The Laws of Medicine by Siddhartha Mukherjee Read Free Book Online

were coming apart at the seams, I suspected, and a novel way of thinking about an old problem was being born.
    We have spent much of our time in medicine dissecting and understanding what we might call the “inlier” problem. By “inliers,” I am referring to the range of normalcy; we have compiled a vast catalog of normal physiological parameters: blood pressure, height, body mass, metabolic rate. Even pathological states are described in terms that have been borrowed from normalcy: there is an average diabetic, a typical case of heart failure, and a standard responder to cancer chemotherapy.
    But we have little understanding of what makes an individual lie outside the normal range. “Inliers” allow us to create rules—but “outliers” act as portals to understand deeper laws. The standard formula—height (in cms) − 100 = average weight plus 10 percent (in kgs)—is a rule that works for most of the human population. But it takes a single encounter with a person with genetic dwarfism to know that there are genes that control this relationship and that mutations can disrupt it quite acutely.
    In his 1934 book, The Logic of Scientific Discovery , the philosopher Karl Popper proposed a crucial criterion for distinguishing a scientific system from an unscientific one. The fundamental feature of a scientific system, Popper argued, is not that its propositions are verifiable, but that its propositions are falsifiable —i.e., every theory carries an inherent possibility of proving it false. A theory or proposition can only be judged “scientific” if it carries within it a prediction or observation that will prove it false. Theories that fail to generate such “falsifiable” conjectures are not scientific. If medicine is to become a bona fide science, then we will have to take up every opportunity to falsify its models, so that they can be replaced by new ones.
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LAW THREE
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    For every perfect medical experiment, there is a perfect human bias.

I n the summer of 2003, I finished my three-year residency in internal medicine and began a fellowship in oncology. It was an exhilarating time. The Human Genome Project had laid the foundation for the new science of genomics—the study of the entire genome. Although frequent criticism of the project appeared in the media—it had not lived up to its promises, some complained—it was nothing short of a windfall for cancer biology. Cancer is a genetic disease, an illness caused by mutations in genes. Until that time, most scientists had examined cancer cells one gene at a time. With the advent of new technologies to examine thousands of genes in parallel, the true complexity of cancers was becoming evident. The human genome has about twenty-four thousand genes in total. In some cancers, up to a hundred and twenty genes were altered—one in every two hundred genes—while in others, only two or three genes were mutated. (Why do some cancers carry such complexity, while others are genetically simpler? Even the questions—not just the answers—thrown up by the genome-sequencing project were unexpected.)
    More important, the capacity to examine thousands of genes in parallel, without making any presuppositions about the mutant genes, allowed researchers to find novel, previously unknown genetic associations with cancer. Some of the newly discovered mutations in cancer were truly unexpected: the genes did not control growth directly, but affected the metabolism of nutrients or chemical modifications of DNA. The transformation has been likened to the difference between measuring one point in space versus looking at an entirelandscape—but it was more. Looking at cancer before genome sequencing was looking at the known unknown. With genome sequencing at hand, it was like encountering the unknown unknown.
    Much of the excitement around the discovery of these genes was driven by the idea that