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mutated). The woman’s tumor had 140 mutations. Of all those, two stood out: one in a gene named TSC1 and another in a gene named NF2. Both these genes had been suspected to modulate the response to everolimus, but before Solit, no one had found formal proof of the link in human patients.
    But this was still a “single patient anecdote”; scientists would still roll their eyes. Solit’s team now returned to the original trial and sequenced the same genes in the larger cohort of patients. A pattern emerged immediately. Four other patients who had mutations in the TSC1 gene had shown modest responses, while none of the other patients, with mutations in other genes but not in TSC1, had shown even a sliver of a response. Via just one variable—the mutation in the TSC1 gene—you could segregate the trial into moderate or strong responders versus nonresponders. “Single patient anecdotes are often dismissed,” Solit wrote. But here, exactly such an anecdote had turned out to be a portal to a new scientific direction. In a future trial, a cohort of patients might be sequenced up front , and only those with mutations in the TSC1 gene might be treated with the drug. Perhaps more important, the relationship between the gene and the susceptibility of the tumor cells opened a new series of scientific investigations into the mechanism for this selective vulnerability, leading to yet new trials and novel drugs.
    But is it a law of medicine that such outliers will providethe most informative pieces of data in our attempt to revamp the core of our discipline? In Lewis Thomas’s time, such a law would have made no sense: there was nothing to “outlie.” The range of medical and surgical interventions was so severely limited that any assessment of variations in response was useless; if every patient with heart failure was destined to die, then it made little sense discriminating one from another (and even if some survived long term, no tools existed to investigate them). But this is precisely what has changed: pieces of data that do not fit our current models of illness have become especially important not only because we are reassessing the nature of our knowledge, but also because we are generating more such pieces of data every day. Think about the vast range of medicines and surgical procedures not as therapeutic interventions but as investigational probes. Think of every drug as a chemical tool—a molecular scalpel—that perturbs human physiology. Aspirin flicks off a switch in the inflammatory system. Lipitor tightens a screw on cholesterol metabolism. The more such investigational probes we use, the more likely we are to alter physiology. And the more we alter physiology, the more we will find variations in response, and thereby discover its hidden, inner logic.
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One morning in the spring of 2015, I led a group of medical students at Columbia University on what I called “outlier rounds.” We were hunting for variant responses to wound healing. Most patients with surgical incisions heal their wounds in a week. But what about the few patients whose wounds don’t heal? We moved from room to room across the hospital, trying to find cases where postsurgical wounds had failed to heal. Most of these were predictable—elderly patients with complex surgical incisions, or diabetics, who are known to heal poorly. But after about nine such cases, we entered the room of a young woman recovering from an abdominal procedure whose incision was still raw and unhealed. The students looked puzzled. Nothing about this woman, or her incision, seemed any different from the hundreds of others that had healed perfectly. After a long pause they began to ask questions. One of them asked about her family history: Had anyone else in her family had a similar experience? Another wondered if he might swab the tissue to check for unusual, indolent infections. The orthodox models of wound healing