Incorporating genomics into the cancer clinical trial process

Incorporating

Genomics

Into

the Cancer

Soonmyung The effectiveness of current chemotherapeutic proaches for the treatment of solid tumors a near plateau, suggesting we are nearing

has reached the limit

cytoreduction.

may

It is hypothesized

to “subset effect,” ing to responses within the population

that

this

ap-

and that drugs administered predicted for particular being treated could

be

of due

accordsubgroups overcome

what appears to be a limit of cytoreduction. However, the clinical trial process, as currently structured, prevents efficient discovery and validation of predictive markers of treatment is proposed, based throughput in, unbiased dictive Semin Saunders

on

multiplexing discovery

markers. Oncol

Z&305-309.

response. preoperative and

An

alternative therapy

of markers validation

to provide process

Copyright

0

2001

process and higha builtfor preby

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UMOR KINETICS models predict that a solid tumor can eventually be eliminated if a sufficient dose of chemotherapy can be delivered.’ From this premise came the development of combination chemotherapy regimens and methods for dose intensification in cancer treatment. However, negative results from recent clinical trials of highdose chemotherapy challenge this prediction and suggest that there may be a limit to the cytoreduction that can be achieved by chemotherapy.1 Although the underlying mechanism for such a limit is unknown, the phenomenon points to the need for a more rational method of selecting the patients who may or may not benefit from specific chemotherapeutic agents. POTENTIAL FOR THE

MECHANISMS RESPONSIBLE LIMIT OF CYTOREDUCTION

Because the concept of a limit of cytoreduction was derived from the results of patient cohort studies and not from studies of one particular kind of tumor treated with different doses of chemotherapy, one can only speculate about why it occurs. It could be argued that tumor cell heterogeneity is ultimately responsible for limiting cytoreduction. While a mixture of chemotherapeutic agents targeted at different molecules can kill most tumor cells, due to the heterogeneity and genomic instability within the tumor cell population, there will be always some tumor cells that are resistant to a given therapy. If one accepts this argument, there Seminars

in Oncology.

Vol 28, No 3 (June),

2001:

pp 305-309

Clinical

Trial

Process

Paik

is no way that the ceiling of cytoreduction can be broken through. One could also hypothesize that the apparent existence of the limit of cytoreduction for a patient cohort is a result of nonselective administration of chemotherapy. In such a case, while a particular subset of patients in the cohort would benefit from a given therapy, the rest would only contribute to the dilution of the survival benefit. This hypothesis offers hope of overcoming cytoreduction limits, since it might be possible that the use of predictive markers of response or resistance would allow for the rational selection of particular chemotherapy regimens for given patients, and the use of such markers could allow us to break through the limit of cytoreduction. EVIDENCE

FOR THE

SUBSET

EFFECT

Results from the National Surgical Adjuvant Breast and Bowel Project (NSABP) B-18 trial lend support to the second hypothesis. In that study, patients with operable breast cancer were randomized to receive a doxorubicin-cyclophosphamide (AC) regimen either preoperatively or postoperative1y.z There was no difference in clinical outcome between the preoperative and postoperative arms, suggesting that the same therapy given postoperatively can be administered preoperatively without harm to the patient. More importantly, initial pathologic responses to AC chemotherapy in the preoperative chemotherapy arm correlated with clinical outcome. Those patients whose tumors showed complete response (ie, the complete disappearance of tumor cells after AC) demonstrated better clinical outcomes than did those

From the Division of Pathology, National Surgical Adjutant Breast and Bowel Project, Pittsburgh, PA. Supported by Public Health Service Grants No. UlO-CA12027, LJlO-CA-69651, UlO-CA-373777, and UlO-CA69974. Address reprint requests to Soonmyung Paik, MD, Division of Pathology, National Surgical Adjutant Breast and Bowel Project, Four Allegheny Center, 5th Floor, East Commons Professional Building, Pittsburgh, PA 15212. Copyright 0 2001 by W.B. Saunders Company 0093-7754/01/2803-0011$35.00/O doi:l0.1053/sonc.2001.23496 305

SOONMYUNG

whose tumors retained invasive cancer cells after AC. In addition, those with any invasive tumor cells remaining after preoperative chemotherapy showed a worse clinical outcome than did those who received postoperative chemotherapy. These data suggest that the overall clinical outcome results in the B-18 postoperative chemotherapy arm were averaged outcomes of those who actually benefited and those who did not. These data also suggest that only a small subset (9%) of patients with breast cancer fully responds to AC, and that for tumors in these patients, the limit of cytoreduction may not exist for AC. HYPOTHESES Using the data obtained from the NSABP trial B-18, we can formulate some basic assumptions about how the apparent limit of cytoreduction with the various chemotherapeutic regimens can be overcome: (1) only a unique subset of tumors responds (“complete pathologic response”) to each specific chemotherapeutic agent; (2) tumor response to each chemotherapeutic agent is governed by the specific gene expression profile within the cells of that tumor; (3) tumor response to each chemotherapeutic agent can be predicted from the gene expression profile, once that profile is determined; and (4) the use of predictive markers of response or resistance would permit the rational selection of chemotherapy regimen for each patient and allow us to break through the limit of cytoreduction. IDENTIFICATION OF SYSTEM BOTTLENECKS The idea of predictive markers for treatment response is not novel. Prognostic indicators for breast cancer have been around for more than a decade. But why are there no predictive markers available for the drugs being used clinically for chemotherapy? The problem lies in part in the current structure of the clinical trials system. The drug evaluation process usually spans three phases of clinical trials, which together routinely take about 10 years. Toxicity and dosing are tested in phase 1 trials. Response rates are assessed in phase 2 trials; for breast cancer, these rates are typically approximately 30%. Finally, phase 3 randomized adjuvant trials are conducted with the knowledge that more than 70% of the patients may not derive additional benefit from the new regimen. Because

PAIK

the typical expected benefit from the addition of a new drug to an existing standard regimen is very small (-5% to 10% absolute benefit), phase 3 adjuvant therapy trials require several thousand patients. But it is generally only after the demonstration of overall benefit in phase 3 trials that we attempt to discover whether certain subsets did or did not benefit from the therapy. Thus our starting point is when we retrospectively address the factors that may predict or correlate with the response to a specific therapy. Since candidate markers are not initially sought for the purpose of their potential worth as predictive markers of response, they typically go through a development process that parallels that of drug development-and that also takes about 10 years. However, one problem with this conventional approach is that even the large sample sizes available for analysis from phase 3 randomized trials will not necessarily provide results that confirm the predictive nature of these markers, because sample size in phase 3 trials is not based on testing the marker/ treatment interaction, but on testing the superiority of the test drug arm over the control arm. The only absolute way to confirm the role of a molecule as a predictive marker is to conduct another study in which the marker is used as a stratifying variable. Thus, it actually will take more than 10 years from the time the phase 1 trial begins until predictive markers can be validated, even when there are candidate markers to be tested. Another reason for the lack of success in identifying predictive markers in conventional clinical trials is the fact that molecular targets for chemotherapeutic agents are not clearly defined. Experiments using model systems comprised of mutant strains of yeast have shown that chemotherapeutic agents once thought to have defined molecular targets actually are rather promiscuous about their target specificity.3 This is most likely because the redundancy in DNA repair and the cell cycle control mechanism in higher organisms renders assaying for a single or a handful of predefined molecular targets unable to provide clear prediction of response to specific drugs. An unbiased look at the entire genome and its expression pattern may be necessary to identify true predictors of response. Unfortunately, within the current multicenter adjuvant trial process, it is difficult to set up tissue banking in such a way as to collect the ideal materials required for genomic scale assays, in

GENOMICS

IN CLINICAL

TRIALS

307

part because a patient’s decision to enroll in a given clinical trial is usually made after diagnostic procedures are performed. By the time a patient provides informed consent to participate in the trial, the window of opportunity for fresh tissue procurement is usually over. In summary, problems with the conventional clinical trials approach in terms of identifying predictive markers include the facts that (1) testing of the markers is not the primary aim of the clinical trials; (2) sample size calculations are based on demonstrating the effect of the treatment, and not on marker-treatment interaction, which would require a much larger sample size; (3) tissue procurement is not ideal for genomic profiling, since the patients are enrolled into the trials after the window of opportunity to procure specimens is over; and (4) there is no “discovery tool” for predictive markers built into the trial process. DEVELOPMENT

OF A NEW

PARADIGM

Is it possible to devise a systematic trial process that contains a built-in tool for the identification and confirmation of predictive markers? Preoperative chemotherapy trials may be ideal for this purpose. As noted above, information obtained from NSABP trial B-18 serves to illustrate why. In B-18, initial tumor responses to preoperative AC correlated with clinical outcome.* Ultimately, 9% of patients showed complete pathologic response and subsequently enjoyed a 90% 5-year survival rate; 91% did not. Theoretically, we could increase the proportion of responders from 9% to 100% by using other drugs; this could lead to as much as a 90% 5year survival rate for all breast cancer patients. This concept could be tested preliminarily by adding to the regimen a drug that would increase the proportion of responders to, for example, 30%; this percentage could then be translated into changes in survival rates. In fact, this is one of the aims of the NSABP’s currently ongoing trial B-27. In this protocol, we have hypothesized that the addition of Taxotere (docetaxel; Aventis, Strasbourg, France) to AC will increase the complete pathologic response rate, with the expectation that approximately 90% of these responders will enjoy 5year survival. If results from this study confirm this hypothesis, one could envision a trial process in which index tumors could be treated with a series of systemic therapies until they show complete local response

Proposed Clinical New drag development Preclinical Q Phase

Trial Process New Molecular

targets

studies oxicity

and dosing)

Phase Ii(response - molecular pattern correlation in Pre-op) muitiplex markers and drugs in pre-op with discovery aims Phase III (Validation

of prediction

pattens

in Pre-op)

Fig I. Overall flow of the prediction-based clinical trial process. After the conventional phase I trial, phase 2 trials are conducted in the preoperative setting in operable breast cancer with the aim of discovering predictive markers. A phase 3 trial, in the preoperal:ive setting, is designed to confirm the markers discovered at the phase 2 stage. New molecular targets will be also identified through this trial process.

and the response of disseminated metastatic cells is ensured. The problem with this concept is that such consecutive Itreatments without the preselection of patients would prove too toxic. Additionally, during the course of any ineffective treatments, the tumor could progress to the point at which it would be inoperable. One way such a strategy could be (employed is if we had predictive markers by which we could preselect the drugs to which the tumor cells in a given patient would have a fair chance of responding. For the purpose of this report, I will call this concept the “prediction-based clinical trial process.” HOW

WOULD

THE

PROCESS

WORK?

The prediction-based clinical trial process has two essential components (Fig 1): (1) a built-in discovery tool for predictive markers, using arraybased, high-throughput determination of gene expression profile and genomic abnormalities (in the discovery phase of the trial); and (2) rapid validation of identified predictive markers (in the validation phase). The preoperative phase of a trial could be used as an experimental (“discovery”) phase of the protocol to generate tumor response information. Patients could then be treated with standard chemotherapy after surgery, guaranteeing adequate therapy. Under this scenario, a phase 2 trial could be safely performed in patients with operable breast cancer, as well as in those with advanced

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disease. Discovery tools for predictive markers could be built into the preoperative experimental phase (Fig 2) of phase 2 trials. High-throughput technologies such as cDNA microarray, which permits the assessment of expression patterns of literally all expressed genes from tumor cells, and array-based comparative genomic hybridization (CGH),5 which allows high-resolution mapping of amplified and deleted chromosomal regions, are two examples of such tools. A profile of gene expression and gene copy number changes could then be correlated with tumor response to specific drugs, and candidate predictive markers or patterns could be identified at the end of the phase 2 trial. This process should be applicable either to a single drug or to multiple drugs to be tested, and in any given trial, both the drugs and markers could be multiplexed. The next phase of the trial would be designed to validate any candidate markers discovered (Fig 3). Thus, for example, if gene expression pattern A were correlated with a response to drug A in rhe discovery phase, then in the validation phase of the trial, patients whose tumors showed expression pattern A would be assigned to drug A. Theoretically, this would then result in a drastically increased response rate if pattern A actually was a

Discovery Phase Core Biopsy

A

B

C 30

R 20

E 25

A

B

C

D

E

Standard Post-op therapy Fig 2. The discovery phase of the prediction-based trial (phase 2 trial). Drugs are randomly assigned tients. Core biopsy will be taken before preoperative therapy and used to profile gene expression and number attempt

changes will be

sponse to each gene expression

using made drug profile

microarray. to discover used, and

clinical to the pachemogene copy

At the end of the trial, markers that predict

by correlating response with gene copy number changes.

an rethe

PAIK

Validation Phase Core Biopsy Multiplex Markers (H

-throughput analysis)

I

Multiplex Drugs in Pre-op Test Phase Pattern A B C D Drug A B C D

E E

I

Validation of response correlation Response 90% 80 90 90

80

I

Standard Post-op therapy Fig 3. trial (phase predictive response the phase

The

validation phase of the prediction-based clinical 3 trial). Drugs are assigned to patients based on pattern. Increased response rates compared to the rates obtained when drugs were randomly assigned in 2 trial would be the end point of the trial.

true predictive marker of response. As in the discovery phase, the validation phase would be able to accommodate the testing of either a single drug or multiple drugs in one trial. FEASIBILITY OF THE PREDICTION-BASED CLINICAL TRIAL PROCESS

Several issuesmust be resolved before the concept proposed here could be implemented. The logistics involved in collecting biopsy tissuewith adequatepreservation of mRNA speciesis the first. A recently introduced reagent called RNAlater (Ambion, Austin, TX) allows for the collection and storageof tissueat room temperature without significant degradation of mRNA. For groups like the NSABP, this may mean that we will be able to collect biopsy tissue at the local membership(institutional) sites without requiring sophisticated freezing methods to preserve mRNA. A second problem is that large amounts of mRNA are required for cDNA microarray experiments (typically 2 pg). Becausecore biopsy materials will not provide such amounts, we need a method by meansof which we can reliably amplify all types of mRNA from the tumor cells without losing the original relative quantity information among the different transcripts (i.e., linear amplification of mRNA species).Although there may never be a perfect solution to this problem, the use of a T7

GENOMICS

IN CLINICAL

TRIALS

polymerase-based amplification system shows promise.” Another important technical question the prediction-based clinical trial process presents is what statistical methods one should use to analyze the daunting amount of information that will result. There are approximately 100,000 genes expressed in the human cell; multivariate analyses of the data gathered from such trials will be almost impossible using standard statistical methods. Sample size estimation is another problem, although rein prediction-based trials sponse correlation should be much simpler than are the time-series analyses that are associated with the conventional trial mechanism. In essence, what we need are computer programs capable of automatically classifying tumors based on a correlation between gene expression and/or gene copy number profiles and response to preoperative chemotherapy. One example of such a program has been developed by a group at the Massachusetts Institute of Technology based on a “neighborhood algorithm” that was used to autoclassify subtypes of leukemia.7 In the latter study, the investigators have used microarray of more than 8,000 genes to examine the mRNA expression pattern in tumor cells obtained from patients diagnosed with acute myelocytic leukemia and acute lymphoblastic leukemia. In the end, the use of a set of 10 to 100 genes identified from the original more than 8,000 genes could accurately classify between the two types of leukemia. However, even with this somewhat sophisticated analysis tool, an accurate prediction of response to chemotherapy could not be achieved in a set of, for example, 15 leukemia patients with known response data. On the other hand, Alizadeh et a1,8 using a custom cDNA microarray of 17,856 genes enriched for genes expressed in germinal center B cells and hierarchical cluster analysis, identified two molecularly distinct forms of diffuse large B-cell lymphoma (DLBCL) that had gene expression patterns indicative of different stages of B-cell differentiation. One type expressed genes characteristic of germinal center B cells (germinal center B-like DLBCL); the second type expressed genes normally induced during in vitro activation of peripheral blood B cells (activated B-like DLBCL). Patients with germinal center B-like DLBCL had significantly better overall

survival than did those with activated B-like DLBCL.8 Undoubtedly, genomic science is rapidly evolving, and more sophisticated analysis tools will be developed in the next few years. CONCLUSION The prediction-based clinical trial process has two major advantages over conventional clinical trial evaluation: (1) a built-in discovery tool for predictive markers, and (2) shortened drug evaluation time, due to a dependency on response information, rather than long-term follow-up, as a determinant of clinical outcome. For this concept to go forward, the correlation between initial tumor response to preoperative chemotherapy and eventual clinical outcome must be validated. NSABP’s trial B-27, which compares preoperative AC to preoperative AC followed by preoperative Taxotere, is expected to provide such data within the next few years. By that time, tools for reliable mRNA amplification and data analysis for complex cDNA microarray experiments are expected to have matured enough to be implemented into the clinical trial process. ACKNOWLEDGMENT The NSABP

author

acknowledges

for her

role

Barbara

as scientific

Good,

editor

PhD,

at

the

of the manuscript.

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