Biology subjects

Velculescu, V.

Publications and source records attributed to Velculescu, V..

3 recordsLinked to original sources

bioRxiv2026

Structural variation in repeat elements is widespread in normal human tissues and in tumorigenesis

Somatic mosaicism contributes to genomic variation, yet postzygotic structural variants remain under-characterized. We performed long- and short-read WGS from multiple individuals (n=47 normal tissues; n=168 samples) and identified mosaic structural variants in all individuals and germ layers, impacting a median 285.2 kb/genome. Nearly half of breakpoints were independently validated, with tissue distributions reflecting both early and late developmental origins. Most mosaic variants were repeat-mediated and 8.3% overlapped functional elements, an enrichment compared to germline variants. To extend these analyses in samples where long-read sequencing is infeasible, we measured repeat alterations from short-read sequencing, recapitulating mosaic tissue-specific differences. We characterized tumor- and tissue- specific variation in repeats across 15 cancer types and found tumor-related repeat variation to be similar in scale to that of normal mosaic variation. Tracking repeat changes in cell-free DNA provided a noninvasive approach for tumor monitoring. Our analyses revealed widespread repeat-driven structural variation in health and disease.

genomics↗

bioRxiv2025

Sensitizing Immune-Refractory Ovarian Tumors via p53 Mutation-Tailored Immunotherapy

High-grade serous ovarian cancer demonstrates limited responsiveness to immune checkpoint inhibitors, owing in part to immunosuppressive environments shaped by nearly universal p53 aberrations. Utilizing an immunocompetent mouse model and individual p53 mutations, we identified a dependence of the p53-R270H mutation (equivalent of human R273H) on regulatory T cells (Tregs) and the PD-1/PD-L1 axis. Analysis of patient datasets associated R273H with elevated levels of two p53 targets, PD-L1 and amphiregulin (AREG), a Tregs growth factor. In contrast to p53-R172H tumors, where there was limited activity, dual antibody therapy targeting AREG and PD-L1 selectively and effectively inhibited R270H tumors. This involved polarization toward M1 macrophages, infiltration of CD8+ T cells, diminished Ly6G+ neutrophils and downregulation of interleukin-4. In patient-derived R273C organoids, the combination treatment reduced the CD4/CD8 ratio. This study is the first to establish a mutation-tailored therapeutic approach that leverages the capacity of p53 to modulate immunosuppressive mechanisms.

cancer biology↗

bioRxiv2019

High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets

Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins is an emerging biomarker for predicting patient response to cancer immunotherapy. Current neoantigen predictors focus on in silico estimation of MHC binding affinity and are limited by low positive predictive value for actual peptide presentation, inadequate support for rare MHC alleles and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method to predict peptide-MHC binding. MHCnuggets is the only method to handle binding prediction for common or rare alleles of MHC Class I or II, with a single neural network architecture. Using a long short-term memory network (LSTM), MHCnuggets accepts peptides of variable length and is capable of faster performance than other methods. When compared to methods that integrate binding affinity and HLAp data from mass spectrometry, MHCnuggets yields a fourfold increase in positive predictive value on independent MHC-bound peptide (HLAp) data. We applied MHCnuggets to 26 cancer types in TCGA, processing 26.3 million allele-peptide comparisons in under 2.3 hours, yielding 101,326 unique candidate immunogenic missense mutations (IMMs). Predicted-IMM hotspots occurred in 38 genes, including 24 driver genes. Predicted-IMM load was significantly associated with increased immune cell infiltration (p<2e-16) including CD8+ T cells. Notably, only 0.16% of predicted immunogenic missense mutations were observed in >2 patients, with 61.7% of these derived from driver mutations. Our results provide a new method for neoantigen prediction with high performance characteristics and demonstrate its utility in large data sets across human cancers.\n\nSynopsisWe developed a new in silico predictor of Major Histocompatibility Complex (MHC) ligand binding and demonstrated its utility to assess potential neoantigens and immunogenic missense mutations (IMMs) in 6613 TCGA patients.

immunology↗