Biology subjects

Martinez-Ledesma, E.

Publications and source records attributed to Martinez-Ledesma, E..

3 recordsLinked to original sources

bioRxiv2026

Chromatin Landscape of Cancer Cell Lines Identifies Enhancer Subtypes

Epigenetic aberrations are a hallmark of cancer; however, systematic chromatin state maps of cancer cells are unavailable. We generated and analyzed 803 histone mark profiles in 142 cancer cell lines and 114 human tumors belonging to 9 solid tumor types. Irrespective of their cell-of-origin, cancer cells segregate from normal tissues based on their enhancer patterns, suggesting enhancer deregulation is a fundamental epigenetic feature in cancer. Enhancer based clustering defined 5 distinct subgroups of cancer cells (EpiC1-5) with unique developmental trajectories, molecular features and dependencies. Importantly, we define a set of core TFs that are critical for EpiC-specific enhancer patterns and survival. Notably, EpiC4 represented a predominantly epigenetic, pan-cancer subtype that displays poor survival, activation and dependence on a FN1-CAV1-SRC-PI3K-AKT signaling network. Together, these data uncover enhancer heterogeneity in pan-cancer systems with identification of a novel enhancer-based subtype and identify potential new therapeutic targets associated with unique epigenetic features.

cancer biology↗

bioRxiv2019

Exploring Survival Models Associated with MCI to AD Conversion: A Machine Learning Approach

Several studies have documented that structural MRI findings are associated with the presence of early-stage Alzheimer Disease (AD). However, the association of each MRI feature with the rate of conversion from mild cognitive impairment (MCI) to AD in a multivariate setting has not been studied fully. The objective of this work is the comprehensive exploration of four different machine learning (ML) strategies to build MRI-based multivariate Cox regression models. These models evaluated the association of MRI features with the time of MCI to clinical AD conversion. We used 442 MCI subjects from the Alzheimers disease Neuroimaging Initiative (ADNI) study. Each subject was described by 346 MRI features and time to AD conversion. Cox regression models then estimated the rate of conversion. Models were built using four ML methodologies in a cross-validation (CV) setting. All the ML methods returned successful Cox models with different CV performances. The best model exhibited a concordance index of 0.84 (95% CI: 0.82-0.86). The final analysis described the hazard ratios (HR) of the top ten MRI features associated with MCI to AD conversion. Our results suggest ML exploration is a viable strategy for building and analyzing survival models that predict subjects at risk of AD conversion.

bioinformatics↗

bioRxiv2019

Comparative molecular life history of spontaneous canine and human gliomas

Sporadic gliomas in companion dogs provide a window on the interaction between tumorigenic mechanisms and host environment. We compared the molecular profiles of canine gliomas with those of human pediatric and adult gliomas to characterize evolutionarily conserved mammalian mutational processes in gliomagenesis. Employing whole genome-, exome-, transcriptome-and methylation-sequencing of 81 canine gliomas, we found alterations shared between canine and human gliomas such as the receptor tyrosine kinases, p53 and cell cycle pathways, and IDH1 R132. Canine gliomas showed high similarity with human pediatric gliomas per robust aneuploidy, mutational rates, relative timing of mutations, and DNA methylation patterns. Our cross-species comparative genomic analysis provides unique insights into glioma etiology and the chronology of glioma-causing somatic alterations.\n\nSignificanceDiffuse gliomas are the most common malignant brain tumors, with high-grade tumors carrying a dismal prognosis. Preclinical models have proven themselves as poor predictors of clinical efficacy. Spontaneous glioma in dogs provides an attractive alternative model, because of their comparable tumor microenvironment and tumor life history. We determined the similarities and differences between human and canine gliomas through genomic profiling, and leveraged our datasets to identify conserved somatic drivers, mutational processes and temporal ordering of somatic glioma events across species. We show that canine gliomas resemble human gliomas at (epi-)genetic levels and are more reminiscent of pediatric than adult disease, thus rationalizing sporadic canine glioma as a preclinical model tailored to measuring treatment efficacies in patients with canine or human glioma.

cancer biology↗