With Cancer, Don’t Ask the Experts

I was recently provided a video link to a December 2013 TEDx conference presentation entitled, “Big Data Meets Cancer” by Neil Hunt, product manager for Netflix. Mr. Hunt’s background has nothing to do with cancer or cancer research. His expertise is in technology, product development, leadership and strategy and has personally shepherded Netflix to its current market dominance. With his background and lack of expertise in cancer, he is an ideal person to examine cancer research from a fresh perspective.

The Long Tail of CancerMr. Hunt begins with a (admittedly) simplistic look at cancer research today. Because he is a data guy, naïve to all of the reasons why cancer cannot be cured, he can look anew at how it might be cured. Using a graphic, he defines cancer as “a long-tail disease” made up of outliers. He points out that most 20th century medical successes have been in the common diseases that fall close to the thick end of the curve. As one moves to the less common illnesses data becomes more scant. Echoing a new conceptual thinking, he points out that cancer is not a single disease but many, possibly thousands.  His concept is to accumulate all of the individual patient data to allow investigators to explore patterns and trends: a bottom up model of cancer biology. Many of his points bear consideration.

For those of you who have read these blogs, you know that I am an adherent to the concept of personalized cancer care. I have articulated repeatedly that cancer patients must be treated as individuals. Each tumor must be profiled using available platforms so that time and resources will not be wasted. We have used the same term “N-of-1” (a clinical trial for one patient) that Mr. Hunt uses in his discussion. He provides two anecdotes regarding patients who benefitted dramatically from unexpected treatment choices. His rallying cry is that contemporary clinical trials are failing. Again, this is an issue that I have addressed many times. He then describes broad-brush clinical protocols as the “tyranny of the average.”

The remainder of the discussion focuses upon possible solutions. Among the obvious hurdles:
1.    Cancer centers are hesitant to share data.
2.    The publication process is slow.
3.    Few are willing to publish negative trials.

To counter these challenges, he points out that small organizations are more incentivized to share and that successes in long-tail diseases can resurrect failed drugs, thereby repaying the costs. Several points were particularly resonant as he pointed out that early adopters face outsized resistance but their perseverance against adversity ultimately evolves the field. He sees this as a win-win-win scenario with patients receiving better care, physicians witnessing better outcomes, and pharmaceutical companies gaining more rapid approval of drugs.

As I watched, it occurred to me that Mr. Hunt was articulating many points that we have raised for over the last decade. As an outsider, he can see, only too clearly, the shortcomings of current methods. His clear perceptions reflect the luxury of distance from the field he is describing. Mr. Hunt’s grasp of cancer research is direct and open-minded. Many problems need fresh eyes. Indeed as we confront problems as complex as cancer it may be best not to ask the experts.

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.”