Biology subjects

Hernandez-Verdin, I.

Publications and source records attributed to Hernandez-Verdin, I..

3 recordsLinked to original sources

bioRxiv2026

Morphologic intratumoral heterogeneity from routine whole-slide histopathology is prognostic for survival in primary central nervous system lymphoma: development in the LOC Network and international external validation

Background: Clinical scores incompletely capture outcomes in primary central nervous system lymphoma (PCNSL). We quantified morphologic heterogeneity in pretreatment hematoxylin and eosin (H\&E) whole slides. Patients and methods: Three independent cohorts of immunocompetent, HIV- and EBV-negative patients treated recently were analyzed: LOC 2023 (122 slides), phase III BLOCAGE-01 (245 slides; NCT02313389), and external Barcelona (BCN; 41 slides). UNI embeddings, prototype learning, spatial metrics, and elastic-net Cox regression defined ITH-C. Results: Models achieved bootstrap-corrected concordance of 0.797--0.834. Age-, sex-, and KPS-adjusted ITH-C HRs were 1.29 (95\% CI 1.01--1.64), 1.27 (1.07--1.51), and 2.13 (1.35--3.37), respectively. Adding ITH-C increased MSKCC C-index from 0.671 to 0.717, 0.560 to 0.593, and 0.588 to 0.706. Spatial transcriptomics linked ITH-C to immune programs. Conclusions: Routine H\&E encodes prognostic spatial heterogeneity in PCNSL. ITH-C complements clinical scores, supporting prospective risk stratification.

bioinformatics↗

bioRxiv2026

h5adify: neuro-symbolic metadata harmonizationenables scalable AnnData integration with locallarge language models

BackgroundThe rapid growth of public single-cell and spatial transcriptomics repositories has shifted the main bottleneck for atlas-scale integration from data generation to metadata heterogeneity. Even when datasets are released in the AnnData H5AD format, inconsistent column naming, partial annotations, and mixed gene identifier conventions frequently prevent reproducible merging, downstream benchmarking, and reuse in foundation model training. Automated approaches that resolve semantic inconsistency while preserving biological validity are therefore essential for scalable data reuse. ResultsWe present h5adify, a neuro-symbolic toolkit that combines deterministic biological inference with locally deployed large language models to transform heterogeneous AnnData objects into schema-normalized, integration-ready representations. The framework performs metadata field discovery, gene identifier harmonization, optional paper-aware extraction, and consensus resolution with explicit uncertainty logging. Benchmarking four open-weight model families deployed through Ollama (Gemma, Llama, Mistral, and Qwen) demonstrates that small local models achieve high semantic accuracy in metadata resolution with low hallucination rates and modest computational requirements. In controlled simulations introducing annotation noise into single-cell and Visium-like datasets, harmonization improves integration benchmarking and reduces spurious batch effects. Application to sex-annotated glioblastoma datasets recovers biologically coherent microenvironmental patterns and cell type-specific genomic differences not explained by differential expression alone. ConclusionsTogether, h5adify provides a reproducible framework for evaluating LLM-assisted biocuration and enables scalable, privacy-preserving metadata harmonization for modern single-cell atlases and foundation model pipelines. These results demonstrate that modular neuro-symbolic integration of deterministic biological inference and small local language models can effectively resolve semantic heterogeneity while remaining computationally accessible.

bioinformatics↗

bioRxiv2021

Pan-cancer landscape of AID-related mutations, composite mutations and its potential role in the ICI response

Activation-induced cytidine deaminase, AICDA or AID, is a driver of somatic hypermutation and class-switch recombination in immunoglobulins. In addition, this deaminase belonging to the APOBEC family, may have off-target effects genome-wide, but its effects at pan-cancer level are not well elucidated. Here, we used different pan-cancer datasets, totaling more than 50,000 samples analyzed by whole-genome, whole-exome or targeted sequencing. AID synergizes initial hotspot mutations by a second composite mutation. Analysis of 2.5 million cells, normal and oncogenic, revealed AICDA expression activation after oncogenic transformation and cell cycle regulation loss. AID mutational load was found to be independently associated with favorable outcome in immune-checkpoint inhibitors (ICI) treated patients across cancers after analyzing 2,000 samples. Finally, we found that AID related neoepitopes, resulting from mutations at more frequent hotspots if compared to other mutational signatures, enhance CXCL13/CCR5 expression, immunogenicity and T-cell exhaustion, which may increase ICI sensitivity. In BriefA combined bulk and single cell multi-omic analysis of over 50,000 patients and 2.5 million cells across 80 tumor types reveals oncogenic acquired AICDA expression inducing composite mutations and clonal immunogenic neoepitopes that are associated with favorable outcome in patients treated by immune-checkpoint inhibitors. Highlights* Pan-cancer analysis of AID mutations using > 50,000 samples, 2,000 ICI treated cases and 2.5 million cells with genome, exome and transcriptome data * Oncogenic transient AICDA expression induces mutations mainly during transcription of its off-target genes in virtually all cancers * AID is implicated in composite mutations on weakly functional alleles and immunogenic clonal neoepitopes at hotspots with greater positive selection * AID mutational load predicts response and is associated with favorable outcome in ICI treated patients

bioinformatics↗