The Emperor of All Maladies’ New Clothes

Ken Burn’s series “The Emperor of All Maladies” from Siddhartha Mukherjee’s book of the same title ppbs logorovides an interesting and informative historical perspective on mankind’s efforts to confront cancer as a disease.

Beginning with ancient references to human malignancy, the series goes on to explore radical surgery and the earliest use of radiation but really gains traction in the mid-20th century with the discovery of the first chemotherapy drugs. While the nitrogen mustard derivatives were being studied under a veil of military secrecy, Dr. Sidney Farber in Boston explored the B-vitamin analogue, aminopterin, for the treatment of childhood leukemia. (You can read more about this in my book Outliving Cancer.)

Through the ensuing decades, seemingly stunning victories ultimately fell in crushing defeats, while the promise of single agents, then multi-drug combinations, followed by dose-intensive therapies, and finally bone marrow transplantation yielded few cures but delivered ever increasing toxicities. Clifton Leaf, a cancer survivor himself who created a stir with his controversial 2003 Fortune Magazine article entitled “Why We Are Losing the War on Cancer and How to Win It” described his own disappointment with the slow pace of progress.

Screen shot Emperor of All MaladiesThe last episode examined our growing understanding of human genomics and segued by interviews with Richard Klausner, former director of the National Cancer Institute; and Harold Varmus, the current NCI director; to Michael Bishop, Eric Lander and Francis Collins who luxuriated in the clinical potential of human genomics and the coming era of big science.

The final part was an interview with Steven Rosenberg, one of the earliest pioneers in immunotherapy and Carl June whose groundbreaking work with chimeric antigen receptor T-cells is among the most recent applications of this important field.

The take-home message would seem to be that despite the fits and starts we are now at the dawn of a new age of big science, big data and genomic breakthroughs. What was missing however was an examination of where we had gone wrong. It would seem that the third rail for this community is an honest assessment of how a small coterie of investigators who championed only certain ways of thinking over all others commandeered all the money, grants, publications, chairmanships and public attention, while patients were left to confront a disease from which survival has changed very little, at ever increasing costs and toxicities.

Another thing that came through was the very human side of cancer as a disease and the kindness and emotional support that family members and parents provided to those afflicted. I couldn’t help but feel that these individuals had been cheated: cheated of the lives of their family members, cheated of the resources that could have pursued other options and cheated of the well-being that these poisonous and dose-intensive regimens rained upon them in their last days.

As science has become the new religion and scientists the new gurus, one message that resonated was that many of these gurus were false prophets. They are too self-absorbed to question their own dogmatic belief systems in dose-intensity or multi-agent combinations, all of which fell painfully by the way side as the next therapeutic fad emerged. Will our current love affair with the gene prove to be little more than the most current example of self-congratulatory science conducted in the echo chamber of modern academia?

Victories against cancer will be won incrementally. Each patient must be addressed as an individual, unique in their biology and unique in their response probability. No gene profile, heat map, DNA sequence or transcriptomic profile has answered the questions that every patient asks; “What treatment is best for me?” Dr. Mukherjee himself used the analogy of the blind men and the elephant. Unfortunately, there was little discussion of how much that parable may apply to our current scientific paradigms.

It is time for patients to demand better and refuse to participate in cookie-cutter protocols.
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Physicians should become more familiar with the fundamentals of physiology and biochemistry to better understand the principles of cancer prevention at the level of diet and lifestyle.

Finally, while we wait with bated breath, for the arrival of glorious gene profiles widely touted as the future answer to all of cancer’s most vexing questions, patients should throw off the yoke of one-size-fits-all approaches and demand laboratory platforms, such as the EVA-PCD assay, that are available today to make better use of existing treatments.

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. Signal_transduction_pathways.svgThe 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.

220px-PI3kinaseAmong 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.

In Cancer Research: An Awakening?

In 2005, as the Iraq War reached a low point with casualties mounting and public support dwindling, Sunni tribesman in the Anbar Province arose to confront the enemy. Joining together as an ad hoc army these fighters turned the tide of the war and achieved victories in the face of what had appeared at the time, to be overwhelming odds.

I am reminded of this by an article in The Wall Street Journal by Peter Huber and Paul Howard of the Manhattan Institute that examined the bureaucracy of drug development. It raised the question: Are new cancer treatments failures or is the process by which they are approved a failure? They describe “exceptional responders” defined as patients who show unexpected benefits from drug treatments. Using molecular profiles, they opine, scientists will unravel the mysteries of these individuals and usher in an era of personalized medicine. Thus, rigid protocols that use drugs based upon tumor type e.g. lung vs. colon fail because they do not incorporate the features that make each patient unique – an awakening.

The example cited is from Memorial Sloan-Kettering where a patient with bladder cancer had an unexpected response to the drug Everolimus (approved for kidney cancer). Subsequent deep sequencing identified a genetic signature associated with sensitivity to this drug. While it is a nice story, I already knew it very well because it had been repeated many times before and would in the past have been dismissed as an “anecdote.” It is precisely because of its rarity that it has been repeated so many times.

The WSJ analysis strikes a familiar chord. For decades, we have decried the failure of rigid clinical trials that underestimate a patient’s unique biology yet cost millions, even billions of dollars, while denying worthy candidates new treatments under stultifying disease-specific designs.

Well Tray Closeup2 smallWe pioneered phenotypic (functional) analyses (the EVA-PCD platform) to examine whole cell models as we explored drug response profiles, novel combinations and new targets. It is regrettable that these WSJ authors, having raised such important issues, then stumble into the same tantalizing trap of molecular diagnostics, and call for bigger, better, faster genomic analyses.

Cancer patients need to receive treatments that work. They do not particularly care why or how they work, just that they work. These authors seem to perpetuate the myth that we must first understand why a patient responds before we can treat them. Nothing could be further from the truth.

Alexander Fleming knew little about bacterial cell wall physiology when he discovered penicillin in 1928, and William Withering knew nothing about the role of muscle enzymes in congestive heart failure when he discovered digoxin extracts in 1785. Would anyone argue that we should have waited decades, even centuries to apply manifestly effective therapies to patients because we did not have the “genes sequenced?’

We may be witness to an awakening in cancer drug development. It may be that a new understanding of individualized patient response will someday provide better outcomes, but platforms with the proven capacity to connect patients to available treatments should be promoted and applied today.

Genomic Profiling for Lung Cancer: the Good, the Bad and the Ugly

Genomic profiling has gained popularity in medical oncology. Using NextGen platforms, protein coding regions of human tumors known as exomes can be examined for mutations, amplifications, deletions, splice variants and SNPs. In select tumors the results can be extremely helpful. Among the best examples are adenocarcinomas of the lung where EGFr, ALK and ROS-1 mutations, deletions and/or re-arrangements identified by DNA analysis can guide the selection of “targeted agents” like Erlotinib and Crizotinib.

An article published in May 2014 issue of JAMA reported results using probes for 10 “oncogenic driver” mutations in lung cancer patients. They screened for at least one gene in 1,007 patients and all 10 genes in 733. The most common was k-ras at 25%, followed by EGFR in 17% and ALK in 8%. The incidence then fell off with other EGFr mutations in 4%, B-raf mutations in 2%, with the remaining mutations each found in less than 1%.

Median survival at 3.5 vs 2.4 years was improved for patients who received treatments guided by the findings (Kris MG et al, Using multiplex assays of oncogenic drivers in lung cancers to select targeted drugs. JAMA, May 2014). Do these results indicate that genomic analyses should be used for treatment selection in all patients? Yes and no.

Noteworthy is the fact that 28% of the patients had driver mutations in one of three genes, EGFr, HER2 or ALK. All three of these mutations have commercially available chemotherapeutic agents in the form of Erlotinib, Afatinib and Crizotinib. Response rates of 50% or higher, with many patients enjoying durable benefits have been observed. Furthermore, patients with EGFr mutations are often younger, female and non-smokers whose tumors often respond better to both targeted and non-targeted therapies. These factors would explain in part the good survival numbers reported in the JAMA article. Today, a large number of commercial laboratories offer these tests as part of standard panels. And, like k-ras mutations in colon cancer or BCR-abl in CML (the target of Gleevec), the arguments in favor of the use of these analyses is strong.

Non-small cell lung cancer

Non-small cell lung cancer

But what of the NSCLC patients for whom no clear identifiable driver can be found? What of the 25% with k-ras mutations for whom no drug exists? What of those with complex mutational findings? And finally what of those patients whose tumors are driven by normal genes functioning abnormally? In these patients no mutations exists at all. How best do we manage these patients?

I was reminded of this question as I reviewed a genomic analysis reported to one of my colleagues. He had submitted a tissue block to an east coast commercial lab when one of his lung cancer patients relapsed. The results revealed mutations in EGFr L858R & T790M, ERBB4, HGF, JAK2, PTEN, STK11, CCNE1, CDKN2A/B, MYC, MLL2 W2006, NFKB1A, and NKX2-1. With a tumor literally bristling with potential targets, what is a clinician to do? How do we take over a dozen genetically identified targets and turn them into effective treatment strategies? In this instance, too much information can be every bit as paralyzing as too little.

Our preferred approach is to examine the small molecule inhibitors that target each of the identified aberrancies in our laboratory platform. We prefer to drill down to the next level of certainty e.g. cellular function. After all, the presence of a target does not a response make.

In this patient I would conduct a biopsy. This would enable us to examine the drugs and combinations that are active against the targets. A “hit” by the EVA-PCD assay would then isolate the “drivers” from the “passengers” and enable the clinician to intelligently select effective treatments. Combining genomic analyses with functional profiling (phenotypic analyses) provides the opportunity to turn speculative observations into actionable events.

This is the essence of Rational Therapeutics.

The Changing Landscape in Non-small Cell Lung Cancer (NSCLC)

In October 2012, we published a study of patients with metastatic NSCLC whose treatment was guided by EVA-PCD laboratory analysis. The trial selected drugs from FDA approved, compendium listed chemotherapies and every patient underwent a surgical biopsy under an IRB-approved protocol to provide tissue for analysis.

The EVA-PCD patients achieved an objective response rate of 64.5 percent (2-fold higher than national average, P < 0.0015) and median overall survival of 21.3 months (nearly 2-fold longer than the national average of 12.5 months).

Non-small cell lung cancer

Non-small cell lung cancer

The concept of conducting biopsies in patients with metastatic NSCLC was not only novel in 2004, it was downright heretical. Physicians argued forcefully that surgical procedures should not be undertaken in metastatic disease fearing risks and morbidity. Other physicians were convinced that drug selection could not possibly improve outcomes over those achieved with well-established NCCN guidelines. One oncologist went so far as to demand a formal inquiry. When the hospital was forced to convene an investigation, it was the co-investigators on the IRB approved protocol and the successfully treated patients who ultimately rebuffed this physician’s attempt to stifle our work.

With the publication of our statistically superior results and many of our patients surviving more than 5 years, we felt vindicated but remain a bit battle scarred.

I was amused when one of my study co-authors (RS) recently forwarded a paper authored at the University of California at Davis about surgical biopsies and tumor molecular profiling published by The Journal of Thoracic and Cardiovascular Surgery. This single institution study of twenty-five patients with metastatic NSCLC reported their experience-taking patients with metastatic disease to surgical biopsy for the express purpose of selecting therapy. Sixty four percent were video assisted thoracic (VATS) wedge biopsies, 16 percent pleural biopsies, 8 percent mediastinoscopies, 12 percent supraclavicular biopsies and 8 percent rib/chest wall resections. Tissues were submitted to a commercial laboratory in Los Angeles for genomic profiling.

The authors enthusiastically described their success conducting surgical procedures to procure tissue for laboratory analysis. Gone was the anxiety surrounding the risk of surgical morbidity. Gone were the concerns regarding departure from “standard” treatment. In their place were compelling arguments that recapitulated the very points that we had articulated ten years earlier in our protocol study. While the platforms may differ, the intent, purpose and surgical techniques applied for tissue procurement were exactly the same.

What the Cooke study did not describe was the response rate for patients who received “directed therapy.” Instead they provide the percent of patients with “potentially targetable” findings (76 percent) and the percent that had a “change in strategy” (56 percent) as well as those that qualified for therapeutic trials (40 percent). Though, laudable, changing strategies and qualifying for studies does not equal clinical responsiveness. One need only examine the number of people who are “potential winners” at Black Jack or those who “change their strategies” (by changing tables/dealers for example) or, for that matter, those who qualify for “high roller status” to understand the limited practical utility of these characterizations.

Nonetheless, the publication of this study from UC Davis provides a landmark in personalized NSCLC care. It is no longer possible for oncologists to decry the use of surgical biopsies for the identification of active treatments.

As none of the patients in this study signed informed consents for biopsy, we can only conclude that the most august institutions in the US now view such procedures as appropriate for the greater good of their patients. Thus, we are witness to the establishment of a new paradigm in cancer medicine. Surgical biopsies in the service of better treatment are warranted, supported and recommended. Whatever platform, functional or genomic, patient-directed therapy is the new normal and the landscape of lung cancer management has changed for the better.

In Cancer – If It Seems Too Good to Be True, It Probably Is

The panoply of genomic tests that have become available for the selection of chemotherapy drugs and targeted agents continues to grow. Laboratories across the United States are using gene platforms to assess what they believe to be driver mutations and then identify potential treatments.

Among the earliest entrants into the field and one of the largest groups, offers a service that examines patient’s tumors for both traditional chemotherapy and targeted agents. This lab service was aggressively marketed under the claim that it was “evidence-based.” A closer examination of the “evidence” however, revealed tangential references and cell-line data but little if any prospective clinical outcomes and positive and negative predictive accuracies.

I have observed this group over the last several years and have been underwhelmed by the predictive validity of their methodologies. Dazzled by the science however, clinical oncologists began sending samples in droves, incurring high costs for these laboratory services of questionable utility.

In an earlier blog, I had described some of the problems associated with these broad brush genomic analyses. Among the greatest shortcomings are Type 1 errors.  These are the identification of the signals (or analytes) that may not predict a given outcome. They occur as signal-to-noise ratios become increasingly unfavorable when large unsupervised data sets are distilled down to recommendations, without anyone taking the time to prospectively correlate those predictions with patient outcomes.

Few of these companies have actually conducted trials to prove their predictive values. This did not prevent these laboratories from offering their “evidence-based” results.

In April of 2013, the federal government indicted the largest purveyor of these techniques.  While the court case goes forward, it is not surprising that aggressively marketed, yet clinically unsubstantiated methodologies ran afoul of legal standards.

A friend and former professor at Harvard Business School once told me that there are two reasons why start-ups fail.  The first are those companies that “can do it, but can’t sell it.”  The other types are companies that “can sell it, but can’t do it.”  It seems that in the field of cancer molecular biology, companies that can sell it, but can’t do it, are on the march.

Cancer Patients, Genetic Testing and Clinical Outcomes

Two years ago in this blog, I described a young man with an aggressive non-small cell lung cancer. Following his diagnosis he was screened for EGFR mutation (the target of Erlotinib [Tarceva]) and ALK gene rearrangement (the target of Crizotinib [Xalkori]). Found negative for both, his options were limited to chemotherapy.

When I met the patient, a PleurX catheter had already been inserted to remove fluid that was rapidly re-accumulating in his right chest. This provided access to cancer-laden fluid and offered an excellent opportunity for EVA-PCD® laboratory analysis.

The results showed the expected resistance to Erlotinib (for which no mutation was found) but very high activity for Crizotinib. When he returned for follow-up we repeated a second analysis. The results were identical. One possibility was that the patient carried a second mutation sensitive to this class of drugs, like ROS-1 or MET, both targets of Crizotinib. However, at the time, MET and ROS-1 gene testing was not readily available. I referred the patient to a colleague who was conducting Crizotinib trials. Fluid was re-aspirated and submitted to a different reference lab for genomic analysis. The finding: The original laboratory test had been erroneous. The patient was indeed, ALK gene rearranged.

After a course of chemotherapy, he qualified for and responded beautifully to single-agent Crizotinib. In my blog, I examined how our functional profile more closely approximated the patient’s biology (phenotype) over the genomic profile (genotype). However appealing these genomic tests may be, they can only identify potential targets for therapy that may or may not be relevant to a patient’s ultimate clinical response.

A year later, a female patient with a mucinous adenocarcinoma presented with brain metastases. An EVA-PCD analysis revealed relative chemotherapy resistance and no activity for Erlotinib (Tarceva). She was found EGFR non-mutated. Unfortunately, there was insufficient tissue for the EVA-PCD to test Crizotinib.

During subsequent Cyber-Knife treatment for her brain metastases, a specimen of tumor showed the ALK gene rearrangement and the patient started Crizotinib. She responded promptly.

At the one-year point, signs of progression appeared in the opposite lung, but while she continued to experience good response in the original sites, a repeat biopsy was performed. This time the EVA-PCD functional profile revealed no activity for Crizotinib, but identified activity for the combination of Platinum and Vinorelbine. We combined these two drugs with the Crizotinib and she remained in remission for an additional year. Low blood counts forced us to withhold chemotherapy and her disease progressed. She was referred to a clinical trial with a second-generation ALK inhibitor. By the second month, her disease had progressed rapidly.

Cancerous cells from a bronchoscopic biopsy were submitted for analysis. The finding: No ALK gene mutation. Instead her tumor carried a MET mutation. The patient now rapidly progressing will require immediate therapy, but what?  Fortunately, a small sample of fluid aspirated from the lung provided adequate cells for analysis. The results are striking since they confirm persistent activity for Crizotinib. The patient has now been re-challenged with Crizotinib and we await clinical follow-up.

Taken together, these cases offer interesting insights. The first reflects the medical community’s preternatural faith in genomics. We, as a society, have so completely accepted the accuracy and predictive validity of genetic tests, that no one seems willing to scrutinize the data for its ultimate accuracy. This may not be serving our patients well, as both these cases exemplify. An error that missed the ALK gene re-arrangement in the first patient almost cost this young man his life, despite our protestations. Then, an error in this woman’s analysis serendipitously led to her response to the right drug for the wrong reason, her gene results notwithstanding

We forget at our peril, that all tests are fallible. Clinicians must recognize that highly sophisticated analyses using the most advanced technologies still function within the infinitely complex confines of human biology. The crosstalk, redundancy and promiscuity of human cellular circuitry remain demonstrably more complex than our best artificial neural networks. Genomic analyses and companion diagnostics now dictate who can and who cannot receive drugs, but as can be seen here, these wonders of modern science are not perfect predictors. They have the potential to deprive patients of life-saving treatment while subjecting others to drugs with little chance of benefit. Physicians must remember to be artful as we apply the science of our trade.

The Information Disconnect

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.

The High Cost of Cancer Care

Scott GottleibAn 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 linCray Computer v2ear 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.

Personalized Cancer Care: N-of-1

The New York Yankees catcher Yogi Berra famous quote, “Déjà vu all over again,” reminds me of the growing focus on the concept of “N- of-1.” For those of you unfamiliar with the catchphrase, it refers to a clinical trial of one subject.

In clinical research, studies are deemed reportable when they achieve statistical significance. The so-called power analysis is the purview of the biostatistician who examines the desired outcome and explores the number of patients (subjects) required to achieve significance. The term “N” is this number. The most famous clinical trials are those large, cooperative group studies that, when successful, are considered practice-changing. That is, a new paradigm for a disease is described. To achieve this level of significance it is generally necessary to accrue hundreds, even thousands of patients. This is the “N” that satisfies the power analysis and fulfills the investigators expectations.

So what about an N-of-1? This disrupts every tenet of cancer research, upends every power analysis, and completely rewrites the book of developmental therapeutics. Every patient is his or her own control. Their good outcome reflects the success or failure of “the trial.” There is no power analysis. It is an “N” of 1.

This “breakthrough” concept however, has been the underpinning of the work of investigators like Drs. Larry Weisenthal, Andrew Bosanquet, Ian Cree, myself and all the other dedicated researchers who pioneered the concept of advancing cancer outcomes one patient at a time. These intrepid scientists described the use of each patient’s tissue to guide therapy selection. They wrote papers, conducted trials and reported their successful results in the peer-reviewed literature. These results I might add have provided statistically significant improvements in clinical responses, times to progression, even survival. By incorporating the contribution of the cellular milieu into clinical response prediction, these functional platforms have consistently outperformed their genomic counterparts in therapy selection So why, one might ask, have the efforts of these dedicated investigators fallen on deaf ears?

I think that the explanation lies in the fact that we live in a technocracy. In this environment, science has replaced religion and medical doctors have abdicated control of clinical development to the basic scientists and basic scientists love genomics. It is no longer enough to have good results; you have to get the results the right way. And so, meaningful advances in therapeutics based on functional platforms have been passed over in favor of marginal advances based on genomic platforms.

There is nothing new about N-of-1. It has been the subject of these investigators compelling observations for more than two decades. Though functional platforms (such as our EVA-PCD®) are not perfect, they provide a 2.04 (1.62 to 2.57, P < 0.001) fold improvement in clinical response for virtually all forms of cancer – as we will be reporting (Apfel C, et al Proc ASCO, 2013).

It seems that in the field of cancer therapeutics “perfect is the enemy of good.” By this reasoning, good tests should not be used until perfect tests are available. Unfortunately, for the thousands of Americans who confront cancer each day there are no perfect tests. Perhaps we should be more willing to use good ones while we await the arrival of perfect ones. After all, it was Yogi Berra who said, “If the world was perfect, it wouldn’t be.”