Yet Another Study Agrees: Functional Profiling Provides Insight

It was during the last weeks of December that a particularly interesting article crossed my desk. The study done by a group from Toronto, Canada, is entitled Variable Clonal Repopulation Dynamics Influence Chemotherapy Response in Colorectal Cancer. The study examined the proliferative capacity and drug sensitivity in colorectal cancer cells that were tracked using a process known as lentiviral lineage tracking. The investigators showed that despite serial passages, the cell populations remained stable from a genomic standpoint.

What was most interesting was the finding that these genomically related subpopulations became progressively more resistant to oxaliplatin after drug exposure, suggesting what they described as “inherent functional variability.”

As one of several investigators engaged in the field of functional profiling (EVA-PCD), I found the article both interesting and extremely consistent with our laboratory observations. First, cancer cells display biological differences that may reflect environmental (microenvironmental) influences, epigenetics and other drivers not readily identified at the DNA level.

Second, these investigators, using extremely sophisticated molecular techniques, found, as the lead investigator said, “We should not be putting our eggs exclusively in the genetics basket.” This quote from the lead investigator, John Dick, was particularly resonant.

As many of you who read my blogs know, a recurring theme in these pages is the need to broaden our scope and examine the protein, metabolic and functional characteristics of the cancer cells in their native state. Once again we find that as our most accomplished molecular brethren drill down to the bedrock of cancer biology, they are confronted by complexities and crosstalk that can only be effectively studied at the level of cell biology.

I wish all of readers of this blog a happy New Year, and look forward to a healthy and productive 2013.

Systems Biology Comes of Age: Metastatic Lung Cancer in the Crosshairs

Cancer therapists have long sought mechanisms to match patients to available therapies. Current fashion revolves around DNA mutations, gene copy and rearrangements to select drugs. While every cancer patient may be as unique as their fingerprints, all of the fingerprints on file with the federal AFIS (automated fingerprint identification system) database don’t add up to a hill of genes (pun intended), if you can’t connect them to the criminal.

To continue the analogy, it doesn’t matter why the individual chose a life of crime, his upbringing, childhood traumas or personal tragedies. What matters is that you capture him in the flesh and incarcerate him (or her, to be politically correct).

The term we apply to the study of cancer, as a biological phenomenon is “systems biology.” This discipline strikes fear into the heart of molecular biologists, for it complicates their tidy algorithms and undermines the artificial linearity of their cancer pathways. We frequently allude to the catchphrase, genotype ≠ phenotype, yet it is the cancer phenotype that we must confront if we are to cure this disease.

Using a systems biology approach, we applied the ex-vivo analysis of programmed cell death (EVA-PCD®) to the study of previously untreated patients with non-small cell lung cancer. Tissue aggregates isolated from their surgical specimens were studied in their native state against drugs and signal transduction inhibitors. This methodology captures all of the interacting “systems,” as they respond to cytotoxic agents and growth factor withdrawal. The trial was powered to achieve a two-fold improvement in response.

At interim analysis, we had more than accomplished our goal. The results speak for themselves.

First: a two-fold improvement in clinical response – from the national average of 30 percent we achieved 64.5 percent (p – 0.00015).

Second: The median time to progression was improved from 6.4 to 8.5 months.

Third: And most importantly the median overall survival was improved from an average of 10 – 12 months to 21.3 months, a near doubling.

These results, from a prospective clinical trial in which previously untreated lung cancer patients were provided assay directed therapy, reflects the first real time application of systems biology to chemotherapeutics. The closest comparison for improved clinical outcome with chemotherapeutic drugs chosen from among all active agents by a molecular platform in a prospective clinical trial is . . .

Oh, that’s right there isn’t any.

Type I Error

Scientific proof is rarely proof, but instead our best approximation. Beyond death and taxes, there are few certainties in life. That is why investigators rely so heavily on statistics.

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. To wit, 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, we are just reporting a paper in the 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.

Today, many laboratories offer tests that claim to select candidates for treatment. Almost all of these laboratories are conducting gene-based analysis. While there are no good prospective studies that prove that these genomic analyses accurately predict response, this has not prevented these companies from marketing their tests aggressively. Indeed, many insurers are covering these services despite the lack of proof.

So let’s examine why these tests may encounter difficulties now and in the future. The answer to put it succinctly is Type I errors. In the statistical literature, a Type I error occurs when a premise cannot be rejected.  The statistical term for this is to reject the “null” hypothesis. Type II errors occur when the null hypothesis is falsely rejected.

Example: The scientific community is asked to test the hypothesis that Up is Down. Dedicated investigators conduct exhaustive analyses to test this provocative hypothesis but cannot refute the premise that Up is Down. They are left with no alternative but to report according to their carefully conducted studies that Up is Down.

The unsuspecting recipient of this report takes it to their physician and demands to be treated based on the finding. The physician explains that, to his best recollection, Up is not Down.  Unfazed the patient, armed with this august laboratory’s result, demands to be treated accordingly. What is wrong with this scenario? Type I error.

The human genome is comprised of more than 23,000 genes: Splice variants, duplications, mutations, SNPs, non-coding DNA, small interfering RNAs and a wealth of downstream events, which make the interpretation of genomic data highly problematic. The fact that a laboratory can identify a gene does not confer a certainty that the gene or mutation or splice variant will confer an outcome. To put it simply, the input of possibilities overwhelms the capacity of the test to rule in or out, the answer.

Yes, we can measure the gene finding, and yes we have found some interesting mutations. But no we can’t reject the null hypothesis. Thus, other than a small number of discreet events for which the performance characteristics of these genomic analyses have been established and rigorously tested, Type I errors undermine and corrupt the predictions of even the best laboratories. You would think with all of the brainpower dedicated to contemporary genomic analyses that these smart guys would remember some basic statistics.

Stalking Leukemia Genes One Whole Genome at a Time

An article by Gina Kolata on the front page of the July 8, Sunday New York Times, “In Leukemia Treatment, Glimpses of the Future,” tells the heartwarming story of a young physician afflicted with acute lymphoblastic leukemia. Diagnosed in medical school, the patient initially achieved a complete remission, only to suffer a recurrence that led him to undergo a bone marrow transplant. When the disease recurred a second time years later, his options were more limited.

As a researcher at Washington University himself, this young physician had access to the most sophisticated genomic analyses in the world. His colleagues and a team of investigators put all 26 of the University’s gene sequencing machines to work around the clock to complete a whole genome sequence, in search of a driver mutation. The results identified FLT3. This mutation had previously been described in acute leukemia and is known to be a target for several available small molecule tyrosine kinase inhibitors. After arranging to procure sunitinib (Sutent, Pfizer Pharmaceuticals), the patient began treatment and had a prompt and complete remission, one that he continues to enjoy to this day.

The story is one of triumph over adversity and exemplifies genomic analysis in the identification of targets for therapy. What it also represents is a labor-intensive, costly, and largely unavailable approach to cancer management. While good outcomes in leukemia have been the subject of many reports, imatinib for CML among them, this does not obtain for most of the common, solid tumors that lack targets for these new silver bullets. Indeed, the article itself describes unsuccessful efforts on the part of Steve Jobs and Christopher Hitchens, to probe their own genomes for effective treatments. More to the point, few patients have access to 26 gene-sequencing machines capable of identifying genomic targets. A professor of bioethics from the University of Washington, Wiley Burke, raised additional ethical questions surrounding the availability of these approaches only to the most connected and wealthiest of individuals.

While brute force sequencing of human genomes are becoming more popular, the approach lacks scientific elegance. Pattern recognition yielding clues, almost by accident, relegates scientists to the role of spectator and removes them from hypothesis-driven investigation that characterized centuries of successful research.

The drug sunitinib is known for its inhibitory effect upon VEGF 1, 2 and 3, PDGFr, c-kit and FLT3. Recognizing the attributes of this drug and being well aware of C-KIT and FLT3’s role in leukemias, we regularly add sunitinib into our leukemia tissue cultures to test for cytotoxic effects in malignantly transformed cells.  The insights gained enable us to simply and quickly gauge the likelihood of efficacy in patients for drugs like sunitinib.

Once again we find that expensive, difficult tests seem preferable to inexpensive, simple ones. While the technocrats at the helm of oncology research promise to drive the price of these tests down to a level of affordability, everyday we wait 1,581 Americans die of cancer. Perhaps, while we await perfect tests that might work tomorrow, we should use good tests that work today.

Venture Capital Goes Genomic

During the 1960s, 70s and into the 90s, a field of investigation arose that examined buyer’s practices when it came to the consumption of goods and services. Algorithms were developed to interrogate consumer choice. One such treatise was reported in 1994 (Carson, RT et al, Experimental Analysis of Choice, Marketing Letters 1994). What these researchers explored were the motivations and forces that drove consumption. When choices are offered, decisions are driven by such factors as complexity and utility. Complexity demands personal expertise or failing that, input from experts, while utility places a value on the good or service.

A recent report from a small biotechnology company called Foundation Medicine has brought this field of endeavor to mind. It seems that this group will be offering DNA sequencing to select chemotherapy drugs. This service, currently priced at $5,800, will focus upon a small cassette of genes that they described as “key” in tumor growth. Based on their technology they have already raised $33.5 million from the likes of Third Rock, Google and Kleiner Perkins Caulfield & Byers, venture capital sources. The CEO of Foundation substantiates the approach by pointing out that fully 150 people have already used their services. One hundred and fifty!

It seems from this report that our colleagues in the field of molecular profiling have studied the dictates of “Experimental Analysis of Choice” to a “T.” What we have is the perfect storm of medical marketing.

First, the technology is so complex as to be beyond the ken of both patients and physicians alike. Thus, expertise is required and that expertise is provided by those engaged in the field. Second, the utility of drug selection is beyond reproach. Who in their right mind wouldn’t want to receive a drug with a higher likelihood of a response when we consider the toxicities and costs, as well as the consequences of the wrong treatment? Dazzled by the prospect of curative outcomes, patients will, no doubt, be lining up around the block.

But, let’s deconstruct what this report is actually telling us. First, a scientifically interesting technology has been brought to the market. Second, it exists to meet an unmet need. So far, so good. What is lacking, however, is evidence. Not necessarily evidence in the rarefied Cochrane sense of idealized survival curves, nor even Level II evidence, but any evidence at all. Like whirling dervishes, patients and their physicians are drawn into a trancelike state, when terms like NextGen sequencing, SNP analysis and splice variants are bandied about.

Despite the enthusiastic reception by investors, I fear a lack of competent due diligence. To wit, a recent article in Biotechniques, “Will the Real Cancer Cell Please Stand Up,” comes to mind. It seems that cancer cells are not individual entities but networks. A harmonic oscillation develops between tumor, stroma, vasculature and cytokines. In this mix, the cancer cell is but one piece of the puzzle.

Indeed, according to recent work from Baylor, some of the tumor promotion signals in the form of small interfering RNAs, may arise not from the cancer cells, but instead from the surrounding stroma. How then, will even the most punctiliously perfect genomic analyses of a cancer cells play out in the real world of human tumor biology and clinical response prediction? Not very well I fear. But then again such a discussion would require data on the predictive validity of the method, something that appears to be sorely lacking.

Will today’s gene profile companies prove to be the biotech Facebook IPOs of tomorrow?

No One is More Interested in Curing Your Cancer Than You

A diagnosis of cancer thrusts a, heretofore, healthy individual into the strange and unfamiliar territory of medical oncology. Many of my patients describe this transition as “entering the cancer bubble.” Suddenly, you are on the inside and everyone on the outside is talking at you about what to do, where to go, whom to see, and what treatments to receive.

From the inside of the bubble however, all of this has a hollow ring as you ponder many options, few good and some, positively frightening. Unfortunately, few patients have the time to complete a MD, or PhD, between diagnosis and the initiation of treatment. Lacking the requisite expertise, they turn to the “authorities” for advice.

Depending on which “authority” one consults, the recommendations may be colored by prejudices and biases. Some physicians adhere strictly to the National Comprehensive Cancer Network guidelines. Others insist upon accrual to Cooperative Group and Phase II trials. University-based investigators will often recommend developmental studies. And some physicians will follow the path of least resistance, examining such issues as cost, chair time and reimbursement, before considering what treatment to deliver.

It is in this milieu, that patients find themselves adrift. Who exactly should you trust? What is their motivation? To put it crassly, when they recommend a specific treatment, what’s in it for them: Cooperative Group points (provided to the most active accruers), academic accolades (the currency of junior faculty), cost containment (the purview of the managed care physicians), or finally, profit margins? Yes, there are a small number of physicians whose choices reflect their own pecuniary interests.

The antidote to all this uncertainty lies within each patient; answers to vexing questions crying out to be heard. These answers reflect the biologic features of each individual’s tumor. What pathway, what repair mechanism, what survival signal drives your tumor? No one has a perfect answer, not the genomic investigators (despite their protestations to the contrary), nor the immunohistochemists, despite the significant appeal of the platform. And not the immunologist (despite brilliant progress in this field over recent years). The closest approximation to human tumor biology is, well, human tumor biology. Using cellular constructs, in the form of native state microspheroids, we can today approximate the response profiles of patients undergoing systemic therapies. Using systems approaches to complex questions, the multitude of factors that contribute to objective response can be examined and elucidated.

No test is perfect. No patient is guaranteed a good outcome. Yet, doubling the objective response rate, and as we and others have documented, improving the time to progression and overall survival can be achieved with available methodologies that apply functional profiling to individual tumors.

No one would walk away from an investment formula that doubled the value of their portfolio. Few would turn down the opportunity to enhance their real estate positions predicated on reliable information from a realtor. Yet everyday, physicians convince patients to walk away from available, published, established methods that can improve response rates, diminish toxicities and avoid futile care. In this environment it is critical for patients to take charge of their own cancer management. Patients must not be dissuaded from seeking the best possible outcomes. Physicians, no matter how well intentioned, are human. Their opinions can be colored by misconceptions and an incomplete understanding of the questions at hand. Laboratory analysis empowers patients to make smart decisions.

In the game of cancer we need all the help we can get. After all, no one is more interested in saving your life than you.

Gee (G719X) Whiz: Novel Mutations and Response to Targeted Therapies

In a recent online forum a patient described her experience using Tarceva as a therapy for an EGFR mutation negative lung cancer. For those of you familiar with the literature you will know that Lynch and Paez both described the sensitizing mutations that allow patients with certain adenocarcinoma to respond beautifully to the small molecule inhibitors.  The majority of these mutations are found in Exon 19 and Exon 21, within the EGFR domain. Response rates for the EGFR-TKI (gefitinib and erlotinib) clearly favor mutation positive patients. Depending upon the study, mutation positive patients have response rates from 53 – 100 percent, generally around 70 percent, while mutation negative response patients have a response rate of 0 – 25 percent, generally about 10 percent.So why don’t all the mutation positive patients respond and conversely why do some mutation negative patients respond?

The story outlined in this online forum gives some insight. The individual in question carried a rare, and only recently recognized, Exon 18 mutation known as a G719X. This uncommon form of mutation had previously been unknown and few laboratories knew to test for it. Nonetheless, G719X positive patients respond to erlotinib and related agents. Indeed, there may be reason to believe that the more potent irreversible EGFR/HER2 dual inhibitor HKI-272, may be even more selective for this point mutation.

The excellent and durable response described by this individual, would not have been possible had the patient’s first physician followed the rules. That is, had her physician refused to give erlotinib to an (putatively) EGFR mutation negative patient she might well not be here to tell her story. More to the point, her good response (a clinical observation) led to the next level of investigation, namely the identification of this specific EGFR variant

The lessons from this experience are numerous. The first is that cancer biology is complex and, to paraphrase E.O. Wilson, was not put on earth for us to necessarily figure it out. The second, is that molecular biologists can only seek and identify that which they know about apriori.  To wit, if you don’t know about it (G719X) and you don’t have a test for it, and you don’t know to look for it, then it’s a virtual certainty that you aren’t going to find it.

The premise of our work at Rational Therapeutics is that the observation of a biological signal identifies a candidate for therapy whether we understand or recognize the target. Crizotinib was originally developed as a clinical therapy for patients who carried the CMET mutation. Serendipity led to the recognition that the responding subpopulation was actually carrying a heretofore-unrecognized ALK gene rearrangement. Sorafenib was originally evaluated for the treatment of BRAF mutation positive diseases. Yet it was the drug’s cross-reactivity with the VGEF tyrosine kinases that lead to its broad clinical applications. Each of these phenomena represents accidental successes. Were it not for the clinical observation of response in patients, the investigators conducting these trials would have been unlikely to make the discoveries that today provide such good clinical responses in others.

To put it quite simply, these patients and their disease entities educated the molecular biologists.

When we first identified lung cancer as a target for gefitinib, and began to administer the closely related erlotinib to lung cancer patients, neither Lynch nor Paez had identified the sensitizing EGFR mutations. That had absolutely no impact upon the excellent responses that we observed. It didn’t matter why it worked, but that it worked.  While the EGFR story has now been well-described, might we not use functional analytical platforms (functional profiling) to gain insights into the next, and the next generation of drugs and therapies that target pathways like MEK, ERK, SHH, FGFR, PI3K, etc., etc., etc. . . .

Time for Rational Therapy?

At the 2012 American Association for Cancer Research (AACR) meeting recently held in Chicago, I again observed that the AACR presentations continue to diverge from those at the American Society of Clinical Oncology (ASCO). At this year’s meeting, I’m not sure I heard the word “chemotherapy” a single time. That is, all of the alphabet soup combinations that make up the sessions at ASCO are nowhere to be found at the AACR meeting. Instead, targeted agents, genomics, proteomics and the growing field of metabolomics reign supreme.

Over the coming weeks, I will blog about some of the more interesting presentations I attended. However, I note below several themes that seemed to emerge.

First: That cancer patients are highly unique. In one presentation using phosphoprotein signatures to connect genetic features to phenotypic expression, the investigator conducted 21 phosphoprotein signatures and found 21 different patterns. This, he noted, reflected the “uniqueness” of each individual.

Additional themes included the growing development of meaningfully effective immune therapies. There was evidence of a renewed interest in tissue cultures as the best platform to study drug effects and interactions. Although virtually every presentation began with the obligatory reference to genomic analysis, almost every one of them then doubled back to metabolism as the principal driver of human cancer.

Interestingly, the one phrase that cropped up time and time again was rational therapeutics. Although they did not appear to be referring to our group, it was comforting to note that they are at least, finally coming around to our philosophy.

The Unfulfilled Promise of Genomic Analysis

In the March 8 issue of the New England Journal of Medicine, investigators from London, England, reported disturbing news regarding the predictive validity and clinical applicability of human tumor genomic analysis for the selection of chemotherapeutic agents.

As part of an ongoing clinical trial in patients with metastatic renal cell carcinoma (the E-PREDICT) these investigators had the opportunity to conduct biopsies upon metastatic lesions and then compare their genomic profiles with those of the primary tumors. Their findings are highly instructive, though not terribly unexpected. Using exon-capture they identified numerous mutations, insertions and deletions. Sanger sequencing was used to validate mutations. When they compared biopsy specimens taken from the kidney they found significant heterogeneity from one region to the next.

Similar degrees of heterogeneity were observed when they compared these primary lesions with the metastatic sites of spread. The investigators inferred a branched evolution where tumors evolved into clones, some spreading to distant sites, while others manifested different features within the primary tumor themselves. Interestingly, when primary sites were matched with metastases that arose from that site, there was greater consanguinity between the primary and met than between one primary site and another primary site in the same kidney. Another way of looking at this is that your grandchildren look more like you, than your neighbor.

Tracking additional mutations, these investigators found unexpected changes that involved histone methyltransferase, histone d-methyltransferase and the phosphatase and tensin homolog (PTEN). These findings were perhaps among the most interesting of the entire paper for they support the principal of phenotypic convergence, whereby similar genomic changes arise by Darwinian selection. This, despite the observed phenotypes arising from precursors with different genomic heritages. This fundamental observation suggests that cancers do not arise from genetic mutation, but instead select advantageous mutations for their survival and success.

The accompanying editorial by Dr. Dan Longo makes several points worth noting.  First he states that “DNA is not the whole story.” This should be familiar to those who follow my blogs, as I have said the same on many occasions.  In his discussion, Dr Longo then references Albert Einstein, who said “Things should be made as simple as possible, but not simpler.” Touché.

I appreciate and applaud Dr. Longo’s comments for they echo our sentiments completely. This article is only the most recent example of a growing litany of observations that call into question molecular biologist’s preternatural fixation on genomic analyses. Human biology is not simple and malignantly transformed cells more complex still. Investigators who insist upon using genomic platforms to force disorderly cells into artificially ordered sub-categories, have once again been forced to admit that these oversimplifications fail to provide the needed insights for the advancement of cancer therapeutics. Those laboratories and corporations that offer “high price” genomic analyses for the selection of chemotherapy drugs should read this and related articles carefully as these reports portend a troubling future for their current business model.

If It is Too Good to Be True . . .

The February 12, 2012, CBS 60 Minutes covered a story that has sparked a great deal of interest among cancer patients and medical professionals. The topic was an investigator named Anil Poti who, while working at Duke University developed a laboratory platform for the study of human lung cancer.

Using molecular profiling, Dr. Poti and his collaborators, reported their capacity to distinguish responding and non-responding cancer patients, providing survival curves that were nothing short of astonishing. I recall attending the original lectures given by these investigators at the American Association of Cancer Research meeting several years ago.

As an investigator in the field of drug response prediction, working in lung cancer I had a particular interest in their platform and I was extremely impressed by the outcomes they reported. At the time, I wondered how the static measurement of gene profiles could possibly characterize the nuances of human biology, to encompass the epigenetic, siRNA, pseudogene, non-coding DNA and protein kinetics that ultimately characterize the human phenotype. Nonetheless, with such compelling data I was prepared to be convinced.

That is until a relatively unheralded report in the Cancer Letter raised concerns by several biostatisticians regarding the reproducibility of Dr. Poti’s findings. And then more comments were followed by a full NIH investigation. A panel of biostatisticians was convened and a formal report provided the explanation for Dr. Poti’s excellent results.

They had been invented. The clinical outcomes were not real results. The findings had been retrofitted to match the patient responses and this was the subject of the 60 Minutes report.

What the 60 Minutes report did not address however, was the real problem. That being the inability of contemporary genetic profiling to truly define human biology. For all the reasons enumerated above, siRNA, non-coding DNA, etc., the simple measurement of gene sequences cannot accurately predict biological behavior. This is what the 60 Minutes reporters and the physicians they interviewed, never discussed. The problem at hand is not an errant investigator but an errant scientific community. Our love affair with the gene that began in 1953 (Watson and Crick) has now been confronted by a most heartbreaking example of infidelity (pun intended).

Genes do not make us what we are; they only (sometimes) permit us to become what we are, with the vagaries of transcription and translation lying between.

This leads us to the reasons I find this so critically important:

1. I cannot stress strongly enough that this is NOT what I do. Genomic analysis (their work) and functional analysis (our work) are distinctly different platforms.
2. I strenuously resist any attempt on the part of anyone to tar me or my work with this brush.
3. It is precisely because genomic analysis cannot accurately predict cancer patient outcomes, that these investigators found it necessary to invent their data.
4. Despite this, functional analyses can and do provide these types of predictive results in lung cancers and other diseases as we have reported in numerous publications.
5. Finally, while imitation is the sincerest form of flattery, this is one instance in which I would prefer to decline the compliment.