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Paton V, Ramirez Flores RO, Gabor A, Badia-I-Mompel P, Tanevski J, Garrido-Rodriguez M, Saez-Rodriguez J. Assessing the impact of transcriptomics data analysis pipelines on downstream functional enrichment results. Nucleic Acids Res 2024:gkae552. [PMID: 38943333 DOI: 10.1093/nar/gkae552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 06/03/2024] [Accepted: 06/19/2024] [Indexed: 07/01/2024] Open
Abstract
Transcriptomics is widely used to assess the state of biological systems. There are many tools for the different steps, such as normalization, differential expression, and enrichment. While numerous studies have examined the impact of method choices on differential expression results, little attention has been paid to their effects on further downstream functional analysis, which typically provides the basis for interpretation and follow-up experiments. To address this, we introduce FLOP, a comprehensive nextflow-based workflow combining methods to perform end-to-end analyses of transcriptomics data. We illustrate FLOP on datasets ranging from end-stage heart failure patients to cancer cell lines. We discovered effects not noticeable at the gene-level, and observed that not filtering the data had the highest impact on the correlation between pipelines in the gene set space. Moreover, we performed three benchmarks to evaluate the 12 pipelines included in FLOP, and confirmed that filtering is essential in scenarios of expected moderate-to-low biological signal. Overall, our results underscore the impact of carefully evaluating the consequences of the choice of preprocessing methods on downstream enrichment analyses. We envision FLOP as a valuable tool to measure the robustness of functional analyses, ultimately leading to more reliable and conclusive biological findings.
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Affiliation(s)
- Victor Paton
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany
| | - Ricardo Omar Ramirez Flores
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany
| | - Attila Gabor
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany
| | - Pau Badia-I-Mompel
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany
| | - Jovan Tanevski
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany
| | - Martin Garrido-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany
- European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
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2
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Schrod S, Zacharias HU, Beißbarth T, Hauschild AC, Altenbuchinger M. CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations. Bioinformatics 2024; 40:i91-i99. [PMID: 38940173 PMCID: PMC11211812 DOI: 10.1093/bioinformatics/btae261] [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] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION High-throughput screens (HTS) provide a powerful tool to decipher the causal effects of chemical and genetic perturbations on cancer cell lines. Their ability to evaluate a wide spectrum of interventions, from single drugs to intricate drug combinations and CRISPR-interference, has established them as an invaluable resource for the development of novel therapeutic approaches. Nevertheless, the combinatorial complexity of potential interventions makes a comprehensive exploration intractable. Hence, prioritizing interventions for further experimental investigation becomes of utmost importance. RESULTS We propose CODEX (COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations) as a general framework for the causal modeling of HTS data, linking perturbations to their downstream consequences. CODEX relies on a stringent causal modeling strategy based on counterfactual reasoning. As such, CODEX predicts drug-specific cellular responses, comprising cell survival and molecular alterations, and facilitates the in silico exploration of drug combinations. This is achieved for both bulk and single-cell HTS. We further show that CODEX provides a rationale to explore complex genetic modifications from CRISPR-interference in silico in single cells. AVAILABILITY AND IMPLEMENTATION Our implementation of CODEX is publicly available at https://github.com/sschrod/CODEX. All data used in this article are publicly available.
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Affiliation(s)
- Stefan Schrod
- Department of Medical Bioinformatics, University Medical Center Göttingen, 37077 Niedersachsen, Germany
| | - Helena U Zacharias
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover Medical School, 30625 Hannover, Germany
| | - Tim Beißbarth
- Department of Medical Bioinformatics, University Medical Center Göttingen, 37077 Niedersachsen, Germany
| | - Anne-Christin Hauschild
- Department of Medical Informatics, University Medical Center Göttingen, 37075 Niedersachsen, Germany
| | - Michael Altenbuchinger
- Department of Medical Bioinformatics, University Medical Center Göttingen, 37077 Niedersachsen, Germany
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3
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Tong X, Qu N, Kong X, Ni S, Zhou J, Wang K, Zhang L, Wen Y, Shi J, Zhang S, Li X, Zheng M. Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery. Nat Commun 2024; 15:5378. [PMID: 38918369 PMCID: PMC11199551 DOI: 10.1038/s41467-024-49620-3] [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: 11/10/2023] [Accepted: 06/10/2024] [Indexed: 06/27/2024] Open
Abstract
Artificial intelligence transforms drug discovery, with phenotype-based approaches emerging as a promising alternative to target-based methods, overcoming limitations like lack of well-defined targets. While chemical-induced transcriptional profiles offer a comprehensive view of drug mechanisms, inherent noise often obscures the true signal, hindering their potential for meaningful insights. Here, we highlight the development of TranSiGen, a deep generative model employing self-supervised representation learning. TranSiGen analyzes basal cell gene expression and molecular structures to reconstruct chemical-induced transcriptional profiles with high accuracy. By capturing both cellular and compound information, TranSiGen-derived representations demonstrate efficacy in diverse downstream tasks like ligand-based virtual screening, drug response prediction, and phenotype-based drug repurposing. Notably, in vitro validation of TranSiGen's application in pancreatic cancer drug discovery highlights its potential for identifying effective compounds. We envisage that integrating TranSiGen into the drug discovery and mechanism research holds significant promise for advancing biomedicine.
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Affiliation(s)
- Xiaochu Tong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Ning Qu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Xiangtai Kong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Shengkun Ni
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Jingyi Zhou
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, 201210, China
- Lingang Laboratory, Shanghai, 200031, China
| | - Kun Wang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
| | - Lehan Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Yiming Wen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China
| | - Jiangshan Shi
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Sulin Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China.
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China.
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China.
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China.
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China.
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China.
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China.
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4
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Powell RT, Rinkenbaugh AL, Guo L, Cai S, Shao J, Zhou X, Zhang X, Jeter-Jones S, Fu C, Qi Y, Baameur Hancock F, White JB, Stephan C, Davies PJ, Moulder S, Symmans WF, Chang JT, Piwnica-Worms H. Targeting neddylation and sumoylation in chemoresistant triple negative breast cancer. NPJ Breast Cancer 2024; 10:37. [PMID: 38802426 PMCID: PMC11130334 DOI: 10.1038/s41523-024-00644-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 05/09/2024] [Indexed: 05/29/2024] Open
Abstract
Triple negative breast cancer (TNBC) accounts for 15-20% of breast cancer cases in the United States. Systemic neoadjuvant chemotherapy (NACT), with or without immunotherapy, is the current standard of care for patients with early-stage TNBC. However, up to 70% of TNBC patients have significant residual disease once NACT is completed, which is associated with a high risk of developing recurrence within two to three years of surgical resection. To identify targetable vulnerabilities in chemoresistant TNBC, we generated longitudinal patient-derived xenograft (PDX) models from TNBC tumors before and after patients received NACT. We then compiled transcriptomes and drug response profiles for all models. Transcriptomic analysis identified the enrichment of aberrant protein homeostasis pathways in models from post-NACT tumors relative to pre-NACT tumors. This observation correlated with increased sensitivity in vitro to inhibitors targeting the proteasome, heat shock proteins, and neddylation pathways. Pevonedistat, a drug annotated as a NEDD8-activating enzyme (NAE) inhibitor, was prioritized for validation in vivo and demonstrated efficacy as a single agent in multiple PDX models of TNBC. Pharmacotranscriptomic analysis identified a pathway-level correlation between pevonedistat activity and post-translational modification (PTM) machinery, particularly involving neddylation and sumoylation targets. Elevated levels of both NEDD8 and SUMO1 were observed in models exhibiting a favorable response to pevonedistat compared to those with a less favorable response in vivo. Moreover, a correlation emerged between the expression of neddylation-regulated pathways and tumor response to pevonedistat, indicating that targeting these PTM pathways may prove effective in combating chemoresistant TNBC.
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Affiliation(s)
- Reid T Powell
- Center for Translational Cancer Research, Institute of Bioscience and Technology Texas A&M Health Science Center, Houston, TX, USA
| | - Amanda L Rinkenbaugh
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lei Guo
- Center for Translational Cancer Research, Institute of Bioscience and Technology Texas A&M Health Science Center, Houston, TX, USA
| | - Shirong Cai
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jiansu Shao
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xinhui Zhou
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xiaomei Zhang
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sabrina Jeter-Jones
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Chunxiao Fu
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yuan Qi
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Faiza Baameur Hancock
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jason B White
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Clifford Stephan
- Center for Translational Cancer Research, Institute of Bioscience and Technology Texas A&M Health Science Center, Houston, TX, USA
| | - Peter J Davies
- Center for Translational Cancer Research, Institute of Bioscience and Technology Texas A&M Health Science Center, Houston, TX, USA
| | - Stacy Moulder
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Eli Lilly and Company, Indianapolis, IN, USA
| | - W Fraser Symmans
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jeffrey T Chang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Integrative Biology and Pharmacology, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Helen Piwnica-Worms
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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5
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Meimetis N, Lauffenburger DA, Nilsson A. Inference of drug off-target effects on cellular signaling using interactome-based deep learning. iScience 2024; 27:109509. [PMID: 38591003 PMCID: PMC11000001 DOI: 10.1016/j.isci.2024.109509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 02/04/2024] [Accepted: 03/13/2024] [Indexed: 04/10/2024] Open
Abstract
Many diseases emerge from dysregulated cellular signaling, and drugs are often designed to target specific signaling proteins. Off-target effects are, however, common and may ultimately result in failed clinical trials. Here we develop a computer model of the cell's transcriptional response to drugs for improved understanding of their mechanisms of action. The model is based on ensembles of artificial neural networks and simultaneously infers drug-target interactions and their downstream effects on intracellular signaling. With this, it predicts transcription factors' activities, while recovering known drug-target interactions and inferring many new ones, which we validate with an independent dataset. As a case study, we analyze the effects of the drug Lestaurtinib on downstream signaling. Alongside its intended target, FLT3, the model predicts an inhibition of CDK2 that enhances the downregulation of the cell cycle-critical transcription factor FOXM1. Our approach can therefore enhance our understanding of drug signaling for therapeutic design.
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Affiliation(s)
- Nikolaos Meimetis
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Douglas A. Lauffenburger
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Avlant Nilsson
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Cell and Molecular Biology, SciLifeLab, Karolinska Institutet, Stockholm, Sweden
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE 41296, Sweden
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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] [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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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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8
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Alzoubi A, Shirazi H, Alrawashdeh A, AL-Dekah AM, Ibraheem N, Kheirallah KA. The Status Quo of Pharmacogenomics of Tyrosine Kinase Inhibitors in Precision Oncology: A Bibliometric Analysis of the Literature. Pharmaceutics 2024; 16:167. [PMID: 38399228 PMCID: PMC10892459 DOI: 10.3390/pharmaceutics16020167] [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: 01/17/2024] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 02/25/2024] Open
Abstract
Precision oncology and pharmacogenomics (PGx) intersect in their overarching goal to institute the right treatment for the right patient. However, the translation of these innovations into clinical practice is still lagging behind. Therefore, this study aimed to analyze the current state of research and to predict the future directions of applied PGx in the field of precision oncology as represented by the targeted therapy class of tyrosine kinase inhibitors (TKIs). Advanced bibliometric and scientometric analyses of the literature were performed. The Scopus database was used for the search, and articles published between 2001 and 2023 were extracted. Information about productivity, citations, cluster analysis, keyword co-occurrence, trend topics, and thematic evolution were generated. A total of 448 research articles were included in this analysis. A burst of scholarly activity in the field was noted by the year 2005, peaking in 2017, followed by a remarkable decline to date. Research in the field was hallmarked by consistent and impactful international collaboration, with the US leading in terms of most prolific country, institutions, and total link strength. Thematic evolution in the field points in the direction of more specialized studies on applied pharmacokinetics of available and novel TKIs, particularly for the treatment of lung and breast cancers. Our results delineate a significant advancement in the field of PGx in precision oncology. Notwithstanding the practical challenges to these applications at the point of care, further research, standardization, infrastructure development, and informed policymaking are urgently needed to ensure widespread adoption of PGx.
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Affiliation(s)
- Abdallah Alzoubi
- Department of Pathological Sciences, College of Medicine, Ajman University, Ajman P.O. Box 346, United Arab Emirates;
- Department of Pharmacology, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Hassan Shirazi
- Department of Pathological Sciences, College of Medicine, Ajman University, Ajman P.O. Box 346, United Arab Emirates;
| | - Ahmad Alrawashdeh
- Department of Allied Medical Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan;
| | | | - Nadia Ibraheem
- Department of Public Health and Community Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan; (N.I.); (K.A.K.)
| | - Khalid A. Kheirallah
- Department of Public Health and Community Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan; (N.I.); (K.A.K.)
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9
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Mondello A, Dal Bo M, Toffoli G, Polano M. Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges. Front Pharmacol 2024; 14:1260276. [PMID: 38264526 PMCID: PMC10803549 DOI: 10.3389/fphar.2023.1260276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 12/26/2023] [Indexed: 01/25/2024] Open
Abstract
Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.
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Affiliation(s)
| | | | | | - Maurizio Polano
- Experimental and Clinical Pharmacology Unit, Centro di Riferimento Oncologico di Aviano (CRO), Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Aviano, Italy
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10
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Chen D, Wang X, Zhu H, Jiang Y, Li Y, Liu Q, Liu Q. Predicting anticancer synergistic drug combinations based on multi-task learning. BMC Bioinformatics 2023; 24:448. [PMID: 38012551 PMCID: PMC10680313 DOI: 10.1186/s12859-023-05524-5] [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: 04/21/2023] [Accepted: 10/09/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND The discovery of anticancer drug combinations is a crucial work of anticancer treatment. In recent years, pre-screening drug combinations with synergistic effects in a large-scale search space adopting computational methods, especially deep learning methods, is increasingly popular with researchers. Although achievements have been made to predict anticancer synergistic drug combinations based on deep learning, the application of multi-task learning in this field is relatively rare. The successful practice of multi-task learning in various fields shows that it can effectively learn multiple tasks jointly and improve the performance of all the tasks. METHODS In this paper, we propose MTLSynergy which is based on multi-task learning and deep neural networks to predict synergistic anticancer drug combinations. It simultaneously learns two crucial prediction tasks in anticancer treatment, which are synergy prediction of drug combinations and sensitivity prediction of monotherapy. And MTLSynergy integrates the classification and regression of prediction tasks into the same model. Moreover, autoencoders are employed to reduce the dimensions of input features. RESULTS Compared with the previous methods listed in this paper, MTLSynergy achieves the lowest mean square error of 216.47 and the highest Pearson correlation coefficient of 0.76 on the drug synergy prediction task. On the corresponding classification task, the area under the receiver operator characteristics curve and the area under the precision-recall curve are 0.90 and 0.62, respectively, which are equivalent to the comparison methods. Through the ablation study, we verify that multi-task learning and autoencoder both have a positive effect on prediction performance. In addition, the prediction results of MTLSynergy in many cases are also consistent with previous studies. CONCLUSION Our study suggests that multi-task learning is significantly beneficial for both drug synergy prediction and monotherapy sensitivity prediction when combining these two tasks into one model. The ability of MTLSynergy to discover new anticancer synergistic drug combinations noteworthily outperforms other state-of-the-art methods. MTLSynergy promises to be a powerful tool to pre-screen anticancer synergistic drug combinations.
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Affiliation(s)
- Danyi Chen
- School of Software Engineering, Tongji University, Shanghai, 201804, China
| | - Xiaowen Wang
- School of Software Engineering, Tongji University, Shanghai, 201804, China
| | - Hongming Zhu
- School of Software Engineering, Tongji University, Shanghai, 201804, China
| | - Yizhi Jiang
- School of Software Engineering, Tongji University, Shanghai, 201804, China
| | - Yulong Li
- School of Software Engineering, Tongji University, Shanghai, 201804, China
| | - Qi Liu
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Qin Liu
- School of Software Engineering, Tongji University, Shanghai, 201804, China.
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11
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Zhou K, Wang W, Tang J. Editorial: Functional screening for cancer drug discovery: from experimental approaches to data integration. Front Genet 2023; 14:1201454. [PMID: 37485338 PMCID: PMC10359426 DOI: 10.3389/fgene.2023.1201454] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 06/30/2023] [Indexed: 07/25/2023] Open
Affiliation(s)
- Kecheng Zhou
- School of Life Sciences, Anhui Medical University, Hefei, China
| | - Wenyu Wang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
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12
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Mundi PS, Dela Cruz FS, Grunn A, Diolaiti D, Mauguen A, Rainey AR, Guillan K, Siddiquee A, You D, Realubit R, Karan C, Ortiz MV, Douglass EF, Accordino M, Mistretta S, Brogan F, Bruce JN, Caescu CI, Carvajal RD, Crew KD, Decastro G, Heaney M, Henick BS, Hershman DL, Hou JY, Iwamoto FM, Jurcic JG, Kiran RP, Kluger MD, Kreisl T, Lamanna N, Lassman AB, Lim EA, Manji GA, McKhann GM, McKiernan JM, Neugut AI, Olive KP, Rosenblat T, Schwartz GK, Shu CA, Sisti MB, Tergas A, Vattakalam RM, Welch M, Wenske S, Wright JD, Hibshoosh H, Kalinsky K, Aburi M, Sims PA, Alvarez MJ, Kung AL, Califano A. A Transcriptome-Based Precision Oncology Platform for Patient-Therapy Alignment in a Diverse Set of Treatment-Resistant Malignancies. Cancer Discov 2023; 13:1386-1407. [PMID: 37061969 PMCID: PMC10239356 DOI: 10.1158/2159-8290.cd-22-1020] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 01/14/2023] [Accepted: 03/14/2023] [Indexed: 04/17/2023]
Abstract
Predicting in vivo response to antineoplastics remains an elusive challenge. We performed a first-of-kind evaluation of two transcriptome-based precision cancer medicine methodologies to predict tumor sensitivity to a comprehensive repertoire of clinically relevant oncology drugs, whose mechanism of action we experimentally assessed in cognate cell lines. We enrolled patients with histologically distinct, poor-prognosis malignancies who had progressed on multiple therapies, and developed low-passage, patient-derived xenograft models that were used to validate 35 patient-specific drug predictions. Both OncoTarget, which identifies high-affinity inhibitors of individual master regulator (MR) proteins, and OncoTreat, which identifies drugs that invert the transcriptional activity of hyperconnected MR modules, produced highly significant 30-day disease control rates (68% and 91%, respectively). Moreover, of 18 OncoTreat-predicted drugs, 15 induced the predicted MR-module activity inversion in vivo. Predicted drugs significantly outperformed antineoplastic drugs selected as unpredicted controls, suggesting these methods may substantively complement existing precision cancer medicine approaches, as also illustrated by a case study. SIGNIFICANCE Complementary precision cancer medicine paradigms are needed to broaden the clinical benefit realized through genetic profiling and immunotherapy. In this first-in-class application, we introduce two transcriptome-based tumor-agnostic systems biology tools to predict drug response in vivo. OncoTarget and OncoTreat are scalable for the design of basket and umbrella clinical trials. This article is highlighted in the In This Issue feature, p. 1275.
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Affiliation(s)
- Prabhjot S. Mundi
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
| | - Filemon S. Dela Cruz
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY USA 10065
| | - Adina Grunn
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
| | - Daniel Diolaiti
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY USA 10065
| | - Audrey Mauguen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY USA 10065
| | - Allison R. Rainey
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY USA 10065
| | - Kristina Guillan
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY USA 10065
| | - Armaan Siddiquee
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY USA 10065
| | - Daoqi You
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY USA 10065
| | - Ronald Realubit
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
| | - Charles Karan
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
| | - Michael V. Ortiz
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY USA 10065
| | - Eugene F. Douglass
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
| | - Melissa Accordino
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Suzanne Mistretta
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
| | - Frances Brogan
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
| | - Jeffrey N. Bruce
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Neurological Surgery, Columbia University Irving Medical Center, 710 W 168th Street, New York, NY USA 10032
| | - Cristina I. Caescu
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
| | - Richard D. Carvajal
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Katherine D Crew
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Guarionex Decastro
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Urology, Columbia University Irving Medical Center, 160 Fort Washington Ave, New York, NY USA 10032
| | - Mark Heaney
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Brian S Henick
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Dawn L Hershman
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
- Department of Epidemiology, Columbia University Mailman School of Public Health, 722 West 168th St. NY, NY 10032
| | - June Y. Hou
- Department of Obstetrics & Gynecology, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY USA 10032
| | - Fabio M. Iwamoto
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Neurology, Columbia University Irving Medical Center, 710 W 168th Street, New York, NY USA 10032
| | - Joseph G. Jurcic
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Ravi P. Kiran
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Surgery, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY USA 10032
| | - Michael D Kluger
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Surgery, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY USA 10032
| | - Teri Kreisl
- Novartis Five Cambridge, MA 02142, United States
| | - Nicole Lamanna
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Andrew B. Lassman
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Neurology, Columbia University Irving Medical Center, 710 W 168th Street, New York, NY USA 10032
| | - Emerson A. Lim
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Gulam A. Manji
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Guy M McKhann
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Neurological Surgery, Columbia University Irving Medical Center, 710 W 168th Street, New York, NY USA 10032
| | - James M. McKiernan
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Urology, Columbia University Irving Medical Center, 160 Fort Washington Ave, New York, NY USA 10032
| | - Alfred I Neugut
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
- Department of Epidemiology, Columbia University Mailman School of Public Health, 722 West 168th St. NY, NY 10032
| | - Kenneth P. Olive
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Todd Rosenblat
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Gary K. Schwartz
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Catherine A Shu
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Michael B. Sisti
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Neurological Surgery, Columbia University Irving Medical Center, 710 W 168th Street, New York, NY USA 10032
- Department of Otolaryngology Head and Neck Surgery, Columbia University Irving Medical Center, 710 W 168th Street, New York, NY USA 10032
- Department of Radiation Oncology, Columbia University Irving Medical Center, 161 Fort Washington Avenue, New York, NY 10032, United States
| | - Ana Tergas
- Department of Obstetrics & Gynecology, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY USA 10032
| | - Reena M Vattakalam
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Obstetrics & Gynecology, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY USA 10032
| | - Mary Welch
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Neurology, Columbia University Irving Medical Center, 710 W 168th Street, New York, NY USA 10032
| | - Sven Wenske
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Urology, Columbia University Irving Medical Center, 160 Fort Washington Ave, New York, NY USA 10032
| | - Jason D. Wright
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Obstetrics & Gynecology, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY USA 10032
| | - Hanina Hibshoosh
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Kevin Kalinsky
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Winship Cancer Institute of Emory University and Department of Hematology and Medical Oncology, Emory University School of Medicine, 1365-C Clifton Road NE, Atlanta, GA 30322, United States
| | - Mahalaxmi Aburi
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
| | - Peter A. Sims
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, 701 W 168th Street, New York, NY USA 10032
| | - Mariano J. Alvarez
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- DarwinHealth Inc. New York
| | - Andrew L. Kung
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY USA 10065
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, 701 W 168th Street, New York, NY USA 10032
- Department of Biomedical Informatics, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY USA 10032
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY USA 10032
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13
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Van de Graaf MW, Eggertsen TG, Zeigler AC, Tan PM, Saucerman JJ. Benchmarking of protein interaction databases for integration with manually reconstructed signalling network models. J Physiol 2023:10.1113/JP284616. [PMID: 37199469 PMCID: PMC11073820 DOI: 10.1113/jp284616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 05/10/2023] [Indexed: 05/19/2023] Open
Abstract
Protein interaction databases are critical resources for network bioinformatics and integrating molecular experimental data. Interaction databases may also enable construction of predictive computational models of biological networks, although their fidelity for this purpose is not clear. Here, we benchmark protein interaction databases X2K, Reactome, Pathway Commons, Omnipath and Signor for their ability to recover manually curated edges from three logic-based network models of cardiac hypertrophy, mechano-signalling and fibrosis. Pathway Commons performed best at recovering interactions from manually reconstructed hypertrophy (137 of 193 interactions, 71%), mechano-signalling (85 of 125 interactions, 68%) and fibroblast networks (98 of 142 interactions, 69%). While protein interaction databases successfully recovered central, well-conserved pathways, they performed worse at recovering tissue-specific and transcriptional regulation. This highlights a knowledge gap where manual curation is critical. Finally, we tested the ability of Signor and Pathway Commons to identify new edges that improve model predictions, revealing important roles of protein kinase C autophosphorylation and Ca2+ /calmodulin-dependent protein kinase II phosphorylation of CREB in cardiomyocyte hypertrophy. This study provides a platform for benchmarking protein interaction databases for their utility in network model construction, as well as providing new insights into cardiac hypertrophy signalling. KEY POINTS: Protein interaction databases are used to recover signalling interactions from previously developed network models. The five protein interaction databases benchmarked recovered well-conserved pathways, but did poorly at recovering tissue-specific pathways and transcriptional regulation, indicating the importance of manual curation. We identify new signalling interactions not previously used in the network models, including a role for Ca2+ /calmodulin-dependent protein kinase II phosphorylation of CREB in cardiomyocyte hypertrophy.
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Affiliation(s)
- Matthew W. Van de Graaf
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
- Children’s National Hospital, Washington, District of Columbia, USA
| | - Taylor G. Eggertsen
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Angela C. Zeigler
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
- Yale New Haven Hospital, New Haven, Connecticut, USA
| | - Philip M. Tan
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Jeffrey J. Saucerman
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
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14
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Vasciaveo A, Arriaga JM, de Almeida FN, Zou M, Douglass EF, Picech F, Shibata M, Rodriguez-Calero A, de Brot S, Mitrofanova A, Chua CW, Karan C, Realubit R, Pampou S, Kim JY, Afari SN, Mukhammadov T, Zanella L, Corey E, Alvarez MJ, Rubin MA, Shen MM, Califano A, Abate-Shen C. OncoLoop: A Network-Based Precision Cancer Medicine Framework. Cancer Discov 2023; 13:386-409. [PMID: 36374194 PMCID: PMC9905319 DOI: 10.1158/2159-8290.cd-22-0342] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 08/22/2022] [Accepted: 11/10/2022] [Indexed: 11/16/2022]
Abstract
Prioritizing treatments for individual patients with cancer remains challenging, and performing coclinical studies using patient-derived models in real time is often unfeasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework that predicts drug sensitivity in human tumors and their preexisting high-fidelity (cognate) model(s) by leveraging drug perturbation profiles. As a proof of concept, we applied OncoLoop to prostate cancer using genetically engineered mouse models (GEMM) that recapitulate a broad spectrum of disease states, including castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of human prostate cancer cohorts by Master Regulator (MR) conservation analysis revealed that most patients with advanced prostate cancer were represented by at least one cognate GEMM-derived tumor (GEMM-DT). Drugs predicted to invert MR activity in patients and their cognate GEMM-DTs were successfully validated in allograft, syngeneic, and patient-derived xenograft (PDX) models of tumors and metastasis. Furthermore, OncoLoop-predicted drugs enhanced the efficacy of clinically relevant drugs, namely, the PD-1 inhibitor nivolumab and the AR inhibitor enzalutamide. SIGNIFICANCE OncoLoop is a transcriptomic-based experimental and computational framework that can support rapid-turnaround coclinical studies to identify and validate drugs for individual patients, which can then be readily adapted to clinical practice. This framework should be applicable in many cancer contexts for which appropriate models and drug perturbation data are available. This article is highlighted in the In This Issue feature, p. 247.
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Affiliation(s)
- Alessandro Vasciaveo
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Juan Martín Arriaga
- Department of Molecular Pharmacology and Therapeutics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY USA 10032
| | - Francisca Nunes de Almeida
- Department of Molecular Pharmacology and Therapeutics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY USA 10032
| | - Min Zou
- Department of Molecular Pharmacology and Therapeutics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY USA 10032
| | - Eugene F. Douglass
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Florencia Picech
- Department of Molecular Pharmacology and Therapeutics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY USA 10032
| | - Maho Shibata
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- Department of Genetics and Development, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY USA 10032
- Department of Urology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY USA 10032
| | - Antonio Rodriguez-Calero
- Department for Biomedical Research, University of Bern, Bern, Switzerland 3008
- Institute of Pathology, University of Bern and Inselspital, Bern, Switzerland 3008
| | - Simone de Brot
- COMPATH, Institute of Animal Pathology, University of Bern, Switzerland 3012
| | - Antonina Mitrofanova
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Chee Wai Chua
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- Department of Genetics and Development, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY USA 10032
- Department of Urology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY USA 10032
| | - Charles Karan
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY USA 10032
| | - Ronald Realubit
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY USA 10032
| | - Sergey Pampou
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY USA 10032
| | - Jaime Y. Kim
- Department of Molecular Pharmacology and Therapeutics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY USA 10032
| | - Stephanie N. Afari
- Department of Molecular Pharmacology and Therapeutics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY USA 10032
| | - Timur Mukhammadov
- Department of Molecular Pharmacology and Therapeutics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY USA 10032
| | - Luca Zanella
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Eva Corey
- Department of Urology, University of Washington, Seattle, WA USA 98195
| | - Mariano J. Alvarez
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- DarwinHealth Inc, New York, NY
| | - Mark A. Rubin
- Department for Biomedical Research, University of Bern, Bern, Switzerland 3008
- Bern Center for Precision Medicine (BCPM) Bern, Switzerland 3008
| | - Michael M. Shen
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- Department of Genetics and Development, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY USA 10032
- Department of Urology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY USA 10032
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, USA 10032
| | - Andrea Califano
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY USA 10032
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, USA 10032
- Department of Biochemistry & Molecular Biophysics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- Department of Biomedical Informatics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Cory Abate-Shen
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- Department of Molecular Pharmacology and Therapeutics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY USA 10032
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- Department of Urology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY USA 10032
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, USA 10032
- Department of Pathology and Cell Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY USA 10032
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15
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Duran-Frigola M, Cigler M, Winter GE. Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence. J Am Chem Soc 2023; 145:2711-2732. [PMID: 36706315 PMCID: PMC9912273 DOI: 10.1021/jacs.2c11098] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities.
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Affiliation(s)
- Miquel Duran-Frigola
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria,Ersilia
Open Source Initiative, 28 Belgrave Road, CB1 3DE, Cambridge, United Kingdom,
| | - Marko Cigler
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria
| | - Georg E. Winter
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria,
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16
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Fernández-Torras A, Duran-Frigola M, Bertoni M, Locatelli M, Aloy P. Integrating and formatting biomedical data as pre-calculated knowledge graph embeddings in the Bioteque. Nat Commun 2022; 13:5304. [PMID: 36085310 PMCID: PMC9463154 DOI: 10.1038/s41467-022-33026-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 08/30/2022] [Indexed: 12/25/2022] Open
Abstract
Biomedical data is accumulating at a fast pace and integrating it into a unified framework is a major challenge, so that multiple views of a given biological event can be considered simultaneously. Here we present the Bioteque, a resource of unprecedented size and scope that contains pre-calculated biomedical descriptors derived from a gigantic knowledge graph, displaying more than 450 thousand biological entities and 30 million relationships between them. The Bioteque integrates, harmonizes, and formats data collected from over 150 data sources, including 12 biological entities (e.g., genes, diseases, drugs) linked by 67 types of associations (e.g., ‘drug treats disease’, ‘gene interacts with gene’). We show how Bioteque descriptors facilitate the assessment of high-throughput protein-protein interactome data, the prediction of drug response and new repurposing opportunities, and demonstrate that they can be used off-the-shelf in downstream machine learning tasks without loss of performance with respect to using original data. The Bioteque thus offers a thoroughly processed, tractable, and highly optimized assembly of the biomedical knowledge available in the public domain. Biomedical data is accumulating at a fast pace and integrating it into a unified framework is a major challenge. Here, the authors present a resource that contains pre-calculated biomedical descriptors derived from a very large knowledge graph.
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Affiliation(s)
- Adrià Fernández-Torras
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Miquel Duran-Frigola
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.,Ersilia Open Source Initiative, Cambridge, UK
| | - Martino Bertoni
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Martina Locatelli
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Patrick Aloy
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain. .,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.
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17
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Laise P, Bosker G, Califano A, Alvarez MJ. A Patient-to-Model-to-Patient (PMP) Cancer Drug Discovery Protocol for Identifying and Validating Therapeutic Agents Targeting Tumor Regulatory Architecture. Curr Protoc 2022; 2:e544. [PMID: 36083100 DOI: 10.1002/cpz1.544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The current Achilles heel of cancer drug discovery is the inability to forge precise and predictive connections among mechanistic drivers of the cancer cell state, therapeutically significant molecular targets, effective drugs, and responsive patient subgroups. Although advances in molecular biology have helped identify molecular markers and stratify patients into molecular subtypes, these associational strategies typically fail to provide a mechanistic rationale to identify cancer vulnerabilities. Recently, integrative systems biology methodologies have been used to reverse engineer cellular networks and identify master regulators (MRs), proteins whose activity is both necessary and sufficient to implement phenotypic states under physiological and pathological conditions, which are organized into highly interconnected regulatory modules called tumor checkpoints. Because of their functional relevance, MRs represent ideal pharmacological targets and biomarkers. Here, we present a six-step patient-to-model-to-patient protocol that employs computational and experimental methodologies to reconstruct and interrogate the regulatory logic of human cancer cells for identifying and therapeutically targeting the tumor checkpoint with novel as well as existing pharmacological agents. This protocol systematically identifies, from specific patient tumor samples, the MRs that comprise the tumor checkpoint. Then, it identifies in vitro and in vivo models that, by recapitulating the patient's tumor checkpoint, constitute the appropriate cell lines and xenografts to further elucidate the tissue context-specific drug mechanism of action (MOA) and permit precise, biomarker-based preclinical validations of drug efficacy. The combination of determination of a drug's context-specific MOA and precise identification of patients' tumor checkpoints provides a personalized, mechanism-based biomarker to enrich prospective clinical trials with patients likely to respond. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.
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Affiliation(s)
- Pasquale Laise
- DarwinHealth, New York, New York
- Department of Systems Biology, Columbia University Irving Medical Center, New York, New York
| | | | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, New York, New York
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, New York
- Department of Medicine, Columbia University Irving Medical Center, New York, New York
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, New York
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York
| | - Mariano J Alvarez
- DarwinHealth, New York, New York
- Department of Systems Biology, Columbia University Irving Medical Center, New York, New York
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18
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A model for network-based identification and pharmacological targeting of aberrant, replication-permissive transcriptional programs induced by viral infection. Commun Biol 2022; 5:714. [PMID: 35854100 PMCID: PMC9296638 DOI: 10.1038/s42003-022-03663-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 06/29/2022] [Indexed: 11/08/2022] Open
Abstract
SARS-CoV-2 hijacks the host cell transcriptional machinery to induce a phenotypic state amenable to its replication. Here we show that analysis of Master Regulator proteins representing mechanistic determinants of the gene expression signature induced by SARS-CoV-2 in infected cells revealed coordinated inactivation of Master Regulators enriched in physical interactions with SARS-CoV-2 proteins, suggesting their mechanistic role in maintaining a host cell state refractory to virus replication. To test their functional relevance, we measured SARS-CoV-2 replication in epithelial cells treated with drugs predicted to activate the entire repertoire of repressed Master Regulators, based on their experimentally elucidated, context-specific mechanism of action. Overall, 15 of the 18 drugs predicted to be effective by this methodology induced significant reduction of SARS-CoV-2 replication, without affecting cell viability. This model for host-directed pharmacological therapy is fully generalizable and can be deployed to identify drugs targeting host cell-based Master Regulator signatures induced by virtually any pathogen.
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Fisher JL, Jones EF, Flanary VL, Williams AS, Ramsey EJ, Lasseigne BN. Considerations and challenges for sex-aware drug repurposing. Biol Sex Differ 2022; 13:13. [PMID: 35337371 PMCID: PMC8949654 DOI: 10.1186/s13293-022-00420-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 03/06/2022] [Indexed: 01/09/2023] Open
Abstract
Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration [33]. The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health's (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER) policies to motivate researchers to consider sex differences [204]. However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses [7, 11, 14, 33]. Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information [1, 7, 155]. They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex [114]. Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods [7]. However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods [151, 159]. Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.
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Affiliation(s)
- Jennifer L. Fisher
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Emma F. Jones
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Victoria L. Flanary
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Avery S. Williams
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Elizabeth J. Ramsey
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Brittany N. Lasseigne
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
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