In the last two decades, many detailed full transcriptomic studies on complex biological samples have been published and included in large gene expression repositories. These studies primarily provide a bulk expression signal for each sample, including multiple cell-types mixed within the global signal. The cellular heterogeneity in these mixtures does not allow the activity of specific genes in specific cell types to be identified. Therefore, inferring relative cellular composition is a very powerful tool to achieve a more accurate molecular profiling of complex biological samples. In recent decades, computational techniques have been developed to solve this problem by applying deconvolution methods, designed to decompose cell mixtures into their cellular components and calculate the relative proportions of these elements. Some of them only calculate the cell proportions (supervised methods), while other deconvolution algorithms can also identify the gene signatures specific for each cell type (unsupervised methods). In these work, five deconvolution methods (CIBERSORT, FARDEEP, DECONICA, LINSEED and ABIS) were implemented and used to analyze blood and immune cells, and also cancer cells, in complex mixture samples (using three bulk expression datasets). Our study provides three analytical tools (corrplots, cell-signature plots and bar-mixture plots) that allow a thorough comparative analysis of the cell mixture data. The work indicates that CIBERSORT is a robust method optimized for the identification of immune cell-types, but not as efficient in the identification of cancer cells. We also found that LINSEED is a very powerful unsupervised method that provides precise and specific gene signatures for each of the main immune cell types tested: neutrophils and monocytes (of the myeloid lineage), B-cells, NK cells and T-cells (of the lymphoid lineage), and also for cancer cells.

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.

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. 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. 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 data are available at Bioinformatics Advances online.

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.

The consensus molecular subtypes (CMSs) classification of colorectal cancer (CRC) is a system for patient stratification that can be potentially applied to therapeutic decisions. Hakai (CBLL1) is an E3 ubiquitin–ligase that induces the ubiquitination and degradation of E-cadherin, inducing epithelial-to-mesenchymal transition (EMT), tumour progression and metastasis. Using bioinformatic methods, we have analysed CBLL1 expression on a large integrated cohort of primary tumour samples from CRC patients. The cohort included survival data and was divided into consensus molecular subtypes. Colon cancer tumourspheres were used to analyse the expression of stem cancer cells markers via RT-PCR and Western blotting. We show that CBLL1 gene expression is specifically associated with canonical subtype CMS2. WNT target genes LGR5 and c-MYC show a similar association with CMS2 as CBLL1. These mRNA levels are highly upregulated in cancer tumourspheres, while CBLL1 silencing shows a clear reduction in tumoursphere size and in stem cell biomarkers. Importantly, CMS2 patients with high CBLL1 expression displayed worse overall survival (OS), which is similar to that associated with CMS4 tumours. Our findings reveal CBLL1 as a specific biomarker for CMS2 and the potential of using CMS2 with high CBLL1 expression to stratify patients with poor OS.