Jorge Mata-Garrido

Background: Colorectal cancer is a multifactorial malignancy implicating a wide variety of risk factors, such as genetic, environmental, nutritional, and lifestyle factors, leading to a certain heterogeneity in the development of the disease. Colorectal cancer is generally classified in terms of a Warburg metabolic phenotype, characterized by an excess of glycolytic axes as compared to oxidative phosphorylation. It is therefore important to better characterize the metabolic heterogeneity of these tumors in relation to their mitochondrial activity. Materials and Methods: Two R-packages (keggmetascore and mitoscore) were developed to explore metabolism, based on KEGG metabolism pathways, and mitochondrial activities, based on mitocarta V3 annotations, for the investigation of diverse transcriptomics data such as bulk or single cell experiments at the single-sample level. Results: Using the two R-packages, we functionally confirmed both regulation of metabolism and mitochondrial activities in LOVO cells after stimulation with metformin. At the single-cell level, in single-cell RNA-sequencing of colorectal tumors, we conjointly observed an activation of metabolism and mitochondrial activities in tumor cells from MSI-high tumors, in contrast to a conjoint repression of metabolism and mitochondrial activity in tumor cells from POLE-mutated tumors. These two types of tumors have distinct responses to immune checkpoint blockade therapy. At the bulk transcriptome level, colorectal tumors present less metabolism/mitochondria activities as compared to normal tissues. Multi-modal integration by co-expression network analysis showed that metabolism/mitochondrial activities are associated with a consensus molecular subtype (CMS) classification of colorectal cancer. Regarding KRAS, BRAF, and TP53 driver gene mutation status, strong repression of metabolism pathways was observed, mainly associated with fewer intra-mitochondrial membrane interactions in tumors harboring a BRAF-V600E mutation. Machine learning using Elastic-net allowed us to build a mixed metabolism/mitochondrial activity score, which was found to be increased in the CMS1-MSI subtype and metastatic samples and to be an independent parameter predictive of BRAF-V600E mutation status in colorectal cancer. Conclusions: These findings underscore the pivotal role of mitochondrial metabolism in colorectal cancer subtyping and highlight its value as a predictive biomarker for personalized therapeutic strategies.

Background/Objectives: Glioblastoma multiforme (GBM) is an aggressive primary brain tumor with limited therapeutic options. Neoantigen-based immunotherapy offers a promising avenue, but its efficacy primarily depends on the ability of somatic mutations to generate immunogenic peptides effectively presented by HLA class I molecules and recognized by cytotoxic T cells, in concert with innate immune mechanisms such as NK-cell activation and DAMP/PAMP signaling. This study aimed to characterize the MHC-I binding diversity of peptides derived from GBM-associated somatic variants, with a particular focus on interactions involving HLA-A68:01 and HLA-B15:01 alleles. These alleles were selected based on their ethnic prevalence and potential structural compatibility with neoepitopes. Methods: Somatic missense variants from TCGA-GBM were filtered using high-confidence genomic databases, including dbSNP, COSMIC, and MANE. Neoepitope prediction was performed across multiple HLA class I alleles using binding affinity algorithms (MHCflurry2). Peptide–HLA interactions were characterized through motif analysis and anchor residue enrichment. Structural modeling of peptide–HLA complexes was conducted using ColabFold (AlphaFold2-multimer v3) to evaluate conformational stability. The population frequency of selected HLA alleles was examined through epidemiological comparisons. Results: Canonical GBM driver mutations (e.g., EGFR, TP53, PIK3R1) are recurrent and biologically relevant, although pharmacological inhibition of EGFR alone has not consistently improved patient outcomes, underscoring the complex signaling redundancy in glioblastoma. HLA-A68:01 exhibited high binding affinity and favorable motif compatibility, supporting its potential for effective neoantigen presentation. HLA-B15:01 was identified as a viable presenter for the EGFR p.Arg108Lys variant. Structural modeling confirmed stable peptide insertion into the MHC-I binding groove, with high-confidence folding and preserved interface integrity. Ethnic distribution analysis revealed varying GBM incidence across populations expressing these alleles. Conclusions: This integrative analysis identified structurally validated, immunogenically promising neoantigens derived from GBM mutations, particularly for HLA-A68:01 and HLA-B15:01. These findings support allele-informed neoepitope prioritization in personalized immunotherapy, especially for patient populations with corresponding HLA genotypes and MHC-I presentation capacity.

Background: Medulloblastoma is an aggressive pediatric brain tumor characterized by marked molecular heterogeneity, which significantly impacts prognosis. The low frequency of genomic mutations in medulloblastoma suggests that alternative mechanisms, such as epigenetic regulation, may play a critical role in its pathogenesis. Methods: Using the EpiFactors database, we investigated the expression of epigenetic regulators in two independent RNA sequencing cohorts [Pediatric Brain Tumor Atlas (PBTA) and Williamson], stratified by molecular subgroups and clinical outcomes. We further analyzed expression heterogeneity at the single-cell level in malignant medulloblastoma cells using single-cell RNA sequencing. Results: Members of the SWI/SNF superfamily were dysregulated across all four molecular subtypes of medulloblastoma. Subtype-specific alterations were also observed: the acetyltransferase complex was shared between Group 3 (with SMARCD3 as a potential marker) and Group 4 (with RBM24 as a potential marker); SWR1, β-catenin/TCF, and protein–DNA complexes were specifically enriched in SHH-MB (with EYA1 and SATB2 as SHH markers); and RSC-type, PRC1, DNA polymerase complexes, and X-chromosome-related factors were enriched in WNT-MB (with FOXA1 and PIWIL4 as WNT markers). An epigenetic score (epi-score), linked to RNA metabolism and S-adenosyl-L-methionine pathways, was developed and identified as an independent adverse prognostic factor. High epi-scores were associated with proliferative, stem-like SHH malignant cells (characterized by G2/M phase, low pseudotime, and high entropy), exhibiting alterations in RNA splicing, DNA recombination, and nuclear division. Conclusions: Expression heterogeneity of epigenetic regulators is closely associated with molecular subgroups and clinical outcomes in medulloblastoma. These findings highlight the role of epigenetic dysregulation in RNA metabolism and tumor progression, particularly in SHH-driven proliferative cells.

In the original publication [...].

Background/Objectives: Medulloblastoma is the most common malignant brain tumor in children and comprises four molecular subtypes—WNT, SHH, Group 3, and Group 4—each with distinct genetic, epigenetic, and metabolic features. Increasing evidence highlights the critical role of metabolic reprogramming and epigenetic alterations in driving tumor progression, therapy resistance, and clinical outcomes. This review aims to explore the interplay between metabolic and epigenetic mechanisms in medulloblastoma, with a focus on their functional roles and therapeutic implications. Methods: A comprehensive literature review was conducted using PubMed and relevant databases, focusing on recent studies examining metabolic pathways and epigenetic regulation in medulloblastoma subtypes. Particular attention was given to experimental findings from in vitro and in vivo models, as well as emerging preclinical therapeutic strategies targeting these pathways. Results: Medulloblastoma exhibits metabolic adaptations such as increased glycolysis, lipid biosynthesis, and altered amino acid metabolism. These changes support rapid cell proliferation and interact with the tumor microenvironment. Concurrently, epigenetic mechanisms—including DNA methylation, histone modification, chromatin remodeling, and non-coding RNA regulation—contribute to tumor aggressiveness and treatment resistance. Notably, metabolic intermediates often serve as cofactors for epigenetic enzymes, creating feedback loops that reinforce oncogenic states. Preclinical studies suggest that targeting metabolic vulnerabilities or epigenetic regulators—and particularly their combination—can suppress tumor growth and overcome resistance mechanisms. Conclusions: The metabolic–epigenetic crosstalk in medulloblastoma represents a promising area for therapeutic innovation. Understanding subtype-specific dependencies and integrating biomarkers for patient stratification could facilitate the development of precision medicine approaches that improve outcomes and reduce long-term treatment-related toxicity in pediatric patients.

Background/Objectives: Hepatic cancers, including hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), are major global health concerns due to rising incidence and limited therapeutic success. While traditional risk factors include chronic liver disease and environmental exposures, recent evidence underscores the significance of genetic alterations and gut microbiota in liver cancer development and progression. This review aims to integrate emerging knowledge on the interplay between host genomic changes and gut microbial dynamics in the pathogenesis and treatment of hepatic cancers. Methods: We conducted a comprehensive review of current literature on genetic and epigenetic drivers of HCC and CCA, focusing on commonly mutated genes such as TP53, CTNNB1, TERT, IDH1/2, and FGFR2. In parallel, we evaluated studies addressing the gut–liver axis, including the roles of dysbiosis, microbial metabolites, and immune modulation. Key clinical and preclinical findings were synthesized to explore how host–microbe interactions influence tumorigenesis and therapeutic response. Results: HCC and CCA exhibit distinct but overlapping genomic landscapes marked by recurrent mutations and epigenetic reprogramming. Alterations in the gut microbiota contribute to hepatic inflammation, genomic instability, and immune evasion, potentially enhancing oncogenic signaling pathways. Furthermore, microbiota composition appears to affect responses to immune checkpoint inhibitors. Emerging therapeutic strategies such as probiotics, fecal microbiota transplantation, and precision oncology based on mutational profiling demonstrate potential for personalized interventions. Conclusions: The integration of host genomics with microbial ecology provides a promising paradigm for advancing diagnostics and therapies in liver cancer. Targeting the gut–liver axis may complement genome-informed strategies to improve outcomes for patients with HCC and CCA.