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Elemento O. How Artificial Intelligence Unravels the Complex Web of Cancer Drug Response. Cancer Res 2024; 84:1745-1746. [PMID: 38588311 DOI: 10.1158/0008-5472.can-24-1123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 04/05/2024] [Indexed: 04/10/2024]
Abstract
The intersection of precision medicine and artificial intelligence (AI) holds profound implications for cancer treatment, with the potential to significantly advance our understanding of drug responses based on the intricate architecture of tumor cells. A recent study by Park and colleagues titled "A Deep Learning Model of Tumor Cell Architecture Elucidates Response and Resistance to CDK4/6 Inhibitors" epitomizes this intersection by leveraging an interpretable deep learning model grounded in a comprehensive map of multiprotein assemblies in cancer, known as Nested Systems in Tumors. This study not only elucidates mechanisms underlying the response to CDK4/6 inhibitors in breast cancer therapy but also highlights the critical role of model interpretability leading to new mechanistic insights.
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Affiliation(s)
- Olivier Elemento
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York
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2
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Clark T, Mohan J, Schaffer L, Obernier K, Al Manir S, Churas CP, Dailamy A, Doctor Y, Forget A, Hansen JN, Hu M, Lenkiewicz J, Levinson MA, Marquez C, Nourreddine S, Niestroy J, Pratt D, Qian G, Thaker S, Bélisle-Pipon JC, Brandt C, Chen J, Ding Y, Fodeh S, Krogan N, Lundberg E, Mali P, Payne-Foster P, Ratcliffe S, Ravitsky V, Sali A, Schulz W, Ideker T. Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.21.589311. [PMID: 38826258 PMCID: PMC11142054 DOI: 10.1101/2024.05.21.589311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
This article describes the Cell Maps for Artificial Intelligence (CM4AI) project and its goals, methods, standards, current datasets, software tools , status, and future directions. CM4AI is the Functional Genomics Data Generation Project in the U.S. National Institute of Health's (NIH) Bridge2AI program. Its overarching mission is to produce ethical, AI-ready datasets of cell architecture, inferred from multimodal data collected for human cell lines, to enable transformative biomedical AI research.
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3
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Shahrouzi P, Forouz F, Mathelier A, Kristensen VN, Duijf PHG. Copy number alterations: a catastrophic orchestration of the breast cancer genome. Trends Mol Med 2024:S1471-4914(24)00120-5. [PMID: 38772764 DOI: 10.1016/j.molmed.2024.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 04/12/2024] [Accepted: 04/26/2024] [Indexed: 05/23/2024]
Abstract
Breast cancer (BCa) is a prevalent malignancy that predominantly affects women around the world. Somatic copy number alterations (CNAs) are tumor-specific amplifications or deletions of DNA segments that often drive BCa development and therapy resistance. Hence, the complex patterns of CNAs complement BCa classification systems. In addition, understanding the precise contributions of CNAs is essential for tailoring personalized treatment approaches. This review highlights how tumor evolution drives the acquisition of CNAs, which in turn shape the genomic landscapes of BCas. It also discusses advanced methodologies for identifying recurrent CNAs, studying CNAs in BCa and their clinical impact.
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Affiliation(s)
- Parastoo Shahrouzi
- Department of Medical Genetics, Institute of Basic Medical Science, Faculty of Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway.
| | - Farzaneh Forouz
- School of Pharmacy, University of Queensland, Woolloongabba, Brisbane, Australia
| | - Anthony Mathelier
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0318 Oslo, Norway; Center for Bioinformatics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway; Department of Medical Genetics, Institute of Clinical Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Vessela N Kristensen
- Department of Medical Genetics, Institute of Clinical Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway; Division of Medicine, Department of Clinical Molecular Biology and Laboratory Science (EpiGen), Akershus University Hospital, Lørenskog, Norway; Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Pascal H G Duijf
- Department of Medical Genetics, Institute of Clinical Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway; Centre for Cancer Biology, UniSA Clinical and Health Sciences, University of South Australia and SA Pathology, Adelaide, Australia.
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4
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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] [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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5
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Kenchappa R, Radnai L, Young EJ, Zarco N, Lin L, Dovas A, Meyer CT, Haddock A, Hall A, Canoll P, Cameron MD, Nagaiah NK, Rumbaugh G, Griffin PR, Kamenecka TM, Miller CA, Rosenfeld SS. MT-125 Inhibits Non-Muscle Myosin IIA and IIB, Synergizes with Oncogenic Kinase Inhibitors, and Prolongs Survival in Glioblastoma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.27.591399. [PMID: 38746089 PMCID: PMC11092436 DOI: 10.1101/2024.04.27.591399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
We have identified a NMIIA and IIB-specific small molecule inhibitor, MT-125, and have studied its effects in GBM. MT-125 has high brain penetrance and retention and an excellent safety profile; blocks GBM invasion and cytokinesis, consistent with the known roles of NMII; and prolongs survival as a single agent in murine GBM models. MT-125 increases signaling along both the PDGFR- and MAPK-driven pathways through a mechanism that involves the upregulation of reactive oxygen species, and it synergizes with FDA-approved PDGFR and mTOR inhibitors in vitro . Combining MT-125 with sunitinib, a PDGFR inhibitor, or paxalisib, a combined PI3 Kinase/mTOR inhibitor significantly improves survival in orthotopic GBM models over either drug alone, and in the case of sunitinib, markedly prolongs survival in ∼40% of mice. Our results provide a powerful rationale for developing NMII targeting strategies to treat cancer and demonstrate that MT-125 has strong clinical potential for the treatment of GBM. Highlights MT-125 is a highly specific small molecule inhibitor of non-muscle myosin IIA and IIB, is well-tolerated, and achieves therapeutic concentrations in the brain with systemic dosing.Treating preclinical models of glioblastoma with MT-125 produces durable improvements in survival.MT-125 stimulates PDGFR- and MAPK-driven signaling in glioblastoma and increases dependency on these pathways.Combining MT-125 with an FDA-approved PDGFR inhibitor in a mouse GBM model synergizes to improve median survival over either drug alone, and produces tumor free, prolonged survival in over 40% of mice.
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Hu M, Alkhairy S, Lee I, Pillich RT, Fong D, Smith K, Bachelder R, Ideker T, Pratt D. Evaluation of large language models for discovery of gene set function. ARXIV 2024:arXiv:2309.04019v2. [PMID: 37731657 PMCID: PMC10508824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Gene set analysis is a mainstay of functional genomics, but it relies on curated databases of gene functions that are incomplete. Here we evaluate five Large Language Models (LLMs) for their ability to discover the common biological functions represented by a gene set, substantiated by supporting rationale, citations and a confidence assessment. Benchmarking against canonical gene sets from the Gene Ontology, GPT-4 confidently recovered the curated name or a more general concept (73% of cases), while benchmarking against random gene sets correctly yielded zero confidence. Gemini-Pro and Mixtral-Instruct showed ability in naming but were falsely confident for random sets, whereas Llama2-70b had poor performance overall. In gene sets derived from 'omics data, GPT-4 identified novel functions not reported by classical functional enrichment (32% of cases), which independent review indicated were largely verifiable and not hallucinations. The ability to rapidly synthesize common gene functions positions LLMs as valuable 'omics assistants.
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Affiliation(s)
- Mengzhou Hu
- Department of Medicine, University of California San Diego, La Jolla, California, USA
| | - Sahar Alkhairy
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
| | - Ingoo Lee
- Department of Medicine, University of California San Diego, La Jolla, California, USA
| | - Rudolf T. Pillich
- Department of Medicine, University of California San Diego, La Jolla, California, USA
| | - Dylan Fong
- Department of Medicine, University of California San Diego, La Jolla, California, USA
| | - Kevin Smith
- Department of Physics, University of California San Diego, La Jolla, California, USA
| | - Robin Bachelder
- Department of Medicine, University of California San Diego, La Jolla, California, USA
| | - Trey Ideker
- Department of Medicine, University of California San Diego, La Jolla, California, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
| | - Dexter Pratt
- Department of Medicine, University of California San Diego, La Jolla, California, USA
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Park S, Silva E, Singhal A, Kelly MR, Licon K, Panagiotou I, Fogg C, Fong S, Lee JJY, Zhao X, Bachelder R, Parker BA, Yeung KT, Ideker T. A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors. NATURE CANCER 2024:10.1038/s43018-024-00740-1. [PMID: 38443662 DOI: 10.1038/s43018-024-00740-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 02/07/2024] [Indexed: 03/07/2024]
Abstract
Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6is) have revolutionized breast cancer therapy. However, <50% of patients have an objective response, and nearly all patients develop resistance during therapy. To elucidate the underlying mechanisms, we constructed an interpretable deep learning model of the response to palbociclib, a CDK4/6i, based on a reference map of multiprotein assemblies in cancer. The model identifies eight core assemblies that integrate rare and common alterations across 90 genes to stratify palbociclib-sensitive versus palbociclib-resistant cell lines. Predictions translate to patients and patient-derived xenografts, whereas single-gene biomarkers do not. Most predictive assemblies can be shown by CRISPR-Cas9 genetic disruption to regulate the CDK4/6i response. Validated assemblies relate to cell-cycle control, growth factor signaling and a histone regulatory complex that we show promotes S-phase entry through the activation of the histone modifiers KAT6A and TBL1XR1 and the transcription factor RUNX1. This study enables an integrated assessment of how a tumor's genetic profile modulates CDK4/6i resistance.
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Affiliation(s)
- Sungjoon Park
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Erica Silva
- Program in Biomedical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Akshat Singhal
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA
| | - Marcus R Kelly
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
- Moores Cancer Center, University of California, San Diego, San Diego, CA, USA
| | - Kate Licon
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Isabella Panagiotou
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Catalina Fogg
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Samson Fong
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - John J Y Lee
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Xiaoyu Zhao
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Robin Bachelder
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Barbara A Parker
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
- Moores Cancer Center, University of California, San Diego, San Diego, CA, USA
| | - Kay T Yeung
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
- Moores Cancer Center, University of California, San Diego, San Diego, CA, USA
| | - Trey Ideker
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA.
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA.
- Moores Cancer Center, University of California, San Diego, San Diego, CA, USA.
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
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Zhao X, Singhal A, Park S, Kong J, Bachelder R, Ideker T. Cancer Mutations Converge on a Collection of Protein Assemblies to Predict Resistance to Replication Stress. Cancer Discov 2024; 14:508-523. [PMID: 38236062 PMCID: PMC10905674 DOI: 10.1158/2159-8290.cd-23-0641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 10/25/2023] [Accepted: 12/21/2023] [Indexed: 01/19/2024]
Abstract
Rapid proliferation is a hallmark of cancer associated with sensitivity to therapeutics that cause DNA replication stress (RS). Many tumors exhibit drug resistance, however, via molecular pathways that are incompletely understood. Here, we develop an ensemble of predictive models that elucidate how cancer mutations impact the response to common RS-inducing (RSi) agents. The models implement recent advances in deep learning to facilitate multidrug prediction and mechanistic interpretation. Initial studies in tumor cells identify 41 molecular assemblies that integrate alterations in hundreds of genes for accurate drug response prediction. These cover roles in transcription, repair, cell-cycle checkpoints, and growth signaling, of which 30 are shown by loss-of-function genetic screens to regulate drug sensitivity or replication restart. The model translates to cisplatin-treated cervical cancer patients, highlighting an RTK-JAK-STAT assembly governing resistance. This study defines a compendium of mechanisms by which mutations affect therapeutic responses, with implications for precision medicine. SIGNIFICANCE Zhao and colleagues use recent advances in machine learning to study the effects of tumor mutations on the response to common therapeutics that cause RS. The resulting predictive models integrate numerous genetic alterations distributed across a constellation of molecular assemblies, facilitating a quantitative and interpretable assessment of drug response. This article is featured in Selected Articles from This Issue, p. 384.
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Affiliation(s)
- Xiaoyu Zhao
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California, San Diego, La Jolla, California
| | - Akshat Singhal
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California
| | - Sungjoon Park
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California, San Diego, La Jolla, California
| | - JungHo Kong
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California, San Diego, La Jolla, California
- Moores Cancer Center, School of Medicine, University of California, San Diego, La Jolla, California
| | - Robin Bachelder
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California, San Diego, La Jolla, California
| | - Trey Ideker
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California, San Diego, La Jolla, California
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California
- Moores Cancer Center, School of Medicine, University of California, San Diego, La Jolla, California
- Department of Bioengineering, University of California, San Diego, La Jolla, California
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9
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Chen HM, Liu JX, Liu D, Hao GF, Yang GF. Human-virus protein-protein interactions maps assist in revealing the pathogenesis of viral infection. Rev Med Virol 2024; 34:e2517. [PMID: 38282401 DOI: 10.1002/rmv.2517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 09/12/2023] [Accepted: 01/16/2024] [Indexed: 01/30/2024]
Abstract
Many significant viral infections have been recorded in human history, which have caused enormous negative impacts worldwide. Human-virus protein-protein interactions (PPIs) mediate viral infection and immune processes in the host. The identification, quantification, localization, and construction of human-virus PPIs maps are critical prerequisites for understanding the biophysical basis of the viral invasion process and characterising the framework for all protein functions. With the technological revolution and the introduction of artificial intelligence, the human-virus PPIs maps have been expanded rapidly in the past decade and shed light on solving complicated biomedical problems. However, there is still a lack of prospective insight into the field. In this work, we comprehensively review and compare the effectiveness, potential, and limitations of diverse approaches for constructing large-scale PPIs maps in human-virus, including experimental methods based on biophysics and biochemistry, databases of human-virus PPIs, computational methods based on artificial intelligence, and tools for visualising PPIs maps. The work aims to provide a toolbox for researchers, hoping to better assist in deciphering the relationship between humans and viruses.
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Affiliation(s)
- Hui-Min Chen
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
| | - Jia-Xin Liu
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
| | - Di Liu
- CAS Key Laboratory of Special Pathogens and Biosafety, Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy of Sciences, Wuhan, China
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang, China
| | - Guang-Fu Yang
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
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10
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Baron M, Tagore M, Wall P, Zheng F, Barkley D, Yanai I, Yang J, Kiuru M, White RM, Ideker T. Desmosome mutations impact the tumor microenvironment to promote melanoma proliferation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.19.558457. [PMID: 37786690 PMCID: PMC10541613 DOI: 10.1101/2023.09.19.558457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Desmosomes are transmembrane protein complexes that contribute to cell-cell adhesion in epithelia and other tissues. Here, we report the discovery of frequent genetic alterations in the desmosome in human cancers, with the strongest signal seen in cutaneous melanoma where desmosomes are mutated in over 70% of cases. In primary but not metastatic melanoma biopsies, the burden of coding mutations on desmosome genes associates with a strong reduction in desmosome gene expression. Analysis by spatial transcriptomics suggests that these expression decreases occur in keratinocytes in the microenvironment rather than in primary melanoma tumor cells. In further support of a microenvironmental origin, we find that loss-of-function knockdowns of the desmosome in keratinocytes yield markedly increased proliferation of adjacent melanocytes in keratinocyte/melanocyte co-cultures. Thus, gradual accumulation of desmosome mutations in neighboring cells may prime melanocytes for neoplastic transformation.
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Affiliation(s)
- Maayan Baron
- Department of Medicine, University of California San Diego, La Jolla, CA USA
| | - Mohita Tagore
- Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Patrick Wall
- Department of Medicine, University of California San Diego, La Jolla, CA USA
| | - Fan Zheng
- Department of Medicine, University of California San Diego, La Jolla, CA USA
| | - Dalia Barkley
- Institute for Computational Medicine, NYU School of Medicine, New York, NY USA
| | - Itai Yanai
- Institute for Computational Medicine, NYU School of Medicine, New York, NY USA
| | - Jing Yang
- Department of Pharmacology, University of California San Diego, La Jolla, CA USA
| | - Maija Kiuru
- Depts. of Dermatology and Pathology, University of California Davis, Sacramento, CA USA
| | - Richard M White
- Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Trey Ideker
- Department of Medicine, University of California San Diego, La Jolla, CA USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA USA
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11
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Hu M, Alkhairy S, Lee I, Pillich RT, Bachelder R, Ideker T, Pratt D. Evaluation of large language models for discovery of gene set function. RESEARCH SQUARE 2023:rs.3.rs-3270331. [PMID: 37790547 PMCID: PMC10543283 DOI: 10.21203/rs.3.rs-3270331/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Gene set analysis is a mainstay of functional genomics, but it relies on manually curated databases of gene functions that are incomplete and unaware of biological context. Here we evaluate the ability of OpenAI's GPT-4, a Large Language Model (LLM), to develop hypotheses about common gene functions from its embedded biomedical knowledge. We created a GPT-4 pipeline to label gene sets with names that summarize their consensus functions, substantiated by analysis text and citations. Benchmarking against named gene sets in the Gene Ontology, GPT-4 generated very similar names in 50% of cases, while in most remaining cases it recovered the name of a more general concept. In gene sets discovered in 'omics data, GPT-4 names were more informative than gene set enrichment, with supporting statements and citations that largely verified in human review. The ability to rapidly synthesize common gene functions positions LLMs as valuable functional genomics assistants.
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Affiliation(s)
- Mengzhou Hu
- Department of Medicine, University of California San Diego, La Jolla, California, USA
| | - Sahar Alkhairy
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
| | - Ingoo Lee
- Department of Medicine, University of California San Diego, La Jolla, California, USA
| | - Rudolf T. Pillich
- Department of Medicine, University of California San Diego, La Jolla, California, USA
| | - Robin Bachelder
- Department of Medicine, University of California San Diego, La Jolla, California, USA
| | - Trey Ideker
- Department of Medicine, University of California San Diego, La Jolla, California, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
| | - Dexter Pratt
- Department of Medicine, University of California San Diego, La Jolla, California, USA
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12
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Li Y, Porta-Pardo E, Tokheim C, Bailey MH, Yaron TM, Stathias V, Geffen Y, Imbach KJ, Cao S, Anand S, Akiyama Y, Liu W, Wyczalkowski MA, Song Y, Storrs EP, Wendl MC, Zhang W, Sibai M, Ruiz-Serra V, Liang WW, Terekhanova NV, Rodrigues FM, Clauser KR, Heiman DI, Zhang Q, Aguet F, Calinawan AP, Dhanasekaran SM, Birger C, Satpathy S, Zhou DC, Wang LB, Baral J, Johnson JL, Huntsman EM, Pugliese P, Colaprico A, Iavarone A, Chheda MG, Ricketts CJ, Fenyö D, Payne SH, Rodriguez H, Robles AI, Gillette MA, Kumar-Sinha C, Lazar AJ, Cantley LC, Getz G, Ding L. Pan-cancer proteogenomics connects oncogenic drivers to functional states. Cell 2023; 186:3921-3944.e25. [PMID: 37582357 DOI: 10.1016/j.cell.2023.07.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 12/30/2022] [Accepted: 07/10/2023] [Indexed: 08/17/2023]
Abstract
Cancer driver events refer to key genetic aberrations that drive oncogenesis; however, their exact molecular mechanisms remain insufficiently understood. Here, our multi-omics pan-cancer analysis uncovers insights into the impacts of cancer drivers by identifying their significant cis-effects and distal trans-effects quantified at the RNA, protein, and phosphoprotein levels. Salient observations include the association of point mutations and copy-number alterations with the rewiring of protein interaction networks, and notably, most cancer genes converge toward similar molecular states denoted by sequence-based kinase activity profiles. A correlation between predicted neoantigen burden and measured T cell infiltration suggests potential vulnerabilities for immunotherapies. Patterns of cancer hallmarks vary by polygenic protein abundance ranging from uniform to heterogeneous. Overall, our work demonstrates the value of comprehensive proteogenomics in understanding the functional states of oncogenic drivers and their links to cancer development, surpassing the limitations of studying individual cancer types.
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Affiliation(s)
- Yize Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Eduard Porta-Pardo
- Josep Carreras Leukaemia Research Institute (IJC), Badalona 08916, Spain; Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain
| | - Collin Tokheim
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Matthew H Bailey
- Department of Biology and Simmons Center for Cancer Research, Brigham Young University, Provo, UT 84602, USA
| | - Tomer M Yaron
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA; Englander Institute for Precision Medicine, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Vasileios Stathias
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Yifat Geffen
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Cancer Center and Department of Pathology, Massachusetts General Hospital, Boston, MA 02115, USA
| | - Kathleen J Imbach
- Josep Carreras Leukaemia Research Institute (IJC), Badalona 08916, Spain; Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain
| | - Song Cao
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Shankara Anand
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Yo Akiyama
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Wenke Liu
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Matthew A Wyczalkowski
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Yizhe Song
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Erik P Storrs
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Michael C Wendl
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA; Department of Genetics, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Mathematics, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Wubing Zhang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Mustafa Sibai
- Josep Carreras Leukaemia Research Institute (IJC), Badalona 08916, Spain; Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain
| | - Victoria Ruiz-Serra
- Josep Carreras Leukaemia Research Institute (IJC), Badalona 08916, Spain; Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain
| | - Wen-Wei Liang
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Nadezhda V Terekhanova
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Fernanda Martins Rodrigues
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Karl R Clauser
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - David I Heiman
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Qing Zhang
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Francois Aguet
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Anna P Calinawan
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Saravana M Dhanasekaran
- Michigan Center for Translational Pathology, Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Chet Birger
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Shankha Satpathy
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Daniel Cui Zhou
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Liang-Bo Wang
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Jessika Baral
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Jared L Johnson
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Emily M Huntsman
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Pietro Pugliese
- Department of Science and Technology, University of Sannio, 82100 Benevento, Italy
| | - Antonio Colaprico
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Antonio Iavarone
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Department of Neurological Surgery, Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Milan G Chheda
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Neurology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Christopher J Ricketts
- Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - David Fenyö
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Samuel H Payne
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Michael A Gillette
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Chandan Kumar-Sinha
- Michigan Center for Translational Pathology, Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alexander J Lazar
- Departments of Pathology & Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Lewis C Cantley
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA.
| | - Gad Getz
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Cancer Center and Department of Pathology, Massachusetts General Hospital, Boston, MA 02115, USA; Harvard Medical School, Boston, MA 02115, USA.
| | - Li Ding
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA; Department of Genetics, Washington University in St. Louis, St. Louis, MO 63130, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA.
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13
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Kratz A, Kim M, Kelly MR, Zheng F, Koczor CA, Li J, Ono K, Qin Y, Churas C, Chen J, Pillich RT, Park J, Modak M, Collier R, Licon K, Pratt D, Sobol RW, Krogan NJ, Ideker T. A multi-scale map of protein assemblies in the DNA damage response. Cell Syst 2023; 14:447-463.e8. [PMID: 37220749 PMCID: PMC10330685 DOI: 10.1016/j.cels.2023.04.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 01/30/2023] [Accepted: 04/25/2023] [Indexed: 05/25/2023]
Abstract
The DNA damage response (DDR) ensures error-free DNA replication and transcription and is disrupted in numerous diseases. An ongoing challenge is to determine the proteins orchestrating DDR and their organization into complexes, including constitutive interactions and those responding to genomic insult. Here, we use multi-conditional network analysis to systematically map DDR assemblies at multiple scales. Affinity purifications of 21 DDR proteins, with/without genotoxin exposure, are combined with multi-omics data to reveal a hierarchical organization of 605 proteins into 109 assemblies. The map captures canonical repair mechanisms and proposes new DDR-associated proteins extending to stress, transport, and chromatin functions. We find that protein assemblies closely align with genetic dependencies in processing specific genotoxins and that proteins in multiple assemblies typically act in multiple genotoxin responses. Follow-up by DDR functional readouts newly implicates 12 assembly members in double-strand-break repair. The DNA damage response assemblies map is available for interactive visualization and query (ccmi.org/ddram/).
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Affiliation(s)
- Anton Kratz
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA; The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Minkyu Kim
- University of California San Francisco, Department of Cellular and Molecular Pharmacology, San Francisco, CA 94158, USA; The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA 94158, USA; Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA; The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA; University of Texas Health Science Center San Antonio, Department of Biochemistry and Structural Biology, San Antonio, TX 78229, USA
| | - Marcus R Kelly
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA
| | - Fan Zheng
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA; The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Christopher A Koczor
- University of South Alabama, Department of Pharmacology and Mitchell Cancer Institute, Mobile, AL 36604, USA
| | - Jianfeng Li
- University of South Alabama, Department of Pharmacology and Mitchell Cancer Institute, Mobile, AL 36604, USA
| | - Keiichiro Ono
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA
| | - Yue Qin
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA
| | - Christopher Churas
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA
| | - Jing Chen
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA
| | - Rudolf T Pillich
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA
| | - Jisoo Park
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA; The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Maya Modak
- University of California San Francisco, Department of Cellular and Molecular Pharmacology, San Francisco, CA 94158, USA; The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA 94158, USA; Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA; The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Rachel Collier
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA
| | - Kate Licon
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA
| | - Dexter Pratt
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA
| | - Robert W Sobol
- University of South Alabama, Department of Pharmacology and Mitchell Cancer Institute, Mobile, AL 36604, USA; Brown University, Department of Pathology and Laboratory Medicine and Legorreta Cancer Center, Providence, RI 02903, USA.
| | - Nevan J Krogan
- University of California San Francisco, Department of Cellular and Molecular Pharmacology, San Francisco, CA 94158, USA; The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA 94158, USA; Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA; The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.
| | - Trey Ideker
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA; The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.
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14
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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 : THE PREPRINT SERVER FOR BIOLOGY 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] [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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15
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Sharon M, Gruber G, Argov CM, Volozhinsky M, Yeger-Lotem E. ProAct: quantifying the differential activity of biological processes in tissues, cells, and user-defined contexts. Nucleic Acids Res 2023:7173756. [PMID: 37207335 DOI: 10.1093/nar/gkad421] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/25/2023] [Accepted: 05/08/2023] [Indexed: 05/21/2023] Open
Abstract
The distinct functions and phenotypes of human tissues and cells derive from the activity of biological processes that varies in a context-dependent manner. Here, we present the Process Activity (ProAct) webserver that estimates the preferential activity of biological processes in tissues, cells, and other contexts. Users can upload a differential gene expression matrix measured across contexts or cells, or use a built-in matrix of differential gene expression in 34 human tissues. Per context, ProAct associates gene ontology (GO) biological processes with estimated preferential activity scores, which are inferred from the input matrix. ProAct visualizes these scores across processes, contexts, and process-associated genes. ProAct also offers potential cell-type annotations for cell subsets, by inferring them from the preferential activity of 2001 cell-type-specific processes. Thus, ProAct output can highlight the distinct functions of tissues and cell types in various contexts, and can enhance cell-type annotation efforts. The ProAct webserver is available at https://netbio.bgu.ac.il/ProAct/.
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Affiliation(s)
- Moran Sharon
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, POB 653 Beer-Sheva 8410501, Israel
| | - Gil Gruber
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, POB 653 Beer-Sheva 8410501, Israel
| | - Chanan M Argov
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, POB 653 Beer-Sheva 8410501, Israel
| | - Miri Volozhinsky
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, POB 653 Beer-Sheva 8410501, Israel
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, POB 653 Beer-Sheva 8410501, Israel
- The National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, POB 653 Beer-Sheva 8410501, Israel
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16
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Petri BJ, Klinge CM. m6A readers, writers, erasers, and the m6A epitranscriptome in breast cancer. J Mol Endocrinol 2023; 70:JME-22-0110. [PMID: 36367225 PMCID: PMC9790079 DOI: 10.1530/jme-22-0110] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 11/11/2022] [Indexed: 11/13/2022]
Abstract
Epitranscriptomic modification of RNA regulates human development, health, and disease. The true diversity of the transcriptome in breast cancer including chemical modification of transcribed RNA (epitranscriptomics) is not well understood due to limitations of technology and bioinformatic analysis. N-6-methyladenosine (m6A) is the most abundant epitranscriptomic modification of mRNA and regulates splicing, stability, translation, and intracellular localization of transcripts depending on m6A association with reader RNA-binding proteins. m6A methylation is catalyzed by the METTL3 complex and removed by specific m6A demethylase ALKBH5, with the role of FTO as an 'eraser' uncertain. In this review, we provide an overview of epitranscriptomics related to mRNA and focus on m6A in mRNA and its detection. We summarize current knowledge on altered levels of writers, readers, and erasers of m6A and their roles in breast cancer and their association with prognosis. We summarize studies identifying m6A peaks and sites in genes in breast cancer cells.
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Affiliation(s)
- Belinda J. Petri
- Department of Biochemistry & Molecular Genetics, University of Louisville School of Medicine; Louisville, KY 40292 USA
| | - Carolyn M. Klinge
- Department of Biochemistry & Molecular Genetics, University of Louisville School of Medicine; Louisville, KY 40292 USA
- University of Louisville Center for Integrative Environmental Health Sciences (CIEHS)
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17
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Yang H, Gan L, Chen R, Li D, Zhang J, Wang Z. From multi-omics data to the cancer druggable gene discovery: a novel machine learning-based approach. Brief Bioinform 2023; 24:6896032. [PMID: 36515158 DOI: 10.1093/bib/bbac528] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 08/31/2022] [Accepted: 11/07/2022] [Indexed: 12/15/2022] Open
Abstract
The development of targeted drugs allows precision medicine in cancer treatment and optimal targeted therapies. Accurate identification of cancer druggable genes helps strengthen the understanding of targeted cancer therapy and promotes precise cancer treatment. However, rare cancer-druggable genes have been found due to the multi-omics data's diversity and complexity. This study proposes deep forest for cancer druggable genes discovery (DF-CAGE), a novel machine learning-based method for cancer-druggable gene discovery. DF-CAGE integrated the somatic mutations, copy number variants, DNA methylation and RNA-Seq data across ˜10 000 TCGA profiles to identify the landscape of the cancer-druggable genes. We found that DF-CAGE discovers the commonalities of currently known cancer-druggable genes from the perspective of multi-omics data and achieved excellent performance on OncoKB, Target and Drugbank data sets. Among the ˜20 000 protein-coding genes, DF-CAGE pinpointed 465 potential cancer-druggable genes. We found that the candidate cancer druggable genes (CDG) are clinically meaningful and divided the CDG into known, reliable and potential gene sets. Finally, we analyzed the omics data's contribution to identifying druggable genes. We found that DF-CAGE reports druggable genes mainly based on the copy number variations (CNVs) data, the gene rearrangements and the mutation rates in the population. These findings may enlighten the future study and development of new drugs.
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Affiliation(s)
- Hai Yang
- Department of Computer Science and Engineering, East China University of Science and Technology, 200237 Shanghai, PR China
| | - Lipeng Gan
- Department of Computer Science and Engineering, East China University of Science and Technology, 200237 Shanghai, PR China
| | - Rui Chen
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA
| | - Dongdong Li
- Department of Computer Science and Engineering, East China University of Science and Technology, 200237 Shanghai, PR China
| | - Jing Zhang
- Department of Computer Science and Engineering, East China University of Science and Technology, 200237 Shanghai, PR China
| | - Zhe Wang
- Department of Computer Science and Engineering, East China University of Science and Technology, 200237 Shanghai, PR China
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18
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Holguin-Cruz JA, Foster LJ, Gsponer J. Where protein structure and cell diversity meet. Trends Cell Biol 2022; 32:996-1007. [PMID: 35537902 DOI: 10.1016/j.tcb.2022.04.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 04/08/2022] [Accepted: 04/12/2022] [Indexed: 01/21/2023]
Abstract
Protein-protein interaction networks - interactomes - are charted with the hope to understand how phenotypes emerge and how they are altered in disease states. Early efforts to map interactomes have focused on the assembly of context agnostic, reference networks. However, recent studies have mapped interactomes across different cell lines and tissues, finding highly variable interactomes due to the rewiring of protein-protein interactions in different contexts. Increasing evidence points to significant links between protein structure and interactome diversity seen across cell types and tissues. We discuss how recent findings support the key role of alternative splicing and phosphorylation, two well-established regulators of protein structural and functional diversity, in defining cell type- and tissue-specific interactomes. Moreover, we show that intrinsically disordered protein regions are most favorably equipped to support interactome rewiring by acting as hubs of protein structure and function regulation.
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Affiliation(s)
- Jorge A Holguin-Cruz
- Michael Smith Laboratories, Department of Biochemistry and Molecular Biology, The University of British Columbia, Vancouver, Canada
| | - Leonard J Foster
- Michael Smith Laboratories, Department of Biochemistry and Molecular Biology, The University of British Columbia, Vancouver, Canada
| | - Jörg Gsponer
- Michael Smith Laboratories, Department of Biochemistry and Molecular Biology, The University of British Columbia, Vancouver, Canada.
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19
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Multi-omics peripheral and core regions of cancer. NPJ Syst Biol Appl 2022; 8:47. [PMID: 36446819 PMCID: PMC9707100 DOI: 10.1038/s41540-022-00258-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 11/07/2022] [Indexed: 11/30/2022] Open
Abstract
Thousands of genes are perturbed by cancer, and these disturbances can be seen in transcriptome, methylation, somatic mutation, and copy number variation omics studies. Understanding their connectivity patterns as an omnigenic neighbourhood in a molecular interaction network (interactome) is a key step towards advancing knowledge of the molecular mechanisms underlying cancers. Here, we introduce a unified connectivity line (CLine) to pinpoint omics-specific omnigenic patterns across 15 curated cancers. Taking advantage of the universality of CLine, we distinguish the peripheral and core genes for each omics aspect. We propose a network-based framework, multi-omics periphery and core (MOPC), to combine peripheral and core genes from different omics into a button-like structure. On the basis of network proximity, we provide evidence that core genes tend to be specifically perturbed in one omics, but the peripheral genes are diversely perturbed in multiple omics. And the core of one omics is regulated by multiple omics peripheries. Finally, we take the MOPC as an omnigenic neighbourhood, describe its characteristics, and explore its relative contribution to network-based mechanisms of cancer. We were able to present how multi-omics perturbations percolate through the human interactome and contribute to an integrated periphery and core.
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20
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Tsitsiridis G, Steinkamp R, Giurgiu M, Brauner B, Fobo G, Frishman G, Montrone C, Ruepp A. CORUM: the comprehensive resource of mammalian protein complexes-2022. Nucleic Acids Res 2022; 51:D539-D545. [PMID: 36382402 PMCID: PMC9825459 DOI: 10.1093/nar/gkac1015] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 10/18/2022] [Accepted: 10/21/2022] [Indexed: 11/17/2022] Open
Abstract
The CORUM database has been providing comprehensive reference information about experimentally characterized, mammalian protein complexes and their associated biological and biomedical properties since 2007. Given that most catalytic and regulatory functions of the cell are carried out by protein complexes, their composition and characterization is of greatest importance in basic and disease biology. The new CORUM 4.0 release encompasses 5204 protein complexes offering the largest and most comprehensive publicly available dataset of manually curated mammalian protein complexes. The CORUM dataset is built from 5299 different genes, representing 26% of the protein coding genes in humans. Complex information from 3354 scientific articles is mainly obtained from human (70%), mouse (16%) and rat (9%) cells and tissues. Recent curation work includes sets of protein complexes, Functional Complex Groups, that offer comprehensive collections of published data in specific biological processes and molecular functions. In addition, a new graphical analysis tool was implemented that displays co-expression data from the subunits of protein complexes. CORUM is freely accessible at http://mips.helmholtz-muenchen.de/corum/.
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Affiliation(s)
- George Tsitsiridis
- Institute of Experimental Genetics, Helmholtz Center Munich (GmbH), German research Center for environmental Health, Neuherberg D-85764, Germany
| | - Ralph Steinkamp
- Institute of Experimental Genetics, Helmholtz Center Munich (GmbH), German research Center for environmental Health, Neuherberg D-85764, Germany
| | - Madalina Giurgiu
- Experimental and Clinical Research Center, Max Delbrück Center for Molecular Medicine and Charité Universitätsmedizin Berlin, Berlin 13125, Germany
| | - Barbara Brauner
- Institute of Experimental Genetics, Helmholtz Center Munich (GmbH), German research Center for environmental Health, Neuherberg D-85764, Germany
| | - Gisela Fobo
- Institute of Experimental Genetics, Helmholtz Center Munich (GmbH), German research Center for environmental Health, Neuherberg D-85764, Germany
| | - Goar Frishman
- Institute of Experimental Genetics, Helmholtz Center Munich (GmbH), German research Center for environmental Health, Neuherberg D-85764, Germany
| | - Corinna Montrone
- Institute of Experimental Genetics, Helmholtz Center Munich (GmbH), German research Center for environmental Health, Neuherberg D-85764, Germany
| | - Andreas Ruepp
- To whom correspondence should be addressed. Tel: +49 89 3187 3189; Fax: +49 89 3187 3500;
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21
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Jiang P, Sinha S, Aldape K, Hannenhalli S, Sahinalp C, Ruppin E. Big data in basic and translational cancer research. Nat Rev Cancer 2022; 22:625-639. [PMID: 36064595 PMCID: PMC9443637 DOI: 10.1038/s41568-022-00502-0] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/26/2022] [Indexed: 02/07/2023]
Abstract
Historically, the primary focus of cancer research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen the rapid accumulation of large-scale cancer omics data catalysed by breakthroughs in high-throughput technologies. This fast data growth has given rise to an evolving concept of 'big data' in cancer, whose analysis demands large computational resources and can potentially bring novel insights into essential questions. Indeed, the combination of big data, bioinformatics and artificial intelligence has led to notable advances in our basic understanding of cancer biology and to translational advancements. Further advances will require a concerted effort among data scientists, clinicians, biologists and policymakers. Here, we review the current state of the art and future challenges for harnessing big data to advance cancer research and treatment.
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Affiliation(s)
- Peng Jiang
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Sanju Sinha
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Kenneth Aldape
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sridhar Hannenhalli
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Cenk Sahinalp
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
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22
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Tang K, Tang J, Zeng J, Shen W, Zou M, Zhang C, Sun Q, Ye X, Li C, Sun C, Liu S, Jiang G, Du X. A network view of human immune system and virus-human interaction. Front Immunol 2022; 13:997851. [PMID: 36389817 PMCID: PMC9643829 DOI: 10.3389/fimmu.2022.997851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 10/11/2022] [Indexed: 11/30/2022] Open
Abstract
The immune system is highly networked and complex, which is continuously changing as encountering old and new pathogens. However, reductionism-based researches do not give a systematic understanding of the molecular mechanism of the immune response and viral pathogenesis. Here, we present HUMPPI-2022, a high-quality human protein-protein interaction (PPI) network, containing > 11,000 protein-coding genes with > 78,000 interactions. The network topology and functional characteristics analyses of the immune-related genes (IRGs) reveal that IRGs are mostly located in the center of the network and link genes of diverse biological processes, which may reflect the gene pleiotropy phenomenon. Moreover, the virus-human interactions reveal that pan-viral targets are mostly hubs, located in the center of the network and enriched in fundamental biological processes, but not for coronavirus. Finally, gene age effect was analyzed from the view of the host network for IRGs and virally-targeted genes (VTGs) during evolution, with IRGs gradually became hubs and integrated into host network through bridging functionally differentiated modules. Briefly, HUMPPI-2022 serves as a valuable resource for gaining a better understanding of the composition and evolution of human immune system, as well as the pathogenesis of viruses.
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Affiliation(s)
- Kang Tang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Jing Tang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Jinfeng Zeng
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Wei Shen
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
- Department of Rheumatology and Immunology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Min Zou
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Chi Zhang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Qianru Sun
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Xiaoyan Ye
- Department of Otolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Chunwei Li
- Department of Otolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Caijun Sun
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Siyang Liu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Guozhi Jiang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Xiangjun Du
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
- Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou, China
- *Correspondence: Xiangjun Du,
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23
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Braberg H, Echeverria I, Kaake RM, Sali A, Krogan NJ. From systems to structure - using genetic data to model protein structures. Nat Rev Genet 2022; 23:342-354. [PMID: 35013567 PMCID: PMC8744059 DOI: 10.1038/s41576-021-00441-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/25/2021] [Indexed: 12/11/2022]
Abstract
Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyse both physical and functional consequences of sequence changes at systems-wide and mechanistic scales. To achieve a systems view, protein interaction networks map which proteins physically interact, while genetic interaction networks inform on the phenotypic consequences of perturbing these protein interactions. Until recently, understanding the molecular mechanisms that underlie these interactions often required biophysical methods to determine the structures of the proteins involved. The past decade has seen the emergence of new approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical-genetic interaction mapping that enable modelling of the structures of individual proteins or protein complexes. Here, we review the emerging use of large-scale genetic datasets and deep learning approaches to model protein structures and their interactions, and discuss the integration of structural data from different sources.
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Affiliation(s)
- Hannes Braberg
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Ignacia Echeverria
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Robyn M Kaake
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Gladstone Institutes, San Francisco, CA, USA
| | - Andrej Sali
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Nevan J Krogan
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Gladstone Institutes, San Francisco, CA, USA.
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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24
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Swaney DL, Ramms DJ, Wang Z, Park J, Goto Y, Soucheray M, Bhola N, Kim K, Zheng F, Zeng Y, McGregor M, Herrington KA, O'Keefe R, Jin N, VanLandingham NK, Foussard H, Von Dollen J, Bouhaddou M, Jimenez-Morales D, Obernier K, Kreisberg JF, Kim M, Johnson DE, Jura N, Grandis JR, Gutkind JS, Ideker T, Krogan NJ. A protein network map of head and neck cancer reveals PIK3CA mutant drug sensitivity. Science 2021; 374:eabf2911. [PMID: 34591642 DOI: 10.1126/science.abf2911] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Danielle L Swaney
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.,J. David Gladstone Institutes, San Francisco, CA, USA.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA
| | - Dana J Ramms
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Department of Pharmacology, University of California San Diego, La Jolla, CA.,Moores Cancer Center, University of California San Diego, La Jolla, CA
| | - Zhiyong Wang
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Moores Cancer Center, University of California San Diego, La Jolla, CA
| | - Jisoo Park
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA
| | - Yusuke Goto
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Moores Cancer Center, University of California San Diego, La Jolla, CA
| | - Margaret Soucheray
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.,J. David Gladstone Institutes, San Francisco, CA, USA.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA
| | - Neil Bhola
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Kyumin Kim
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.,J. David Gladstone Institutes, San Francisco, CA, USA.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA
| | - Fan Zheng
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA
| | - Yan Zeng
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Michael McGregor
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.,J. David Gladstone Institutes, San Francisco, CA, USA.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA
| | - Kari A Herrington
- Department of Biochemistry and Biophysics Center for Advanced Light Microscopy at UCSF, University of California San Francisco, San Francisco, CA, USA
| | - Rachel O'Keefe
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Nan Jin
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Nathan K VanLandingham
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Helene Foussard
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.,J. David Gladstone Institutes, San Francisco, CA, USA.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA
| | - John Von Dollen
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.,J. David Gladstone Institutes, San Francisco, CA, USA.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA
| | - Mehdi Bouhaddou
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.,J. David Gladstone Institutes, San Francisco, CA, USA.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA
| | - David Jimenez-Morales
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.,J. David Gladstone Institutes, San Francisco, CA, USA.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA
| | - Kirsten Obernier
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.,J. David Gladstone Institutes, San Francisco, CA, USA.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA
| | - Jason F Kreisberg
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA
| | - Minkyu Kim
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.,J. David Gladstone Institutes, San Francisco, CA, USA.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA
| | - Daniel E Johnson
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Natalia Jura
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Cardiovascular Research Institute, University of California San Francisco, San Francisco, CA, USA
| | - Jennifer R Grandis
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - J Silvio Gutkind
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Department of Pharmacology, University of California San Diego, La Jolla, CA.,Moores Cancer Center, University of California San Diego, La Jolla, CA
| | - Trey Ideker
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA.,Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.,Department of Computer Science, University of California San Diego, La Jolla, CA, USA
| | - Nevan J Krogan
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.,J. David Gladstone Institutes, San Francisco, CA, USA.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA
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25
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Abstract
[Figure: see text].
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Affiliation(s)
- Ran Cheng
- Department of Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Peter K Jackson
- Baxter Laboratory, Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
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26
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Kim M, Park J, Bouhaddou M, Kim K, Rojc A, Modak M, Soucheray M, McGregor MJ, O'Leary P, Wolf D, Stevenson E, Foo TK, Mitchell D, Herrington KA, Muñoz DP, Tutuncuoglu B, Chen KH, Zheng F, Kreisberg JF, Diolaiti ME, Gordan JD, Coppé JP, Swaney DL, Xia B, van 't Veer L, Ashworth A, Ideker T, Krogan NJ. A protein interaction landscape of breast cancer. Science 2021; 374:eabf3066. [PMID: 34591612 PMCID: PMC9040556 DOI: 10.1126/science.abf3066] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Minkyu Kim
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Jisoo Park
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.,Department of Medicine, University of California, San Diego, CA, USA
| | - Mehdi Bouhaddou
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Kyumin Kim
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Ajda Rojc
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Maya Modak
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Margaret Soucheray
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Michael J McGregor
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Patrick O'Leary
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.,Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Denise Wolf
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.,Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Erica Stevenson
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Tzeh Keong Foo
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Dominique Mitchell
- Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA.,Division of Hematology/Oncology, University of California, San Francisco, CA, USA
| | - Kari A Herrington
- Department of Biochemistry and Biophysics, Center for Advanced Light Microscopy, University of California, San Francisco, CA, USA
| | - Denise P Muñoz
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.,Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Beril Tutuncuoglu
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Kuei-Ho Chen
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Fan Zheng
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.,Department of Medicine, University of California, San Diego, CA, USA
| | - Jason F Kreisberg
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.,Department of Medicine, University of California, San Diego, CA, USA
| | - Morgan E Diolaiti
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.,Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - John D Gordan
- Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA.,Division of Hematology/Oncology, University of California, San Francisco, CA, USA
| | - Jean-Philippe Coppé
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.,Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Danielle L Swaney
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Bing Xia
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Laura van 't Veer
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.,Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Alan Ashworth
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.,Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Trey Ideker
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.,Department of Medicine, University of California, San Diego, CA, USA.,Department of Bioengineering, University of California, San Diego, CA, USA
| | - Nevan J Krogan
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
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