Abstract
Cancers that are histologically defined as the same type of cancer often need a distinct therapy based on underlying heterogeneity; likewise, histologically disparate cancers can require similar treatment approaches due to intrinsic similarities. A comprehensive analysis integrated with drug response data and molecular alterations, particularly to reveal therapeutic concordance mechanisms across histologically disparate tumor subtypes, has not yet been fully exploited. In this study, we integrated pharmacological, genomic, and transcriptomic profiling data provided from the Cancer Genome Project (CGP) in a systematic in silico investigation of the pharmacological subtypes of cancers and the intrinsic concordance of molecular mechanisms leading to similar therapeutic responses across histologically disparate tumor subtypes. We further developed a novel approach to redefine cell-to-cell similarity and drug-to-drug similarity from the therapeutic concordance, providing a new point of view to study cancer heterogeneity. This study demonstrates how pharmacological and omics data can be used to systematically classify cancers in terms of response to various compounds and provides us with a purely therapy-oriented perspective to view tumor classifications independent of histology subtypes. The knowledge of pharmacological subtypes of 367 drugs are available via our website (http://www.hywanglab.cn/dtdb/), providing the resources for precision medicine in the perspective of therapeutic response-based re-classification of tumor.