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Szallasi Z, Diossy M, Tisza V, Li H, Sahgal P, Zhou J, Sztupinszki Z, Young D, Nuosome D, Kuo C, Jiang J, Chen Y, Ebner R, Sesterhenn I, Moncur J, Chesnut G, Petrovics G, T Klus G, Valcz G, Nuzzo P, Ribli D, Börcsök J, Prósz A, Krzystanek M, Ried T, Szüts D, Rizwan K, Kaochar S, Pathania S, D'Andrea A, Csabai I, Srivastava S, Freedman M, Dobi A, Spisak S. Increased frequency of CHD1 deletions in prostate cancers of African American men is associated with rapid disease progression without inducing homologous recombination deficiency. Res Sq 2024:rs.3.rs-3995251. [PMID: 38645014 PMCID: PMC11030533 DOI: 10.21203/rs.3.rs-3995251/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
We analyzed genomic data derived from the prostate cancer of African and European American men in order to identify differences that may contribute to racial disparity of outcome and that could also define novel therapeutic strategies. In addition to analyzing patient derived next generation sequencing data, we performed FISH based confirmatory studies of Chromodomain helicase DNA-binding protein 1 (CHD1) loss on prostate cancer tissue microarrays. We created CRISPR edited, CHD1 deficient prostate cancer cell lines for genomic, drug sensitivity and functional homologous recombination (HR) activity analysis. We found that subclonal deletion of CHD1 is nearly three times as frequent in prostate tumors of African American men than in men of European ancestry and it associates with rapid disease progression. We further showed that CHD1 deletion is not associated with homologous recombination deficiency associated mutational signatures in prostate cancer. In prostate cancer cell line models CHD1 deletion did not induce HR deficiency as detected by RAD51 foci formation assay or mutational signatures, which was consistent with the moderate increase of olaparib sensitivity. CHD1 deficient prostate cancer cells, however, showed higher sensitivity to talazoparib. CHD1 loss may contribute to worse outcome of prostate cancer in African American men. A deeper understanding of the interaction between CHD1 loss and PARP inhibitor sensitivity will be needed to determine the optimal use of targeted agents such as talazoparib in the context of castration resistant prostate cancer.
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Affiliation(s)
| | | | - Viktoria Tisza
- Institute of Enzymology, Research Centre for Natural Sciences
| | - Hua Li
- Center for Prostate Cancer Research
| | | | - Jia Zhou
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | | | - Denise Young
- Center for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD
| | - Darryl Nuosome
- Center for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD
| | - Claire Kuo
- Center for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD
| | - Jiji Jiang
- Center for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD
| | - Yongmei Chen
- Center for Prostate Disease Research, Murtha Cancer Center Research Program, Department Uniformed Services University of the Health Sciences, Bethesda, MD
| | | | | | - Joel Moncur
- Joint Pathology Center, Silver Spring, Maryland, USA
| | - Gregory Chesnut
- Center for Prostate Disease Research, Murtha Cancer Center Research Program, Department Uniformed Services University of the Health Sciences, Bethesda, MD
| | - Gyorgy Petrovics
- Computational Health Informatics Program, Boston Children's Hospital, USA, Harvard Medical School, Boston, USA
| | | | - Gábor Valcz
- ELKH Translational Extracellular Vesicle Research Group, Budapest, Hungary
| | - Pier Nuzzo
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Dezso Ribli
- Department of Physics of Complex Systems, Eotvos Lorand University, Budapest, Hungary
| | | | | | | | | | - Dávid Szüts
- HUN-REN Research Centre for Natural Sciences
| | - Kinza Rizwan
- Department of Medicine, Baylor College of Medicine, Houston, USA
| | - Salma Kaochar
- Department of Medicine, Baylor College of Medicine, Houston, USA
| | | | | | | | - Shib Srivastava
- Center for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD
| | - Matthew Freedman
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Albert Dobi
- Center for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD
| | - Sandor Spisak
- Institute of Enzymology, Research Centre for Natural Sciences, Eötvös Loránd Research Network
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Robert PA, Akbar R, Frank R, Pavlović M, Widrich M, Snapkov I, Slabodkin A, Chernigovskaya M, Scheffer L, Smorodina E, Rawat P, Mehta BB, Vu MH, Mathisen IF, Prósz A, Abram K, Olar A, Miho E, Haug DTT, Lund-Johansen F, Hochreiter S, Haff IH, Klambauer G, Sandve GK, Greiff V. Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for antibody specificity prediction. Nat Comput Sci 2022; 2:845-865. [PMID: 38177393 DOI: 10.1038/s43588-022-00372-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 11/09/2022] [Indexed: 01/06/2024]
Abstract
Machine learning (ML) is a key technology for accurate prediction of antibody-antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: the lack of a unified ML formalization of immunological antibody-specificity prediction problems and the unavailability of large-scale synthetic datasets to benchmark real-world relevant ML methods and dataset design. Here we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based three-dimensional antibody-antigen-binding structures with ground-truth access to conformational paratope, epitope and affinity. We formalized common immunological antibody-specificity prediction problems as ML tasks and confirmed that for both sequence- and structure-based tasks, accuracy-based rankings of ML methods trained on experimental data hold for ML methods trained on Absolut!-generated data. The Absolut! framework has the potential to enable real-world relevant development and benchmarking of ML strategies for biotherapeutics design.
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Affiliation(s)
- Philippe A Robert
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
| | - Rahmad Akbar
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Robert Frank
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | | | - Michael Widrich
- ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
| | - Igor Snapkov
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Andrei Slabodkin
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Maria Chernigovskaya
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | | | - Eva Smorodina
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Brij Bhushan Mehta
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Mai Ha Vu
- Department of Linguistics and Scandinavian Studies, University of Oslo, Oslo, Norway
| | | | - Aurél Prósz
- Danish Cancer Society Research Center, Translational Cancer Genomics, Copenhagen, Denmark
| | - Krzysztof Abram
- The Novo Nordisk Foundation Center for Biosustainability, Autoflow, DTU Biosustain and IT University of Copenhagen, Copenhagen, Denmark
| | - Alex Olar
- Department of Complex Systems in Physics, Eötvös Loránd University, Budapest, Hungary
| | - Enkelejda Miho
- Institute of Medical Engineering and Medical Informatics, School of Life Sciences, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
- aiNET GmbH, Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | | | - Sepp Hochreiter
- ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
- Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria
| | | | - Günter Klambauer
- ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
| | | | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
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