Network based stratification of major cancers by integrating somatic mutation and gene expression data

Abstract

The stratification of cancer into subtypes that are significantly associated with clinical outcomes is beneficial for targeted prognosis and treatment. In this study, we integrated somatic mutation and gene expression data to identify clusters of patients. In contrast to previous studies, we constructed cancer-type-specific significant co-expression networks (SCNs) rather than using a fixed gene network across all cancers, such as the network-based stratification (NBS) method, which ignores cancer heterogeneity. For each type of cancer, the gene expression data were used to construct the SCN network, while the gene somatic mutation data were mapped onto the network, propagated, and used for further clustering. For the clustering, we adopted an improved network-regularized non-negative matrix factorization (netNMF) (netNMF_HC) for a more precise classification. We applied our method to various datasets, including ovarian cancer (OV), lung adenocarcinoma (LUAD) and uterine corpus endometrial carcinoma (UCEC) cohorts derived from the TCGA (The Cancer Genome Atlas) project. Based on the results, we evaluated the performance of our method to identify survival-relevant subtypes and further compared it to the NBS method, which adopts priori networks and netNMF algorithm. The proposed algorithm outperformed the NBS method in identifying informative cancer subtypes that were significantly associated with clinical outcomes in most cancer types we studied. In particular, our method identified survival-associated UCEC subtypes that were not identified by the NBS method. Our analysis indicated valid subtyping of patient could be applied by mutation data with cancer-type-specific SCNs and netNMF_HC for individual cancers because of specific cancer co-expression patterns and more precise clustering.

The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine

“…NetNorM normalizes patient mutation profiles based on gene network topology, adding or removing mutations for particular patients conditional on the distribution of mutations on the network [98] instead of network smoothing to overcome the sparse and binary nature of mutation data. NBS or similar approaches have successfully stratified cancer cohorts into subtypes that are related to clinical outcomes such as patient survival, response to therapy or tumor histology in ovarian, lung [97], colorectal, head and neck, kidney [99], endometrial [97,99,100] and prostate cancers [101].…”

“…It has recently been suggested that interpatient heterogeneity could be better overcome by using cancer-type-specific networks. To this end, He et al [100] employed expression data to create cancer-type-specific significant co-expression networks (SCNs) that were then used with somatic mutation data in an NBS approach. By focusing on the disease-specific network, it was possible to identify survival-associated subtypes in uterine corpus endometrial carcinoma (UCEC) cancer that were not detected by the original NBS method.…”

Development of somatic mutation signatures for risk stratification and prognosis in lung and colorectal adenocarcinomas

“…However, gene-gene networks vary from tissue to tissue and a single set of canonical gene-gene networks as prior knowledge may omit or overemphasize some interactions [9]. To address this issue, other studies have elected to use cancer-specific co-expression networks based on RNA expression data [10] or canonical pathways [11].…”

Protein interaction disruption in cancer

“…An example of such network based methods is network smoothing, which fuses network structure with prior knowledge, and produces a measure for each node that respects both the input data and the structure of the network [12]. Such smoothing methods are widely used, with applications ranging from identification of cancer genes [13, 14], identification of gained/lost cellular functions [15] and more [12].…”