PICan: An integromics framework for dynamic cancer biomarker discovery

Abstract

A B S T R A C TModern cancer research on prognostic and predictive biomarkers demands the integration of established and emerging high-throughput technologies. However, these data are meaningless unless carefully integrated with patient clinical outcome and epidemiological information. Integrated datasets hold the key to discovering new biomarkers and therapeutic targets in cancer. We have developed a novel approach and set of methods for integrating and interrogating phenomic, genomic and clinical data sets to facilitate cancer biomarker discovery and patient stratification. Applied to a known paradigm, the biological and clinical relevance of TP53, PICan was able to recapitulate the known biomarker status and prognostic significance at a DNA, RNA and protein levels.

Challenging the Cancer Molecular Stratification Dogma: Intratumoral Heterogeneity Undermines Consensus Molecular Subtypes and Potential Diagnostic Value in Colorectal Cancer

“…2-4] that molecular classification, irrespective of tumor tissue site of origin, can be reliably employed to underpin treatment algorithms in colorectal cancer. We propose that a morpho-molecular strategy (27), integrating tissue morphology/origin, molecular evaluation (e.g., gene expression profiling, next-generation sequencing, tissue microarray analyses), and clinical phenotype in a common integromics framework (28), is necessary for precise, robust and clinically relevant dissection of tumor biology that will inform the next generation of investigator-led biomarker-driven interventional trials in this common disease.…”

Stratified analysis reveals chemokine-like factor (CKLF) as a potential prognostic marker in the MSI-immune consensus molecular subtype CMS1 of colorectal cancer

“…While this issue will continue to limit the application of in silico clinical discovery strategies, the responsible but effective sharing of genomic and clinical data as espoused by, for example, the Global Alliance for Genomics and Health [35] to allow complete testing of this approach. Implementing such a molecular pathology “integromics” approach is becoming increasingly relevant in the era of stratified medicine [36–38]. …”

Building a ‘Repository of Science’: The importance of integrating biobanks within molecular pathology programmes

“…An example of a successful biobank-integrative model is PICan, Pathology Integromics in Cancer, which marries this data capable biobank system to data analytics for researchers to warehouse information for data persistence and analyse in-situ [19] . The full capabilities of informatics synergistically leveraging Biobank capabilities are beyond the scope of this review and the authors would point to this perspective for added insight [20] .…”