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How Molecular Profiling Works


gpawelski

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There is molecular profiling and there is functional profiling. The former testing is theoretical and the latter is actual.

In drug selection, molecular (genetic) testing examines a single process within the cell or a relatively small number of processes. The aim is to tell if there is a theoretical predisposition to drug response. It attempts to link surrogate gene expression to a theoretical potential for drug activity. Patients' cancer cells are never exposed to chemotherapy drugs. It relies upon a handful of gene patterns which are thought to imply a potential for drug susceptibility. In other words, molecular testing tells us whether or not the cancer cells are potentially susceptible to a mechanism/pathway of attack. It doesn't tell you if one drug is better or worse than another drug which may target a certain mechanism/pathway.

Functional profiling doesn't dismiss DNA testing, it uses all the information, both genomic and functional, to design the best treatment for each individual, not populations. Laboratories like Rational Therapeutics and Weisenthal Cancer Group test for a lot more than just a few mutations. The cell is a system, an integrated, interacting network of genes, proteins and other cellular constituents that produce functions. One needs to analyze the systems' response to drug treatments, not just a few targets (pathways).

Their functional profiling test assesses the activity of a drug upon combined effect of all cellular processes, using several metabolic (cell metabolism) and morphologic (structure) endpoints, at the cell "population" level, rather than at the "single cell" level, measuring the interaction of the entire genome.

Examining a patient's DNA can give physicians a lot of information, but as the NCI has concluded (J Natl Cancer Inst. March 16, 2010), it cannot determine treatment plans for patients. It cannot test sensitivity to any of the targeted therapies, just "theoretical" candidates for targeted therapy.

Just recently, an International consortium of cell biologists decided to name it Personalized Cancer Cytometric testing, after the first head-to head clinical trial comparing molecular gene testing vs personalized cancer cytometrics.

http://cancerfocus.org/forum/showthread.php?t=3474

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Cancer biomarker test development and adoption has "lagged far behind" recent advances in cancer therapies, according to a commentary published in the July 31, 2013 issue of Science Translational Medicine.

"Despite prodigious advances in tumor biology research, few tumor biomarker tests have been adopted as standard clinical practice," write the authors, a blue ribbon panel of representatives from industry, academia, and professional organizations led by Daniel Hayes, MD, clinical director of the breast oncology program at the University of Michigan Comprehensive Cancer Center in Ann Arbor.

This is a problem for a variety of reasons, including that fact that clinicians need tools to identify the patients who will most likely benefit — or definitely not benefit — from therapies, they explain.

Without robust biomarker testing, the "promise of personalized medicine" in oncology is being jeopardized, the authors say.

The note that there is no easy answer here because the tumor biomarker test conundrum is a many-sided problem that is rooted in "undervaluation" by its stakeholders.

The "lack of reliable tests stems from a vicious cycle of undervaluation, resulting from inconsistent regulatory standards and reimbursement, as well as insufficient investment in research and development, scrutiny of biomarker publications by journals, and evidence of analytical validity and clinical utility," they explain.

Undeterred by the magnitude of the problem, Dr. Hayes and colleagues propose "ambitious" reforms that, if enacted, "should result in a virtuous cycle" of biomarker valuation.

They identify 5 causes of the vicious cycle and 5 corresponding remedial recommendations.

However, they do not acknowledge what another expert in the field has identified as a key to the shortage of tumor biomarkers and, by extension, tests.

"It has proven extremely difficult to find and validate a cancer biomarker," Eleftherios P. Diamandis, MD, PhD, professor of pathology and laboratory medicine at Mount Sinai Hospital in Toronto, previously told Medscape Medical News. In 2010, he wrote an essay on the paucity of cancer biomarkers (J Natl Cancer Inst. 2010;102:1462-1467).

Many scientists, including Nobel Prize winners in basic sciences, have embarked on a path of biomarker discovery and validation and come up empty handed, said Dr. Diamandis at the time.

Causes and Recommendations

A biomarker is a biological indicator that "objectively measures or evaluates physiological or pathophysiological processes or pharmacological responses to a therapeutic intervention," Dr. Hayes and colleagues explain.

A tumor biomarker test is used to detect and quantitate the biomarker. Tests can be used for a variety of purposes, including screening for cancers and selecting optimal therapy.

There are 2 values associated with every tumor biomarker test related to therapeutics: clinical and financial. The clinical utility is fairly simple; it "provides an indication of whether a cancer patient is likely to benefit from a given treatment," the authors write.

The financial utility is a related value. "A test has financial value if its use permits the application of expensive therapeutic strategies only in a targeted population of likely responders."

However, the American marketplace does not equally value tests and drugs, they note.

"The marketplace has recognized the value of advances in cancer care that have resulted from the discovery and development of molecularly targeted therapies but not the value of robust new tumor biomarker tests to guide patient management," they write.

The result is clear. "R&D for such tests and their adoption into standard clinical practice have lagged behind R&D and clinical use of therapeutics," they assert.

The authors believe that the entire scientific and commercial apparatus involved in biomarker testing is second rate, compared with that of drugs.

"The research, regulatory, clinical-use, and reimbursement standards are not as well defined or as rigorous as those applied to therapeutics," they write.

The authors identify 5 causes of the resulting vicious cycle of second rate operations and related recommendations that would break the cycle.

Table. The Tumor Biomarker Test Vicious Cycle: Causes and Recommendations

Cause: Inconsistent regulatory standards for clinical data needed to approve tests

Recommendation: Reform regulatory review of tests and ensure that "high levels of evidence" are provided, on par with that of therapeutics

Cause: Poor reimbursement levels for tests with established clinical utility

Recommendation: Increase reimbursement for proven tests that help determine which therapies work

Cause: Insufficient investment for test development and clinical research

Recommendation: Increase government and industry investment for test research so it is comparable to new drug research

Cause: Insufficient scrutiny by peer-reviewed journals for tumor biomarker research publications

Recommendation: Increase the rigor for peer review

Cause: Lack of high-level evidence for tests supporting recommendations for clinical use

Recommendation: Include only proven tests in evidence-based care guidelines

"These recommendations are not about creating more regulation; they are about creating an even playing field that allows tumor biomarker tests to be developed and proven clinically relevant. We want to stimulate innovation yet hold investigators and clinicians to the highest scientific standards — as we now do for therapeutics," Dr. Hayes said in a press statement. "We need to change the way we value tumor biomarkers in this country."

Dr. Hayes reports being a consultant for Oncimmune LLC, Inbiomotion, and Biomarker Strategies; receiving research funding from Novartis, Veridex (Johnson & Johnson), and Janssen R&D, LLC (Johnson & Johnson); being a coinventor on a patent for a method of predicting progression-free and overall survival in metastatic breast cancer patients using circulating tumor cells; and having applied for patents on a test for the diagnosis and treatment of breast cancer and for circulating tumor cell capturing techniques and devices.

Sci Transl Med. 2013;15:196cm6.

http://stm.sciencemag.org/content/5/196/196cm6

Citation: New Cancer Biomarker Tests Stunted by 'Vicious Cycle'. Medscape. Aug 05, 2013.

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The era of personalized medicine based on validated tumor biomarkers is indeed at hand. As the increasing numbers and types of cancer drugs are developed, oncologists become more and more likely to misuse them in their practice. Developing a good and clinically practical drug selection system is no less important than the discovery of new drugs or how to put them into the body.

The idea of simply finding a mutation and then pick an appropriately targeted drug seems like a nice idea. However, not every key that looks like it will fit a lock will actually turn it. The same is likely to be the case with targeted drugs. There are numerous common mutations in various tumor types, but they don't know that all those mutations are going to turn out to be relevant, as many of them are essentially bystanders.

All the mutation or amplification studies tell us is whether or not the cells are potentially susceptible to a mechanism of attack. They don't tell you if one drug is better or worse than some other drug which may target this. The cell is a system, an integrated, interacting network of genes, proteins and other cellular constituents that produce functions. You need to analyze the systems' response to drug treatments, not just one target or pathway.

Giving instructions on the genetic differences that determine how a person responds to a drug will still have cancer medicine being prescribed on a trial-and-error basis, with adverse drug reactions remaining a major cause of injury and hospitalizations.

There have been technologies, developed over the last twenty years, that hold the key to solving some of the problems confronting a healthcare system that is seeking ways to best allocate available resources while accomplishing the critical task of matching individual patients with the treatments most likely to benefit them.

It isn't just molecular analysis, it is whether the capacity to judge phenotypes will be easily achieved at the genotype level. Systems biology suggests that the simple knowledge of a gene's presence or absence does not confer a biological behavior. No good gardener would attempt to tell you how your future bouquet will look by simply examining a packet of flower seeds.

Similarly, no good doctor should attempt to choose drugs based solely on genotype analysis. The particular sequence of DNA that an organism possesses (genotype) does not determine what bodily or behavioral form (phenotype) the organism will finally display.

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Rationalizing Treatment and Coverage Decisions With Predictive Biomarkers

Using only FDA-approved, standard lung cancer drugs available to all oncologists, a process of laboratory selection provided a 64.5 percent response rate - more than double the national average of 30 percent (p = 0.00015), well established in medical literature. More importantly, the median overall survival of 21.3 months was nearly two-fold longer than the best results of 13.5 months reported for non-assay based standard treatments. Strikingly, among the stage IV (metastatic) patients, there are several who remain alive approaching eight years since diagnosis.

According to a Phase II clinical trial conducted by investigators at Rational Therapeutics and the Memorial Care Todd Cancer Institute, Long Beach, CA, and published in the October issue of Anticancer Research, functional cytometric profiling of programmed cell death doubles the response rate and improves time-to-progression and survival in patients with advanced lung cancer. According to Dr. Robert A. Nagourney, lead investigator, this study confirms the ability of a laboratory test to accurately predict drug activity for individual cancer patients.

The article, titled "Functional Profiling to Select Chemotherapy in Untreated, Advanced or Metastatic Non-Small Cell Lung Cancer," describes results achieved in patients who received first-line chemotherapy based on their phenotype analysis. Functional profiling provides a window into the dynamic process by which human tumor cells respond to therapy. By capturing cells within their natural microenvironment, human biology is recreated in the laboratory.

Statistical analyses enable researchers to establish “levels” of certainty. Reported as “p-values,” these metrics offer the reader levels of statistical significance indicating that a given finding is not simply the result of chance. A p-value equal to 0.1 (1 in 10) means that the findings are 90 percent likely to be true with a 10 percent error. A p-value of 0.05 (1 in 20) tells the reader that the findings are 95 percent likely to be true. While a p-value equal to 0.01 (1 in 100) tells the reader that the results are 99 percent likely to be true. For an example in real time, this paper is reporting in lung cancer literature that doubled the response rate for metastatic disease compared with the national standard. The results achieved statistical significance where p = 0.00015. That is to say, that there is only 15 chances out of 100,000 that this finding is the result of chance.

The biomarker-based paradigm will require us to consider the level of evidence necessary to declare true activity. Daniel J. Sargent, PhD, Professor of Cancer Research at the Mayo Clinic, tells us that it may become impossible to perform traditional trials with requirements to achieve a P-value less than 0.05, high statistical power, and an OS advantage.

When the patient population becomes small, we’re going to have to consider either other endpoints or other statistical philosophies. Should we use a Bayesian strategy, in which we borrow information from other clinical trials to help make decisions? Or do we loosen the P-value requirements, that a P-value of less than 0.1 or 0.2, for example, be considered a sufficient level of evidence for activity?

These are active areas of research that need to be fully considered as we enter this era of truly personalized therapy with patient populations that are becoming smaller and smaller. I do know that the Bayesian method is no stranger to the functional profiling platform. It’s what gives credit to the accuracy of the assay tests.

The absolute predictive accuracy of cell culture assay tests varies according to the overall response rate in the patient population, in accordance with Bayesian principles. The actual performance of assays in each type of tumor precisely match predictions made from Bayes’ Theorem.

Bayes’ Theorem is a tool for assessing how probable evidence makes some hypothesis. It is a powerful theorem of probability calculus which is used as a tool for measuring propensities in nature rather than the strength of evidence (Solving a Problem in the Doctrine of Changes).

Sources:

Anticancer Research October 2012 vol. 32 no. 10; 4454-4460

Daniel J. Sargent, PhD, is the Ralph S. and Beverly E. Caulkins Professor of Cancer Research at the Mayo Clinic Cancer Center in Rochester, Minnesota, and Group Statistician for the Alliance for Clinical Trials in Oncology. "Commentary on clinical endpoints, validation of surrogate endpoints and biomarkers in oncology clinical trials."

http://ar.iiarjournals.org/content/32/1 ... e7c0d554dc

Functional Profiling Leads to Identification of Accurate Genomic Findings

http://cancerfocus.org/forum/showthread.php?t=3987

Examination of Xalkori activity in human tumor primary culture micro-spheroids

http://cancerfocus.org/forum/showthread.php?t=4004

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  • 4 weeks later...

Robert A. Nagourney, M.D.

Medical and Laboratory Director

Rational Therapeutics, Inc.

Long Beach, California

I recently had an interesting conversation with a physician regarding her patient with an aggressive breast cancer.

A portion of tumor had been submitted to our laboratory for analysis and we identified activity for the alkylating agents and the Taxanes, but not for Doxorubicin. After our report was submitted to the treating physician she contacted me to discuss our findings, as well as the results from a genomic/proteomic laboratory that conducted a parallel analysis upon a portion of the patient’s tumor. The physician was kind enough to forward me their report. Their results recommended doxorubicin while ours did not. The treating physician asked for my input. Here, I thought, was a “teachable moment.”

Our discussion turned to the profound difference between analyte-based laboratory tests e.g. genomic and proteomic, and functional platforms like our own (EVA-PCD). Genomic, transcriptomic and proteomic platforms measure the presence or absence of genes, RNA or protein. Gene amplification, deletions or mutations and protein and phosphoprotein expressions are examined. These platforms dichotomize patients into those who do and those who do not express the given analyte, with cutoffs for gene copy number or intensity of staining.

These platforms have worked very well in diseases where there is a linear connection between the gene (or protein) and the disease state, e.g. BCR-ABL in CML for which imatinib has proven so effective. These tests have worked reasonably well in EGFR mutated and ALK gene rearranged in lung cancer, but even here response rates and response durations have been less dramatic. However, they have not worked very well at all for the vast majority of cancers that do not carry specific and well-characterized targets. These cancers reflect polygenic phenomena and are not defined by a single gene or protein expression.

Functional platforms look at cellular response to injury at the systems level and measure the end result of drug exposure to gauge the likelihood of a clinical response. Our focus on cell biology allows us to determine whether a drug or combination induces programmed cell death. After all, regardless of what gene elements are operational, it is the ultimate eradication of the cancer clone (its loss of viability) that results in clinical response.

As we reported in a recent paper in non-small cell lung cancer, patients who revealed the most sensitive ex-vivo profile to erlotinib (Tarceva) lived far longer than the general clinical experience for those patients who were selected for erlotinib by EGFr mutation analysis alone. Some of these patients are alive at 5, even 9 years since diagnosis.

We live in a technocracy where process has taken precedence over results. We are enamored with complex scientific technologies sometimes at the expense of simple answers. A metallurgist, familiar with every last detail of the alloys used in a Boeing 747 wouldn’t necessarily be your first choice for pilot. A skilled pathologist, intimately familiar with the most detailed intricacies of human diagnostics would not likely be your preferred surgeon for cardiac bypass.

Cancer diagnosis and cancer treatment are two distinctly different disciplines. While we use the ER (estrogen receptor) status in breast cancer to select treatment, few oncologists would select Tamoxifen for their NSCLC patients even though many NSCLC patients express ER in their tissue. ER + NSCLC does not respond to tamoxifen and V600E BRAF mutated (+) colon cancer patients do not respond to vemurafenib, the very drug that works so well in BRAF V600E (+) melanoma.

Cancer is contextual and responses are not solely predicated upon the presence or absence of a gene element alone. We must use a broader brush when we paint the likeness of our patients in the laboratory, one that encompasses the vicissitudes of human biology in all of its complexities.

Where I took issue with the report, however, was its “evidence-based” moniker. The evidentiary manuscripts cited to support the drug recommendations, with titles like “Overexpression of COX-2 in celecoxib-resistant breast cancer cell lines” provided little evidence that a (+) COX-2 finding by IHC on this patient’s biopsy specimen would offer any real hope of response. It seemed that with all of the really interesting science going on here, no one had taken the time to do the hard work to figure out whether any of these observations had a basis in reality. The failure of ERCC1 expression in lung cancer to correlate with response and survival or the Duke University debacle with gene profiling in NSCLC are just the most recent examples of how “lovely theories can be spoiled by a little fact.”

As we and our colleagues in cell profiling have actually taken the time to correlate predictions with clinical outcomes we have shown a 2.04 fold higher objective response rate (p 0.001) and significantly improved 1-year survival (p=0.02). (Apfel, C. et al Proc ASCO, 2013). To the contrary, it is of interest to examine the comparatively scant published literature on genomic and IHC profiling for drug selection under similar circumstances. While one group reported an underwhelming objective response rate of 10 percent in their study, (Von Hoff, J Clin Oncol 2010) a more recent study is even more illuminating. A Spanish group used genomic profiling in 254 colon cancer patients to select candidates for gene-targeted agents (KRAS/BRAF/PI3K/PTEN/MET) and provided therapy for 82. They reported a significantly shorter time to progression for targeted treatments compared with conventional therapies 7.9 vs 16.3 week (P<0.001) and an overall objective response rate of 1.2 percent, yes that’s 1.2% (1/82).

Human tumor biology is many things, but simple is not one of them. Reductionist thinking is not providing the insights that our patients desperately need. While we await the arrival of a perfect test for the prediction of response to cancer therapy, perhaps we as physicians and our patients should use a good one, one that works.

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John L. Marshall, M.D.

Medscape Oncology

I have been focused these past few months on the concept of molecular profiling. For the cost of a CT scan, you can send your patient's tumor for a deep sequence, a next-generation sequencing. You can do proteins and get a lot of data from your patient's tumor. Then you get back a report that has 80 pages in it, and you are flipping through this thing with lists and lists and lists of different molecular targets, some of which surface up to the first page. And then a recommendation is made, or at least a suggestion is made, to you about how to treat the patient.

Has this been useful for you yet? There are times when it really is compelling. You get one of these reports back and you think, "Maybe I should use this" or "Maybe I should follow it." In our world, we are mostly using these kinds of reports to try to help us pick which phase 1 trial a patient should go into, not whether to give oxaliplatin or irinotecan. And which tumor are you sending? Are you sending something from right now or are you sending something from years ago that you have in the basement in the pathology department? There are so many variables.

And, of course, patients are hearing about personalized medicine. It is obviously a very attractive subject. They want us to do these tests. They want us to interpret these tests. Are you qualified to interpret these tests? I'm not, and I'm a pretty smart guy who is focused on these things a lot, but I'm not sure that I am qualified to take one of these reports and make a clinician recommendation. I wonder if we are going to ultimately need something called a molecular oncologist or a pathway specialist -- someone who can take these reports and try to make some sense out of them.

There are so many things up in the air with these, and I am wondering if this is still research or if it is now a practice. Should we be incorporating this more and more or is it still only under a research umbrella? That question has been plaguing me, so this year our symposium here at Georgetown University, to which we invite folks from all walks of life to come and talk about this, will be focused on molecular profiling. We have representation from the US Food and Drug Administration and from the Centers for Medicare & Medicaid Services. Should Medicare pay for a drug that the profile says is the right drug to give to that patient but is not on the National Comprehensive Cancer Network pathway? We have private payers coming. We have bioethicists for privacy and the management of these data. We have good folks from the pharmaceutical industry, oncologists, patients, and patient advocates all getting together on December 6-7, here in Washington, DC, to really drill down on the subject and see where we are in terms of the interface between research and practice.

I think we are right there and that we will bust through this over the course of the next few years. We need some guidance. We are going to need to discuss this openly among ourselves. What are the best ways to do this? Should we be looking at genes? Should we be looking at proteins? They don't always agree. Should we be looking at circulating factors such as microRNA? What really defines key targeted actionable items in our patients? Of course, there are a bunch of different assays out there and a bunch of different times that you could biopsy your patient and do the analysis. The next time you decide to order one of those things, be sure about what you are going to do with it. Are you going to make a clinical treatment when you get that result back? Are you doing it just to appease the patient and learn as we go? Are you doing it for research? Are you doing it for practice? If you know the answers to those questions, I think it will help once that big report comes back to your desk and you have to figure out how to make a therapeutic decision.

Come to our meeting, Fighting a Smarter War Against Cancer 2013: A Three-Part Symposium, on December 6-7 in Washington, DC.

https://secure.alumni.georgetown.edu/ol ... _id=159447

Citation: Dazed and Confused About Molecular Data? Medscape. Oct 23, 2013.

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  • 3 months later...

In general, expression profiling studies report those genes that showed statistically significant differences under changed experimental conditions. This is typically a small fraction of the genome for several reasons.

First, different cells and tissues express a subset of genes as a direct consequence of cellular differentiation so many genes are turned off.

Second, many of the genes code for proteins that are required for survival in very specific amounts so many genes do not change.

Third, cells use many other mechanisms to regulate proteins in addition to altering the amount of mRNA, so these genes may stay consistently expressed even when protein concentrations are rising and falling.

Fourth, financial constraints limit expression profiling experiments to a small number of observations of the same gene under identical conditions, reducing the statistical power of the experiment, making it impossible for the experiment to identify important but subtle changes.

Finally, it takes a great amount of effort to discuss the biological significance of each regulated gene, so scientists often limit their discussion to a subset. Newer microarray analysis techniques automate certain aspects of attaching biological significance to expression profiling results, but this remains a very difficult problem.

The relatively short length of gene lists published from expression profiling experiments limits the extent to which experiments performed in different laboratories appear to agree. Placing expression profiling results in a publicly accessible microarray database makes it possible for researchers to assess expression patterns beyond the scope of published results, perhaps identifying similarity with their own work.

Preservation of the tissue and handling of the tissue is a major problem in molecular profiling. The tissues used in all the histopathologic diagnosis such as formalin-fixed tissue cannot be used for Molecular Profiling. The best solution to this problem is to use non-formalin alcohol-based fixatives.

Tumor tissue is generally a mixture of several elements such as tumor, adjacent normal and stromal elements.

Lack of prospective studies which is also hampering the performance of many molecular profiling experiments. Cost of the equipment and running of many molecular profiling is very high.

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  • 5 months later...

United States vs Caris Life Sciences, Caris Diagnostics, Miraca Life Sciences

It is familiar and makes me somewhat feel slightly ill, but it was refreshing to see that action is being taken. Just a note, however, that the fraud was not discovered by CMS but instead the case derived its impetus from the persistence of two (justifiably) disgruntled former employees. I suspect that many companies are just one disgruntled employee away from facing a similar experience. In this case, except for the monumental stupidity that these complainants' superiors evinced in ineptly dealing with their employees, the violations likely would never have been detected by CMS.

http://pathologyblawg.com/wp-content/up ... plaint.pdf

Wild West of Molecular Testing? Lawsuit Alleges Caris Engaged in Aggressive Marketing

http://www.cancerletter.com/articles/20140808_1

http://www.cancerletter.com/articles/20140808_3

Note: Foundation Medicine is not any different than Caris Diagnostics in Phoenix (now Miraca Life Sciences), beyond testing for standard pathology "targets" such as ER, PR, Her2, EGFR mutations, KRAS, BRAF. They aren't worth much for the sorts of chemotherapy which is used in 95% of all cancers and useless with respect to drug combinations. While fresh tissue is very dear and hard to come by, function trumps structure, in terms of potency and robustness of information provided than using archival paraffin blocks.

Batch Processing of tumor biopsies for cell markers

viewtopic.php?f=8&t=50687

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