Integration and comparison of different genomic data for outcome prediction in cancer

Abstract

BackgroundIn cancer, large-scale technologies such as next-generation sequencing and microarrays have produced a wide number of genomic features such as DNA copy number alterations (CNA), mRNA expression (EXPR), microRNA expression (MIRNA), and DNA somatic mutations (MUT), among others. Several analyses of a specific type of these genomic data have generated many prognostic biomarkers in cancer. However, it is uncertain which of these data is more powerful and whether the best data-type is cancer-type dependent.Therefore, our purpose is to characterize the prognostic power of models obtained from different genomic data types, cancer types, and algorithms. For this, we compared the prognostic power using the concordance and prognostic index of models obtained from EXPR, MIRNA, CNA, MUT data and their integration for ovarian serous cystadenocarcinoma (OV), multiform glioblastoma (GBM), lung adenocarcinoma (LUAD), and breast cancer (BRCA) datasets from The Cancer Genome Atlas repository. We used three different algorithms for prognostic model selection based on constrained particle swarm optimization (CPSO), network feature selection (NFS), and least absolute shrinkage and selection operator (LASSO).ResultsThe integration of the four genomic data produced models having slightly higher performance than any single genomic data. From the genomic data types, we observed better prediction using EXPR closely followed by MIRNA and CNA depending on the cancer type and method. We observed higher concordance index in BRCA, followed by LUAD, OV, and GBM. We observed very similar results between LASSO and CPSO but smaller values in NFS. Importantly, we observed that model predictions highly concur between algorithms but are highly discordant between data types, which seems to be dependent on the censoring rate of the dataset.ConclusionsGene expression (mRNA) generated higher performances, which is marginally improved when other type of genomic data is considered. The level of concordance in prognosis generated from different genomic data types seems to be dependent on censoring rate.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-015-0065-1) contains supplementary material, which is available to authorized users.

Tumor copy number alteration burden is a pan-cancer prognostic factor associated with recurrence and death

“…Another interesting feature of the association of tumor CNA burden with outcome demonstrated here is that it has prognostic significance independent of tumor mutation burden (TMB). This is consistent with recent work in glioblastoma, breast, lung, and ovarian cancer showing that CNA-derived signatures have more prognostic power than somatic point mutation-based signatures, as measured by concordance index ( Gómez-Rueda et al, 2015 ). Thus, tumor CNA burden could complement clinical analyses of actionable driver mutations using a single panel-based sequencing assay.…”

Classification based on extensions of LS-PLS using logistic regression: application to clinical and multiple genomic data

“…Large-scale technologies have produced a wide variety of genomic features, such as mRNA-gene expression, DNA methylation, microRNA, and copy number alterations (CNAs), among others. Many genomic data of these types have been generated and analyzed in numerous studies with the aim of predicting a specific outcome [ 1 , 2 ]. In this article, we focus on binary class prediction where the outcome can be for instance alive/dead, or therapeutic success/failure.…”

Ensemble Feature Selection and Meta-Analysis of Cancer miRNA Biomarkers

“…12 Succeeding the earliest evidence of miRNA involvement in human cancer by Croce 13 and collaborators [9], various studies demonstrate that miRNA expression is deregulated 14 in human cancer through diverse mechanisms [10]. Additionally, in comparison to the 15 impractical and invasive methods currently used for cancer diagnosis [11,12], miRNA 16 biomarkers can be detected directly from biological fluids (such as blood, urine, saliva 17 and pleural fluid [13]), and they can also be used as biomarkers to detect tumors at an 18 early stage, which is extremely important for survival. For example, the 5-year survival 19 rate for lung cancer is 5%, but an early diagnosis can boost it to almost 50% [14].…”

“…Thus, 20 miRNA expression profiles correlate with clinical variables, highlighting their potential 21 value as prognostic and/or diagnostic tools. 22 In such a context of increasing availability of data, it is of utmost practical 23 importance to build databases of miRNA expressions data for cancer research [15][16][17][18][19], 24 and also to extract features that could be used as cancer biomarkers [20][21][22]. For 25 example, miRNA hsa-mir-21 is mentioned as a marker for patients with squamous cell 26 lung carcinoma [23], with astrocytoma [24], breast cancer [25], and gastric cancer [26].…”