would like to then

23 eliminate the alternative hypothesis and prove to

24 Dr. Burke that we have indeed done science in

25 this setting.


1 DR. FERGUSON: Can you do that in the

2 final hour?


4 DR. BOSANQUET: It was stated that

5 there was no independent validation of this. We

6 actually took the data that we published in the

7 Lancet the following year. This paper that we're

8 discussing is 1990; we published this work in

9 1991 in the Lancet, using CLL patients. And we

10 also looked at extreme drug resistance in these

11 patients.

12 We got this. We found 22 of 119

13 patients had extreme drug resistance in vitro,

14 and none of these patients responded. So here is

15 one of the things that we would speak to, which

16 was independent validation in a completely

17 different set of circumstances in a different

18 laboratory, and we find exactly the same thing,

19 extreme drug resistance, no response.

20 DR. FERGUSON: Using the same cutoff

21 points that were determined by Kern Weisenthal, I

22 guess.

23 DR. BURKE: Just to respond briefly to

24 that, two points. One, that is not a replication

25 of the 1990 paper.


1 And number two, I do suspect that

2 that's exactly correct, that it is disease and

3 treatment specific. And that's exactly my

4 point. That's exactly my point. You have to

5 talk a specific disease, a specific treatment,

6 how does the test do? Not a conglomeration of

7 diseases and treatments together. That's exactly

8 my point. Thank you.

9 DR. FERGUSON: Thank you. Dr. Burken.

10 DR. BURKEN: Hi, everybody. Can

11 everybody hear me okay? I am Dr. Mitch Burken, a

12 medical officer with the coverage and analysis

13 group at HCFA. What I'd like to do is try to tie

14 together some of the presentations from earlier

15 in the day. There will be a lot of material in

16 here that you've seen before, but what I want to

17 do is try to wrap it up, and wrap it up in a way

18 that's consistent with Dr. Bagley's opening

19 remarks around 8:00 this morning, looking at the

20 broad sweep of the evidence, not spending as much

21 time on specific papers as much as trying to see

22 the bigger picture, cutting across many assay

23 formats.

24 Well, as I said, for the first several

25 minutes I want to be as conceptual as possible,


1 and then we'll get more into the bulk of the

2 evidence itself.

3 But let's think about why we would

4 order any type of lab test, okay? A lab test has

5 its maximum clinical utility when the disease

6 probability is most uncertain. In other words,

7 we heard a little bit about the 50-50 point, and

8 the naive Bayes condition, and so forth, but let

9 me just try to restate that in a slightly

10 different way. If we have any type of lab test

11 we're looking at, and exploring questions of

12 clinical utility, okay? What's the probability

13 that the patient has a disease? If a patient is

14 very unlikely to have the disease, then what kind

15 of information do you have when you get a lab

16 test result? It's certainly not very very high.

17 And the reciprocal situation, where we

18 have a very very high probability or prevalence

19 of disease, and then the lab test doesn't really

20 add a whole lot, because we're almost positive

21 the patient has the disease. It's when we're

22 unsure of ourselves, and when we are at that

23 50-50 point, that's when a lab result can really

24 begin to add value.

25 Well, where are we in the Medicare


1 program? The panel is charged with trying to

2 demarcate what's reasonable and necessary with

3 respect to human tumor assay systems, okay? And

4 we need to find a spot in this, or we need to

5 kind of bracket an area of this graph where lab

6 testing -- and again, we'll talk about the HTAS

7 in a second. But where is lab testing most

8 reasonable and necessary? Where does it add

9 information?

10 Let's talk about now about applying

11 this more generic situation to human tumor assay

12 systems. Well, let's talk about the

13 chemosensitivity scenario. We talked all day

14 about how this testing can assist clinicians in

15 selecting effective single agents. Okay?

16 Conversely, the chemoresistance scenario is where

17 this assay, or where these assay systems can

18 avoid ineffective agents. And what's our

19 reference here? The reference is data from

20 published clinical trials; maybe they're in peer

21 reviewed journals, maybe unfortunately they're

22 just in abstracts that are available at ASCO

23 meetings. But again, there is information from

24 clinical trials that does provide a backdrop

25 against which one can look at this lab testing


1 and the added value thereof.

2 So let's go back to our graph again.

3 In vitro testing has the greatest clinical

4 utility when the presumed sensitivity or

5 resistance, because remember, they're really

6 reciprocal functions of each other, is most

7 uncertain. And going to our X axis here, what's

8 the real question? The question is, is tumor X

9 sensitive or resistant to drug Y in patient Z?

10 Therefore, we need to be specific as to what

11 questions we're posing.

12 Well, let's talk some more about

13 clinical utility, because as I said, what we want

14 to do is look at the broad sweep of the

15 evidence. We've talked about different outcome

16 measures today; we've talked about clinical

17 response; survival; we've talked even a little

18 bit about quality of life, although in the packet

19 of materials there is really not a lot of quality

20 of life literature to discuss, so we won't really

21 get into that.

22 And in looking at clinical responses

23 and outcome, we need to identify robust

24 two-by-two data using a valid gold standard, and

25 from there we can look at different performance


1 measures. In this case, I think it's valuable to

2 look at positive predictive value as a marker for

3 chemosensitivity, and negative predictive value

4 as a marker for chemoresistance. One could also

5 talk about the sensitivity or specificity, but

6 let's try to keep it just a little bit simpler,

7 let's focus in on some concepts, and not worry so

8 much about the math. There are others in the

9 room who may be more expert, but let's try to

10 keep it simple, and not get too wrapped up in the

11 numbers, but let's try to get wrapped up until

12 the themes and the concepts.

13 As we discussed earlier this morning,

14 as Dr. Bagley emphasized, we have to insure that

15 the biases, such as insufficient sample sizes,

16 don't substantially influence our results.

17 Going back to our graph now, in 2-D

18 rather than 3-D, let me emphasize a point that

19 I've said already, but let me reemphasize it

20 again. That if you are at the extremes of this

21 utility function, okay, where the lab test,

22 whether it's human tumor assay system, or a serum

23 sodium, or whatever it is, or a chest x-ray, any

24 type of diagnostic test, if you're at the extreme

25 regions of this utility function, it doesn't


1 really matter what your predictive values are.

2 If the predictive value is high, it can be offset

3 by the fact that you are in an extreme region of

4 the utility function where those numbers don't

5 really mean as much. Okay? And we'll talk more

6 about that. Okay?

7 Well, what kinds of measures do we need

8 to evaluate test accuracy? The ones that I

9 talked about below, predictive values, but there

10 are also sensitivity, specificity, area under the

11 ROC curve, but let's talk about something else.

12 What about some of the physician concerns. In

13 the lab, what kinds of things can a physician

14 tell his or her patient when a particular tumor

15 can or cannot be assayed by the lab?

16 On the right side of the slide are what

17 I would call the quality control measures, and

18 we're not really going to spend time on those in

19 this particular, at least my particular

20 presentation, but you've heard from FDA earlier.

21 So let's just kind of stay on the left-hand side

22 of the slide for now.

23 Well, just to get back to a couple of

24 those issues that really cut to the heart what

25 physician concerns might be, you know, is there


1 sufficient assessability or evaluability of the

2 tumor cells from the submitted specimens? And we

3 found out that some of the earlier clonogenic

4 assays had very very -- had relatively low

5 assessability or evaluability rates. But let me

6 pose another question.

7 Even if a particular assay format is

8 evaluable 90 percent of the time, it still might

9 mean that 10 percent of the time the physician

10 speaks with his or her patient, and they really

11 just can't get an adequate result. So I think

12 that's an issue. You know, even if it's 90 or 95

13 percent, there is still some percentage of the

14 time when you don't have a result and you come

15 back. Okay?

16 There are other issues that come into

17 play. What's the effect of tumor heterogeneity?

18 We talked a little bit this morning about tumor

19 heterogeneity, but there's another type of tumor

20 heterogeneity as well, and that's the type that

21 can occur within the same patient, so that a

22 primary tumor and its metastatic lesions have

23 different in vitro patterns. And again, that is

24 a consideration to keep in mind when we're

25 thinking about this type of testing.


1 So as I mentioned, in vitro results for

2 solid tumors from one site may not always provide

3 the same result as other sites. However, there

4 is a paper back in 1986, we'll get to it a little

5 bit later, I'll touch on it again, but it shows

6 that this problem may not be quite as pronounced

7 in clonic lesions such as leukemias.

8 Well, there is a whole host of in vitro

9 assay formats and we can, as I say, just kind of

10 go through those. But I think it's important to

11 mention at the end here that we will not in this

12 presentation be going through the clonogenic

13 literature. When we reviewed this material at

14 HCFA, we didn't feel there would be a lot of

15 value in discussing the older technologies that

16 had the lower evaluability rates, and just felt

17 it would be better to present it to the panel

18 this way.

19 Well, what kinds of criteria do we use

20 to evaluate the literature? Again, our goal here

21 is to be broad based. We looked at peer review

22 journals in English. There were some manuscripts

23 pending publication that were necessary for panel

24 discussion. There were a couple of the assay

25 formats that were relatively recently developed


1 that we felt we would not be fair to the

2 requesters if we excluded some of the

3 manuscripts. There was, for example, Bartels

4 chemoresponse assay was FDA approved back in 1996

5 and package inserted in the summary of the safety

6 evaluation data was included as a way of

7 evaluating that. And we did not look at abstract

8 data.

9 Based on that, what types of additional

10 search methods? Well, we -- again, we looked at

11 articles submitted to HCFA prior to November 1st,

12 1999.

13 DR. FERGUSON: Mitch, can you speak

14 into the microphone?

15 DR. BURKEN: Right. When we started

16 reviewing this sometime, sometime before the

17 panel itself, we found that the Fruehauf and

18 Bosanquet review article from the 1993 PPO

19 updates, crystallized many of the issues. And

20 what we did, based on it, there were some summary

21 tables that were actually presented this morning,

22 where they looked at groups of studies. And I

23 again refer you to summary table seven and eight

24 from the 1993 PPO. And as a result, we really

25 focused our efforts, our literature efforts on


1 the EDR, as well as also some of the other

2 thymidine assays, because there were some other

3 thymidine uridine incorporation assays pertinent

4 to bring to the panel. And then we also did a

5 lot of sampling of DiSC and MTT, using a MEDLINE

6 search, and we did not have any time limit on our

7 studies.

8 And when we went through and did our

9 literature search, then we had to figure out what

10 we would want to present to the panel. And since

11 clinical response was one of the outcomes we

12 looked at, as well as survival, we needed to have

13 confidence in the viability of our two-by-two

14 tables. So as a result, any study that lacked

15 the clinical criteria -- either the -- the

16 clinical criteria either had to be documented or

17 referenced, you know, for clinical response. We

18 only looked at adult patients.

19 And just for the record here, in the

20 rather extensive handout which has been provided

21 for this session, we do list the pediatric

22 studies that have not been summarized in this

23 presentation, but there is a notebook of all the

24 studies that are being presented in this

25 presentation, are available. I know it's kind of


1 hard to read the whole notebook tonight, but as a

2 supplement to the materials you already received,

3 there are papers in here such that anybody that

4 has any questions about any of the bullet items

5 from this afternoon's presentation can go back to

6 this, as well as your other materials.

7 We included both prospective and

8 retrospective two-by-two data designs. The only

9 thing we did exclude for this, again, for this

10 panel presentation, were descriptive type studies

11 that didn't use any quantitative summaries.

12 There were some studies that went beyond

13 two-by-two tables, used regression analysis and

14 some other techniques, and those were included.

15 Now talking about all these studies,

16 you know, how can we present these studies to the

17 impact panel? Can we group them or pool them, or

18 do we need to go through them individually? It

19 was something that we really had to spend some

20 time thinking about. And we came to the very,

21 very strong conclusion that data from the

22 individual studies should not be pooled. The

23 reason being is that they're, the studies are so

24 heterogeneous, they use different cutoff points,

25 different tumor drug combinations, different


1 clinical response criteria, that we just felt

2 very uncomfortable about doing a meta-analysis

3 for the purposes of presenting data to the panel,

4 okay? So therefore, each study must be presented

5 on its own merit, and I think that's a

6 fundamental approach to presenting data this

7 afternoon, and probably lengthens the

8 presentation a little bit, but we feel it's

9 important.

10 So now, let's just go through the

11 evidence. Let me just walk through the handout

12 with you. I don't -- there is a lot of bulk on

13 my slides, but again, it's in the handout and it

14 is really set up to be a reference guide to

15 trying to put it all together.

16 The assay formats I start with are not

17 based on cell death versus cell proliferation.

18 It's not done that way. I went from the assay

19 formats that we concentrated on, as in the EDR,

20 the DiSC and the MTT, where we really had the

21 most literature, and then towards the end I have

22 some of the other formats where there was a

23 little less literature that came up, based on the

24 criteria that were described in previous slides.

25 The Kern and Weisenthal article from


1 1990 is a complex article that was referred over

2 to Dr. Burke and Dr. Handelsman for separate

3 review. Again, a central piece of evidence, but

4 highly complex.

5 But let me go through some of the other

6 articles that pertain to EDR as well as some of

7 the other thymidine uridine incorporation

8 formats.

9 Eltabakkh, '98, shows, you know, some

10 PPVs and NPVs. There were some confidence

11 intervals that are reported. As you can see, the

12 NPVs in this study is actually fairly low. Let

13 me start, as I said, rather than to go through

14 all the bullets, let me try to highlight what are

15 some of the themes. As I said, there were a

16 hundred new patients with ovarian cancers. We

17 find out in this study, all the patients were

18 recruited prior to chemotherapy, which is

19 important when we think about selection bias.

20 And we found 75 evaluable patients, so we went

21 from about a hundred down to 75, which is really

22 pretty good.

23 Fernandez-Trigo is a study, again, that

24 also has some case loss of about roughly 25

25 percent. But in this case, there was a very rare


1 site cancer that was selected, so I would just

2 keep that in mind.

3 Moving on to some of the other

4 thymidine uridine formats, you know, I talked

5 about the CRA, the Bartels CRA, and it turns out

6 that there was a study by Elledge back in '95

7 that enumerates the findings of clinical trial.

8 And one little, kind of I suppose warning to the

9 panel when evaluating this paper, I mentioned

10 that NPV is a marker for chemoresistance and PPV

11 being a marker for chemosensitivity. Well, in

12 this particular article, not the article but the

13 package insert, and the summary safety and

14 evaluation data, it's flip-flopped, so you have

15 to be a little bit careful there. It turns out

16 you have to reverse that, so you have to really

17 be wide awake when you read these two-by-two

18 tables. And I mention that down here, that the

19 two-by-two table design differs from the other

20 studies presented, even differs from the Elledge

21 paper, which is -- the Elledge paper comes out

22 before the FDA submission data. And this was a

23 prospective blind enrollment of 60 relapsed

24 breast cancer patients. The interesting thing

25 about this particular assay format is that it was


1 very specific for breast cancer and 5-FU.

2 Well, what about some of these earlier

3 five-day thymidine uptake assays? Sondak in '84

4 had a series of 142 patients with successful

5 assays out of a pool of 219, with 33 clinical

6 correlations. Quite a bit of case loss here,

7 even though, again, you know -- well, the numbers

8 are small, but the NPV, again, is high. You

9 know. But one, again, has to be concerned about

10 possible selection bias.

11 Sondak in '85, 819 mixed solid tumors,

12 again, if you use different cutoffs, you're going

13 to have different PPVs and NPVs.

14 Sanfilippo, in '81, there were several

15 studies on three-hour incubation, rather than the

16 five days, you know, and there were -- as I said,

17 you can see the numbers here. I think the

18 interesting thing about this particular study in

19 '81 was the use of subsets for high

20 proliferative and low proliferative non-Hodgkins

21 lymphoma cases.

22 And in '86, Sanfilippo, the same group,

23 went ahead and studied 169 patients with various

24 types of germ cell testicular tumors, but only 29

25 cases were available for clinical correlation.


1 Again, we didn't really know how many people were

2 previously treated or untreated, and that could

3 inject some bias.

4 More three-hour uptake assays, two

5 studies by Silvestrini in 1985 and Daidone in

6 1985 from the same institution as Silvestrini.

7 Different tumor types.

8 Well, let's kind of move along, and we

9 finished up with the thymidine/uridine

10 incorporation assays, and let's move on to the

11 DiSC assay. And as I say, there were several

12 papers that were reviewed in the Cortazar and

13 Johnson article, which is the review article in

14 1999, which did a MEDLINE search, and targeted 12

15 studies, and four of those 12 studies were DiSC

16 assay approaches in solid tumors, three of the

17 four studies being small cell lung carcinoma, the

18 other study being a non-small cell lung

19 carcinoma. And I think, you know, in each of the

20 studies, the test groups did at least as well as

21 the control groups. The survival data was not

22 particularly convincing. In the Gazdar study,

23 the survival rates were similar; in the Wilbur

24 study, the survival rate comparisons were not

25 am. Again, that is survival rate of assay


1 directed versus, you know, empiric therapy

2 groups. In both the Shaw and the Cortazar

3 articles, their survival rates were really not --

4 there was really not enough of a difference to

5 really hold much discussion.

6 But I think where we find a lot more

7 evidence, again, based on our structured review,

8 is looking at this, and hematologic tumors. And

9 we start with Dr. Weisenthal's study in '86 where

10 there is 70 cases. What we did was we subtracted

11 out the 29 cases of ALL. Again, it's just a

12 judgment case one makes as to how you want to

13 treat the pediatric tumors. I can tell you that

14 the pediatric performance table was just about

15 the same as for adults, and there weren't

16 significant differences, so I think what one

17 could --

18 DR. FERGUSON: Mitch, closer to the

19 microphone please, or maybe you should hold it.

20 DR. BURKEN: Yeah. As I said, one can

21 scan through some of the pediatric studies

22 quickly, and I think get a flavor for that. But

23 moving on, looking at the adult data, we had PPVs

24 and NPVs that were over 80 percent.

25 Dr. Bosanquet in 1999 had a fairly


1 elegant study, and it was reviewed earlier

2 today. I will leave the details to the group.

3 Another study that was also discussed

4 earlier today was a study by Mason that did some

5 modeling. In this particular case, not only was

6 there some clinical response data and survival

7 data that was looked at, but there was some

8 modeling done using regressions, where if you --

9 I know that the print is a little small at the

10 bottom, but I just wanted to mention that if you

11 look at the life years gained per assay, the

12 modeling here said that if you had a simulated

13 50-year old with stage C chronic lymphocytic

14 leukemia, there would be a life years gain would

15 be about six months, and about three weeks if it

16 was a simulated age 70 stage AB female. Again,

17 these are all simulations that are based on the

18 assumptions in the regression modeling. But it's

19 a little bit more of a sophisticated approach, as

20 I said.

21 And continuing on, there's been, as I

22 said, a fair amount of work in hematology.

23 Tidefelt in 1989, with more than 90 percent of

24 the patients not being previously treated. There

25 is a complex predictive value studies in this


1 paper with varying anthracycline concentrations

2 and different treatment regimens. But if you

3 flush out all 40, actually 40 out of 53 patients

4 were available for clinical correlations, and you

5 can see the PPVs and the NPVs.

6 To go on now, to continue more on the

7 hematologic DiSC studies, Bird in '86 was a small

8 study. That's the one I quoted earlier because

9 there was peripheral blood and bone marrow that

10 were used as two separate sites. And there

11 seemed to be reasonably good concordance between

12 in vitro testing, between peripheral blood and

13 bone marrow. But again, I caution, the sample

14 sizes are fairly small here. Bird in '88, again,

15 another small sample study.

16 I'm going back just some more. More

17 small sample studies. Dr. Bosanquet in 1983.

18 Dr. Beksac in '88. I think the interesting thing

19 about this study was it's kind of a mixed

20 retrospective prospective approach, so it was a

21 somewhat complicated study even though there were

22 only 16 patients.

23 Dr. Bosanquet's 1991 study that was

24 actually just up on the screen a few minutes ago,

25 showed 67 patients with CLL where there was a


1 survival benefit. But just before we leave DiSC

2 and the hematologic applications, you know, there

3 were some articles that didn't have documented

4 clinical criteria, and you see down, Dr.

5 Bosanquet's article. Again, we certainly looked

6 at the survival data, but we didn't feel that the

7 clinical criteria were adequately specified in

8 this particular paper even though, as I said, it

9 showed some survival data.

10 And Kirkpatrick from 1990 was a paper,

11 again, all in the backup book here that has some

12 pediatric data.

13 Moving on to MTT, several articles did

14 not have documented clinical criteria so that for

15 the purposes of this panel presentation, we

16 didn't feel it would be useful to present

17 clinical response data. And it's interesting

18 that three -- we talked about the 12 studies from

19 Cortazar and Johnson. Three of them were Yamaue,

20 1991, 1992 and 1996, but unfortunately, none of

21 those articles had, you know, adequately

22 described criteria, and we just didn't feel we

23 could construct good enough two-by-two tables.

24 And the survival rates in these studies do not

25 compare test versus control groups. And so, the


1 pediatric neoplasm studies are listed there.

2 Veerman, I think was also mentioned this

3 morning. But again, you know, it's our choice.

4 Well, let's move along then to some of

5 the solid tumor studies for MTT, and this goes

6 more or less in chronological order. We had Suto

7 in 1989, with GI solid tumors. Again, a very

8 small number of clinical correlations are

9 available.

10 We had Tsai in 1990. This was from

11 cell lines from 25 patients with small cell lung

12 cancer. In this case we have regression modeling

13 as opposed to two-by-two data.

14 Furukawa has a larger sample, but

15 again, only 22 patients available for clinical

16 correlation. So the numbers may be high, you

17 know, an NPV of 100 percent and a PPV of 75

18 percent but again, with small sample sizes and

19 this degree of case loss, one really has to

20 wonder about the possible selection bias. And

21 then we see some survival benefits in this

22 study.

23 Saikawa in 1994, 50 patients, 40 of

24 whom received post-surgical chemotherapy. This

25 was basically just divided up into two groups, an


1 adaptive group versus a non-adapted group.

2 Again, we have some survival data here as well.

3 Sargent, '94, 206 confirmed or

4 suspected epithelial ovarian adenocarcinoma

5 patients. 37 were previously untreated. And

6 again, we have a -- we were able to have survival

7 data on 37 of those 206.

8 We have a more recent study by Taylor,

9 again, stage three, four, previously untreated

10 adenocarcinoma. 43 available for clinical

11 correlation, or roughly 50 percent out of the

12 starting 90 were finally available after you

13 consider tumor evaluability and clinical

14 correlation. And we have a couple of subgroups

15 here for all treatments and platinum only.

16 Xu, 1999, it's in your packet, your

17 original green book. 156 advanced breast cancer

18 patients. And they actually noted in the study

19 itself that the source of selection bias -- well,

20 they didn't say they had selection bias, but they

21 did say that they preferentially recruited worse

22 prognosis patients in the MTT directed versus the

23 control group, which was certainly a source of

24 concern for those reviewing it.

25 And just a -- hematologic MTT tumors,


1 there's just a lot of those. If you go several

2 slides back, a lot of those studies like Veerman

3 and Hongo, and Hongo were excluded because they

4 were pediatric studies, but if you look at 23

5 patients with de novo AML and five in CML blast

6 crisis, 21 were available for clinical

7 correlations, with again, good looking predicted

8 values, but I think people should evaluate the

9 robustness of the numbers.

10 Then, just to kind of close out, again,

11 we wanted to be fair and not just presenting the

12 thymidine incorporation assays as well as DiSC

13 and MTT, so we did look at some studies from FCA

14 and some of the other assay formats. Leone had

15 78 cases in 1991. This is again, for those of us

16 that are trying to keep up with the different

17 abbreviations, this is the fluorescent cytoprin

18 assay, and see, the Leone study.

19 And then Meitner in '91 actually

20 extended the Leone data set and worked it up to a

21 total of 101 cases with similar NPVs and PPVs.

22 FMCA is a similar fluorescent method, a

23 little bit more recent. Some of the literature,

24 a Larsson study had 43 samples with 27 clinical

25 correlations. Again, the numbers look pretty


1 good. In this circumstance we did find

2 blinding. I'll tell you a little bit about

3 blinding towards the end, but not terribly often

4 did we see evidence of blinding in the studies.

5 Csoka is a more recent article, as I

6 mentioned. 125 patients with newly diagnosed or

7 relapsed ovarian cancers. 45 available for

8 clinical correlation. He did have a breakdown of

9 previously treated versus drug naive patients.

10 Blinding again was reported. And there was, in a

11 small group again, an NPV of 100 percent.

12 Again, moving along, Dr. Nagourney was

13 kind enough to submit a manuscript to HCFA on his

14 apoptotic assay. One thing I would mention is,

15 or question I would raise is, the manuscript was

16 just a summary manuscript, and from it we were

17 not able to determine how the EVA assay improved

18 treatment management beyond empiric treatment

19 regimens for the refractory patients. So that is

20 a question.

21 Then we also looked at several of the

22 HDRA papers that were submitted to us by Dr.

23 Hoffman and his company. Many of the articles

24 that were submitted to us are in a -- again, all

25 of it is available to the panel in a notebook


1 form, but what we did is we pulled out, and

2 they're also in here, the four, three articles in

3 a manuscript that are clinical correlations.

4 Many of the other patients were experimental and

5 pharmacologic studies.

6 I might add that there was a lot more

7 material that was submitted to HCFA than my

8 presentation would suggest, many many more papers

9 that we looked at. But many of them were

10 experimental pharmacology studies, and we didn't

11 feel that this particular venue looking at

12 medical necessity would be quite the right place

13 to get into a lot of extensive experimental

14 pharmacology.

15 So looking at these four papers from

16 HDRA, again, small sample sizes, but again, you

17 can see the NPVs. Again, very few of the studies

18 had confidence intervals calculated, as you can

19 see throughout my presentation.

20 Furukawa in '95, this was presented

21 earlier in the day. Post-surgical stage three to

22 four patients. Mixture of gastric and colorectal

23 tumors, and similar findings that we've seen.

24 Kubota, 1995, stage three four gastric

25 cancer with somewhat -- when I bulleted these for


1 you, what I have done is I've not gone into all

2 the different subgroupings, so therefore, some of

3 the sensitive groups range from a sample of 20

4 to 38, and the resistant groups from 89 to 99,

5 but I have not gone into great detail to specify

6 all those subgroupings. I am trying to give the

7 main message here.

8 And then Dr. Hoffman's article that's a

9 manuscript in press. Again, more gastric and

10 colorectal tumors.

11 Well, as we try to summarize all the

12 literature and take this kind of view from the

13 hillside here, the Cortazar and Johnson article

14 helps do that a little bit, in the sense that

15 they've selected out 12 prospective trials. I

16 mentioned that four of them were the DiSC studies

17 that I outlined, the three MTT studies that I

18 didn't present to the panel because of the lack

19 of documented clinical criteria, and five of

20 those 12 studies were the earlier clonogenic

21 assays.

22 Overall findings from the 12 study

23 review, showing that only a small percentage of

24 patients have actually been treated with an in

25 vitro selected regimen, and that's certainly


1 consistent with many of the studies that I have

2 presented along the way, demonstrating case

3 loss. And most of the patients have had advanced

4 stage solid tumors. The overall assessability

5 rate only being 72 percent, but I think it's

6 really quite fair to mention that five of those

7 12 studies were from the earlier clonogenic

8 methods, so that would have a negative impact on

9 the overall evaluability rate since again, we're

10 talking about five assay formats that were not as

11 technically advanced as DiSC and MTT.

12 And these trials, the response rates

13 among the directed therapy patients were at least

14 as good as those achieved with empiric therapy,

15 and five of the 12 trials illustrated survival

16 data for the directed versus empiric therapy, but

17 it was difficult to determine overall trends in

18 these five studies, including three DiSC trials.

19 In only one of those trials was there

20 randomization, and in that particular trial all

21 the experimental arms consisted of small sample

22 sizes.

23 So where are we, at nearly the end of

24 the day here? I think we found in going through

25 this systematic review that there is not strong


1 convincing medical evidence to support the

2 overall clinical utility of human tumor assay

3 systems. The comprehensive literature review

4 demonstrates that there were many different tumor

5 drug combinations among different studies and

6 this made it difficult to really make conclusions

7 about particular tumor drug combinations because

8 of this variability. And that's really kind of

9 what I would call a structural feature of

10 reviewing so many articles. Many of them had

11 small sample sizes. We had frequent selection

12 bias, recruiting documented or possible

13 refractory patients.

14 Remember, let's go back to our utility

15 function where we are thinking about being in the

16 center of that function or at the extremes, and

17 if you are recruiting patients into the study who

18 are at the extremes of that utility function,

19 then there is a concern that regardless of

20 whether the negative predicted values are high or

21 not, you're not getting a lot of clinical

22 utility. And in the same vein, by recruiting

23 advance stage patients, you may be getting

24 yourself into a situation where without lab

25 testing, you pretty much know that a patient


1 isn't going to respond anyway, therefore your

2 negative predictive value or your positive

3 predictive values are going to be adversely

4 affected by such selection bias.

5 And I've noted before that there was

6 only rare or occasional documented use of

7 blinding.

8 Well, that's the broad sweep, but when

9 we go down and we look at it in a little more

10 detail, we really should note that there were a

11 relatively higher number of clinical correlations

12 available for DiSC and MTT assay formats. As a

13 result, the human tumor assay systems may have a

14 greater potential clinical utility for

15 hematologic neoplasms such as CLL, where there

16 has been really a fair amount of work, then solid

17 tumor. And when considering this literature,

18 let's never forget, you know, the importance of

19 evaluability and heterogeneity in making

20 determinations.

21 And again, we are still in the tough

22 spot of trying to apply single agent drug tumor

23 interactions to multiple agent regimens, and I

24 think a certain amount of inferences have to be

25 made from these, you know, in vitro studies.


1 Thank you.

2 DR. FERGUSON: Thank you. Dr.

3 Bosanquet?

4 DR. BOSANQUET: Thank you, Dr. Burken,

5 for summarizing that. I'm glad to see the up to

6 date work has been included in the production.

7 You spent some time on your clinical

8 utility curve at the beginning. I wonder if you

9 could explain to us all how that was

10 mathematically derived, because I would have

11 drawn a different curve.

12 DR. BURKEN: Well, one could, I suppose

13 one could argue that rather than being a

14 triangular distribution, it could be a normal

15 distribution and have a slightly different look

16 to it. But I think what we ought to do is agree

17 on the fact that a lab test is most valuable when

18 you're most unsure of whether the patient has a

19 disease or not.

20 DR. BOSANQUET: I quite agree with

21 that.

22 DR. BURKEN: I think that's a critical

23 point, and I think that ought to be established,

24 and that the value of any lab test is going to

25 drop off considerably if you're at the extremes


1 of prevalence.

2 DR. BOSANQUET: Well, that's the bit

3 that I would necessarily disagree with. You have

4 drawn a triangular curve here, if I can call it a

5 curve. We all agree, I think, with the 0 percent

6 on the left and the 0 percent on the right, or

7 the low added information at both left and right,

8 and the very high added information in the

9 middle. But just the shape of that curve, and

10 you have spent some time on it, and I just would

11 ask you again, how did you mathematically define

12 that? Because if you look at the Bayesian

13 curves, I think you would find a mathematical, if

14 you define that mathematically from the Bayesian

15 curves, I think you'd get rather a different

16 curve, and your conclusions form this bit of the

17 talk would then be different.

18 DR. BURKEN: Yeah. Let me just say

19 that I, you know, I'll admit up front that this

20 could have been a normal curve rather than a

21 triangular function. But the most -- rather than

22 getting bogged down in the mathematics, I think

23 it's important for the panelists to consider the

24 question of mapping out -- let me go to this next

25 graph. What we need to do is we need to kind of


1 map out an area where we feel laboratory testing

2 is reasonable and necessary. Now we're not --

3 I'm not standing up here and telling you that

4 that cutoff point -- it happens that I have the

5 yellow box here at maybe 20 percent or 80

6 percent. I'm not coming out and telling you that

7 there's any mathematical validity to making the

8 box 20 percent and 80 percent. What I'm trying

9 to do is illustrate a concept of how a lab test

10 becomes less useful as it drops off away from the

11 50-50 point.

12 DR. BAGLEY: Would it be fair to say

13 that although the Bayesian curve which you

14 showed, which is mathematically derived, deals

15 with the probability of a correct diagnosis,

16 whereas what we're dealing with here is not the

17 probability but the clinical utility of

18 increasing that probability? I mean, as the

19 certainty of the disease goes higher, the

20 probability, you know, based on a combination of

21 tests, is also going to go up. But as that

22 probability becomes more certain, the clinical

23 utility or the incremental value of that

24 additional information becomes less. And I think

25 this is an expression of the value of the


1 information.

2 DR. BOSANQUET: I quite agree with you,

3 but you see, many of these tests are used on

4 resistant patients, where the pretest probability

5 of response is very low. And what Dr. Burken is

6 implying by this curve is that if you have less

7 than 20 percent pretest probability of response,

8 then these tests aren't very useful. And I would

9 challenge him, and I think he admitted that this

10 is not mathematically defined.

11 If we could just have a look at the one

12 slide that I've got? We've seen this slide

13 before, and the important thing is, if you take a

14 pretest probability of response of, say 5

15 percent, Dr. Burken was suggesting that anything

16 below 20, the test was not going to add any

17 information. But if you look at this, the

18 information is added at very low levels, because

19 if you take a patient who has a pretest

20 probability of response of 5 percent, you can

21 split that into test sensitive patients who have

22 a probability of response of 20 percent, and

23 those who have a probability of response of 1

24 percent. And I think that's, I would disagree,

25 and I think that would be a useful addition of


1 information to those patients with very low

2 pretest probability of response, so anything from

3 5 percent on. And therefore, I would suggest

4 that the curves you were showing were somewhat

5 misleading. That's all I'd like to say.

6 DR. BURKEN: Yeah. What I'm going to

7 do is I'm going to let Dr. Burke pitch in a

8 little bit with some of the mathematics. But

9 again, I want to emphasize that the schematic

10 that was put up did not, you know, was not --

11 that box did not mean to imply that 20 percent or

12 80 percent or 30 percent would be some type of

13 cutoff that this panel would be expected to

14 respond to. The diagram is simply there to

15 conceptually show in actually probably more even

16 a qualitative way than a quantitative way, that

17 there is simply less value from a lab test among,

18 in a situation where you are very sure of a

19 disease, or you think the probability of disease

20 is so low, that's also another scenario where the

21 test wouldn't be terribly useful, and that the

22 inference from that diagram -- so let me flip it

23 later on to this one.

24 And again, please don't read any

25 cutoffs in here that, where the red triangles


1 begin at 20 percent or 80 percent, please don't

2 read it that way. But the purpose of that

3 diagram is simply to show that at the extreme

4 regions of low probability or high probability,

5 if you have studies with selection bias, where

6 patients who were recruited into a study are in

7 the extreme regions of that utility function,

8 what it can do is detract from the power, or I

9 don't want to use that word power because that's

10 a statistical term and I'll get myself into

11 trouble with some of the statisticians. It can

12 detract from the ability to use positive and

13 predictive negative values as a marker of

14 clinical utility. You just have to be aware of

15 what kinds of patients you have in your study

16 before you can go to bat with high NPVs or PPVs

17 to make a case for a lab test, any lab test.

18 DR. FERGUSON: Dr. Burke.

19 DR. BURKE: Thank you. There's a

20 couple points that have to be made. One is, when

21 you -- I mentioned briefly the 50-50 situation,

22 which is the fair test for a test. But what

23 happens is if you look at the accuracy of a test,

24 it's very difficult to find therapy dependent

25 prognostic factors that are really accurate.


1 It's really hard to do. If you're looking at

2 estrogen receptor status, the area under the ROC

3 is about .62. Okay? So it turns out that these

4 factors are fairly weak. This test is a therapy

5 dependent prognostic factor. And the issue

6 becomes, if your test has an accuracy of .62, but

7 being a naive Bayesian gives you an accuracy of

8 90 percent correct, okay, then the issue is, what

9 are you going to be? So the bottom line is that

10 you have to look at the marginal utility of your

11 test in relation to the population you're using

12 it in.

13 And I think what Mitch's slide is

14 pointing out is not a particular shape, but the

15 fact that it becomes harder and harder to exhibit

16 any marginal utility as the prevalence of the

17 disease goes up. Because eventually, you're

18 going to become a naive Bayesian, because that is

19 the correct approach when the prevalence becomes

20 very high. And in fact, it's true, that is the

21 correct thing you should become, because your

22 test is not as good as predicting the

23 prevalence.

24 Now, the other thing is, you could say

25 well, maybe my test can help a little bit at the


1 limit. But the problem is, your test has a

2 variance associated with it. And then the

3 question becomes, as the prevalence goes up, it

4 becomes almost acentotic, and so you have very

5 very little room to move, and your test variance

6 can take up that room, so you'll never know

7 whether you're doing anything good or not.

8 DR. FERGUSON: Dr. Fruehauf?

9 DR. FRUEHAUF: I would like to thank

10 Dr. Burken and Dr. Burke for telling me that I

11 should be a naive Bayesian, although I don't know

12 what that is yet. I think statistically, I'd

13 like to use this curve, because I agree with

14 this. I think this is true. I think your points

15 are valid and I'd like to use this as an example

16 of how there is a relationship between what we're

17 talking about and what you're talking about,

18 because I don't think they are separated.

19 Now, let's use this curve. Here we

20 have the probability of response in the middle of

21 50 percent, because we're using these tests to

22 predict response, not whether disease is there,

23 so we're making an assumption that disease is

24 equivalent to resistance or sensitivity; true?

25 DR. BURKEN: Well, I'm a little


1 concerned. You know, we may not want to overplay

2 this one issue.

3 DR. FRUEHAUF: This is your model, and

4 you're relating it to in vitro drug response, so

5 please tell me, how does this curve relate to in

6 vitro drug response? What is the relationship

7 between the X and Y axis, and treating patients

8 with chemotherapy?

9 DR. BURKE: Well, let me tell you the

10 way I would choose to use it, okay? And just to

11 make sure we're on the same wavelength here. The

12 way this graph is designed is that that block,

13 the granite block in the middle that's gray,

14 wherever we should have those cutoffs, and we

15 won't argue about that, demonstrates that there

16 is a lot of information added by lab testing in

17 that region, because there is enough uncertainty

18 about whether a patient is either resistant or

19 sensitive to that particular drug, and again,

20 they're reciprocals of each other, so I can use

21 them interchangeably.

22 DR. FRUEHAUF: Okay. Can I go from

23 there? I understand that.

24 DR. BURKE: But let me say, let's not

25 talk about that it measures response. It's the


1 height of the graph, the Y axis, is how much

2 value there is from the lab test result.

3 (Inaudible question from audience.)

4 DR. BURKE: Basically it is simply

5 saying that the greatest uncertainty is at 50

6 percent prevalence, right, of response,

7 non-response, whatever the case might be that is

8 your gold standard.


10 DR. BURKE: And the issue simply is

11 that if you set your study for a 50 percent

12 response or non-response, and then you test your

13 drug or whatever in that population, then the

14 prevalence isn't going to help you or hinder you

15 in your predictions.

16 DR. FRUEHAUF: Yes, I appreciate that.

17 So let's take Tamoxifen and breast cancer,

18 previously untreated breast cancer. If we give

19 Tamoxifen to women without knowing their receptor

20 status, there's a 30 percent response rate across

21 the board. Okay? No test. Everybody gets

22 Tamoxifen, 30 percent response rate. Well,

23 that's okay, but can we do better? Let's get

24 receptors, and now treat according to the

25 results, and see if we change and enrich for


1 response, and eliminate people from what can be

2 toxic therapy because of side effects. And what

3 we find is that if you have estrogen receptor in

4 the tumor, there is a 75 percent response rate.

5 And if you don't have those receptors, there is a

6 10 percent response rate. So my question to you

7 is, can you relate that knowledge and that test

8 to this curve for me?

9 DR. FERGUSON: I am going to take the

10 prerogative of cutting this off right now so the

11 panel can have a discussion. I'm getting hints.

12 DR. WEISENTHAL: Before this, you said

13 that I would have the chance to respond.

14 DR. FERGUSON: I did actually. It's

15 going to have to be very brief.

16 DR. WEISENTHAL: The point I was trying

17 to make, Dr. Burke was implying bad science or

18 sloppy science, or whatever. And also, HCFA in

19 their review specifically excluded the pediatric

20 ALL patients. I just want to make the following

21 very brief points.

22 Firstly, medicine is imperfect.

23 Secondly, medical oncology is inadequate. 70

24 percent of all the treatments that we give don't

25 work. More than half of the chemotherapies for


1 non-FDA approved indications, none of which would

2 stand up to the level of rigor that Dr. Burke is

3 asking here.

4 So I think it's very important that you

5 have to look at the information as a whole.

6 Earlier in my presentation I presented what I

7 call the central hypothesis, and the central

8 hypothesis simply stated is this: You test

9 tumors in vitro, you get a spectrum of responses

10 in vitro, and that the responses in vitro are

11 related in some way to the responses in vivo.

12 I showed you 35 studies which were

13 admittedly, and you know, you got some detail

14 there, of variable quality, and some were very

15 marginal quality, but some, particularly the ones

16 that were excluded, and I don't think there is a

17 meaningful biological reason for excluding

18 pediatric patients, other than they don't get

19 Medicare, but the disease is enough similar.

20 But if you look at the work that was

21 done at the free University of Amsterdam, which

22 was excluded, and I want to tell you about that,

23 that would stand up, I believe, to Dr. Burke's

24 level of rigor. What they did there was they

25 first of all did their training set studies where


1 they did their retrospective analysis. They got

2 their criteria for sensitivity resistance and so

3 forth. And then in a prospective blinded

4 fashion, using those criteria which had been

5 established from the retrospective study, they

6 prospectively tested it in the cooperative group

7 study in a double blinded fashion, published peer

8 reviewed in the journal Blood, which is one of

9 the most rigorously peer reviewed journals

10 around, and it showed absolutely astonishing

11 great results. And you have to include that

12 paper in the context of everything that you've

13 heard.

14 Now, the point that I wanted to

15 conclude with is that if all we're talking about

16 is validating an estrogen receptor, it would be

17 very simple. We're talking about one test, a

18 very common disease, breast cancer. But what we

19 tried to do, beginning 20 years ago, is we said

20 we have this morass, we've got hundreds of

21 diseases, hundreds of potential therapies, which

22 are increasing every year dramatically, and there

23 just has to be some way of matching patient to

24 treatment.

25 DR. FERGUSON: Okay. Thank you.



2 DR. FERGUSON: Are there some

3 questions?

4 DR. HELZLSOUER: I would just like to

5 throw out one comment, with the estrogen receptor

6 analogy is that the reason we know all this is

7 because they were done in clinical trials. They

8 were evaluated in clinical trials, and I didn't

9 want to lose that momentum for tomorrow's

10 discussion.

11 DR. FERGUSON: Other questions from the

12 panel? Let me just ask a point of order here.

13 (Discussion off the record.)

14 DR. FERGUSON: Mr. Barnes?

15 Mr. BARNES: Actually, I have a

16 question for Dr. Burke. Would it make any sense

17 to go back over data, or would it in fact be too

18 hard or impossible, to do a disease specific

19 analysis based on test by test? I mean, it seems

20 to me that we're all bumping up against the fact

21 that there is a bunch of different tests, and

22 about 30 different types of cancers.

23 DR. BURKE: No, I'm -- I, for all my

24 strong comments, I'm agnostic as to the test

25 itself. I have no opinion on it one way or


1 another. But that is the only way to evaluate

2 the claim that they really want to make.

3 MR. BARNES: Right. But what I mean

4 is, using the data that are either in the

5 articles, or could the data be generated some

6 other way, to reevaluate.

7 DR. BURKE: Well, let me make two brief

8 comments. One is, it's striking that there isn't

9 a large cohort study for a particular disease,

10 which is what one would expect, given the

11 frequency with which this test seems to be done.

12 But number two, yes. For some diseases, for

13 example CLL, it may in fact be the case that

14 there is sufficient evidence, okay, to evaluate a

15 particular test for that particular disease. And

16 the reason why I say particular disease is

17 because diseases are kind of strange, as you well

18 know and I well know, and CLL is a very strange

19 disease, but it has its own characteristics

20 associated with it. And so yes, you'd want to

21 look at CLL in terms of CLL. Why CLL? You're

22 saying well, for this test, given the

23 characteristics of CLL as a disease, does this

24 test help us? In early stage disease, in late

25 stage disease, for particular treatments, if


1 there are effective treatments, because remember,

2 for therapy significant prognostic factors, if

3 there is no effective treatment, then there is no

4 need for therapy specific prognostic factors.

5 MR. BARNES: Right. Well, let me ask

6 my question a different way. Of the 12 studies,

7 or 13 or 14 or whatever they are, is there a way

8 to go back to them and dissect out the

9 histologies, CLL or whatever, according to test

10 result, specific test by test, and get data? So

11 in other words, do you think that anyone, not

12 necessarily you, could go back to the actual

13 publications and dissect that out?

14 DR. BURKE: It depends on the

15 publication, it depends on the study. Some yes,

16 some no. It depends on the adequacy of the

17 study. Some studies you, like retrospective

18 studies, it would be very difficult to do that.

19 Prospective studies that were done properly would

20 be much easier to do, because you would have

21 complete information, which you most of the time

22 don't have in retrospective. But yes, it could

23 be done, if the data were there.

24 MR. BARNES: I'd just like to add a

25 couple of comments as well. Many of the studies


1 on solid tumors and even hematologic tumors or

2 mixture of tumors, and the studies do not specify

3 histologic subtypes, and it becomes very

4 difficult to create a laundry list of studies by

5 disease type. I think if you go through the

6 handout from this presentation, you will see that

7 unfold, because I do specify the, you know,

8 whether it's mixed or what tumor types it is, you

9 know, and sometimes it's just very difficult.

10 DR. FERGUSON: Other questions from the

11 panel? I have a question I'll throw out. It

12 seems that in a number of the papers that we've

13 seen, when there were comparison groups that they

14 were, if there were patients whose cancer was

15 sensitive to a drug, they were given that drug,

16 whereas the other quote, control group, was one

17 who showed resistance, and those were allowed to

18 have physician's choice in the chemotherapy. Now

19 that seems to me to bias the two groups if the

20 test has any validity at all, so that they really

21 aren't comparable. That is, the ones with the

22 drug, the cancer showed sensitivity, were treated

23 by guidance from that test, whereas the ones that

24 didn't or were resistant, were treated by

25 physician's choice. And then one -- that group


1 comes out worse, and why wouldn't we expect

2 that? And I guess I'm asking for a comment or an

3 explanation for why that's the best thing to do,

4 because it does not seem to me to be the best

5 thing to do. Yes?

6 DR. HOFFMAN: My name's Robert

7 Hoffman. We performed such a prospective study.

8 I think in the previous retrospective studies we

9 showed very extensive correlation between

10 survival and response in the drug response assay,

11 in our case the histoculture drug response

12 assay. So we then designed a trial as you

13 mentioned, comparing outcome of patients who were

14 treated by assay guided therapy if their tumors

15 were responsive in the assay, to clinician's

16 choice in the resistant patients.

17 I think it would have been unethical to

18 treat the resistant patients with the resistant

19 drugs as a matter of course. So that was, I

20 think, the criteria in our study. Of course the

21 next step, I think, would be an absolute

22 randomized trial where you separate the patients

23 beforehand, but I think if knowing someone is

24 resistant, given not only the data from our

25 studies, but the very very extensive data


1 presented by the other groups here, I think it's

2 not, and respectfully in my opinion, it's not

3 ethical to treat with a resistant drug.

4 DR. FERGUSON: Are there any other

5 questions or comments? Go ahead, Dr. Klee.

6 DR. KLEE: The study that Dr. Bosanquet

7 alluded to, at one point in the presentation they

8 were talking about this MRC study, I guess is

9 ongoing, the randomization for, which really is

10 randomizing against use of the drug testing

11 versus not using the drug testing. Does that --

12 that seems like a rather fundamental type study,

13 and I was surprised that hadn't been done

14 earlier, it's ongoing now, but it would be sort

15 of the basis of much of the clinical trial work

16 that's been done on a lot of the therapeutic side

17 of things, so it just surprises me that there was

18 no published study along that line. And

19 apparently there are numerous difficulties in

20 trying to carry that out, but I don't know why

21 that hasn't been done or what has precluded doing

22 that.

23 DR. FERGUSON: Yes, Dr. Weisenthal?

24 DR. WEISENTHAL: As one who

25 participated in the design and funding of such


1 studies, what I have to tell you, it's one of

2 these things that's easier said than done. In

3 1985 I had a large grant from the VA, had 31 VA

4 hospitals, it was a cooperative VA study in

5 multiple myeloma, standard therapy versus assay

6 directed therapy. It was several years in

7 planning, we had two national investigators

8 meetings, one was held here in Baltimore. A

9 tremendous amount of work and everything went

10 into that. What happened was that eight months

11 into the study, accrual was running only about

12 one-fourth of what had been projected, they

13 decided that the study just would not be ever

14 completed and so it was cancelled.

15 Subsequently, we got a study going in

16 the Eastern Cooperative Oncology Group, which was

17 to lead to a randomized trial in non-small cell

18 lung cancer. Again, in the first six months the

19 study accrued six patients, although we had 51

20 hospitals eligible to contribute patients, and

21 that was closed.

22 And I keep mentioning Dan von Hoff,

23 who's the most energetic effective clinical

24 trials organizer I've ever seen, tried several

25 times, and never completed a single prospective


1 randomized trial. It's just much easier than

2 said, for all sorts of reasons, that we could

3 discuss over a margarita.

4 DR. FERGUSON: Thank you. Yes, Dr.

5 Burke?

6 DR. BURKE: The cooperative groups and

7 other randomized trialists are collecting frozen

8 tissue, and an issue that may be available to

9 you. I mean, they know the different treatments,

10 they know the outcomes, they have snap frozen

11 tissue. The question is, can these assays be

12 done on snap frozen tissue, because if they

13 could, the outcome is already known.

14 DR. FRUEHAUF: It would be really

15 wonderful if we could use frozen tissue for the

16 assays, and this is really one of the technical

17 issues of doing a prospective randomized study,

18 and we did this with the GOG. GOG-118 was a

19 prospective study, wasn't randomized, but to be

20 obtain fresh tissue at surgery, send it to the

21 laboratory, and I am a member of SWOG, and I

22 attend GOG meetings, I'm a member of ASCO, and I

23 can tell you that tissue banks are a great idea,

24 but they haven't really reached fruition because

25 of the logistical problems of moving tissue from


1 one place to another is very difficult. And so

2 we have not -- you can't use snap frozen tissue;

3 it has to preserved in a live state in media, and

4 transported so it gets there within 24 hours.

5 DR. FERGUSON: Thank you. Other

6 questions from the panel? Yes, Dr. Helzlsouer?

7 DR. HELZLSOUER: I have a question in

8 terms of these assays, and I'm having a little

9 trouble lumping them all together. But is it my

10 understanding that there is only maybe one that

11 tests combinations routinely, all the rest are

12 single chemotherapy assays?

13 DR. FRUEHAUF: The question of single

14 agents and combinations is kind of a tempest in a

15 teapot in a way. Every lab that I know of tests

16 drug combinations. We test drug combinations at

17 Oncotech, AntiCancer tests drug combinations, Dr.

18 Nagourney tests drug combinations, Dr. Weisenthal

19 tests drug combinations. I think one of the

20 issues that is fundamental is, is there drug

21 synergy, and if you don't test two drugs two

22 together where there could be synergy, what are

23 you going to miss in the information? And this

24 goes to the issue of why we use multi-agent

25 therapy. You are an oncologist and an


1 epidemiologist, and I'm sure your thinking like

2 many oncologists is, we use multi-agent

3 chemotherapy because of the gold ecomin

4 hypothesis, that there are multiple subsets

5 within each tumor that are differentially

6 sensitive to different agents in the

7 combination. So when platinum was added to

8 testicular chemotherapy regimens, that additional

9 activity killed a subset that was there

10 microscopically. Even though people had CRs,

11 they weren't surviving.

12 So single agents should be active in

13 combinations. So our view is, if you test a

14 single agent and it can't reach its drug target,

15 there's extreme resistance to that single agent,

16 and it can't reach its target because of protein

17 or rapid adduct repair or what have you, that

18 single agent isn't going to add a synergistic

19 effect in the absence of its own effect. So,

20 Dr. DeVita, in the third edition of Principles

21 and Practices, made a statement in his chapter on

22 chemotherapy that combinations should always be

23 made up of active single agents. And so we look

24 for single agent activity against the cancer in

25 the salvage setting, and then we'll move this


1 agent up into the adjuvant setting, when it's

2 been proven to have activity.

3 So single agent testing is predicated

4 on finding out if the single agent would have no

5 benefit as a single agent, it's unlikely then

6 that it would have benefit in a combination, but

7 we all test combinations.

8 DR. FERGUSON: But let me -- as I

9 recall reading these papers, that none of them

10 actually routinely were testing two agents

11 simultaneously on one. I mean, the majority of

12 the papers that we read and that Dr. Burken

13 presented single agents. Maybe serially they

14 would test several agents, but not together in

15 one Petri dish routinely.

16 DR. FRUEHAUF: Yes. I think that

17 Dr. Nagourney presented evidence, and I will let

18 them speak, but just for our role, we tested the

19 concept of whether single agent testing was

20 predictive in combination therapy in breast

21 cancer. So we took the single agents and looked

22 at their activity as single agents, and added up

23 their scores as I presented this morning, and

24 that was predictive of how the person did in

25 response to the combination. Now other people


1 have tested combinations as well, and I'm sure

2 they will comment on that.

3 DR. FERGUSON: Okay. Was I misreading

4 these papers?

5 DR. HELZLSOUER: That's the same way, I

6 interpreted them the same way, they were all

7 single agent tests, and not combinations.

8 DR. FERGUSON: Yeah. I mean, all the

9 published stuff we saw was single agent.

10 DR. HANDELSMAN: The bulk of it was,

11 but not all of it.

12 DR. FERGUSON: Okay. Yes?

13 DR. HOFFMAN: Technically, to test

14 combinations is entirely feasible. We're dealing

15 with most of the tests with culture dishes,

16 culture wells, with medium. You can add one

17 drug, two drugs --

18 DR. FERGUSON: I don't disagree with

19 that.

20 DR. HOFFMAN: You can add ten drugs.

21 Most of the studies have, as has been mentioned,

22 have focused on single drugs to understand their

23 individual activity. We've done a study as yet

24 unpublished that shows predictivity to the

25 combination treatment for ovarian cancer as


1 predicted by Cisplatin alone, but to mix drugs in

2 the cultures is technically trivial.

3 DR. NAGOURNEY: Yeah, if I might just

4 address that. Actually we specifically do focus

5 on drug combinations, and as Dr. Fruehauf alluded

6 to, most drug combinations are basically

7 additive, and in some cases subadditive or

8 antagonistic. There are a small number of

9 combinations that are genuinely truly

10 synergistic, and which are extremely attractive

11 and interesting as therapists. One of the most

12 attractive are the interactions between

13 alkylating agents or platinum, and

14 antimetabolites, a couple of examples of which

15 were cited in some things we referenced, one

16 paper in the British Journal of Cancer,

17 indicating true synergy between alkylating agents

18 and CDA, and that observation has now resulted in

19 a 100 percent response rate in an ECOG trial.

20 Similarly, Cisplatin and Gemcitabine as

21 a related combination in solid tumors is

22 presenting us with really one of the most active

23 combinations we've ever seen in medical oncology,

24 but those are actually pretty rare. So I think

25 for the most part, most drugs are intelligently


1 given as single agents, but there are a few very

2 beautiful examples of synergy, and they can be

3 test.

4 DR. FERGUSON: Is this in response to

5 that?

6 DR. KERN: Yes. Just briefly. In the

7 now famous, or perhaps infamous Kern and

8 Weisenthal paper of 1990, we had a cohort of 105

9 patients that were treated with combinations, and

10 we showed that --

11 DR. FERGUSON: I don't doubt that the

12 patients are treated with combinations. The

13 issue was, was the test done with two drugs?

14 DR. KERN: That's correct. All the

15 drugs were tested singly and in combination in

16 the laboratory, and correlated with the clinical

17 with the clinical response.

18 DR. FERGUSON: That wasn't clear, at

19 least to me.

20 DR. KERN: I understand. It's in Table

21 5 of that paper. Thank you.

22 DR. HELZLSOUER: Another concern I have

23 which hasn't been addressed, and we didn't really

24 have it in our packet, were the reproducibility

25 issues of these tests. Then I just heard that


1 you have to have the fresh tissue within the lab

2 within 24 hours, and this may need some

3 clarification. Also, we're dealing with home

4 brews that are being done in certain labs, so the

5 tissue has to go to that lab, there won't be

6 kits. So what will be the accessibility of

7 this? Not just -- so we have the reproducibility

8 issue in doing that, but then in general, how

9 would these be able to be done if there is only a

10 few labs doing these?

11 DR. FRUEHAUF: Well, I think that if

12 there's a favorable decision today, there will be

13 many more labs doing this.

14 DR. HELZLSOUER: Well then, I would

15 like to have more information even yet on

16 reproducibility.

17 DR. FRUEHAUF: Yeah. The

18 reproducibility thing is very important. And

19 we're inspected by the College of American

20 Pathologists to fulfill CLIA regulations, and we

21 have to show precision, we have to show

22 sensitivity and specificity, and we do that by

23 looking at thousands of cases in our database to

24 show that the population patterns remain

25 constant.


1 Most of the laboratories, and we work

2 by getting specimens from all over the country,

3 and we set up a system where Federal Express

4 takes the specimen immediately after surgery and

5 brings it to our laboratory. The other labs use

6 similar courier processes. So it's not that it's

7 hard to get a motivated person in the pathology

8 department to send the specimen.

9 DR. HELZLSOUER: Let me ask you this,

10 about your reproducibility studies. So they're

11 done using your known samples, so you have your

12 known controls; is that what you're saying within

13 your lab?

14 DR. FRUEHAUF: That's correct.

15 DR. HELZLSOUER: Is that what the

16 regulations are? My experience with that, with

17 dealing with laboratories, is that's usually not

18 very reproducible when you're dealing with sent

19 specimens that you do not know. So I wonder if

20 those studies have been done in these assays to

21 determine for samples unknown to you --


23 DR. HELZLSOUER: Sent specimens.

24 DR. FRUEHAUF: That was done. SWOG did

25 a study in the '80s where they looked at


1 concordance between laboratories, and they sent

2 the same specimens to different laboratories.

3 And they found a concordance level of about 80

4 percent between laboratories for the same result,

5 and I think that is really significant

6 considering the variability of biological

7 specimens.

8 DR. BURKE: (Inaudible).

9 DR. FRUEHAUF: It's from a book, Tumor

10 Cloning Assays, that was published. Somebody

11 might know that better that I do.

12 DR. BURKE: My question was, did you

13 have a citation on that.

14 DR. FRUEHAUF: I can provide that to

15 you after the meeting.

16 DR. BURKE: Because I share your

17 concern about -- I mean even the most common

18 tests have difficulty, most prognostic factor

19 tests have a great deal of problems with

20 reproducibility. CAP has been trying to do

21 standardization for years in this area, on even

22 automated type tests, and it's very, very

23 difficult to do.

24 DR. FRUEHAUF: I can tell you what

25 we've done. We have cell lines that we study.


1 We have 25 different cell lines with

2 characterized drug response patterns. And we

3 send these as unknowns into the laboratory on a

4 periodic basis, to make sure that every day,

5 every week when we're running the assays, we are

6 getting the appropriate result for these cell

7 lines, which are unknown to the people in the lab

8 who are doing the assay. So we have an internal

9 validation process with 15 to 20 cell lines that

10 we run routinely to validate the Cisplatin result

11 is appropriate for the ovarian cell line, for

12 instance; that the adreomyecin result is

13 appropriate for the breast cancer cell line, and

14 this is an internal validation process which, we

15 use the same one for doing markers, for doing

16 HRCC New, and P-53 as phase for actions, where

17 you have to have internal validations you run in

18 your laboratory to confirm that every time you're

19 running the test, you're getting the same

20 expected results.

21 DR. KASS: Could I ask a follow-up

22 question on that? In that particular study that

23 you referenced, one thing that was of interest to

24 me, we have seen lots of different types of

25 laboratory tests, and I was wondering if in that


1 study they addressed the results comparing the

2 different types of assays that we have heard

3 referred to today, have any studies been done to

4 look at the comparability of the DiSC versus the

5 MMT, versus whatever?

6 DR. FRUEHAUF: Yes, and I think that

7 other people do this all the time. Dr.

8 Weisenthal does three separate assays on each

9 specimen that comes into his laboratory. What we

10 did for GOG 118 internally, we ran a DiSC assay,

11 and we ran an EDR assay on the same specimen, and

12 we looked at the cut points of low, intermediate

13 and extreme resistance, and we found that they

14 were exactly concordant with a very small, one to

15 two percent difference. So, the cut points are

16 very important, reproducibility is very

17 important, but all the people who are doing this

18 have been doing this for 15 or 20 years and

19 have -- there was an NCI consensus conference in

20 the '80s that addressed these specific issues of

21 quality control, because this all stems from the

22 NCI funding these laboratories originally to

23 develop this technology. And it was partly done

24 for drug discovery and it was partly done for

25 helping patients get the right therapy. So the


1 consensus conference looked at the issues of

2 coefficient of variation, out wires, how many

3 standards you needed to run with each assay, et

4 cetera. And they set up a profile of quality

5 control requirements internally in the laboratory

6 that would be necessary, and they compared the

7 different laboratories that were doing the

8 testing, so that there would be a uniformity of

9 process. And so we incorporated into our

10 laboratory procedures those quality assurance and

11 quality control measures, along with the internal

12 standards being run all the time. And what we

13 are doing now is using these cell lines to send

14 to the other labs as proficiency tests, because

15 we have to have proficiency tests to maintain the

16 quality assurance.

17 DR. FERGUSON: Thank you. Very briefly,

18 Dr. Kern.

19 DR. KERN: Yeah, very briefly. There

20 was a study published by NCI a few years ago

21 where we compared four laboratories, UCLA lab,

22 Sid Salmon's lab in Arizona, Dan von Hoff in

23 Texas, and Mayo Clinic, Dr. Liebe's lab, they

24 were all sent -- all labs were sent 20 compounds,

25 blinded, coded. Most of them were anticancer


1 drugs; some of them included sugar and salt. And

2 we published on the very close reproducibility of

3 all four laboratories. I can provide you that

4 reference.

5 DR. FERGUSON: Thank you. Dr. Loy?

6 DR. LOY: I just wanted to ask a

7 question that remains in my mind, and that is,

8 when is the optimal time to biopsy? Certainly

9 you would expect tumor biology to change after,

10 or posttreatment, whether it be chemotherapy or

11 radiation, and I'm just wondering if there's any

12 studies to talk about or clarify when the most

13 appropriate time to biopsy is, and if there's any

14 predictive value in testing those tumors that

15 have not previously been treated.

16 DR. ROBINSON: My name is William

17 Robinson. I'm with the U.S. Harvest Medical

18 Technologies Corporation. We didn't send

19 literature to the panel, but we did get in on

20 this at the end, thank the Lord. One thing we

21 wanted to draw reference to was that question

22 about timing, because according to a research

23 paper that came out of NIH in 1981, they felt the

24 most appropriate time was within the first four

25 hours of biopsy, because I think according to


1 Dr. Wing, that the gethaco protein does get

2 inducted very early on, so therefore, you don't

3 get a real response, a clear response to what the

4 tumor looks like in vivo as opposed to what you

5 actually see in the Petri dish. Some of the

6 literature we sent actually does show you, for

7 those who can actually see this, that we were

8 able to pick up a metabolism very early on,

9 within about minutes. So if it's a case where

10 you're going to compare MTT tests and the DiSC

11 tests, I think the idea is you want to get the

12 tumor in the closest condition that it appears

13 naturally, so as far as automation is concerned,

14 and that's where we come in, we think that this

15 is the kind of tool and the kind of forum for

16 discussion as to how you combine therapies, that

17 this makes this a very good and useful meeting.

18 Thank you.

19 DR. LOY: Thank you for that, but my

20 question was more directed towards when in the

21 course of the history of the disease, is it

22 pretreatment or posttreatment?

23 DR. FRUEHAUF: Acquired resistance is

24 an important question, so that if somebody has a

25 biopsy and you get a result and you treat the


1 patient, and then the patient's failed primary

2 therapy and you want to go back to your result.

3 The question is, is that result still valid to

4 treat the patient now, who's had intervening

5 therapy? Is that part of your question?

6 DR. LOY: That is part of my question,

7 but please address that issue.

8 DR. FRUEHAUF: So first, of course, you

9 have two kinds of variability up front in newly

10 presenting patients; you've got sit, inter-site,

11 and for synchronous lesions, and so we studied in

12 paired cases synchronous lesions and metachronous

13 lesions. And we looked at extreme resistance

14 frequencies for the various drugs, between sites

15 and over time, for ovarian cancer. We presented

16 this at AACR. We found that there is a very low

17 frequency of a two-drug category shift, of about

18 5 percent, in terms of synchronous lesions. So

19 if you looked at platinum resistance in an

20 ovarian cancer patient and you compared the

21 primary ovary with the peritoneal metastases,

22 only 5 percent of the time was there a

23 significant difference in the result. It went up

24 to about 8 percent when it was over time, so the

25 difference over time -- now, I think the key is,


1 there's not a lot of heterogeneity and change in

2 resistance patterns, but there can be a decrease

3 in sensitivity, so that if you're using the assay

4 to identify ineffective agents, an agent that's

5 ineffective initially, after intervening therapy,

6 was still inactive later. It was a loss of

7 sensitivity that was occurring. So there is a

8 robust ability to say if the drug wasn't going to

9 work up front, it's unlikely after failure or

10 progression that that drug is now going to work

11 in the relapse setting.

12 DR. LOY: Have you found the same thing

13 or have studies been shown to show the same thing

14 to be characteristic of hematologic malignancies,

15 which are known to transform after chemotherapy?

16 DR. FRUEHAUF: I would leave that to

17 one of my friends who does this research on that.

18 DR. FERGUSON: Mitch, do you have

19 something?

20 DR. BURKEN: Just a quick comment on a

21 study. Just -- I didn't get it in before. The

22 issue came of up of concordance or discordance

23 between different assay formats. And you know,

24 there have been several studies; as a matter of

25 fact, some of them were listed this morning. One


1 of the studies, I'm not sure whether it was

2 listed or not, was by Tavassol in Oncology in

3 1995, where there were 17 patients that had head

4 to head FCA and EDR, and there was some

5 discordance. At least 12 of the 17 patients had,

6 or 12 of the 17 patients had at least two drugs

7 that had different patterns. The problem with

8 those kinds of studies, as I said, you run up

9 against complicating factors like the tumor

10 heterogeneity that we talked about earlier, where

11 the differences may be due to the fact that

12 there's just intrinsic tumor heterogeneity. And

13 so, it does open up I think another vista of ways

14 of looking at test accuracy.

15 DR. FERGUSON: Dr. Bosanquet, did you

16 have some response?

17 DR. BOSANQUET: Can I address a couple

18 of these points? We very early on looked at

19 different biopsy sites for the hematologics, and

20 compared drug sensitivity. So we looked at

21 blood, bone marrow, lymph node, and found almost

22 identical drug sensitivity from those three

23 sites, in CLL and non-Hodgkins lymphoma, similar

24 diseases.

25 We have also -- I also concur in the


1 ovarian data that John Fruehauf has just

2 mentioned. We also in our laboratory find almost

3 identical results between a situs and a primary

4 tumor in the ovarian setting.

5 The point was raised about the timing

6 of the biopsies. In 1988 we published a paper in

7 Cancer, which hasn't been mentioned, in which we

8 looked at drug sensitivity before and after an

9 intervening period of time. If there was no

10 intervening chemotherapy, there was no difference

11 in drug sensitivity from one to the subsequent

12 test. If there was intervening chemotherapy that

13 was not the drug that you were testing -- I'm

14 sorry -- if there was intervening chemotherapy,

15 for instance, with Doxorubicin, and you looked at

16 the difference in chlorambucil sensitivity before

17 and after the Doxorubicin, there was usually a

18 slight increase in resistance, and chlorambucil

19 resistance.

20 If you looked at the drug that had been

21 given in between, so you tested Doxorubicin, then

22 you gave Doxorubicin, then you tested Doxorubicin

23 again, you saw a greater increase in resistance

24 between the two tests. There was one anomaly to

25 this finding, this universal finding, which is


1 becoming a standard chemotherapy in CLL in

2 Britain. And that is that we found that if

3 patients were treated with chlorambucil, and this

4 is just in CLL, if patients were treated with

5 chlorambucil, they became 10-fold more sensitive,

6 or there or cells became 10-fold more sensitive

7 to the steroids. And this is an anomalous

8 finding, which is really quite exciting. And if

9 you look in the original literature on steroids,

10 not much use in untreated CLL. But we found

11 them, high does methylprednisolone for instance,

12 to be very effective in previously treated CLL,

13 supporting this finding from the laboratory. So

14 that's, as far as I'm aware, the only time that

15 increased sensitivity is induced by treatment.

16 DR. FERGUSON: Other questions from the

17 panel members or comments? If not, we will

18 reconvene tomorrow morning at 8:00.

19 (The panel adjourned at 4:33 p.m.,

20 November 15, 1999.)