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Tumour-specific Causal Inference Discovers Distinct Disease Mechanisms Underlying Cancer Subtypes.

ABSTRACT: Cancer is a disease mainly caused by somatic genome alterations (SGAs) that perturb cellular signalling systems. Furthermore, the combination of pathway aberrations in a tumour defines its disease mechanism, and distinct disease mechanisms underlie the inter-tumour heterogeneity in terms of disease progression and responses to therapies. Discovering common disease mechanisms shared by tumours would provide guidance for precision oncology but remains a challenge. Here, we present a novel computational framework for revealing distinct combinations of aberrant signalling pathways in tumours. Specifically, we applied the tumour-specific causal inference algorithm (TCI) to identify causal relationships between SGAs and differentially expressed genes (DEGs) within tumours from the Cancer Genome Atlas (TCGA) study. Based on these causal inferences, we adopted a network-based method to identify modules of DEGs, such that the member DEGs within a module tend to be co-regulated by a common pathway. Using the expression status of genes in a module as a surrogate measure of the activation status of the corresponding pathways, we divided breast cancers (BRCAs) into five subgroups and glioblastoma multiformes (GBMs) into six subgroups with distinct combinations of pathway aberrations. The patient groups exhibited significantly different survival patterns, indicating that our approach can identify clinically relevant disease subtypes.

SUBMITTER: Xue Y

PROVIDER: S-EPMC6744493 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Tumour-specific Causal Inference Discovers Distinct Disease Mechanisms Underlying Cancer Subtypes.

Xue Yifan Y Cooper Gregory G Cai Chunhui C Lu Songjian S Hu Baoli B Ma Xiaojun X Lu Xinghua X

Scientific reports 20190913 1

Cancer is a disease mainly caused by somatic genome alterations (SGAs) that perturb cellular signalling systems. Furthermore, the combination of pathway aberrations in a tumour defines its disease mechanism, and distinct disease mechanisms underlie the inter-tumour heterogeneity in terms of disease progression and responses to therapies. Discovering common disease mechanisms shared by tumours would provide guidance for precision oncology but remains a challenge. Here, we present a novel computat ...[more]

PMID: 31519988

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Project description:BackgroundGene expression profiling has been used to define molecular phenotypes of complex diseases such as breast cancer. The luminal A and basal-like subtypes have been repeatedly identified and validated as the two main subtypes out of a total of five molecular subtypes of breast cancer. These two are associated with distinctly different gene expression patterns and more importantly, a significant difference in clinical outcome. To further validate and more thoroughly characterize these two subtypes at the molecular level in tumors at an early stage, we report a gene expression profiling study using three different DNA microarray platforms.ResultsExpression data from 20 tumor biopsies of early stage breast carcinomas were generated on three different DNA microarray platforms; Applied Biosystems Human Genome Survey Microarrays, Stanford cDNA Microarrays and Agilent's Whole Human Genome Oligo Microarrays, and the resulting gene expression patterns were analyzed. Both unsupervised and supervised analyses identified the different clinically relevant subtypes of breast tumours, and the results were consistent across all three platforms. Gene classification and biological pathway analyses of the genes differentially expressed between the two main subtypes revealed different molecular mechanisms descriptive of the two expression-based subtypes: Signature genes of the luminal A subtype were over-represented by genes involved in fatty acid metabolism and steroid hormone-mediated signaling pathways, in particular estrogen receptor signaling, while signature genes of the basal-like subtype were over-represented by genes involved in cell proliferation and differentiation, p21-mediated pathway, and G1-S checkpoint of cell cycle-signaling pathways. A minimal set of 54 genes that best discriminated the two subtypes was identified using the combined data sets generated from the three different array platforms. These predictor genes were further verified by TaqMan Gene Expression assays.ConclusionWe have identified and validated the two main previously defined clinically relevant subtypes, luminal A and basal-like, in a small set of early stage breast carcinomas. Signature genes characterizing these two subtypes revealed that distinct molecular mechanisms might have been pre-programmed at an early stage in different subtypes of the disease. Our results provide further evidence that these breast tumor subtypes represent biologically distinct disease entities and may require different therapeutic strategies. Finally, validated by multiple gene expression platforms, including quantitative PCR, the set of 54 predictor genes identified in this study may define potential prognostic molecular markers for breast cancer.

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Tumour-specific Causal Inference Discovers Distinct Disease Mechanisms Underlying Cancer Subtypes.

Publications

Tumour-specific Causal Inference Discovers Distinct Disease Mechanisms Underlying Cancer Subtypes.

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