Immune subtype in Colorectal Cancer: molecular, functional characterization and clinical implications

Trabajo de fin de máster en Bioinformática y Biología ComputacionalPurpose: Cancer initiation and progression are the consequence of a complex interplay between cancer cells and the tumor microenvironment. Recently, a new global transcriptomic immune classification of solid tumors has identified six immune subtypes (ISs) (C1-C6) with distinct immunogenomic features significantly associated with clinical outcome. The aim of our study was to specifically characterize the ISs in colorectal cancer (CRC) patients, and to assess their interplay with the Consensus Molecular Subtypes (CMS). Patients and Methods: CMS and ISs data, as well as clinical and molecular information of CRC patients were obtained from TCGA database27 (N=625). Immune cell populations (CIBERSORT, MCP-Counter methods), differential gene expression analyses and Gene Set Enrichment Analysis (GSEA) were also performed to characterize the immune subtypes in the global CRC population and by CMS subtype. Results: Only 5 of the 6 ISs are present in CRC patients, with 2 predominant ISs: the C1 Wound Healing (77%) and the C2 IFN-γ Dominant (17%) subtypes. CMS1 showed the highest proportion of C2 (53%), while C1 was particularly dominant in CMS2 (91%). CMS3 had the highest representation of C3 Inflammatory (7%) and C4 Lymphocyte Depleted ISs (4%), while C6 TGF-β Dominant cases belonged exclusively to CMS4 (2.3%). The prognostic impact of ISs in CRC was substantially different from that reported for the global TCGA dataset, with best 5-year survival rates observed in C6 and C1 patients (100% and 65%, respectively), while C2 and C3 displayed the worst outcome (49% and 23%, respectively). C2 tumors had a high density of CD8, follicular helper T cells, regulatory T cells, dendritic cells and neutrophils, while C1 was enriched in plasma, CD4 T and activated mast cells. Accordingly, expression of several immunomodulatory genes, including immune-checkpoints (PDL-1, CTL4, LAG 3), was upregulated in C2 tumors. GSEA analysis revealed C2 was characterized by a high activation of the immune system, apoptosis and DNA repair, as well as mTOR signalling and oxidative phosphorylation, while C1 was more dependent of metabolic pathways such as glycolysis and pyruvate metabolism. Conclusion: ISs identify distinct immune profiles within CMS subgroups with relevant clinical and biological implications and may therefore be a valuable tool to improve tailored therapeutic interventions in CRC patients

DREIMT: a drug repositioning database and prioritization tool for immunomodulation

Motivation Drug immunomodulation modifies the response of the immune system and can be therapeutically exploited in pathologies such as cancer and autoimmune diseases. Results DREIMT is a new hypothesis-generation web tool, which performs drug prioritization analysis for immunomodulation. DREIMT provides significant immunomodulatory drugs targeting up to 70 immune cells subtypes through a curated database that integrates 4960 drug profiles and ∼2600 immune gene expression signatures. The tool also suggests potential immunomodulatory drugs targeting user-supplied gene expression signatures. Final output includes drug–signature association scores, FDRs and downloadable plots and results tables. Availabilityand implementation http://www.dreimt.org. Supplementary information Supplementary data are available at Bioinformatics online.Agencia Estatal de Investigación | Ref. RTI2018-097596-B-I00Instituto de Salud Carlos IIIComunidad de Madrid | Ref. PEJD-2019-PRE/BMD-15732Xunta de Galicia | Ref. ED431C2018/55-GRCJunta de Andalucía | Ref. PI-0173-201

Metabolomic profile of neuroendocrine tumors (NETs) identifies methionine, porphyrin and tryptophan metabolism as key dysregulated pathways associated with patient survival

Objective: Metabolic profiling is a valuable tool to characterize tumor biology but remains largely unexplored in neuroendocrine tumors (NETs). Our aim was to comprehensively assess the metabolomic profile of NETs and identify novel prognostic biomarkers and dysregulated molecular pathways.Design and Methods: Multiplatform untargeted metabolomic profiling (GC-MS, CE-MS, and LC-MS) was performed in plasma from 77 patients with G1-2 extra-pancreatic NETs enrolled in the AXINET trial (NCT01744249) (study cohort) and from 68 non-cancer individuals (control). The prognostic value of each differential metabolite (n = 155) in NET patients (P < .05) was analyzed by univariate and multivariate analyses adjusted for multiple testing and other confounding factors. Related pathways were explored by Metabolite Set Enrichment Analysis (MSEA) and Metabolite Pathway Analysis (MPA).Results: Thirty-four metabolites were significantly associated with progression-free survival (PFS) (n = 16) and/or overall survival (OS) (n = 27). Thirteen metabolites remained significant independent prognostic factors in multivariate analysis, 3 of them with a significant impact on both PFS and OS. Unsupervised clustering of these 3 metabolites stratified patients in 3 distinct prognostic groups (1-year PFS of 71.1%, 47.7%, and 15.4% (P = .012); 5-year OS of 69.7%, 32.5%, and 27.7% (P = .003), respectively). The MSEA and MPA of the 13-metablolite signature identified methionine, porphyrin, and tryptophan metabolisms as the 3 most relevant dysregulated pathways associated with the prognosis of NETs.Conclusions: We identified a metabolomic signature that improves prognostic stratification of NET patients beyond classical prognostic factors for clinical decisions. The enriched metabolic pathways identified reveal novel tumor vulnerabilities that may foster the development of new therapeutic strategies for these patients

Comprehensive Plasma Metabolomic Profile of Patients with Advanced Neuroendocrine Tumors (NETs). Diagnostic and Biological Relevance

Purpose: High-throughput “-omic” technologies have enabled the detailed analysis of metabolic networks in several cancers, but NETs have not been explored to date. We aim to assess the metabolomic profile of NET patients to understand metabolic deregulation in these tumors and identify novel biomarkers with clinical potential. Methods: Plasma samples from 77 NETs and 68 controls were profiled by GC−MS, CE−MS and LC−MS untargeted metabolomics. OPLS-DA was performed to evaluate metabolomic differences. Related pathways were explored using Metaboanalyst 4.0. Finally, ROC and OPLS-DA analyses were performed to select metabolites with biomarker potential. Results: We identified 155 differential compounds between NETs and controls. We have detected an increase of bile acids, sugars, oxidized lipids and oxidized products from arachidonic acid and a decrease of carnitine levels in NETs. MPA/MSEA identified 32 enriched metabolic pathways in NETs related with the TCA cycle and amino acid metabolism. Finally, OPLS-DA and ROC analysis revealed 48 metabolites with diagnostic potential. Conclusions: This study provides, for the first time, a comprehensive metabolic profile of NET patients and identifies a distinctive metabolic signature in plasma of potential clinical use. A reduced set of metabolites of high diagnostic accuracy has been identified. Additionally, new enriched metabolic pathways annotated may open innovative avenues of clinical research

DREIMT: a drug repositioning database and prioritization tool for immunomodulation.

Drug immunomodulation modifies the response of the immune system and can be therapeutically exploited in pathologies such as cancer and autoimmune diseases. DREIMT is a new hypothesis-generation web tool, which performs drug prioritization analysis for immunomodulation. DREIMT provides significant immunomodulatory drugs targeting up to 70 immune cells subtypes through a curated database that integrates 4960 drug profiles and ∼2600 immune gene expression signatures. The tool also suggests potential immunomodulatory drugs targeting user-supplied gene expression signatures. Final output includes drug-signature association scores, FDRs and downloadable plots and results tables. http://www.dreimt.org. Supplementary data are available at Bioinformatics online

MicroRNA signature and integrative omics analyses define prognostic clusters and key pathways driving prognosis in patients with neuroendocrine neoplasms

Neuroendocrine neoplasms (NENs) are mutationally quiet (low number of mutations/Mb), and epigenetic mechanisms drive their development and progression. We aimed at comprehensively characterising the microRNA (miRNA) profile of NENs, and exploring downstream targets and their epigenetic modulation. In total, 84 cancer‐related miRNAs were analysed in 85 NEN samples from lung and gastroenteropancreatic (GEP) origin, and their prognostic value was evaluated by univariate and multivariate models. Transcriptomics (N = 63) and methylomics (N = 30) were performed to predict miRNA target genes, signalling pathways and regulatory CpG sites. Findings were validated in The Cancer Genome Atlas cohorts and in NEN cell lines. We identified a signature of eight miRNAs that stratified patients in three prognostic groups (5‐year survival of 80%, 66% and 36%). Expression of the eight‐miRNA gene signature correlated with 71 target genes involved in PI3K–Akt and TNFα–NF‐kB signalling. Of these, 28 were associated with survival and validated in silico and in vitro. Finally, we identified five CpG sites involved in the epigenetic regulation of these eight miRNAs. In brief, we identified an 8‐miRNA signature able to predict survival of patients with GEP and lung NENs, and identified genes and regulatory mechanisms driving prognosis in NEN patients