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Agrawal P, Jain N, Gopalan V, Timon A, Singh A, Rajagopal PS, Hannenhalli S. Network-based approach elucidates critical genes in BRCA subtypes and chemotherapy response in triple negative breast cancer. iScience 2024; 27:109752. [PMID: 38699227 PMCID: PMC11063905 DOI: 10.1016/j.isci.2024.109752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 03/18/2024] [Accepted: 04/12/2024] [Indexed: 05/05/2024] Open
Abstract
Breast cancers (BRCA) exhibit substantial transcriptional heterogeneity, posing a significant clinical challenge. The global transcriptional changes in a disease context, however, are likely mediated by few key genes which reflect disease etiology better than the differentially expressed genes (DEGs). We apply our network-based tool PathExt to 1,059 BRCA tumors across 4 subtypes to identify key mediator genes in each subtype. Compared to conventional differential expression analysis, PathExt-identified genes exhibit greater concordance across tumors, revealing shared and subtype-specific biological processes; better recapitulate BRCA-associated genes in multiple benchmarks, and are more essential in BRCA subtype-specific cell lines. Single-cell transcriptomic analysis reveals a subtype-specific distribution of PathExt-identified genes in multiple cell types from the tumor microenvironment. Application of PathExt to a TNBC chemotherapy response dataset identified subtype-specific key genes and biological processes associated with resistance. We described putative drugs that target key genes potentially mediating drug resistance.
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Affiliation(s)
- Piyush Agrawal
- Cancer Data Science Lab, National Cancer Institute, NIH, Bethesda, MD, USA
| | | | - Vishaka Gopalan
- Cancer Data Science Lab, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Annan Timon
- University of Pennsylvania, Philadelphia, PA, USA
| | - Arashdeep Singh
- Cancer Data Science Lab, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Padma S. Rajagopal
- Cancer Data Science Lab, National Cancer Institute, NIH, Bethesda, MD, USA
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2
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Rosenberger G, Li W, Turunen M, He J, Subramaniam PS, Pampou S, Griffin AT, Karan C, Kerwin P, Murray D, Honig B, Liu Y, Califano A. Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis. Nat Commun 2024; 15:3909. [PMID: 38724493 PMCID: PMC11082183 DOI: 10.1038/s41467-024-47957-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 04/16/2024] [Indexed: 05/12/2024] Open
Abstract
Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.
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Affiliation(s)
- George Rosenberger
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Wenxue Li
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA
| | - Mikko Turunen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Jing He
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Prem S Subramaniam
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Sergey Pampou
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Aaron T Griffin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Medical Scientist Training Program, Columbia University Irving Medical Center, New York, NY, USA
| | - Charles Karan
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Patrick Kerwin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Diana Murray
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Barry Honig
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Yansheng Liu
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA.
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA.
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA.
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
- Chan Zuckerberg Biohub New York, New York, NY, USA.
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Kazansky Y, Mueller HS, Cameron D, Demarest P, Zaffaroni N, Arrighetti N, Zuco V, Mundi PS, Kuwahara Y, Somwar R, Qu R, Califano A, de Stanchina E, Dela Cruz FS, Kung AL, Gounder MM, Kentsis A. Epigenetic targeting of PGBD5-dependent DNA damage in SMARCB1-deficient sarcomas. bioRxiv 2024:2024.05.03.592420. [PMID: 38766189 PMCID: PMC11100591 DOI: 10.1101/2024.05.03.592420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Despite the potential of targeted epigenetic therapies, most cancers do not respond to current epigenetic drugs. The Polycomb repressive complex EZH2 inhibitor tazemetostat was recently approved for the treatment of SMARCB1 -deficient epithelioid sarcomas, based on the functional antagonism between PRC2 and loss of SMARCB1. Through the analysis of tazemetostat-treated patient tumors, we recently defined key principles of their response and resistance to EZH2 epigenetic therapy. Here, using transcriptomic inference from SMARCB1 -deficient tumor cells, we nominate the DNA damage repair kinase ATR as a target for rational combination EZH2 epigenetic therapy. We show that EZH2 inhibition promotes DNA damage in epithelioid and rhabdoid tumor cells, at least in part via its induction of the transposase-derived PGBD5. We leverage this collateral synthetic lethal dependency to target PGBD5-dependent DNA damage by inhibition of ATR but not CHK1 using elimusertib. Consequently, combined EZH2 and ATR inhibition improves therapeutic responses in diverse patient-derived epithelioid and rhabdoid tumors in vivo . This advances a combination epigenetic therapy based on EZH2-PGBD5 synthetic lethal dependency suitable for immediate translation to clinical trials for patients.
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Stokes ME, Vasciaveo A, Small JC, Zask A, Reznik E, Smith N, Wang Q, Daniels J, Forouhar F, Rajbhandari P, Califano A, Stockwell BR. Subtype-selective prenylated isoflavonoids disrupt regulatory drivers of MYCN-amplified cancers. Cell Chem Biol 2024; 31:805-819.e9. [PMID: 38061356 PMCID: PMC11031350 DOI: 10.1016/j.chembiol.2023.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 07/18/2023] [Accepted: 11/13/2023] [Indexed: 01/05/2024]
Abstract
Transcription factors have proven difficult to target with small molecules because they lack pockets necessary for potent binding. Disruption of protein expression can suppress targets and enable therapeutic intervention. To this end, we developed a drug discovery workflow that incorporates cell-line-selective screening and high-throughput expression profiling followed by regulatory network analysis to identify compounds that suppress regulatory drivers of disease. Applying this approach to neuroblastoma (NBL), we screened bioactive molecules in cell lines representing its MYC-dependent (MYCNA) and mesenchymal (MES) subtypes to identify selective compounds, followed by PLATESeq profiling of treated cells. This revealed compounds that disrupt a sub-network of MYCNA-specific regulatory proteins, resulting in MYCN degradation in vivo. The top hit was isopomiferin, a prenylated isoflavonoid that inhibited casein kinase 2 (CK2) in cells. Isopomiferin and its structural analogs inhibited MYC and MYCN in NBL and lung cancer cells, highlighting the general MYC-inhibiting potential of this unique scaffold.
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Affiliation(s)
- Michael E Stokes
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA
| | - Alessandro Vasciaveo
- Department of Systems Biology, Columbia University Medical Center, New York City, NY 10032, USA
| | - Jonnell Candice Small
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA
| | - Arie Zask
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA
| | - Eduard Reznik
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA
| | - Nailah Smith
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA
| | - Qian Wang
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA
| | - Jacob Daniels
- Department of Pharmacology, Columbia University Medical Center, New York City, NY 10032, USA
| | - Farhad Forouhar
- Proteomics and Macromolecular Crystallography Shared Resource (PMCSR), Columbia University Medical Center, New York City, NY 10032, USA
| | - Presha Rajbhandari
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA
| | - Andrea Califano
- Department of Systems Biology, Columbia University Medical Center, New York City, NY 10032, USA.
| | - Brent R Stockwell
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA; Department of Chemistry, Columbia University, New York City, NY 10027, USA; Department of Pathology and Cell Biology and Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA.
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Fernández EC, Tomassoni L, Zhang X, Wang J, Obradovic A, Laise P, Griffin AT, Vlahos L, Minns HE, Morales DV, Simmons C, Gallitto M, Wei HJ, Martins TJ, Becker PS, Crawford JR, Tzaridis T, Wechsler-Reya RJ, Garvin J, Gartrell RD, Szalontay L, Zacharoulis S, Wu CC, Zhang Z, Califano A, Pavisic J. Elucidation and Pharmacologic Targeting of Master Regulator Dependencies in Coexisting Diffuse Midline Glioma Subpopulations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.17.585370. [PMID: 38559080 PMCID: PMC10979998 DOI: 10.1101/2024.03.17.585370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Diffuse Midline Gliomas (DMGs) are universally fatal, primarily pediatric malignancies affecting the midline structures of the central nervous system. Despite decades of clinical trials, treatment remains limited to palliative radiation therapy. A major challenge is the coexistence of molecularly distinct malignant cell states with potentially orthogonal drug sensitivities. To address this challenge, we leveraged established network-based methodologies to elucidate Master Regulator (MR) proteins representing mechanistic, non-oncogene dependencies of seven coexisting subpopulations identified by single-cell analysis-whose enrichment in essential genes was validated by pooled CRISPR/Cas9 screens. Perturbational profiles of 372 clinically relevant drugs helped identify those able to invert the activity of subpopulation-specific MRs for follow-up in vivo validation. While individual drugs predicted to target individual subpopulations-including avapritinib, larotrectinib, and ruxolitinib-produced only modest tumor growth reduction in orthotopic models, systemic co-administration induced significant survival extension, making this approach a valuable contribution to the rational design of combination therapy.
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Bhattacharyya S, Ehsan SF, Karacosta LG. Phenotypic maps for precision medicine: a promising systems biology tool for assessing therapy response and resistance at a personalized level. Front Netw Physiol 2023; 3:1256104. [PMID: 37964768 PMCID: PMC10642209 DOI: 10.3389/fnetp.2023.1256104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 09/28/2023] [Indexed: 11/16/2023]
Abstract
In this perspective we discuss how tumor heterogeneity and therapy resistance necessitate a focus on more personalized approaches, prompting a shift toward precision medicine. At the heart of the shift towards personalized medicine, omics-driven systems biology becomes a driving force as it leverages high-throughput technologies and novel bioinformatics tools. These enable the creation of systems-based maps, providing a comprehensive view of individual tumor's functional plasticity. We highlight the innovative PHENOSTAMP program, which leverages high-dimensional data to construct a visually intuitive and user-friendly map. This map was created to encapsulate complex transitional states in cancer cells, such as Epithelial-Mesenchymal Transition (EMT) and Mesenchymal-Epithelial Transition (MET), offering a visually intuitive way to understand disease progression and therapeutic responses at single-cell resolution in relation to EMT-related single-cell phenotypes. Most importantly, PHENOSTAMP functions as a reference map, which allows researchers and clinicians to assess one clinical specimen at a time in relation to their phenotypic heterogeneity, setting the foundation on constructing phenotypic maps for personalized medicine. This perspective argues that such dynamic predictive maps could also catalyze the development of personalized cancer treatment. They hold the potential to transform our understanding of cancer biology, providing a foundation for a future where therapy is tailored to each patient's unique molecular and cellular tumor profile. As our knowledge of cancer expands, these maps can be continually refined, ensuring they remain a valuable tool in precision oncology.
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Affiliation(s)
- Sayantan Bhattacharyya
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Shafqat F. Ehsan
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Loukia G. Karacosta
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States
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7
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Hu LZ, Douglass E, Turunen M, Pampou S, Grunn A, Realubit R, Antolin AA, Wang ALE, Li H, Subramaniam P, Karan C, Alvarez M, Califano A. Elucidating Compound Mechanism of Action and Polypharmacology with a Large-scale Perturbational Profile Compendium. bioRxiv 2023:2023.10.08.561457. [PMID: 37873470 PMCID: PMC10592689 DOI: 10.1101/2023.10.08.561457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
The Mechanism of Action (MoA) of a drug is generally represented as a small, non-tissue-specific repertoire of high-affinity binding targets. Yet, drug activity and polypharmacology are increasingly associated with a broad range of off-target and tissue-specific effector proteins. To address this challenge, we have implemented an efficient integrative experimental and computational framework leveraging the systematic generation and analysis of drug perturbational profiles representing >700 FDA-approved and experimental oncology drugs, in cell lines selected as high-fidelity models of 23 aggressive tumor subtypes. Protein activity-based analyses revealed highly reproducible, drug-mediated modulation of tissue-specific targets, leading to generation of a proteome-wide polypharmacology map, characterization of MoA-related drug clusters and off-target effects, and identification and experimental validation of novel, tissue-specific inhibitors of undruggable oncoproteins. The proposed framework, which is easily extended to elucidating the MoA of novel small-molecule libraries, could help support more systematic and quantitative approaches to precision oncology.
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8
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Failli M, Demir S, Del Río-Álvarez Á, Carrillo-Reixach J, Royo L, Domingo-Sàbat M, Childs M, Maibach R, Alaggio R, Czauderna P, Morland B, Branchereau S, Cairo S, Kappler R, Armengol C, di Bernardo D. Computational drug prediction in hepatoblastoma by integrating pan-cancer transcriptomics with pharmacological response. Hepatology 2023:01515467-990000000-00573. [PMID: 37729391 DOI: 10.1097/hep.0000000000000601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 08/11/2023] [Indexed: 09/22/2023]
Abstract
BACKGROUND AND AIMS Hepatoblastoma (HB) is the predominant form of pediatric liver cancer, though it remains exceptionally rare. While treatment outcomes for children with HB have improved, patients with advanced tumors face limited therapeutic choices. Additionally, survivors often suffer from long-term adverse effects due to treatment, including ototoxicity, cardiotoxicity, delayed growth, and secondary tumors. Consequently, there is a pressing need to identify new and effective therapeutic strategies for patients with HB. Computational methods to predict drug sensitivity from a tumor's transcriptome have been successfully applied for some common adult malignancies, but specific efforts in pediatric cancers are lacking because of the paucity of data. APPROACH AND RESULTS In this study, we used DrugSense to assess drug efficacy in patients with HB, particularly those with the aggressive C2 subtype associated with poor clinical outcomes. Our method relied on publicly available collections of pan-cancer transcriptional profiles and drug responses across 36 tumor types and 495 compounds. The drugs predicted to be most effective were experimentally validated using patient-derived xenograft models of HB grown in vitro and in vivo. We thus identified 2 cyclin-dependent kinase 9 inhibitors, alvocidib and dinaciclib as potent HB growth inhibitors for the high-risk C2 molecular subtype. We also found that in a cohort of 46 patients with HB, high cyclin-dependent kinase 9 tumor expression was significantly associated with poor prognosis. CONCLUSIONS Our work proves the usefulness of computational methods trained on pan-cancer data sets to reposition drugs in rare pediatric cancers such as HB, and to help clinicians in choosing the best treatment options for their patients.
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Affiliation(s)
- Mario Failli
- Telethon Institute of Genetics and Medicine, Pozzuoli, Naples, Italy
- Department of Chemical, Materials and Industrial Production Engineering, University of Naples "Federico II", Naples, Italy
| | - Salih Demir
- Department of Pediatric Surgery, Dr. von Hauner Children's Hospital, University Hospital, LMU Munich, Germany
| | - Álvaro Del Río-Álvarez
- Childhood Liver Oncology Group (c-LOG), Health Sciences Research Institute Germans Trias i Pujol (IGTP), Badalona, Catalonia, Spain
| | - Juan Carrillo-Reixach
- Childhood Liver Oncology Group (c-LOG), Health Sciences Research Institute Germans Trias i Pujol (IGTP), Badalona, Catalonia, Spain
- Nottingham Clinical Trials Unit, Nottingham, United Kingdom
| | - Laura Royo
- Childhood Liver Oncology Group (c-LOG), Health Sciences Research Institute Germans Trias i Pujol (IGTP), Badalona, Catalonia, Spain
| | - Montserrat Domingo-Sàbat
- Childhood Liver Oncology Group (c-LOG), Health Sciences Research Institute Germans Trias i Pujol (IGTP), Badalona, Catalonia, Spain
| | | | - Rudolf Maibach
- International Breast Cancer Study Group Coordinating Center, Bern, Switzerland
| | - Rita Alaggio
- Pathology Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Piotr Czauderna
- Department of Surgery and Urology for Children and Adolescents, Medical University of Gdansk, Gdansk, Poland
| | - Bruce Morland
- Department of Oncology, Birmingham Women's and Children's Hospital, Birmingham, United Kingdom
| | | | - Stefano Cairo
- XenTech, Evry, France
- Champions Oncology, Rockville, Maryland, USA
| | - Roland Kappler
- Department of Pediatric Surgery, Dr. von Hauner Children's Hospital, University Hospital, LMU Munich, Germany
| | - Carolina Armengol
- Childhood Liver Oncology Group (c-LOG), Health Sciences Research Institute Germans Trias i Pujol (IGTP), Badalona, Catalonia, Spain
- Liver and Digestive Diseases Networking Biomedical Research Centre (CIBEREHD), Madrid, Spain
| | - Diego di Bernardo
- Telethon Institute of Genetics and Medicine, Pozzuoli, Naples, Italy
- Department of Chemical, Materials and Industrial Production Engineering, University of Naples "Federico II", Naples, Italy
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Agrawal P, Jain N, Gopalan V, Timon A, Singh A, Rajagopal PS, Hannenhalli S. Network-based approach elucidates critical genes in BRCA subtypes and chemotherapy response in Triple Negative Breast Cancer. bioRxiv 2023:2023.05.21.541618. [PMID: 37425784 PMCID: PMC10327220 DOI: 10.1101/2023.05.21.541618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Breast cancers exhibit substantial transcriptional heterogeneity, posing a significant challenge to the prediction of treatment response and prognostication of outcomes. Especially, translation of TNBC subtypes to the clinic remains a work in progress, in part because of a lack of clear transcriptional signatures distinguishing the subtypes. Our recent network-based approach, PathExt, demonstrates that global transcriptional changes in a disease context are likely mediated by a small number of key genes, and these mediators may better reflect functional or translationally relevant heterogeneity. We apply PathExt to 1059 BRCA tumors and 112 healthy control samples across 4 subtypes to identify frequent, key-mediator genes in each BRCA subtype. Compared to conventional differential expression analysis, PathExt-identified genes (1) exhibit greater concordance across tumors, revealing shared as well as BRCA subtype-specific biological processes, (2) better recapitulate BRCA-associated genes in multiple benchmarks, and (3) exhibit greater dependency scores in BRCA subtype-specific cancer cell lines. Single cell transcriptomes of BRCA subtype tumors reveal a subtype-specific distribution of PathExt-identified genes in multiple cell types from the tumor microenvironment. Application of PathExt to a TNBC chemotherapy response dataset identified TNBC subtype-specific key genes and biological processes associated with resistance. We described putative drugs that target top novel genes potentially mediating drug resistance. Overall, PathExt applied to breast cancer refines previous views of gene expression heterogeneity and identifies potential mediators of TNBC subtypes, including potential therapeutic targets.
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Affiliation(s)
- Piyush Agrawal
- Cancer Data Science Lab, National Cancer Institute, NIH, Bethesda, MD, USA
| | | | - Vishaka Gopalan
- Cancer Data Science Lab, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Annan Timon
- University of Pennsylvania, Philadelphia, PA, USA
| | - Arashdeep Singh
- Cancer Data Science Lab, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Padma S Rajagopal
- Cancer Data Science Lab, National Cancer Institute, NIH, Bethesda, MD, USA
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Dong X, Ding L, Thrasher A, Wang X, Liu J, Pan Q, Rash J, Dhungana Y, Yang X, Risch I, Li Y, Yan L, Rusch M, McLeod C, Yan KK, Peng J, Chi H, Zhang J, Yu J. NetBID2 provides comprehensive hidden driver analysis. Nat Commun 2023; 14:2581. [PMID: 37142594 PMCID: PMC10160099 DOI: 10.1038/s41467-023-38335-6] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 04/26/2023] [Indexed: 05/06/2023] Open
Abstract
Many signaling and other genes known as "hidden" drivers may not be genetically or epigenetically altered or differentially expressed at the mRNA or protein levels, but, rather, drive a phenotype such as tumorigenesis via post-translational modification or other mechanisms. However, conventional approaches based on genomics or differential expression are limited in exposing such hidden drivers. Here, we present a comprehensive algorithm and toolkit NetBID2 (data-driven network-based Bayesian inference of drivers, version 2), which reverse-engineers context-specific interactomes and integrates network activity inferred from large-scale multi-omics data, empowering the identification of hidden drivers that could not be detected by traditional analyses. NetBID2 has substantially re-engineered the previous prototype version by providing versatile data visualization and sophisticated statistical analyses, which strongly facilitate researchers for result interpretation through end-to-end multi-omics data analysis. We demonstrate the power of NetBID2 using three hidden driver examples. We deploy NetBID2 Viewer, Runner, and Cloud apps with 145 context-specific gene regulatory and signaling networks across normal tissues and paediatric and adult cancers to facilitate end-to-end analysis, real-time interactive visualization and cloud-based data sharing. NetBID2 is freely available at https://jyyulab.github.io/NetBID .
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Affiliation(s)
- Xinran Dong
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Center for Molecular Medicine, Children's Hospital of Fudan University, Shanghai, 201102, P.R. China
| | - Liang Ding
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Andrew Thrasher
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Xinge Wang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, 60607, USA
| | - Jingjing Liu
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Qingfei Pan
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Jordan Rash
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Yogesh Dhungana
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Graduate School of Biomedical Sciences, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Xu Yang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Isabel Risch
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Yuxin Li
- Departments of Structural Biology and Developmental Neurobiology, Centre for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Lei Yan
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Michael Rusch
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Clay McLeod
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Koon-Kiu Yan
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Junmin Peng
- Departments of Structural Biology and Developmental Neurobiology, Centre for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Hongbo Chi
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Jinghui Zhang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Jiyang Yu
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA.
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