Peripheral Blood Mononuclear Cells Predict Therapeutic Efficacy of Immunotherapy in NSCLC

In lung cancer immunotherapy, biomarkers to guide clinical decisions are limited. We now explore whether the detailed immunophenotyping of circulating peripheral blood mononu-clear cells (PBMCs) can predict the efficacy of anti-PD-1 immunotherapy in patients with advanced non-small-cell lung cancer (NSCLC). We determined 107 PBMCs subpopulations in a prospective cohort of NSCLC patients before starting single-agent anti-PD-1 immunotherapy (study group), an-alyzed by flow cytometry. As a control group, we studied patients with advanced malignancies before initiating non-immunotherapy treatment. The frequency of PBMCs was correlated with treatment outcome. Patients were categorized as having either high or low expression for each bi-omarker, defined as those above the 55th or below the 45th percentile of the overall marker expres-ion within the cohort. In the study group, three subpopulations were associated with significant differences in outcome: high pretreatment levels of circulating CD4+CCR9+, CD4+CCR10+, or CD8+CXCR4+ T cells correlated with poorer overall survival (15.7 vs. 35.9 months, HR 0.16, p = 0.003; 22.0 vs. NR months, HR 0.10, p = 0.003, and 22.0 vs. NR months, HR 0.29, p = 0.02). These differences were specific to immunotherapy-treated patients. High baseline levels of circulating T cell subpopulations related to tissue lymphocyte recruitment are associated with poorer outcomes of immunotherapy-treated advanced NSCLC patientsProjects PIE15/00068, PI17/01865, and PI20/01458 (Instituto de Salud Carlos III) awarded to R.C.; Projects FIS PI19/01491 and CIBER Cardiovascular (Fondo de Investigación Sanitaria del Instituto de Salud Carlos III with co-funding from the Fondo Europeo de Desarrollo Regional FEDER) awarded to A.A.; CNIO Bioinformatics Unit is supported by the Instituto de Salud Carlos III (ISCIII); Project RETOS RTI2018-097596-B-I00 (AEI/10.13039/501100011033 MCI/FEDER, UE); Projects PI17/00801 and PI21/01111 grants from Instituto de Salud Carlos III and JR17/00007 awarded to N.R.-L., and Project Molecular Analysis of the Exhaled Breath Condensate in the Management of Solitary Pulmonary Nodule (ideas semilla AECC 2019), from Asociación Española Contra el Cáncer (AECC), awarded to J.

PanDrugs: a novel method to prioritize anticancer drug treatments according to individual genomic data

BACKGROUND: Large-sequencing cancer genome projects have shown that tumors have thousands of molecular alterations and their frequency is highly heterogeneous. In such scenarios, physicians and oncologists routinely face lists of cancer genomic alterations where only a minority of them are relevant biomarkers to drive clinical decision-making. For this reason, the medical community agrees on the urgent need of methodologies to establish the relevance of tumor alterations, assisting in genomic profile interpretation, and, more importantly, to prioritize those that could be clinically actionable for cancer therapy. RESULTS: We present PanDrugs, a new computational methodology to guide the selection of personalized treatments in cancer patients using the variant lists provided by genome-wide sequencing analyses. PanDrugs offers the largest database of drug-target associations available from well-known targeted therapies to preclinical drugs. Scoring data-driven gene cancer relevance and drug feasibility PanDrugs interprets genomic alterations and provides a prioritized evidence-based list of anticancer therapies. Our tool represents the first drug prescription strategy applying a rational based on pathway context, multi-gene markers impact and information provided by functional experiments. Our approach has been systematically applied to TCGA patients and successfully validated in a cancer case study with a xenograft mouse model demonstrating its utility. CONCLUSIONS: PanDrugs is a feasible method to identify potentially druggable molecular alterations and prioritize drugs to facilitate the interpretation of genomic landscape and clinical decision-making in cancer patients. Our approach expands the search of druggable genomic alterations from the concept of cancer driver genes to the druggable pathway context extending anticancer therapeutic options beyond already known cancer genes. The methodology is public and easily integratable with custom pipelines through its programmatic API or its docker image. The PanDrugs webtool is freely accessible at http://www.pandrugs.org .The authors thank Joaquín Dopazo, Patricia León, and José Carbonell for kindly providing the modelled pathways employed in PanDrugs implementation; and Michael Tress for his helpful comments and suggestions in the earlier version of the manuscript.S

A comprehensive database for integrated analysis of omics data in autoimmune diseases

This work is partially funded by FEDER/Junta de Andalucia-Consejeria de Economia y Conocimiento (Grant CV20-36723), Consejeria de Salud (Grant PI-0173-2017) and by EU/EFPIA Innovative Medicines Initiative Joint Undertaking PRECISESADS (115565). JMM is partially funded by Ministerio de Economia, Industria y Competitividad. None of the funding bodies played any role in the design of the study and collection, analysis, and interpretation of data nor in writing the manuscript.Background: Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field. Results: Here, we present Autoimmune Diseases Explorer (https:// adex. genyo. es), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis. Conclusions: This is the first omics database focused on autoimmune diseases. This resource incorporates homogeneously processed data to facilitate integrative analyses among studies.FEDER/Junta de Andalucia-Consejeria de Economia y Conocimiento CV20-36723Consejeria de Salud PI-0173-2017EU/EFPIA Innovative Medicines Initiative Joint Undertaking PRECISESADS 115565Ministerio de Economia, Industria y Competitivida