Characterization of aberrant pathways across human cancers

Abstract

Background Cancer is a broad group of genetic diseases which account for millions of deaths worldwide each year. Cancers are classified by various clinical, pathological and molecular methods, but even within a well-characterized disease, there is a significant inter-patient variability in survival, response to treatment, and other parameters. Especially in molecular level, tumours of the same category can appear significantly dissimilar due to complex combinations of genetic aberrations leading to a similar malignancy. We extended the current classification methods by studying tumour heterogeneity at pathway level. Methods We computed the rate of alterations in 1994 pathways and 2210 tumours consisting of eight different cancers. Using gene set enrichment analysis, each sample was computed a pathway aberration profile that reflected its molecular state. The profiles were analysed together to infer the characteristic aberration rates for each pathway within each cancer. Subgroups of tumours defined by similar pathway aberrations were identified using clustering analyses. The pathway aberration and gene expression profiles of the subgroups were consecutively compared across all eight cancer types to search for similar tumours crossing the standard classification. Results We identified pathways and processes that were common to all cancers as well as traits that are unique to a cancer type or closely related cancers. Studying the gene expression patterns within the pathway context suggested potential alteration mechanisms. Clustering analysis revealed five clinically relevant subgroups of tumours in four cancers that exhibited significant differences in survival compared to others. The cross-cancer analysis of the subgroups resulted in the identification of tumours that shared potentially significant alterations. Conclusions This study represents the first effort to extend the molecular characterizations towards pathway level descriptions across the family of cancers. In addition to providing a proof-of-concept for single sample pathway aberration analysis in this context, we present a comprehensive pathway aberration dataset that can be used to study pathway aberration patterns within or across cancers. Significant similarities between subgroups of different cancers on pathway and gene expression levels provide interesting hypotheses for understanding variable drug response, or transferring treatments across diseases by identifying common druggable pathways or genes, for example.

Revealing Biological Pathways Implicated in Lung Cancer from TCGA Gene Expression Data Using Gene Set Enrichment Analysis

“…Jiang et al 39 used GSEA to indicate enriched genes in order to evaluate the messenger RNA expression of insulin receptor isoform A and insulin receptor isoform B, which used the TCGA LUSC RNA-seq data rather than the gene expression data in our paper. Ylipää et al 40 analyzed the TCGA LUSC gene expression data for the similarities with other types of cancers based on a GSEA-inspiring method of computing the pathway aberration profile for each tumor sample, which was to study the similarities of different cancers rather than to analyze the pathway implication in LUSC in this paper. Above all, to our knowledge, the TCGA LUSC gene expression data, together with the whole set of genes in the pathway, might not have been used to analyze the pathway implications in LUSC via GSEA.…”

A multilevel pan-cancer map links gene mutations to cancer hallmarks

“…Third, integration of additional molecular data, including gene and protein expression as well as epigenetic modifications, would make the information flow across the map more physiologically relevant. There are already some approaches that integrate different data types and interactions in a systematic and quantitative way [ 22 , 23 ]. However, none of these approaches explicitly incorporate hallmarks into their framework.…”

Cancer research in the era of next-generation sequencing and big data calls for intelligent modeling

“…Alternatively, if we compute the enriched pathways for each sample prior to clustering, we can use the enrichment data to cluster tumors into subgroups that might be easier to interpret and understand. Pathway analysis offers intriguing opportunities; for example, if we know that the pathway activation profiles of two subgroups of different cancers are similar, we might hypothesize that both can be treated effectively by the same drug [ 10 ].…”