Identification of a gene expression signature associated with breast cancer survival and risk that improves clinical genomic platforms.

Motivation: Modern genomic technologies allow us to perform genome-wide analysis to find gene markers associated with the risk and survival in cancer patients. Accurate risk prediction and patient stratification based on robust gene signatures is a key path forward in personalized treatment and precision medicine. Several authors have proposed the identification of gene signatures to assign risk in patients with breast cancer (BRCA), and some of these signatures have been implemented within commercial platforms in the clinic, such as Oncotype and Prosigna. However, these platforms are black boxes in which the influence of selected genes as survival markers is unclear and where the risk scores provided cannot be clearly related to the standard clinicopathological tumor markers obtained by immunohistochemistry (IHC), which guide clinical and therapeutic decisions in breast cancer. Results: Here, we present a framework to discover a robust list of gene expression markers associated with survival that can be biologically interpreted in terms of the three main biomolecular factors (IHC clinical markers: ER, PR and HER2) that define clinical outcome in BRCA. To test and ensure the reproducibility of the results, we compiled and analyzed two independent datasets with a large number of tumor samples (1024 and 879) that include full genome-wide expression profiles and survival data. Using these two cohorts, we obtained a robust subset of gene survival markers that correlate well with the major IHC clinical markers used in breast cancer. The geneset of survival markers that we identify (which includes 34 genes) significantly improves the risk prediction provided by the genesets included in the commercial platforms: Oncotype (16 genes) and Prosigna (50 genes, i.e. PAM50). Furthermore, some of the genes identified have recently been proposed in the literature as new prognostic markers and may deserve more attention in current clinical trials to improve breast cancer risk prediction. Availability and implementation: All data integrated and analyzed in this research will be available on GitHub (https://github.com/jdelasrivas-lab/breastcancersurvsign), including the R scripts and protocols used for the analyses. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

A Gene Signature Derived from the Loss of CDKN1A (p21) Is Associated with CMS4 Colorectal Cancer.

The epithelial-mesenchymal transition (EMT) is associated with tumor aggressiveness and increased invasion, migration, metastasis, angiogenesis, and drug resistance. Although the HCT116 p21-/- cell line is well known for its EMT-associated phenotype, with high Vimentin and low E-cadherin protein levels, the gene signature of this rather intermediate EMT-like cell line has not been determined so far. In this work, we present a robust molecular and bioinformatics analysis, to reveal the associated gene expression profile and its correlation with different types of colorectal cancer tumors. We compared the quantitative signature obtained with the NanoString platform with the expression profiles of colorectal cancer (CRC) Consensus Molecular Subtypes (CMS) as identified, and validated the results in a large independent cohort of human tumor samples. The expression signature derived from the p21-/- cells showed consistent and reliable numbers of upregulated and downregulated genes, as evaluated with two machine learning methods against the four CRC subtypes (i.e., CMS1, 2, 3, and 4). High concordance was found between the upregulated gene signature of HCT116 p21-/- cells and the signature of the CMS4 mesenchymal subtype. At the same time, the upregulated gene signature of the native HCT116 cells was similar to that of CMS1. Using a multivariate Cox regression model to analyze the survival data in the CRC tumor cohort, we selected genes that have a predictive risk power (with a significant gene risk incidence score). A set of genes of the mesenchymal signature was proven to be significantly associated with poor survival, specifically in the CMS4 CRC human cohort. We suggest that the gene signature of HCT116 p21-/- cells could be a suitable metric for mechanistic studies regarding the CMS4 signature and its functional consequences in CRC. Moreover, this model could help to discover the molecular mechanisms of intermediate EMT, which is known to be associated with extraordinarily high stemness and drug resistance.

Mining Drug-Target Associations in Cancer: Analysis of Gene Expression and Drug Activity Correlations.

Cancer is a complex disease affecting millions of people worldwide, with over a hundred clinically approved drugs available. In order to improve therapy, treatment, and response, it is essential to draw better maps of the targets of cancer drugs and possible side interactors. This study presents a large-scale screening method to find associations of cancer drugs with human genes. The analysis is focused on the current collection of Food and Drug Administration (FDA)-approved drugs (which includes about one hundred chemicals). The approach integrates global gene-expression transcriptomic profiles with drug-activity profiles of a set of 60 human cell lines obtained for a collection of chemical compounds (small bioactive molecules). Using a standardized expression for each gene versus standardized activity for each drug, Pearson and Spearman correlations were calculated for all possible pairwise gene-drug combinations. These correlations were used to build a global bipartite network that includes 1007 gene-drug significant associations. The data are integrated into an open web-tool called GEDA (Gene Expression and Drug Activity) which includes a relational view of cancer drugs and genes, disclosing the putative indirect interactions found for FDA-approved drugs as well as the known targets of these drugs. The results also provide insight into the complex action of pharmaceuticals, presenting an alternative view to address predicted pleiotropic effects of the drugs.

Survival marker genes of colorectal cancer derived from consistent transcriptomic profiling.

BACKGROUND: Identification of biomarkers associated with the prognosis of different cancer subtypes is critical to achieve better therapeutic assistance. In colorectal cancer (CRC) the discovery of stable and consistent survival markers remains a challenge due to the high heterogeneity of this class of tumors. In this work, we identified a new set of gene markers for CRC associated to prognosis and risk using a large unified cohort of patients with transcriptomic profiles and survival information. RESULTS: We built an integrated dataset with 1273 human colorectal samples, which provides a homogeneous robust framework to analyse genome-wide expression and survival data. Using this dataset we identified two sets of genes that are candidate prognostic markers for CRC in stages III and IV, showing either up-regulation correlated with poor prognosis or up-regulation correlated with good prognosis. The top 10 up-regulated genes found as survival markers of poor prognosis (i.e. low survival) were: DCBLD2, PTPN14, LAMP5, TM4SF1, NPR3, LEMD1, LCA5, CSGALNACT2, SLC2A3 and GADD45B. The stability and robustness of the gene survival markers was assessed by cross-validation, and the best-ranked genes were also validated with two external independent cohorts: one of microarrays with 482 samples; another of RNA-seq with 269 samples. Up-regulation of the top genes was also proved in a comparison with normal colorectal tissue samples. Finally, the set of top 100 genes that showed overexpression correlated with low survival was used to build a CRC risk predictor applying a multivariate Cox proportional hazards regression analysis. This risk predictor yielded an optimal separation of the individual patients of the cohort according to their survival, with a p-value of 8.25e-14 and Hazard Ratio 2.14 (95% CI: 1.75-2.61). CONCLUSIONS: The results presented in this work provide a solid rationale for the prognostic utility of a new set of genes in CRC, demonstrating their potential to predict colorectal tumor progression and evolution towards poor survival stages. Our study does not provide a fixed gene signature for prognosis and risk prediction, but instead proposes a robust set of genes ranked according to their predictive power that can be selected for additional tests with other CRC clinical cohorts.