Reply to D.R. Catchpoole et al

Abstract

We substantially agree with the comments of Catchpoole et al in response to our editorial. The idea that analyses of multidimensional, highly complex datasets of cancer and host factors will lead to more precise and successful individualized therapies is immensely appealing. We agree that building the algorithms for truly personalized medicine indeed will require paradigm shifts in clinical and translational research. The path to tailored therapies in individuals will be paved in large part by a deeper understanding of the complexity and heterogeneity of cancers. Genomics has contributed much to this understanding, as exemplified by the reclassification of breast cancers into many distinct subtypes based on gene expression profiles. Our endorsement of the so-called virtue of complexity is an appeal to strengthen the scientific rigor of genomic studies in cancer. Increasing the number of patients and samples is necessary but far from sufficient. Other important factors in conducting and reporting such studies include patient stratification, integration of various highthroughput technologies, novel trial designs and bioinformatics approaches, integration of emerging concepts in cancer biology, and transparent and complete presentation of statistical analyses. Despite the limitations of reductionism, genomic signatures derived from such approaches have proved useful in defining risks for relapse and appropriate patients for adjuvant therapies in early-stage breast cancers. Moreover, the development of predictive therapeutic biomarkers based on the understanding of molecular pathways, networks, and drug mechanisms has much to contribute to the personalization of cancer therapies, as illustrated by the importance of RAS mutation status in colorectal cancers treated with epidermal growth factor receptor–targeted antibodies. Ultimately, however, we agree that the development of truly personalized therapies will require ascertaining the key differences among individuals as well as similarities between cohorts within a disease type. Proof that tailoring makes a difference, particularly in a highly curable disease like pediatric acute lymphoblastic leukemia, is itself a major challenge in clinical trials designs.