Biology subjects

Chinnaiyan, A.

Publications and source records attributed to Chinnaiyan, A..

5 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↗

bioRxiv2024

Deciphering the Transcription Factor Landscape in Neuroendocrine Prostate Cancer Progression: A Novel Approach to Understand NE Transdifferentiation

Background and ObjectiveProstate cancer (PCa) is a leading cause of cancer mortality in men, with neuroendocrine prostate cancer (NEPC) representing a particularly resistant subtype. The role of transcription factors (TFs) in the progression from prostatic adenocarcinoma (PRAD) to NEPC is poorly understood. This study aims to identify and analyze lineage-specific TF profiles in PRAD and NEPC and illustrate their dynamic shifts during NE transdifferentiation. MethodsA novel algorithmic approach was developed to evaluate the weighted expression of TFs within patient samples, enabling a nuanced understanding of TF landscapes in PCa progression and TF dynamic shifts during NE transdifferentiation. Resultsunveiled TF profiles for PRAD and NEPC, identifying 126 shared TFs, 46 adenocarcinoma-TFs, and 56 NEPC-TFs. Enrichment analysis across multiple clinical cohorts confirmed the lineage specificity and clinical relevance of these lineage-TFs signatures. Functional analysis revealed that lineage-TFs are implicated in pathways critical to cell development, differentiation, and lineage determination. Novel lineage-TF candidates were identified, offering potential targets for therapeutic intervention. Furthermore, our longitudinal study on NE transdifferentiation highlighted dynamic TF expression shifts and delineated a three-phase hypothesis for the process comprised of de-differentiation, dormancy, and re-differentiation. and proposing novel insights into the mechanisms of PCa progression. ConclusionThe lineage-specific TF profiles in PRAD and NEPC reveal a dynamic shift in the TF landscape during PCa progression, highlighting three distinct phases of NE transdifferentiation.

cancer biology↗

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↗

bioRxiv2021

Therapeutic reversal of prenatal pontine ID1 signaling in DIPG

Diffuse intrinsic pontine glioma (DIPG) is a highly aggressive brain tumor with rare survival beyond two years. This poor prognosis is largely due to the tumors highly infiltrative and invasive nature. Previous reports demonstrate upregulation of the transcription factor ID1 with H3K27M and ACVR1 mutations, but this has not been confirmed in human tumors or therapeutically targeted. We developed an in utero electroporation (IUE) murine H3K27M-driven tumor model, which demonstrates increased ID1 expression in H3K27M- and ACVR1-mutated tumor cells. In human tumors, elevated ID1 expression is associated with H3K27M/ACVR1-mutation, brainstem location, and reduced survival. The ID1 promoter demonstrates a similar active epigenetic state in H3K27M tumor cells and murine prenatal hindbrain cells. In the developing human brain, ID1 is expressed highest in oligo/astrocyte-precursor cells (OAPCs). These ID1+/SPARCL1+ cells share a transcriptional program with astrocyte-like (AC-like) DIPG cells, and demonstrate upregulation of gene sets involved with regulation of cell migration. Both genetic and pharmacologic [cannabidiol (CBD)] suppression of ID1 results in decreased DIPG cell invasion/migration in vitro and invasion/tumor growth in multiple in vivo models. CBD reduces proliferation through reactive oxygen species (ROS) production at low micromolar concentrations, which we found to be achievable in the murine brainstem. Further, pediatric high-grade glioma patients treated off-trial with CBD (n=15) demonstrate tumor ID1 reduction and improved overall survival compared to historical controls. Our study identifies that ID1 is upregulated in DIPG through reactivation of a developmental OAPC transcriptional state, and ID1-driven invasiveness of DIPG is therapeutically targetable with CBD. One Sentence SummaryThe transcription factor ID1 is upregulated in a subset of DIPG tumor cells, and ID1-driven invasiveness is therapeutically targetable with CBD.

cancer biology↗

bioRxiv2018

MechRNA: prediction of lncRNA mechanisms from RNA-RNA and RNA-protein interactions

MotivationLong non-coding RNAs (lncRNAs) are defined as transcripts longer than 200 nucleotides that do not get translated into proteins. Often these transcripts are processed (spliced, capped, polyadenylated) and some are known to have important biological functions. However, most lncRNAs have unknown or poorly understood functions. Nevertheless, because of their potential role in cancer, lncRNAs are receiving a lot of attention, and the need for computational tools to predict their possible mechanisms of action is more than ever. Fundamentally, most of the known lncRNA mechanisms involve RNA-RNA and/or RNA-protein interactions. Through accurate predictions of each kind of interaction and integration of these predictions, it is possible to elucidate potential mechanisms for a given lncRNA.\n\nApproachHere we introduce MechRNA, a pipeline for corroborating RNA-RNA interaction prediction and protein binding prediction for identifying possible lncRNA mechanisms involving specific targets or on a transcriptome-wide scale. The first stage uses a version of IntaRNA2 with added functionality for efficient prediction of RNA-RNA interactions with very long input sequences, allowing for large-scale analysis of lncRNA interactions with little or no loss of optimality. The second stage integrates protein binding information pre-computed by GraphProt, for both the lncRNA and the target. The final stage involves inferring the most likely mechanism for each lncRNA/target pair. This is achieved by generating candidate mechanisms from the predicted interactions, the relative locations of these interactions and correlation data, followed by selection of the most likely mechanistic explanation using a combined p-value.\n\nResultsWe applied MechRNA on a number of recently identified cancer-related lncRNAs (PCAT1, PCAT29, ARLnc1) and also on two well-studied lncRNAs (PCA3 and 7SL). This led to the identification of hundreds of high confidence potential targets for each lncRNA and corresponding mechanisms. These predictions include the known competitive mechanism of 7SL with HuR for binding on the tumor suppressor TP53, as well as mechanisms expanding what is known about PCAT1 and ARLn1 and their targets BRCA2 and AR, respectively. For PCAT1-BRCA2, the mechanism involves competitive binding with HuR, which we confirmed using HuR immunoprecipitation assays.\n\nAvailabilityMechRNA is available for download at https://bitbucket.org/compbio/mechrna\n\nContactbackofen@informatik.uni-freiburg.de, cenksahi@indiana.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗