Gene-Guided Chemotherapy Research Questioned

[gdpawel]

It is the hope is that any patient with cancer would have their tumor biopsied and profiled. The profile would then be displayed as a unique genetic signature, which would in turn predict which therapy is most likely to work. However.....

Gene-Expression Signatures in Lung Cancer: Not Ready Yet

Roxanne Nelson - Medscape Medical News

March 17, 2010 — The identification of prognostic markers could assist in the clinical management of nonsmall-cell lung cancers (NSCLC). Although molecular profiling of tumors has led to the identification of gene-expression patterns, a new review has found "little evidence" that any of the signatures are ready for use in the clinical setting.

In addition, the researchers reported that they found "serious problems in the design and analysis of many of the studies" that were included in their review, published online March 16 in the Journal of the National Cancer Institute.

Even in its earliest stages, lung cancer has a very high recurrence rate and mortality, the authors note. Current clinical staging techniques have limitations in terms of predicting recurrence and guiding treatment, but the ability to identify new molecular targets using techniques such as microarray-based gene-expression profiling has the potential to improve patient care.

Inconclusive Results Thus Far

Studies have reported mixed results. As previously reported by Medscape Oncology, one recent review article found that gene-expression profiling failed to outperform standard histologic examinations. However, another study reported that a "5-gene signature" was closely associated with relapse-free and overall survival among patients with NSCLC.

More recently, at the 2010 Joint Conference on Molecular Origins of Lung Cancer, researchers reported that a mutated epidermal growth-factor receptor (EGFR) gene signature was a validated therapeutic target in NSCLC, and suggested that this gene signature might provide "predictive value and biological insights" into EGFR inhibitor responses in lung adenocarcinomas.

For the current review, Jyothi Subramanian, PhD, and Richard Simon, DSc, from the Biometric Research Branch at the National Cancer Institute in Bethesda, Maryland, conducted a literature search of studies published from 2002 to 2009 to critically evaluate studies that reported prognostic gene-expression signatures in NSCLC.

Little Evidence of Gene Signatures

The authors selected 16 studies as being most relevant, and closely assessed them for a number of criteria, including the appropriateness of the study design, the statistical validation of the prognostic signature on independent datasets, the presentation of results in an unbiased manner, and the demonstration of medical utility for the new signature beyond that obtained using existing treatment guidelines.

They noted that one of the "striking findings" is that none of the studies succeeded in showing that gene-expression signatures had better predictive power "over and above known risk factors." In fact, they note, the majority of the risk factors outlined by the National Comprehensive Cancer Network (NCCN) guideline were not even considered by most of the studies they reviewed.

For example, the extent of residual tumor after resection is the most important variable, after stage, when making decisions about adjuvant chemotherapy, according to the NCCN guideline. But only 7 of the studies stated that completeness of resection was a criterion for patient selection.

Drs. Subramanian and Simon point out that "the most important medical question that needs to be answered by a new prognostic signature in NSCLC is whether it can identify the subset of stage IA patients who might benefit from adjuvant chemotherapy." But only 2 studies in their survey included validation results for this subpopulation.

The majority of papers presented overall validation results for stage I patients, and some of the signatures were successful in identifying high-risk stage I patients. However, whether or not the signature was better at predicting overall survival than tumor size or other standard risk factors was not adequately addressed and was unclear from most of these studies, the authors report. Only 1 study, they note, reported a marginal improvement in the predictive accuracy for their gene-expression signature, compared with tumor size, for stage I patients

Another important medical need is the ability to identify the subset of stage IB and stage II patients who are at a low risk for disease recurrence without chemotherapy, the authors explain. But only one of the studies presented separated validation results for this subgroup of patients; a second study was the only one that reported the statistical significance of the prognostic signature for validation in stage II samples. The lack of predictiveness for stage II patients could be the result of the small number of such patients in the study samples, they note.

Most of the studies presented validation results on data that were not used for developing the predictive signatures.

"Most of the studies presented validation results on data that were not used for developing the predictive signatures," they write; in addition, "none of the 16 studies reviewed adequately addressed the question of the predictive power that could be attained by using easily measurable clinicopathological factors for stage I samples."

On the basis of their observations and analyses, the authors suggest a set of guidelines to aid the design, analysis, and evaluation of prognostic gene-expression studies, with a focus on NSCLC.

"Clinical validity of a prognostic signature implies demonstrating that the test result correlates with clinical outcome," they write, whereas "medical utility of a prognostic signature means that the test result is actionable, leading to patient benefit."

Therefore, the ultimate test of clinical validity for a prognostic signature is how well it performs in a prospective clinical trial. Several such trials are currently underway, including the CALGB 30506 trial that was recently initiated to clinically test the lung metagene prognostic signature in lung cancer, the authors point out.

"Regardless of clinical validation, unless a new prognostic signature provides additional risk stratification within the stage and risk-factor groupings on which current treatment guidelines are based, its broad acceptance in medical practice is unlikely," the authors conclude.

J Natl Cancer Inst. Published online March 16, 2010

[gdpawel]

Gene expression (signature) assays are panels of markers that can predict the likelihood of cancer recurrence in various populations. Functonal profiling assay is a test for drug activity against a tumor. Pharmacogenomic testing is a test to identify patients who are likely to have the most toxicity.

By testing the gene expression markers of a patient, oncologists can identify those patients unlikely to benefit from adjuvant chemotherapy from those that would. If the patient needs adjuvant chemotherapy, by testing the patient's tumor cells and testing the patient toxicity tolerance, the oncologist can select drugs that have a higher probability of being effective for an individual patient rather than selecting drugs based on the average responses of many patients in large clinical trials.

What a cancer patient would like ideally, is to know whether they would benefit from adjuvant chemotherapy. If so, which active drugs have the highest probability of working and are relatively non-toxic in a given patient.

Whether a patient would benefit from adjuvant therapy depends on two things: (1) whether the tumor is "destined" to come back in the first place and (2) whether the tumor is "sensitive" to drugs which might be used to keep it from coming back.

The gene expression (signature) marker assays actually could be calibrated to provide information both about the possibility of recurrence and also chemosensitivity. The problem is dissecting one from the other. Studies to date have just looked at whether people had a recurrence.

You can identify gene expression patterns (via assays) which correlate with this. But it can be hard and even impossible to tell what exactly you are measuring: is it intrinsic aggressiveness of the tumor? sensitivity to adriamycin? sensitivity to cyclophosphamide? sensitivity to taxol? sensitivity to tamoxifen? You find a gene expression panel which correlates with something, but picking apart the pieces is hard.

You can begin to do this if you combine gene expression studies (molecular profiling) with cell culture studies (functional tumor cell profiling). Use the functional profiling as the gold standard to define the difference between sensitivity and resistance. Then see which pattern correlates with which for individual tumors and individual drugs.

When the decision is made to treat a patient with chemotherapy, most patients are treated with a combination of drugs. The "functional profiling" method differs from existing DNA and RNA tests in that it assesses the activity of a drug upon combined effect of all cellular processes, using several metabolic and morphologic endpoints. Other tests, such as those which identify DNA or RNA sequences or gene expression signatures of individual proteins often examine only one component of a much larger, interactive process.

No gene-based test can discriminate differing levels of anti-tumor activity occurring among different therapy drugs. Nor can available gene-based tests identify situations in which it is advantageous to combine the new "targeted" drugs with other types of cancer drugs. So far, only cell-based functional profiling has demonstrated this critical ability.

Not only is this an important predictive test, it is also a unique tool that can help to identify newer and better drugs, evaluate promising drug combinations, and serve as a "gold standard" correlative model with which to develop new DNA, RNA, and protein-based tests that better predict for drug activity.

Genomic testing is not the answer, without cell "function" analysis. Functional tumor cell profiling has its own very sophisticted program to discover gene expression microarrays which predict for responsiveness to drug therapy. The way to identify informative gene expression patterns is to have a gold standard and that cell-based functional profiling assays are by far the most powerful, efficient, useful gold standard to have. It grasps the potential value of the assays today to individualize therapy.

And then you come to the 1,000 pound gorilla of a question: What effect will the different individual drugs have in combination in different, individual tumors? This is where cell-based functional profiling assays will always be able to provide uniquely valuable information. But it's not one versus the other. The best thing is to combine these different tests in ways which make the most sense. One month's worth of herceptin + avastin costs $8000. That's without any docetaxel and blood cell growth factors and anti-emetics. If nothing else, we can't afford too much trial and error treatment.

There are hundreds of different therapeutic drug regimens which any one or in combination can help cancer patients. The system is overloaded with drugs and underloaded with the wisdom and expertise for using them. We have produced an entire generation of investigators in clinical oncology who believe that the only valid form of clinical research is to perform "well-designed," prospective, randomized trials in which patients are randomized to receive one empiric drug combination versus another empiric drug combination.

The problem is not with using the prospective, randomized trial as a research instrument. The problem comes from applying this time and resource-consuming instrument to address hypotheses of trivial importance (do most cancers prefer Coke or Pepsi?). The failure of 30 years' worth of clinical trials research into "one-size-fits-all" therapy will eventually force a consideration of new approaches. All the more reason to "test the tumor" first - properly.