Abstract 603: Consensus molecular subtyping through a community of experts advances unsupervised gene expression-based disease classification and facilitates clinical translation
Authors: Guinney Justin , Dienstmann Rodrigo , Wang Xin , de Reynies Aurelien , Schlicker Andreas , Soneson Charlotte , Marisa Laetitia , Roepman Paul , Nyamundanda Gift , Angelino Paolo , Bot Brian , Morris Jeffrey S. , Simon Iris , Gerster Sarah , Fessler Evelyn , de Sousa e Melo Felipe , Missiaglia Edoardo , Ramay Hena , Barras David , Homicsko Krisztian , Maru Dipen , Manyam Ganiraju , Broom Bradley , Boige Valerie , Laderas Ted , Salazar Ramon , Gray Joe W. , Tabernero Josep , Bernards Rene , Friend Stephen , Laurent-Puig Pierre , Medema Jan P. , Sadanandam Anguraj , Wessels Lodewyk , Delorenzi Mauro , Kopetz Scott , Vermeulen Louis , Tejpar Sabine 
Published: August 2015

Abstract
Background: Gene expression-based subtyping is widely accepted as a relevant source of disease stratification. Despite the widespread use, its translational and clinical utility is hampered by discrepant results, likely related to differences in data processing and algorithms applied to diverse patient cohorts, sample preparation methods, and gene expression platforms. In the absence of a clear methodological gold standard to perform such analyses, a more general framework that integrates and compares multiple strategies is needed to define common disease patterns in a principled, unbiased manner.
Methods: We formed a consortium of 6 independent experts groups - each with a previously published CRC classifier, ranging from 3 to 6 subtypes - to understand similarities and differences of their subtyping systems. Sage Bionetworks functioned as neutral party to aggregate public and proprietary data (Synapse platform) and perform meta-analysis. Each group applied its CRC subtyping signature to the collection of data sets with gene expression (n = 4,151, predominantly stage II and III). Using the resulting subtype labels, we developed a network-based model and applied a Markov cluster algorithm to detect robust network substructures that would indicate recurring subtype patterns and therefore a consensus subtyping system. Correlative analyses using clinico-pathological, genomic and epigenomic features was performed to robustly characterize the identified subtypes.
Results: This analytical framework revealed significant interconnectivity between the six independent classification systems, leading to the identification of four biologically distinct consensus molecular subtypes (CMS) enriched for key pathway traits: CMS1 (MSI Immune), hypermutated, microsatellite unstable, with strong immune activation; CMS2 (Canonical), epithelial, chromosomally unstable, with marked WNT and MYC signaling activation; CMS3 (Metabolic), epithelial, with evident metabolic dysregulation; and CMS4 (Mesenchymal), prominent TGFβ activation, angiogenesis, stromal invasion. Patients diagnosed with MSI Immune tumors had worse survival after relapse and those with mesenchymal tumors had increased risk of metastasis and worse overall survival.
Discussion: We describe a novel methodological paradigm for deriving benchmarks of disease subtyping. Our work represents the first example of a community of experts identifying and advocating for a single reproducible model for cancer subtyping, effectively unifying previous classifiers. In the CRC domain, the uniformity afforded by this new classification system and its application to a large data set revealed important subtype-specific biological associations that were previously unnoticed or marginally significant, supporting a new taxonomy of the disease.
Citation Format: Justin Guinney, Rodrigo Dienstmann, Xin Wang, Aurelien de Reynies, Andreas Schlicker, Charlotte Soneson, Laetitia Marisa, Paul Roepman, Gift Nyamundanda, Paolo Angelino, Brian Bot, Jeffrey S. Morris, Iris Simon, Sarah Gerster, Evelyn Fessler, Felipe de Sousa e Melo, Edoardo Missiaglia, Hena Ramay, David Barras, Krisztian Homicsko, Dipen Maru, Ganiraju Manyam, Bradley Broom, Valerie Boige, Ted Laderas, Ramon Salazar, Joe W. Gray, Josep Tabernero, Rene Bernards, Stephen Friend, Pierre Laurent-Puig, Jan P. Medema, Anguraj Sadanandam, Lodewyk Wessels, Mauro Delorenzi, Scott Kopetz, Louis Vermeulen, Sabine Tejpar. Consensus molecular subtyping through a community of experts advances unsupervised gene expression-based disease classification and facilitates clinical translation. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 603. doi:10.1158/1538-7445.AM2015-603