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Reuters
NEW YORK |
(Reuters) - Nearly every oncologist can tell the story of cancer
patients who beat the odds, responding so well to treatment that they
continued to live many years disease-free, while most of their peers
worsened and eventually died....Over the past century, such patients - sometimes called "outliers" or "super responders" - have stood out by staging remarkable recoveries, or long-term benefit, from cancer drugs that provide little or no help to others. Little heed has been paid to them because there was no way to know why they fared so well. In most cases, the drugs that helped them were abandoned because they helped too few patients.......
I read the article and shake my head, what of these outliers? Some private laboratory oncologists have been saying for years, what of those complicated disease entities like cancer, whose complexity and variability challenge even the best of minds? How do we bang the round peg of cancer therapy into the square hole of formulaic care?
ReplyDeleteFor years, they've been used to standardized guidelines that provide the same treatment to every patient with a given diagnosis. Virtually every medical oncologist knows the drill. The result: the average patient has an average outcome with the average treatment. By encompassing regimens into standardized algorithms, they may soon be able to eliminate themselves entirely, with supercomputers.
While clinical trials are designed to identify average improvements for average patients, virtually every trial conducted has patients who live much longer than average. They constitute the tail on the survival curve (the outliers) and almost every trial has several. The job of medical oncologists is to identify those true responders and treat them appropriately.
One of those laboratory oncologists told me one time that the term applied for these failures in the average patient paradigm are "beta errors," meaning that the investigators missed the benefit of a given treatment. By identifying active treatments in small subsets of patients, phenotype analytic tools can enable them to select those small subsets for treatment regardless of average expectations.
thanks for this - one of the very major reasons why we are 'stuck' and not moving forward. Here's hope for the genome.
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