An integrated method and system for identifying functional patient-specific somatic aberations using multi-omic cancer profiles

Abstract

A system and method for determining the functional impact of somatic mutations and genomic aberrations on downstream cellular processes by integrating multi-omics measurements in cancer samples with community-curated biological pathways are disclosed. The method comprises the steps of extracting biological pathway information from well-curated biological pathway sources, using the pathway information to generate an upstream regulatory parent sub-network tree for each gene of interest, integrating measurement-based omic data for both cancer and normal samples to determine a nonlinear function for each gene expression level based on the gene's epigenetic information and regulatory network status, using the nonlinear function to predict gene expression levels and compare activation and consistency scores with inputted patient- specific gene expression data, and using the patient-specific gene expression predictions to identify significant deviations and inconsistencies in gene expressionlevels from expected levels in individual patient samples to identify potential biomarkers in providing predictive information in relation to cancer and cancer treatment.

Description

[0001] related application [0002] This application claims priority to US Provisional Application No. 62 / 210502, filed August 27, 2015, which is hereby expressly incorporated by reference in its entirety. technical field [0003] The present invention relates to data-driven integrated systems and methods for providing patient-specific gene expression predictions by constructing gene-gene regulatory influence networks and comparing with multi-omic patient-specific measurements Includes community-curated biological pathway network information and omics data, e.g., RNAseq-based expression data, copy number variation (CNV) data, and DNA methylation data, that are patient-specific Sexual measures included RNAseq-based gene expression, array-based DNA methylation (epigenetics), and SNP-array-based somatic copy number alterations (sCNA). More specifically, patient-specific gene expression predictions are used to identify significant deviations and inconsistencies in gene expression...

AI Technical Summary

Problems solved by technology

On the other hand, relying heavily on descriptive biological networks ignores their limitations: pathway networks are often constructed based on experimental evidence in specific cellular contexts, which may not always translate to other tissue and pathological contexts

First, these methods fully trust biological pathway network relationships without considering potential tissue-specific changes in pathway network connectivity

A second and more important problem is that these techniques ignore the possibility of functional heterogeneity among interacting links in the network

They assume that the influence of all immediate parental nodes is equal, but in reality some regulatory parental genes may have significantly higher influence than other parental genes

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