LAUSR

Some cancers are driven by targetable genomic alterations that dysregulate key pathways influencing cell growth and survival. These tumors lend themselves to significant, although inevitably transient, clinical responses upon targeted drug treatment. Accordingly, omic-based theranostics (i.e. diagnostics that guide therapeutic interventions) have transformed the management of a substantial number of cancers (King et al., 1985; Druker et al., 2001; Slamon et al., 2001; Demetri et al., 2002; Lynch et al., 2004). However, after a highly productive discovery phase resulting in the identification of multiple clinically actionable genomic targets, new information capabilities have been limited by challenges in target druggability, treatment toxicity, and intratumoral heterogeneity. Therefore, the ability to harness tumor omic information to its full clinical potential has not yet been realized. The impact of these challenges has led to a pivot toward a more comprehensive understanding of the complexity of tumor information and the role of distinct biological processes in driving tumor phenotypes and, hence, patient outcomes. To achieve the requisite level of (epi)genome-phenome knowledge, the interrogation of large-scale datasets for integral biological pathways that regulate tumor behavior has come into greater focus. Technological developments in high-content biological and data platforms have led to the generation of large-scale multi-omics datasets in radiomics, genomics, epigenomics, transcriptomics, proteomics, metabolomics, and phenomics (Song et al., 2020). Collections of publicly available datasets like The Cancer Genome Atlas (TCGA) and The Cancer Imaging Atlas (TCIA) provide critical resources to a research environment that is frequently hindered by access to clinically relevant big data (TCGA Research Network; Cancer Imaging Program). These and similar datasets can have broad utility in advancing a more comprehensive and integrated understanding of indivdiual cancers. OPEN ACCESS