Cancer Research Moves Forward by Fits and Starts

I recently returned from the American Association for Cancer Research (AACR) meeting held in Philadelphia. AACR is attended by basic researchers focused on the molecular basis of oncology. Many of the concepts reported will percolate to the clinical literature over the coming years.

There were many themes including the revolution in immunologic therapy that took center stage, as James Allison, PhD, received the Pezcoller Prize for his groundbreaking work in targeting immune checkpoints. The Princess Takamatsu Award given to Dr. Lewis Cantley, recognized his seminal contribution to our understanding of signal transduction at the level of PI3K. A series of very informative lectures were provided on “liquid biopsies” that examine blood, serum and other bodily fluids to characterize the process of carcinogenesis. These technologies have the potential to revolutionize the diagnosis and monitoring of cancers.

The first symposium I attended described the phenomenon of chromothripsis. This represents a catastrophic cellular trauma that results in the simultaneous fragmentation of chromosomal regions, allowing for rejoining of disparate chromosome components, often leading to malignancy and other diseases. I find the concept intriguing, as it reflects the intersection of oncology with evolutionary developmental biology, reminiscent of the outstanding work of Stephen Jay Gould. His theory of punctuated equilibrium, from 1972, challenged many long held beliefs in the study of evolution.

Since the time of Charles Darwin, we believed that evolution was slow and continual.  New attributes were selected under environmental pressure and the population carried those characteristics forward toward higher complexity. Gould and his associate, Niles Eldredge, stated that evolution was anything but gradual. Indeed, according to their hypothesis, evolution occurred as a state of relative stability, followed by brief episodes of disruption. This came to mind as I contemplated the implications of chromothripsis.

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According to the new thinking (chromothripsis and its related fields), cancer may arise as a single cell forced to recover from what would otherwise be catastrophic injury. The reconfiguring of genetic elements scrambled together to avoid apoptosis (programmed cell death) provides an entirely new biology that can progress to full-blown malignancy.

By this reasoning, each patient’s cancer is unique. The results of damage control whereby chromosomal material is rejoined haphazardly would be largely unpredictable. These cancers would have a fingerprint all their own, depending on which chromosome was disrupted.

As high throughput technologies and next generation sequences continue to unravel the complexity of human cancer, we seem to be more and more like those who practice stone rubbing to create facsimiles of reality from the “surface” of our genetic information. Like stone rubbing, practitioners do not create the images, but simply borrow from them.

With each symposium, we learn that cancer biology does not come to be, but is. Grasping the complexity of cancer requires the next level of depth. That level of depth is slowly being recognized by investigators from Harvard University to Vanderbilt as the measurement of humor tumor phenotypes.

Cancer is phenotypic and human biology is phenotypic. Laboratory analyses that allow us to measure, grasp, and manipulate phenotypes are those that will provide the best outcomes for patients. Laboratory analyses like the EVA-PCD.

Is Cancer a Genetic Disease?

I recently had the opportunity to meet two charming young patients: One, a 32-year-old female with an extremely rare malignancy that arose in her kidney and the other a 33-year-old gentleman with widely metastatic sarcoma.

Both patients had obtained expert opinions from renowned cancer specialists and both had undergone aggressive multi-modality therapies including chemotherapy, radiation and surgery. Although they suffered significant toxicities, both of their diseases had progressed unabated. Each arrived at my laboratory seeking assistance for the selection of effective treatment.

With the profusion of genomic analyses available today at virtually every medical center, it came as no surprise that both patients had undergone genetic profiling. What struck me were the results. The young woman had “no measurable genetic aberrancies” from a panoply of 370 cancer-causing exomes, while the young man’s tumor revealed no somatic mutations and only two germ-line SNV’s (single nucleotide variants) from a 50 gene NextGen sequence, neither of which had any clinical or therapeutic significance.

What are we to make of these findings? By conventional wisdom, cancer is a genetic disease. Yet, neither of these patients carried detectable “driver” mutations. Are we to conclude that the tumors that invaded the cervical vertebra of the young woman, requiring an emergency spinal fusion, or the large mass in the lung of the young man are not “cancers”? It would seem that if we apply contemporary dogma, these patients do not have a cancer at all. But nothing could be further from the truth.

Cancer as a disease is not a genomic phenomenon. It is a phenotypic one. As such, it is extremely likely that these patients’ tumors are successfully exploiting normal genes in abnormal ways. The small interfering RNAs or methylations or acetylation or non-coding DNA’s that conspired to create these monstrous problems are too deeply encrypted to be easily deciphered by our DNA methodologies. These changes are effectively gumming up the works of the cancer cell’s biology without leaving a fingerprint.  

I have long recognized that cellular studies like the EVA-PCD platform provide the answers, through functional profiling, that genetic analyses can only hope to detect. The assay did identify drugs active in these patients’ tumor, which will offer meaningful benefit, despite the utter lack of genetic targets. Once again, we are educated by cellular biology in the absence of genomic insights. This leaves us with a question however – is cancer a genetic disease?

Is There a Role for PI3k Inhibitors in Breast Cancer? Maybe.

Over the past decades oncologists have learned that cancer is driven by circuits known as signal transduction pathways. The first breakthroughs were in chronic myelogenous leukemia (CML) where a short circuit in the gene as c-Abl caused the overgrowth of malignant blasts. The development of Imatinib (Gleevec) a c-Abl inhibitor yielded brilliant responses and durable remissions with a pill a day.

The next breakthrough came with the epidermal growth factor pathway and the development of Gefitinib (Iressa) and shortly thereafter Erlotinib (Tarceva). Good responses in lung cancers, many durable were observed and the field of targeted therapy seemed to be upon us.

Among the other signal pathways that captured the imagination of the pharmaceutical industry as a potential target was phospho-inositol-kinase (PI3K). Following experimental work by Lew Cantley, PhD, who first described this pathway in 1992, more than a dozen small molecules were developed to inhibit this cell signal system.

The PI3K pathway is important for cell survival and regulates metabolic activities like glucose uptake and protein synthesis. It is associated with insulin signaling and many bio-energetic phenomena. The earliest inhibitors functioned downstream at a protein known as mTOR, and two have been approved for breast, neuroendocrine and kidney cancers. Based on these early successes, PI3K, which functions upstream and seemed to have much broader appeal, became a favored target for developmental clinical trials.

The San Antonio Breast Cancer Symposium is one of the most important forums for breast cancer research. The December 2014 meeting featured a study that combined one of the most potent PI3K inhibitors, known as Pictilisib, with a standard anti-estrogen drug, Fulvestrant, in women with recurrent breast cancer. The FERGI Trial only included ER positive patients who had failed prior treatment with an aromatase inhibitor (Aromasin, Arimidex or Femara). The patients were randomized to receive the ER blocker Fulvestrant with or without Pictilisib.

With seventeen months of follow-up there was some improvement in time to progressive disease, but this was not large enough to achieve significance and the benefit remains unproven. A subset analysis did find that for patients who were both ER (+) and PR (+) a significant improvement did occur. The ER & PR (+) patients benefitted for 7.4 months on the combination while those on single agent Fulvestrant for only for 3.7 months.

The FERGI trial is more interesting for what it did not show. And that is that patients who carried the PI3K mutation, the target of Pictilisib, did not do better than those without mutation (known as wild type). To the dismay of those who tout the use of genomic biomarkers like PI3K mutation for patient drug selection, the stunning failure to identify responders at a genetic level should send a chill down the spine of every investor who has lavished money upon the current generation of genetic testing companies.

It should also raise concerns for the new federal programs that have designated hundreds of millions of dollars on the new “Personalized Cancer Therapy Initiatives” based entirely on genomic analyses. The contemporary concept of personalized cancer care is explicitly predicated upon the belief that genomic patient selection will improve response rates, reduce costs and limit exposure to toxic drugs in patients unlikely to respond.

This unanticipated failure is only the most recent reminder that genomic analyses can only suggest the likelihood of response and are not determinants of clinical outcome even in the most enriched and carefully selected individuals. It is evident from these findings that PI3K mutation alone doesn’t define the many bioenergetic pathways associated with the phenotype. This strongly supports phenotypic analyses like EVA-PCD as better predictors of response to agents of this type, as we have shown in preclinical and clinical analyses.

Toward A 100% Response Rate in Human Cancer

Oncologists confront numerous hurdles as they attempt to apply the new cancer prognostic and predictive tests. Among them are the complexities of gene arrays that introduce practicing physicians to an entirely new lexicon of terms like “splice variant, gene-rearrangement, amplification and SNP.”

Although these phrases may roll of the tongue of the average molecular biologists (mostly PhDs), they are foreign and opaque to the average oncologist (mostly MDs). To address this communication shortfall laboratory service providers provide written addenda (some quite verbose) to clarify and illuminate the material. Some institutions have taken to convening “molecular tumor boards” where physicians most adept at genomics serve as “translators.” Increasingly, organizations like ASCO offer symposia on modern gene science to the rank and file, a sort of Cancer Genomics for Dummies. If we continue down this path, oncologists may soon know more but understand less than any other medical sub-specialists.

However well intended these educational efforts may be, none of them are prepared to address the more fundamental question: How well do genomic profiles actually predict response? This broader issue lays bare our tendency to confuse data with results and big data with big results. To wit, we must remember that our DNA, originally provided to each of us in the form of a single cell (the fertilized ovum) carries all of the genetic information that makes us, us. From the hair follicles on our heads to the acid secreting cells in our stomach, every cell in our body carries exactly the same genetic data neatly scripted onto our nuclear hard-drives.

What makes this all work, however, isn’t the DNA on the hard drive, but instead the software that judiciously extracts exactly what it needs, exactly when it needs it. It’s this next level of complexity that makes us who we are. While it is true that you can’t grow hair or secrete stomach acid without the requisite DNA, simply having that DNA does not mean you will grow hair or make acid. Our growing reliance upon informatics has created a “forest for the trees” scenario, focusing our gaze upon nearby details at the expense of larger trends and insights.

What is desperately needed is a better approximation of the next level of complexity. In biology that moves us from the genotype (informatics) to the phenotype (function). To achieve this, our group now regularly combines genomic, transcriptomic or proteomic information with functional analyses. This enables us to interrogate whether the presence or absence of a gene, transcript or protein will actually confer that behavior or response at the system level.

I firmly believe that the future of cancer therapeutics will combine genomic, transcriptomic and/or proteomic analyses with functional (phenotypic) analyses.

Recent experiences come to mind. A charming patient in her 50s underwent a genomic analysis that identified a PI3K mutation. She sought an opinion. We conducted an EVA-PCD assay on biopsied tissue that confirmed sensitivity to the drugs that target PI3K. Armed with this information, we administered Everolimus at a fraction of the normal dose. The response was prompt and dramatic with resolution of liver function abnormalities, normalization of her performance status and a quick return to normal activities. A related case occurred in a young man with metastatic colorectal cancer. He had received conventional chemotherapies but at approximately two years out, his disease again began to progress.

A biopsy revealed that despite prior exposure to Cetuximab (the antibody against EGFR) there was persistent activity for the small molecule inhibitor, Erlotinib. Consistent with prior work that we had reported years earlier, we combined Cetuximab with Erlotinib, and the patient responded immediately.

Each of these patients reflects the intelligent application of available technologies. Rather than treat individuals based on the presence of a target, we can now treat based on the presence of a response. The identification of targets and confirmation of response has the potential to achieve ever higher levels of clinical benefit. It may ultimately be possible to find effective treatments for every patient if we employ multi-dimensional analyses that incorporate the results of both genomic and phenotypic platforms.

The High Cost of Cancer Care

An article by Scott Gottlieb, MD, in Forbes (Medicare Nixes Coverage for New Cancer Tests), described Medicare reimbursement for new molecular diagnostics. As many readers are aware, there have been a growing number of diagnostic tests developed and marketed over recent years designed to identify and monitor the progress of cancer. Many of these tests are multiplexed gene or protein panels that identify prognostic groups using nomograms developed from prospective or retrospective analyses. The 21-gene Oncotype DX and related Mammoprint, are among the most widely used. Related tests for lung, colon, and other cancers are in development.

With the explosion of assays designed to personalize cancer care, comes the expense associated with conducting these analyses. Medicare, as the largest provider of medical insurance in the United States, is at the leading edge of cost containment. Not surprisingly, HHS has a jaundiced view of adding tests without clear cost benefit.

The issue is far broader than cost analysis. It goes to the very heart of what we describe as personalized medicine. Every patient wants the right treatment for their disease. Every laboratory company wants to sell their services. Where the supply and demand curve meet however, is no longer set by market forces. In this instance, third party reimbursers set the fee and the companies then need to determine whether they can provide their service at that cost.

The problem, as with all economic analysis, is meeting patient’s unlimited wants with limited resources. Two solutions can be envisaged. On the one hand, medical care progressively moves to a scenario of haves and have nots wherein only wealthier individuals can afford to obtain those drugs and interventions that are beyond the price range of most. On the other hand, care is rationed and only those treatments and interventions that rise to the highest level of evidence are made available.

While the subject of this article was sophisticated diagnostic tests, it will only be a matter of time before these same econometric analyses begin to limit the availability of costly drugs like highly expensive targeted agents. In a recent editorial published in blood, leading leukemia experts pointed out that 11 of the 12 recently approved drugs each cost $10,000 or more per month.

As we examine the rather grim prospect of unaffordable or rationed care, a glimmer of hope can be seen. Using expensive and relatively insensitive molecular diagnostic tests to select expensive targeted agents could be replaced by less expensive testing platforms. The dramatic, yet brief responses observed for many targeted agents reflect the shortcoming of linear thinking applied to the manifestly non-linear human biology, characterized by cross talk, redundancies and unrecognized hurdles. To address these complexities phenotypic analysis (the phenotype being the end product of genomic, transcriptomic and proteomic events) provide global assessments of tumor response to drugs, combinations and signal transduction inhibitors. These more discriminating results identify cellular response at the level of biology, not just informatics. While it is theoretically possible that high-throughput genomic analyses using neural networks and high throughput computer analyses may ultimately provide similar information, it is unlikely that most patients will have ready access to a Cray computer to decipher their results.

We need to stop working hard and start working smart. The answers to the many questions raised by the Forbes article regarding resource allocation in cancer treatment may already be at hand.

Chemosensitivity Testing Captures Attention of “Nature Biotechnology”

An interesting editorial appeared in the February 2013 issue of Nature Biotechnology titled “Dishing out cancer treatment.” The lead line reads, “Despite their limitations, in-vitro assays are a simple means for assessing the drug sensitivity of a patient’s cancer . . . we think assays deserve a second look.”

The author describes the unequivocal appeal of laboratory analyses that are capable of selecting drugs and combinations for individual patients. At a time when 100’s of new drugs are in development, drug discovery platforms that can mimic human tumor response in the laboratory are becoming increasingly attractive to patients and the pharmaceutical industry. While the author, rooted in contemporary molecular biology, examines the field through the lens of genomic, transcriptomic, proteomic and metabolomic profiling, he recognizes that these analyte-based approaches cannot capture the tumor in its microenvironment, yet we now recognize that these micro-environmental influences are critical to accurate response prediction.

As one reads this piece, it is instructive to remember that no other platform can examine the dynamic interaction between cells and their microenvironment. No other platform can examine drug synergy. And no other platform can examine drug sequence.

It is these complexities however, that will guide the next generation of drug tests and ultimately the process of drug discovery. Even the most ardent adherents to genomic profiling must ultimately recognize that genotype does not equal phenotype. Yet, it is the tumor phenotype that we must study.

I am gratified that the editors of so august a journal as Nature Biotechnology have taken the time to reexamine this important field. Perhaps, if our most scientific colleagues are beginning to recognize the importance of functional analyses, it may be only a matter of time before the clinical oncology community follows suit.

The editor’s final line is poignant, “After years spent on the sidelines, perhaps in-vitro screening methods deserve another look.” We couldn’t agree more.

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.

Truly Personalized Cancer Care

In the mid 1980s, it became apparent to me that cancer did not result from uncontrolled cell proliferation, but instead from the lack of cell death. Yet, cancer research labored for almost a century under the erroneous belief that cancer represented dysregulation of cell proliferation. Today, we confront another falsehood: the complexities and redundancies of human tumor biology can be easily characterized based on genomic analyses.

The process of carcinogenesis reflects the accumulation of cellular changes that provide a selective survival advantage to transformed cells.  However, the intricate circuitry that provide these survival advantages, reflect harmonic osolations between DNA, RNA and protein. Put simply, Genotype does not equal Phenotype. It is the phenotype that determines biological behavior and clinical response in cancer. Thus, it is ridiculously simplistic to imagine that a DNA profile by itself can provide more than a fraction of the information required to make individual patient treatment decisions.

When therapies are based on genomic analysis, only a portion of the patient’s profile is taken into consideration. These analyses disregard the environmental, epigenetic and proteomic factors that make each of us individuals. Though useful prognostically and applicable in select circumstances where a unique genetic perturbation leads to a clinical response (c-ABL and Imatinib response in CML), genomic analyses provide only a veneer of information.

The Rational Therapeutics Ex Vivo Analysis – Programmed Cell Death™ (EVA-PCD) assay focuses upon the complexity of human tumors by measuring cell death, the end result of all cellular mechanisms of response and resistance acting in concert. By incorporating cell-cell, vascular, stromal and inflammatory elements into the tumor response assessment, the EVA-PCD platform provides a robust surrogate for human tumor response. While much of modern cancer research pursues the question of “Why” cancer arises, the clinical oncologist must confront the more practical question of “How” the best outcome can be achieved.

Assay-directed therapy is truly personalized cancer care providing treatments unique to the individual.