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Am Soc Clin Oncol Educ Book. 2014:91-9. doi: 10.14694/EdBook_AM.2014.34.91.

The evolution of our molecular understanding of colorectal cancer: what we are doing now, what the future holds, and how tumor profiling is just the beginning.

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From the Sage Bionetworks, Fred Hutchinson Cancer Research Center, Seattle, WA; Department of Medical Oncology, Translational Research Laboratory, Catalan Institute of Oncology (ICO), Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; Medical Oncology Department, Vall d'Hebron University Hospital and Institute of Oncology (VHIO), Universitat Autònoma de Barcelona, Barcelona, Spain.


Colorectal cancer (CRC) has been extensively molecularly characterized in recent years. In addition to the understanding of biologic hallmarks of the disease, the ultimate goal of these studies was to provide tools that could allow us to differentiate subgroups of CRC with prognostic and predictive implications. So far, subtype classification has been largely driven by well-described features: (1) defective mismatch repair resulting in higher mutation rate; (2) cellular proliferation along with chromosomal instability and copy number aberrations; and (3) an invasive stromal phenotype mainly driven by TGF-β linked to epithelial-mesenchymal transition. Recent studies have outlined the complexity of CRC at the gene expression level, confirming how heterogeneous the disease is beyond currently validated parameters, namely KRAS, BRAF mutations and microsatellite instability. In fact, adopting an extended mutation profile upfront, which includes nonrecurrent KRAS, NRAS, and PIK3CA gene variants, likely improves outcomes. In this article, we review the current trends of translational research in CRC, summarize ongoing genomically driven clinical trials, and describe the challenges for defining a comprehensive, robust, and reproducible disease classification system that links molecular features to personalized medicine. We believe that identification of CRC subtypes based on integrative genomic analyses will provide a better guide for patient stratification and for rational design of drugs targeting specific pathways.

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