Biology subjects

Hosseini, N.

Publications and source records attributed to Hosseini, N..

7 recordsLinked to original sources

bioRxiv2026

Genomic Determinants of Lethality and Therapeutic Vulnerability in Castration-Resistant Prostate Cancer

Despite extensive characterization, the molecular determinants that shape disease progression and therapeutic outcomes in metastatic castration-resistant prostate cancer (mCRPC) remain incompletely defined. This large, multi-institutional analysis reveals that the genomic determinants of outcomes in primary prostate cancer are distinct from those in castration-resistant disease. We identify androgen-regulated MYC fusions as potent primary genetic drivers catalyzing early progression. Additionally, across independent mCRPC cohorts, we validate TP53, RB1, and CDKN1B alterations as the strongest genetic drivers of poor survival. Mechanistically, CDKN1B loss induces an AR-positive epithelial-mesenchymal transition rather than neuroendocrine differentiation, while TP53 loss triggers whole-genome duplication and subsequent adaptive copy-number gains. Within TP53-altered tumors, AR amplifications confer improved survival while AR mutations are protective regardless of TP53 status. Integrating these genomic events with transcriptomic phenotypes, we derive CAPrisk, a multi-omic prognostic classifier. CAPrisk stratifies patients with mCRPC into three risk groups with >33-month survival differences and predicts outcomes across androgen receptor pathway inhibitors, taxanes, PARP-inhibitors, and radium-223. Together, these findings expand the clinical utility of genomic profiling in advanced prostate cancer and establish a framework for risk stratification in lethal disease. IN BRIEF (eTOC BLURB)Rebernick et al. analyze a multi-institutional cohort of 1,331 prostate cancer samples and identify the genomic determinants of progression to lethal, castration-resistant disease. They define rare AR-regulated MYC fusions and haploinsufficient CDKN1B loss as drivers of poor outcome while highlighting AR alterations as predictors of improved prognosis. They develop CAPrisk, a multi-omic classifier that stratifies patient outcomes across androgen receptor pathway inhibitors, taxanes, PARP-inhibitors, and radium-223. HIGHLIGHTS- AR-regulated MYC fusions are rare ([~]2%) truncal events driving aggressive disease. - Distinct AR alterations drive favorable outcomes in TP53-altered and non-altered contexts. - Haploinsufficient CDKN1B loss predicts poor survival in AR-positive tumors. - A multi-omic model predicts outcomes across four standard-of-care therapies. MI-OncoSeq TeamChandan Kumar, Erica Rabban, Kayla Muschong, Lakshmi P. Kunju, Javed Siddiqui, Yu Ning, Rui Wang, Fengyun Su, Yelena Kleyman-Smith, Josh N. Vo, Jin Chen, Rahul Mannan Stand Up To Cancer TeamWassim Abida, Joanna Cyrta, Glenn Heller, Davide Prandi, Joshua Armenia, Ilsa Coleman, Matteo Benelli, Eliezer M. Van Allen, Andrea Sboner, Tarcisio Fedrizzi, Juan Miguel Mosquera, Brian D. Robinson, Navonil De Sarkar, Lakshmi P. Kunju, Scott Tomlins, Daniel Nava Rodrigues, Massimo Loda, Anuradha Gopalan, Victor E. Reuter, Colin C. Pritchard, Joaquin Mateo, Diletta Bianchini, Susana Miranda, Suzanne Carreira, Pasquale Rescigno, Julie Filipenko, Jacob Vinson, Robert B. Montgomery, Himisha Beltran, Elisabeth I. Heath, Howard I. Scher, Philip W. Kantoff, Mary-Ellen Taplin, Nikolaus Schultz, Johann S. deBono, Francesca Demichelis, Peter S. Nelson, Mark A. Rubin, Charles Sawyers

Cancer Biology↗

bioRxiv2026

A structure-aware framework for genomic variant interpretation in genetic skeletal disorders

BackgroundGenetic skeletal disorders (GSDs) comprise a heterogeneous group of rare, predominantly monogenic conditions that are increasingly diagnosed through high-throughput sequencing. While gene discovery has progressed rapidly, interpretation of pathogenic and uncertain variants remains a major bottleneck, in part because their functional consequences are determined at the protein structure level. However, a systematic assessment of structural knowledge across GSD-associated genes is currently lacking. Here, we present a comprehensive protein structure-centric analysis of 674 protein-coding genes implicated in GSDs. MethodsWe integrated experimental structures, AlphaFold2 (AF2) models, multimeric states, protein-protein interactions, and ClinVar variant annotations. ResultsWe quantify experimental structural availability and sequence coverage, revealing that 37% of GSD proteins lack any experimental structure and that, among proteins with structures, sequence coverage is often incomplete. We show that AF2 models provide high-confidence structural information for a substantial subset of proteins lacking experimental data, but that model reliability strongly correlates with existing structural coverage. Analysis of multimeric assemblies and co-occurring partners demonstrates that many GSD proteins function as obligate multimers, highlighting the importance of interface-level interpretation of variants. Finally, mapping clinically annotated missense variants onto representative protein structures illustrates how structural context can inform the interpretation of pathogenic and uncertain variants, particularly at interaction interfaces. ConclusionsTogether, this work provides a structure-aware reference framework for GSD genes, highlighting systematic gaps in current protein knowledge and demonstrating how integration of structural data can guide genomic variant interpretation. Our observations support a broader principle of structural equivalence, whereby distinct variants converge on shared structural perturbations that explain clustering patterns and enable mechanistic interpretation of nearby variants of uncertain significance.

genomics↗

bioRxiv2025

Proteasome Inhibition Enhances Lysosome-mediated Targeted Protein Degradation

Proteasome inhibitor drugs are currently used in the clinic to treat multiple myeloma and mantle cell lymphoma. These inhibitors cause accumulation of undegraded proteins, thus inducing proteotoxic stress and consequent cell death. However, cancer cells counteract this effect by activating an adaptive response through the transcription factor Nuclear factor erythroid 2-related factor 1 (NRF1, also known as NFE2L1). NRF1 induces transcriptional upregulation of proteasome and autophagy/lysosomal genes, thereby reducing proteotoxic stress and diminishing the effectiveness of proteasome inhibition. While suppressing this protective autophagy response is one potential strategy, here we investigated whether this heightened autophagy could instead be leveraged therapeutically. To this end, we designed an autophagy-targeting chimera (AUTAC) compound to selectively degrade the anti-apoptotic protein Mcl1 via the lysosome. Our results show that this lysosome-mediated targeted degradation is significantly amplified in the presence of proteasome inhibition, in a NRF1-dependent manner. Mechanistically, AUTAC-driven Mcl1 clearance requires K63-linked ubiquitination by UBC13 and TRAF6 and recognition by the cargo receptor p62/SQSTM1. The combination of the proteasome inhibitor carfilzomib and Mcl1 AUTAC synergistically promoted cell death in both in vitro models, including wild-type and proteasome inhibitor-resistant multiple myeloma and lung cancer cells, and in mouse tumor xenografts. Thus, our work offers a novel strategy for enhancing proteasome inhibitor efficacy by exploiting the adaptive autophagy response. More broadly, our study establishes a framework for amplifying lysosome-mediated targeted protein degradation, with potential applications in cancer therapeutics and beyond.

biochemistry↗

bioRxiv2024

Targeting of SUMOylation leads to cBAF complex stabilization and disruption of the SS18::SSX transcriptome in Synovial Sarcoma

Synovial Sarcoma (SS) is driven by the SS18::SSX fusion oncoprotein. and is ultimately refractory to therapeutic approaches. SS18::SSX alters ATP-dependent chromatin remodeling BAF (mammalian SWI/SNF) complexes, leading to the degradation of canonical (cBAF) complex and amplified presence of an SS18::SSX-containing non-canonical BAF (ncBAF or GBAF) that drives an SS-specific transcription program and tumorigenesis. We demonstrate that SS18::SSX activates the SUMOylation program and SSs are sensitive to the small molecule SAE1/2 inhibitor, TAK-981. Mechanistically, TAK-981 de-SUMOylates the cBAF subunit SMARCE1, stabilizing and restoring cBAF on chromatin, shifting away from SS18::SSX-ncBAF-driven transcription, associated with DNA damage and cell death and resulting in tumor inhibition across both human and mouse SS tumor models. TAK-981 synergized with cytotoxic chemotherapy through increased DNA damage, leading to tumor regression. Targeting the SUMOylation pathway in SS restores cBAF complexes and blocks the SS18::SSX-ncBAF transcriptome, identifying a therapeutic vulnerability in SS, positioning the in-clinic TAK-981 to treat SS.

cancer biology↗

bioRxiv2023

MHCnvex: Likelihood-based model for calling the copy number variations and loss of heterozygosity in MHC class I and II locus

MotivationAccurate detection of copy number variation (CNV) and loss of heterozygosity (LOH) in the major histocompatibility complex (MHC) locus is of great significance to both clinicians and researchers since it has the potential to inform treatment decisions, particularly in the context of immunotherapy. However, due to the high level of polymorphism in this region, calling copy number variations is a challenging task and requires special methodology. To address this challenge, we have developed a tool with a wide range of applicability to call CNV and LOH in the MHC region. ResultsTo address the challenge mentioned above, we have developed MHCnvex, an algorithm that accurately calls haplotype level CNVs for the genes in the MHC class I and II locus. MHCnvex presents a novel approach based on likelihood models for detecting CNVs at haplotype level. Additionally, this method integrates the MHC locus with other adjacent loci from the short arm of chromosome 6 to enhance the accuracy of the calls. The incorporation of a statistical approach and the examination of the broader chromosome 6 region, rather than just the MHC locus alone, make MHCnvex less vulnerable to local coverage biases (commonly associated with MHC locus). The performance of MHCnvex has been evaluated according to different measures including concordance with MHC flanking regions and changes in the allelic expression of MHC genes due to alteration. MHCnvex has also shown to significantly reduce the variability of calculated coverage for CNV analysis. AvailabilityImplementation of MHCnvex algorithm is available as an R package at: https://github.com/NoshadHo/MHCnvex

bioinformatics↗

bioRxiv2023

HLAProphet: Personalized allele-level quantification of the HLA proteins

Loss of HLA expression in tumor cells is a commonly observed phenotype that is known to be associated with T-cell evasion. Proteogenomic characterizations of the molecular mechanisms underpinning this loss of HLA expression are hindered by the polymorphic nature of the HLA proteins, with most individuals having germline HLA sequences that are highly divergent from the sequences found in standard reference databases. To address this issue, we have developed HLAProphet, an algorithm that utilizes HLA types from paired DNA sequencing data to provide personalized allele-level quantification of the HLA proteins from TMT mass spectrometry data. We show that HLAProphet triples the number of tryptic peptide identifications made by standard reference based approaches, and produces protein expression values that have high concordance with RNA expression and known loss of heterozygosity events.

bioinformatics↗

bioRxiv2023

Distinct mutational processes shape selection of MHC class I and class II mutations across primary and metastatic tumors

Disruption of antigen presentation via loss of MHC expression is a strategy whereby cancer cells escape immune surveillance and develop resistance to immunotherapy. We developed the personalized genomics algorithm Hapster and accurately called somatic mutations within the MHC genes of 10,001 primary and 2,199 metastatic tumors, creating a catalog of 1663 nonsynonymous mutations that provide key insights into MHC mutagenesis. We found that MHC-I genes are among the most frequently mutated genes in both primary and metastatic tumors, while MHC-II mutations are more restricted. Recurrent deleterious mutations are found within haplotype and cancer-type specific hotspots associated with distinct mutational processes. Functional classification of MHC residues revealed significant positive selection for mutations disruptive to the B2M, peptide, and T-cell binding interfaces, as well as MHC chaperones. At the cohort level, all cancers with positive selection for MHC mutations are responsive to immune checkpoint inhibitors, underscoring the translational relevance of our findings.

cancer biology↗