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Cancer is one of the main public health problems in the world. Systemic therapies such as chemotherapy and more recently target therapies as well as immunotherapy have improved the prognosis of this large group of complex malignant diseases. However, the frequent emergence of multidrug resistance (MDR) mechanisms is one of the major impediments towards curative treatment of cancer. While several mechanisms of drug chemoresistance are well defined, resistance to immunotherapy is still insufficiently unclear due to the complexity of the immune response and its dependence on the host. Expression and regulation of immune checkpoint molecules (such as PD-1, CD279; PD-L1, CD274; and CTLA-4, CD152) play a key role in the response to immunotherapy. In this regard, immunotherapy based on immune checkpoints inhibitors (ICIs) is a common clinical approach for treatment of patients with poor prognosis when other first-line therapies have failed. Unfortunately, about 70 % of patients are classified as non-responders, or they progress after initial response to these ICIs. Multiple factors can be related to immunotherapy resistance: characteristics of the tumor microenvironment (TME); presence of tumor infiltrating lymphocytes (TILs), such as CD8 + T cells associated with treatment-response; presence of tumor associated macrophages (TAMs); activation of certain regulators (like PIK3γ or PAX4) found present in non-responders; a low percentage of PD-L1 expressing cells; tumor mutational burden (TMB); gain or loss of antigen-presenting molecules; genetic and epigenetic alterations correlated with resistance. This review provides an update on the current state of immunotherapy resistance presenting targets, biomarkers and remedies to overcome such resistance., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

RNA-seq is currently considered the most powerful, robust and adaptable technique for measuring gene expression and transcription activation at genome-wide level. As the analysis of RNA-seq data is complex, it has prompted a large amount of research on algorithms and methods. This has resulted in a substantial increase in the number of options available at each step of the analysis. Consequently, there is no clear consensus about the most appropriate algorithms and pipelines that should be used to analyse RNA-seq data. In the present study, 192 pipelines using alternative methods were applied to 18 samples from two human cell lines and the performance of the results was evaluated. Raw gene expression signal was quantified by non-parametric statistics to measure precision and accuracy. Differential gene expression performance was estimated by testing 17 differential expression methods. The procedures were validated by qRT-PCR in the same samples. This study weighs up the advantages and disadvantages of the tested algorithms and pipelines providing a comprehensive guide to the different methods and procedures applied to the analysis of RNA-seq data, both for the quantification of the raw expression signal and for the differential gene expression.

Like physics in the 19th century, biology and molecular biology in particular, has been fertilized and enhanced like few other scientific fields, by the incorporation of mathematical methods. In the last decades, a whole new scientific field, bioinformatics, has developed with an output of over 30,000 papers a year (Pubmed search using the keyword "bioinformatics"). Huge databases of mass throughput data have been established, with ArrayExpress alone containing more than 2.7 million assays (October 2019). Computational methods have become indispensable tools in molecular biology, particularly in one of the most challenging areas of cancer research, multidrug resistance (MDR). However, confronted with a plethora of different algorithms, approaches, and methods, the average researcher faces key questions: Which methods do exist? Which methods can be used to tackle the aims of a given study? Or, more generally, how do I use computational biology/bioinformatics to bolster my research? The current review is aimed at providing guidance to existing methods with relevance to MDR research. In particular, we provide an overview on: a) the identification of potential biomarkers using expression data; b) the prediction of treatment response by machine learning methods; c) the employment of network approaches to identify gene/protein regulatory networks and potential key players; d) the identification of drug-target interactions; e) the use of bipartite networks to identify multidrug targets; f) the identification of cellular subpopulations with the MDR phenotype; and, finally, g) the use of molecular modeling methods to guide and enhance drug discovery. This review shall serve as a guide through some of the basic concepts useful in MDR research. It shall give the reader some ideas about the possibilities in MDR research by using computational tools, and, finally, it shall provide a short overview of relevant literature., (Copyright © 2019. Published by Elsevier Ltd.)

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.

Background: Despite the large increase of transcriptomic studies that look for gene signatures on diseases, there is still a need for integrative approaches that obtain separation of multiple pathological states providing robust selection of gene markers for each disease subtype and information about the possible links or relations between those genes., Results: We present a network-oriented and data-driven bioinformatic approach that searches for association of genes and diseases based on the analysis of genome-wide expression data derived from microarrays or RNA-Seq studies. The approach aims to (i) identify gene sets associated to different pathological states analysed together; (ii) identify a minimum subset within these genes that unequivocally differentiates and classifies the compared disease subtypes; (iii) provide a measurement of the discriminant power of these genes and (iv) identify links between the genes that characterise each of the disease subtypes. This bioinformatic approach is implemented in an R package, named geNetClassifier, available as an open access tool in Bioconductor. To illustrate the performance of the tool, we applied it to two independent datasets: 250 samples from patients with four major leukemia subtypes analysed using expression arrays; another leukemia dataset analysed with RNA-Seq that includes a subtype also present in the previous set. The results show the selection of key deregulated genes recently reported in the literature and assigned to the leukemia subtypes studied. We also show, using these independent datasets, the selection of similar genes in a network built for the same disease subtype., Conclusions: The construction of gene networks related to specific disease subtypes that include parameters such as gene-to-gene association, gene disease specificity and gene discriminant power can be very useful to draw gene-disease maps and to unravel the molecular features that characterize specific pathological states. The application of the bioinformatic tool here presented shows a neat way to achieve such molecular characterization of the diseases using genome-wide expression data.

Gene expression profiling signatures may be used to classify the subtypes of Myelodysplastic syndrome (MDS) patients. However, there are few reports on the global methylation status in MDS. The integration of genome-wide epigenetic regulatory marks with gene expression levels would provide additional information regarding the biological differences between MDS and healthy controls. Gene expression and methylation status were measured using high-density microarrays. A total of 552 differentially methylated CpG loci were identified as being present in low-risk MDS; hypermethylated genes were more frequent than hypomethylated genes. In addition, mRNA expression profiling identified 1005 genes that significantly differed between low-risk MDS and the control group. Integrative analysis of the epigenetic and expression profiles revealed that 66.7% of the hypermethylated genes were underexpressed in low-risk MDS cases. Gene network analysis revealed molecular mechanisms associated with the low-risk MDS group, including altered apoptosis pathways. The two key apoptotic genes BCL2 and ETS1 were identified as silenced genes. In addition, the immune response and micro RNA biogenesis were affected by the hypermethylation and underexpression of IL27RA and DICER1. Our integrative analysis revealed that aberrant epigenetic regulation is a hallmark of low-risk MDS patients and could have a central role in these diseases.

Most genetic studies in colorectal carcinomas have focused on those abnormalities that are acquired by primary tumors, particularly in the transition from adenoma to carcinoma, whereas few studies have compared the genetic abnormalities of primary versus paired metastatic samples. In this study, we used high-density 500K single-nucleotide polymorphism arrays to map the overall genetic changes present in liver metastases (n=20) from untreated colorectal carcinoma patients studied at diagnosis versus their paired primary tumors (n=20). MLH1, MSH2 and MSH6 gene expression was measured in parallel by immunohistochemistry. Overall, metastatic tumors systematically contained those genetic abnormalities observed in the primary tumor sample from the same subject. However, liver metastases from many cases (up to 8 out of 20) showed acquisition of genetic aberrations that were not found in their paired primary tumors. These new metastatic aberrations mainly consisted of (1) an increased frequency of genetic lesions of chromosomes that have been associated with metastatic colorectal carcinoma (1p, 7p, 8q, 13q, 17p, 18q, 20q) and, more interestingly, (2) acquisition of new chromosomal abnormalities (eg, losses of chromosomes 4 and 10q and gains of chromosomes 5p and 6p). These genetic changes acquired by metastatic tumors may be associated with either the metastatic process and/or adaption of metastatic cells to the liver microenvironment. Further studies in larger series of patients are necessary to dissect the specific role of each of the altered genes and chromosomal regions in the metastatic spread of colorectal tumors.

Motivation: Alteration of gene expression often results in up- or down-regulated genes and the most common analysis strategies look for such differentially expressed genes. However, molecular disease mechanisms typically constitute abnormalities in the regulation of genes producing strong alterations in the expression levels. The search for such deregulation states in the genomic expression profiles will help to identify disease-altered genes better., Results: We have developed an algorithm that searches for the genes which present a significant alteration in the variability of their expression profiles, by comparing an altered state with a control state. The algorithm provides groups of genes and assigns a statistical measure of significance to each group of genes selected. The method also includes a prefilter tool to select genes with a threshold of differential expression that can be set by the user ad casum. The method is evaluated using an experimental set of microarrays of human control and cancer samples from patients with acute promyelocytic leukemia.