Systematic Discovery of the Functional Impact of Somatic Genome Alterations in Individual Tumors through Tumor-specific Causal Inference

Abstract

We report a tumor-specific causal inference (TCI) framework, which discovers causative somatic genome alterations (SGAs) through inferring causal relationships between SGAs and molecular phenotypes (e.g., transcriptomic, proteomic, or metabolomic changes) within an individual tumor. We applied the TCI algorithm to tumors from The Cancer Genome Atlas (TCGA) and identified those SGAs that causally regulate the differentially expressed genes (DEGs) within each tumor. Overall, TCI identified 634 SGAs that cause cancer-related DEGs in a significant number of tumors, including most of the previously known drivers and many novel candidate cancer drivers. The inferred causal relationships are statistically robust and biologically sensible, and multiple lines of experimental evidence support the predicted functional impact of both well-known and novel candidate drivers. By identifying major candidate drivers and revealing their functional impact in a tumor, TCI shed light on disease mechanisms of each tumor, providing useful information for advancing cancer biology and precision oncology.Significance statementsCancer is mainly caused by SGAs. Precision oncology involves identifying and targeting tumor-specific aberrations resulting from causative SGAs. TCI is a novel computational framework for discovering the causative SGAs and their impact on oncogenic processes, thus revealing tumor-specific disease mechanisms. This information can be used to guide precision oncology.

Comprehensive analyses reveal TKI-induced remodeling of the tumor immune microenvironment in EGFR/ALK-positive non-small-cell lung cancer

“…27 A previous study established that NEFH is a cancer driver because of its functional impact on KRAS and MTORC1 signaling. 28 We conducted survival analyses of LUAD patients with the EGFR 19Del/L858R mutation or EML4-ALK fusion in the TCGA dataset; our findings suggested that patients with high NEFH expression had worse prognoses than patients with low NEFH expression (Figure 3(d,e)). Survival analyses of patients with EGFR or ALK mutations in the Pan-Cancer dataset confirmed that NEFH was a cancer-promoting factor (Supplementary Figure 2(a,b)).…”

Efficient Representations of Tumor Diversity with Paired DNA-RNA Aberrations

“…The major objective in (Cai et al, 2019) is to identify driver genes by estimating likely causal relationships between somatic genomic alterations and genes which are differentially expressed relative to normal. Bayesian inference is applied to each individual tumor sample to associate differential expression with a set of genomic alterations in the sample.…”

“…Whereas this program bears similarities with ours the objectives and methodology are quite different: our analysis is top-down, based on applying a known network to directly characterize a tumor population with a relatively concise set of paired genomic-transcriptomic relationships, and designed for quantification of inter-tumor heterogeneity. In contrast, the approach in (Cai et al, 2019) is model-driven and the networks are learned.…”

cancerAlign: Stratifying tumors by unsupervised alignment across cancer types

“…To better understand cancer heterogeneity, large-scale cancer genomics projects, such as the Cancer Genome Atlas (TCGA), the International Cancer Genome project, and the Memorial Sloan Kettering-Integrated Mutation Profiling project have systematically profiled thousands of tumors, holding the promise to realize personalized treatment [3][4][5][6]. Among the collected genome-scale omics data, somatic mutation profiles have been used to discover causal drivers of tumors [7][8][9][10][11][12][13] and further reveal informative cancer subtypes [14,15]. In pursuit of this vision, computational approaches have been developed to stratify tumors according to high-dimensional, noisy and sparse somatic mutation profiles [1,[16][17][18][19][20][21].…”