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Translating cancer complexity to clinical decisions.

Cells are complex machines that sometimes get out of control and take over their host, i.e., become cancer cells. It goes without saying that to control or repair them, a thorough understanding of how they work and a specific understanding of what has gone awry is required. The study by Chen et al.,1 led by one of the pioneers in this field, is a technically rigorous step toward sorting these errant machines into broad categories that may require different therapeutic approaches. In this study, Chen et al. defined a predictor of survival based on data from 680 tumors and defined a clinically feasible reverse transcriptase polymerase chain reaction classifier that significantly classified a test set of tumors for survival. It is thus a significant study for the size of the training set, the rigor of the analysis, and the translation to a reverse transcriptase polymerase chain reaction assay in a 101-patient test set. Such classification attempts, and in particular attempting to understand what these classifications imply for patient management, are clearly the next frontier in oncology. The last sentence of the abstract looks toward clinical application and states that these data should be incorporated “into new clinical trials with the goal of personalized treatment of lung cancer patients and improving patient survival.” For example, the discovery and definition of subsets of these tumors driven by epidermal growth factor receptor (EGFR) mutations and ALK fusions have revolutionized both our understanding of how cancers work and our therapies for these subsets. Definition of clinically actionable subsets by expression array technology, as described in the article, has to date been much less successful, at least in part because of two significant issues: (1) they are typically tied to a particular way of looking at the cancer cell, ignoring all others, and (2) they fail to carefully define the clinical problem they are addressing and choose sample sets and analyses appropriate to answer this question rather than whatever is conveniently available. This study, although far from unique in this respect, suffers from both these issues. Regarding the first problem, it is now clear that cancer cells are not completely defined and cannot be completely understood by studying their pattern of acquired genetic mutations or by studying RNA expression patterns in isolation. EGFR-mutant tumors are clearly different from EGFR wild-type tumors in almost every way—patients with these tumors on average live longer no matter how you treat them, respond better to chemotherapy, and have different patterns of relapse and metastasis. There are a handful of mutant genes known today (perhaps about 10) that logically define significant subsets of lung cancers, and future studies attempting to define RNA expression classifiers should include these gene mutation variables in their analyses. Important RNA patterns associated with a clinical phenotype might be completely different in each of these gene mutation subsets, and by mixing these all together in the training set, we unnecessarily muddle our data. Although “multidimensional” classification analytic techniques need to be optimized, it is clear that future classification studies should take into account at least these dominant subsets. This study does not do this, even lumping adenocarcinomas with squamous carcinomas, each with clear differences in gene mutation patterns. Conversely, it is also clear that all tumors with EGFR mutations, and even all tumors with exactly the