A practical molecular assay to predict survival in resected non-squamous, non-small-cell lung cancer: development and international validation studies

Johannes R Kratz MD, Prof Jianxing He MD, Stephen K Van Den Eeden PhD, Prof Zhi-Hua Zhu MD, Prof Wen Gao MD, Patrick T Pham BS, Michael S Mulvihill BS, Fatemeh Ziaei BS, Huanrong Zhang MD, Bo Su MD, Prof Xiuyi Zhi MD, Charles P Quesenberry PhD, Laurel A Habel BS, Qiuhua Deng BS, Zongfei Wang BS, Jiangfen Zhou BS, Huiling Li PhD, Mei-Chun Huang PhD, Che-Chung Yeh PhD, Prof Mark R Segal PhD, M Roshni Ray BS, Prof Kirk D Jones MD, Dan J Raz MD, Zhidong Xu MD, Thierry M Jahan MD, David Berryman PharmD, Biao He PhD, Dr Michael J Mann MD, Prof David M Jablons MD

Summary

Background:

The frequent recurrence of early-stage non-small-cell lung cancer (NSCLC) is generally attributable to metastatic disease undetected at complete resection. Management of such patients depends on prognostic staging to identify the individuals most likely to have occult disease. We aimed to develop and validate a practical, reliable assay that improves risk stratification compared with conventional staging.

Methods:

A 14-gene expression assay that uses quantitative PCR, runs on formalin-fixed paraffin-embedded tissue samples, and differentiates patients with heterogeneous statistical prognoses was developed in a cohort of 361 patients with non-squamous NSCLC resected at the University of California, San Francisco. The assay was then independently validated by the Kaiser Permanente Division of Research in a masked cohort of 433 patients with stage I non-squamous NSCLC resected at Kaiser Permanente Northern California hospitals, and on a cohort of 1006 patients with stage I—III non-squamous NSCLC resected in several leading Chinese cancer centres that are part of the China Clinical Trials Consortium (CCTC).

Findings:

Kaplan-Meier analysis of the Kaiser validation cohort showed 5 year overall survival of 71·4% (95% CI 60·5—80·0) in low-risk, 58·3% (48·9—66·6) in intermediate-risk, and 49·2% (42·2—55·8) in high-risk patients (ptrend=0·0003). Similar analysis of the CCTC cohort indicated 5 year overall survivals of 74·1% (66·0—80·6) in low-risk, 57·4% (48·3—65·5) in intermediate-risk, and 44·6% (40·2—48·9) in high-risk patients (ptrend<0·0001). Multivariate analysis in both cohorts indicated that no standard clinical risk factors could account for, or provide, the prognostic information derived from tumour gene expression. The assay improved prognostic accuracy beyond National Comprehensive Cancer Network criteria for stage I high-risk tumours (p<0·0001), and differentiated low-risk, intermediate-risk, and high-risk patients within all disease stages.

Interpretation:

Our practical, quantitative-PCR-based assay reliably identified patients with early-stage non-squamous NSCLC at high risk for mortality after surgical resection.

Funding: UCSF Thoracic Oncology Laboratory and Pinpoint Genomics.

The Lancet, Early Online Publication, 27 January 2012 doi:10.1016/S0140-6736(11)61941-7

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Gregory D. Pawelski

SAN FRANCISCO -- In a finding that could improve the survival odds for early-stage lung cancer patients, UCSF researchers determined a new molecular test can predict more accurately than current diagnostic methods which tumors are more likely to be aggressive and turn deadly.

The study results, published in the medical journal The Lancet, come from the two largest clinical trials ever conducted on the molecular genetics of lung cancer and included early-stage patients from Northern California Kaiser hospitals as well as from China.

In both trial groups, a 14-gene test, which was based on developments originally made at UCSF but created by a Mountain View company, was able to accurately determine a patient's odds of death within five years of surgery by analyzing the biological makeup of the tumor.

This potentially could save lives by helping patients with early-stage but aggressive disease decide after surgery to remove tumors whether to undergo additional treatment such as chemotherapy or targeted radiation, the researchers said.

Dr. David Jablons, chief of UCSF's thoracic oncology program and an author of the study, called the results a breakthrough for earlier-stage patients who have tough treatment decisions to make. "It can help enhance the chance of curing more patients and this is not an insignificant problem," he said.

Lung cancer is the most common cause of cancer death in the United States as well as the world. One reason it's so deadly is that lung cancer is caught in the early stages in only about 30 percent of those who are diagnosed. Also, unlike other types of cancer where early diagnosis can increase survival upwards of 90 percent, as high as 45 percent of people with the earliest stage of lung cancer die within five years, despite seemingly successful surgery.

"The fact of the matter is people do not do well in general, even with early-stage lung cancer," said Jablons. He said current methods of detecting and staging the disease - using scans, surgery and clinical observation - are insufficient to determine the aggressive nature of the disease.

The molecular assay, developed by Mountain View's Pinpoint Genomics, analyzes the activity level of the 14 genes in preserved tissue samples as compared to levels in the normal lung. It then characterizes whether that tumor poses a high, intermediate or low risk of death for the patient.

This study - which was based on tissue samples from 433 Northern California Kaiser patients and 1,006 patients from China - and found that the test very accurately predicted the likelihood of death in both groups.

"There really hasn't been a tool to more clearly identify the patients who have the more difficult biology," said David Berryman, Pinpoint's chief executive officer. "The key to it is to really hone in on a specific set of genes that would be a prognosticator of progression or more aggressive disease."

Berryman said the test, which received the proper approvals last year, is commercially available but the company has been waiting until these results before moving forward with it. He said he hopes Medicare and health insurers will cover the test as those payers have with other gene-based tests.

Health experts say the test is most similar to the diagnostic test Oncotype DX, which can identify the high-risk breast cancer patients who will benefit most from chemotherapy.

But what the research involving the Pinpoint test doesn't yet show is whether additional therapy following surgery for lung-cancer patients actually improves survival rates for those patients.

"Knowing this result will have benefit (to the patient) is the real question of course," said Stephen Van Den Eeden, research scientist at the Kaiser Permanente's Division of Research in Oakland.

Additional research is also needed to identify which chemotherapies would be most beneficial.

While the studies were funded by Pinpoint and private endowments to UCSF, researchers stressed that they were conducted under strict guidelines and using blinded conditions to prevent bias.

Sources: National Cancer Institute; World Health Organization.

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Gregory D. Pawelski

I was wondering when someone was going to develop a test like this since the failed Lung Metagene Predictor, developed at Duke University Medical Center, that was supposed to tell physicians which lung cancer patients would benefit from chemotherapy and which ones would not need to be unnecessarily exposed to toxic chemotherapy cocktails.

The test doesn't predict which patients will benefit from chemotherapy (i.e. which patients are chemosensitive to actual therapies), rather it's like the Oncotype Dx test, which identifies patients who are unlikely to have a recurrence if treated with surgery alone. If you aren't going to have a recurrence, you don't need chemotherapy.

Improving cancer patient diagnosis and treatment through a combination of gene-based and cellular-based testing will offer predictive insight into the nature of an individual's particular cancer and enable oncologists to prescribe treatment more in keeping with the heterogeneity of the disease. The biologies are very different and the "individual" response to given drugs is very different.

Recent caveat about genetic testing: viewtopic.php?f=50&t=46833

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Gregory D. Pawelski

Predictive and Prognostic Factors for Non–Small Cell Lung Cancer—Potholes in the Road to the Promised Land

Adi F. Gazdar and Joan H. Schiller

Affiliations of authors: Hamon Center for Therapeutic Oncology Research, Department of Pathology (AFG), and Division of Hematology and Oncology, Department of Internal Medicine and Simmons Comprehensive Cancer Center (JHS), University of Texas Southwestern Medical Center, Dallas, TX

Surgery offers the best chance of long-term cures for early-stage non–small cell lung cancer, the most common form of lung cancer. However, between one-third and one-half of early-stage tumors will relapse after curative intent resection. Adjuvant chemotherapy (ACT) offers a survival advantage for up to 15% of early-stage resected cancers and has become the standard of care. Thus, having the ability to select the minority of patients who are likely to benefit from ACT would be enormously beneficial to the majority of early-stage patients who undergo resection who could avoid experiencing the morbidity associated with ACT. This strategy would also create economic benefits in addition to the health benefits for patients. In this issue of the Journal, Chen et al. (1) present a microarray-based gene expression signature that has both prognostic and predictive values for early-stage non–small cell lung cancer.

Many markers have been tested for their predictive and prognostic value in lung cancer, and some have entered clinical practice. These include pathological typing that may be used to include or exclude certain forms of therapy—for example, adenocarcinoma histology is a strong predictor of outcome of pemetrexed therapy in advanced patients, and serious hemorrhagic complications may occur after bevacizumab therapy in patients with squamous histologies. Lung cancers are remarkably heterogeneous at the molecular level, and at least nine “driver” oncogene mutations have been described in lung adenocarcinomas (2). These mutations may lead to oncogene addiction, and effective therapies have been approved by the US Food and Drug Administration (FDA) for epidermal growth factor receptor mutations and ALK translocations, and many other individualized forms of therapy are currently in clinical trials (2). However, nearly half of lung adenocarcinomas and most of the squamous cell carcinomas lack known driver mutations.

The completion of the human genome project in the year 2003 was a bonanza for medical research because of the development and clinical applications of genomic-based assays including increasingly dense microarray platforms for global analyses of gene expression, copy number variation, DNA methylation and microRNA. In particular, gene expression studies have grabbed the limelight, with at least 16 prognostic or predictive gene signatures having been described for lung cancer (1,3), as well as many for breast and the other major cancers. Although these studies offer promise for personalized medicine applications (4,5), there are also many pitfalls. Given that tens of thousands of expressed genes are examined simultaneously, it is not surprising that few of the reported gene signatures have multiple genes in common. The biological relevance (if any) of most signature genes is unknown. For continuous variables, patients are usually divided arbitrarily, often on the basis of the median value. Major mistakes in the methods of analysis and even potential fraud have been detected in some published studies, and others cannot be fully interpreted or verified. Independent reviewers could not replicate the data for one highly publicized study for the individualized selection of chemotherapy for breast cancer patients (6). The study was inadequately documented, the role of biostatisticians was minimal, and major analytical mistakes were detected. After a lengthy battle of words between critics and investigators and investigations by the National Cancer Institute and the Institute of Medicine, the study was suspended, several published reports in leading journals were withdrawn, some investigators were reassigned or resigned, and lawsuits were filed on the behalf of patients who claimed they had received wrongfully identified nonstandard therapies.

The above mentioned problems have led to a certain amount of disillusionment and skepticism regarding signature studies, and they stimulated the FDA to declare that “in vitro diagnostic multivariate index assays are also medical devices subject to FDA review” (6). Certain guidelines for the planning, execution, and transparent reporting of such assays have also been instituted (6,7). Baggerly and Coombes (6,7) identified five essential requirements for authors and journals to follow, and they should be fulfilled before starting clinical trials based on “omic” signatures to guide treatment. The requirements are that authors and journals give readers access to 1) the raw data, 2) the code used to derive the results from the raw data, 3) evidence of provenance of the raw data, 4) written descriptions of any nonscriptable analysis steps, and 5) prespecified analysis plans.

Why is there a need for yet another gene expression–based prognostic signature study in lung cancer? A critical appraisal of the published studies of gene expression signatures for lung cancer found serious problems in methodology and analysis in most of them (3). Subramanian and Simon (3) also pointed out that the major clinical usefulness of a prognostic signature for lung cancer is to identify early-stage patients who will benefit from ACT and those who could be spared. The study reported by Chen et al. (1) differs from most other published studies in several aspects. They appear to have satisfied the requirements of Baggerley and Coombes (6), including presenting a detailed description of their non-scriptable analysis steps in Sweave format in the Supplementary Materials (available online). By comparing the expression patterns of nonmalignant and malignant breast tissues, they had previously defined a malignancy-risk gene signature that was associated with cancer risk in nonmalignant breast tissue and was a prognostic factor for breast cancer (8). Whereas most microarray-based signatures largely consist of genes whose relationship to cancer is peripheral or unknown, the malignancy-risk gene signature was markedly enhanced for genes related to cell proliferation (8). As both breast and lung cancers are characterized by early loss of normal growth control mechanisms, they applied the malignancy-risk signature to lung cancers by using three publicly available datasets. Chen et al. (1) used one large dataset to test their hypothesis and the other two to validate their findings by generating an overall malignancy-risk score. Surprisingly, the malignancy-risk gene signature had both prognostic and predictive value: Of patients who did not receive ACT, those with a low–malignancy-risk score had increased overall survival compared with those with a high-malignancy score. Results from the test and validation sets were in agreement, even though the studies used different expression platforms to generate their data. As the authors suggest, this may be the first gene expression signature that has potential clinical value in two major forms of cancer. Another positive aspect is that the first author is a member of the Biostatistics Department, indicating that the authors recognized the importance of this discipline to such studies.

Although the study by Chen et al. (1) suggests that clinically relevant subgroups can be identified by a gene expression–based signature, it cannot be “translated” until it has been independently validated in a prospectively conducted trial. The road to the promised land of personalized therapy is a long one and littered with potholes. Hopefully, Chen et al. have successfully avoided most of them.

References:

1. Chen D-S, Hsu Y-L, Fulp WJ, et al. Prognostic and predictive value of a malignancy-risk gene signature in early-stage non–small cell lung cancer. J Natl Cancer Inst. 2011;103(24):1859-1870.

2. Pao W, Girard N. New driver mutations in non-small-cell lung cancer. Lancet Oncol. 2011;12(2):175-180.

3. Subramanian J, Simon R. Gene expression-based prognostic signatures in lung cancer: ready for clinical use? J Natl Cancer Inst. 2010;102(7):464-474.

4. Xie Y, Minna JD. Predicting the future for people with lung cancer. Nat Med. 2008;14(8):812-813.

5. Xie Y, Minna JD. Non-small-cell lung cancer mRNA expression signature predicting response to adjuvant chemotherapy. J Clin Oncol. 2010;28(29):4404-4407.

6. Baggerly KA, Coombes KR. What information should be required to support clinical “omics” publications? Clin Chem. 2011.

7. Baggerly K. Disclose all data in publications. Nature 2010;467(7314):401.

8. Chen DT, Nasir A, Culhane A, et al. Proliferative genes dominate malignancy-risk gene signature in histologically-normal breast tissue. Breast Cancer Res Treat. 2010;119(2):335-346.

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Gregory D. Pawelski

Just identifying molecular predisposing mechanisms still does not guarantee that a drug will be effective for an individual patient. Nor can it, for any patient or even large group of patients, discriminate the potential for clinical activity among different agents of the same class.

The challenge is to identify which patients targeted treatment will be most effective. One can chase all the mutations they want, because if you miss just one, it may be the one that gets through. Or you can look for the drugs that are "sensitive" to killing all of your cancer cells, not "theoretical candidates.

Tumors can become resistant to a targeted treatment, or the drug no longer works, even if it has previously been effective in shrinking a tumor. Drugs are combined with existing ones to target the tumor more effectively. Most cancers cannot be effectively treated with targeted drugs alone.

Molecular testing methods detect the presence or absence of selected gene or protein mutations which theoretically correlate with single agent drug activity. Cells are never exposed to anti-cancer agents.

Even patients who are found to have an activating EGFR mutation, Tarceva is considered acceptable but not a definite superior choice. Genetic variations alone do not determine response to targeted therapy. Those patients who test negative for EGFR are left to the same guesswork as conventional therapy.

There are lots of things which determine if the drug works, beyond the existence of a given target. Does the drug even get into the cancer cell? Does it get pumped out of the cell? Does the cell have ways of escaping drug effects? Can cells repair damage caused by the drug?

Tarceva could be given selectively to patients with EGFR negative NSCLC. It is a challenge to identify which patients targeted treatments like Tarceva will be effective. Patients across a broad range of clinical characteristics could benefit. Being EGFR negative is no reason not to be given this drug.

What is needed is to measure the net effect of all processes within the cancer, acting with and against each other in real time, and test living cells (not cell lines) actually exposed to drugs and drug combinations of interest. The key to understanding the genome is understanding how cells work. How is the cell being killed regardless of the mechanism?

Functional profiling assesses the net effect of all inter-cellular and intra-cellular processes occurring in real time when cells are exposed to anti-cancer agents (targeted or conventional). Tests are performed using intact, living cancer cells plated in 3D microclusters. It allows for testing of different drugs within the same class and drug combinations to detect drug synergy and drug antagonism.

The core understanding is the cell, composed of hundreds of complex molecules that regulate the pathways necessary for vital cellular functions. If a targeted drug could perturb any of these pathways, it is important to examine the effects of drug combinations within the context of the cell.

Both genomics and proteomics can identify potential therapeutic targets, but these targets require the determination of cellular endpoints. You still need to measure the net effect of all processes, not just the individual molecular targets.

Rating the efficacy of population research vs rating the efficacy of drugs actually tested against an individual's cancer cells.

References:

Eur J Clin Invest, Volume 37(suppl. 1):60, April 2007. Functional profiling with cell culture-based assays for kinase inhibitors and anti-angiogenic agents.

J Clin Onco, 2006 ASCO Annual Meeting Proceedings Part 1. Vol 24, No. 18S (June 20 Supplement), 2006: 17117. Genfitinib-induced cell death in short term fresh tumor cultures predicts for long term patient survival in previously-treated NSCLC.

Nagourney, R. et. al, Horizontal and vertical signal pathway inhibition in human tumor primary culture micro-spheroids. Abstract 1764, proceedings AACR 2010.

"Phase II Trial of Personalized Chemotherapy In Stage IV NSCLC: Clinical Application Of Functional Profiling In First-Line Therapy" (Abstract No. 7617; Citation: J. Clin Oncol 28:7s, 2010)

Nagourney RA, Kollin CA, Sommers B, Su Y-Z, Evans SS. Functional profiling of human tumors in primary culture: a platform for drug discovery and therapy selection, AACR abstract #1546, 2008

Survival among patients with platinum resistant, locally advanced non-small cell lung cancer treated with platinum-based systemic therapy. d'Amato TA, Pettiford BL, Schuchert MJ, Parker R, Ricketts WA, Luketich JD, Landreneau RJ. Ann Surg Oncol. 2009 Oct;16(10):2848-55. Epub 2009 Jul 16.

Functional profiling with cell culture-based assays for kinase and anti-angiogenic agents Eur J Clin Invest 37 (suppl. 1):60, 2007

Functional Profiling of Human Tumors in Primary Culture: A Platform for Drug Discovery and Therapy Selection (AACR: Apr 2008-AB-1546)

Journal of Clinical Oncology, 2006 ASCO Annual Meeting Proceedings Part I. Vol 24, No. 18S (June 20 Supplement), 2006: 17117

Ian A. Cree (ed.), Cancer Cell Culture: Methods and Protocols, Second Edition, Methods in Molecular Biology, vol. 731, DOI 10.1007/978-1-61779-080-5_22.

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Gregory D. Pawelski

Testing for EGFR, KRAS and ALK pathways and coming up negative is no reason not to be given these drugs. All the mutation/amplification study can tell you is whether or not the cancer cells are "potentially" susceptible to this mechanism of attack. It cannot tell you if a targeted drug will actually work for your cancer cells, or not.

Tumor cells have such an uniqueness that not much is known of their respective reaction to targeted therapies. In other words, patients can benefit from targeted drugs regardless of mutation/amplification status. These targeted therapies produce limited results because they can help a relatively small subgroup of cancer patients.

But when they work, they produce very good responses. With targeted therapy, the trick is figuring out which patients will respond. Tests to pinpoint those patients cannot be accomplished with genetic testing.

In a conference sponsored by the Institute of Medicine, scientists representing both public and private institutions examined the obstacles that confront researchers in their efforts to develop effective combinations of targeted cancer agents.

In a periodical published by the American Society of Clinical Oncology (ASCO) in their September 1, 2011 issue of the ASCO Post, contributor Margo J. Fromer, who participated in the conference, wrote about it.

One of the participants, Jane Perlmutter, PhD, of the Gemini Group, pointed out that advances in genomics have provided sophisticated target therapies, but noted, “cellular pathways contain redundancies that can be activated in response to inhibition of one or another pathway, thus promoting emergence of resistant cells and clinical relapse.”

James Doroshow, MD, deputy director for clinical and translational research at the NCI, said, “the mechanism of actions for a growing number of targeted agents that are available for trials, are not completely understood.”

He went on to say that the “lack of the right assays or imaging tools means inability to assess the target effect of many agents.” He added that “we need to investigate the molecular effects . . . in surrogate tissues,” and concluded “this is a huge undertaking.”

Michael T. Barrett, PhD, of TGen, pointed out that “each patient’s cancer could require it’s own specific therapy.” This was followed by Kurt Bachman of GlaxoSmithKline, who opined, “the challenge is to identify the tumor types most likely to respond, to find biomarkers that predict response, and to define the relationship of the predictors to biology of the inhibitors.”

What they were describing was precisely the work that clinical oncologists involved with cell culture assays have been doing for the past two decades. The complexities and redundancies of human tumor biology had finally dawned on these investigators, who have clung to analyte-based molecular platforms.

http://www.ascopost.com/articles/septem ... enges.aspx

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Gregory D. Pawelski