1
|
Failli M, Demir S, Del Río-Álvarez Á, Carrillo-Reixach J, Royo L, Domingo-Sàbat M, Childs M, Maibach R, Alaggio R, Czauderna P, Morland B, Branchereau S, Cairo S, Kappler R, Armengol C, di Bernardo D. Computational drug prediction in hepatoblastoma by integrating pan-cancer transcriptomics with pharmacological response. Hepatology 2024; 80:55-68. [PMID: 37729391 PMCID: PMC11185924 DOI: 10.1097/hep.0000000000000601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 08/11/2023] [Indexed: 09/22/2023]
Abstract
BACKGROUND AND AIMS Hepatoblastoma (HB) is the predominant form of pediatric liver cancer, though it remains exceptionally rare. While treatment outcomes for children with HB have improved, patients with advanced tumors face limited therapeutic choices. Additionally, survivors often suffer from long-term adverse effects due to treatment, including ototoxicity, cardiotoxicity, delayed growth, and secondary tumors. Consequently, there is a pressing need to identify new and effective therapeutic strategies for patients with HB. Computational methods to predict drug sensitivity from a tumor's transcriptome have been successfully applied for some common adult malignancies, but specific efforts in pediatric cancers are lacking because of the paucity of data. APPROACH AND RESULTS In this study, we used DrugSense to assess drug efficacy in patients with HB, particularly those with the aggressive C2 subtype associated with poor clinical outcomes. Our method relied on publicly available collections of pan-cancer transcriptional profiles and drug responses across 36 tumor types and 495 compounds. The drugs predicted to be most effective were experimentally validated using patient-derived xenograft models of HB grown in vitro and in vivo. We thus identified 2 cyclin-dependent kinase 9 inhibitors, alvocidib and dinaciclib as potent HB growth inhibitors for the high-risk C2 molecular subtype. We also found that in a cohort of 46 patients with HB, high cyclin-dependent kinase 9 tumor expression was significantly associated with poor prognosis. CONCLUSIONS Our work proves the usefulness of computational methods trained on pan-cancer data sets to reposition drugs in rare pediatric cancers such as HB, and to help clinicians in choosing the best treatment options for their patients.
Collapse
Affiliation(s)
- Mario Failli
- Telethon Institute of Genetics and Medicine, Pozzuoli, Naples, Italy
- Department of Chemical, Materials and Industrial Production Engineering, University of Naples “Federico II”, Naples, Italy
| | - Salih Demir
- Department of Pediatric Surgery, Dr. von Hauner Children’s Hospital, University Hospital, LMU Munich, Germany
| | - Álvaro Del Río-Álvarez
- Childhood Liver Oncology Group (c-LOG), Health Sciences Research Institute Germans Trias i Pujol (IGTP), Badalona, Catalonia, Spain
| | - Juan Carrillo-Reixach
- Childhood Liver Oncology Group (c-LOG), Health Sciences Research Institute Germans Trias i Pujol (IGTP), Badalona, Catalonia, Spain
- Nottingham Clinical Trials Unit, Nottingham, United Kingdom
| | - Laura Royo
- Childhood Liver Oncology Group (c-LOG), Health Sciences Research Institute Germans Trias i Pujol (IGTP), Badalona, Catalonia, Spain
| | - Montserrat Domingo-Sàbat
- Childhood Liver Oncology Group (c-LOG), Health Sciences Research Institute Germans Trias i Pujol (IGTP), Badalona, Catalonia, Spain
| | | | - Rudolf Maibach
- International Breast Cancer Study Group Coordinating Center, Bern, Switzerland
| | - Rita Alaggio
- Pathology Unit, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Piotr Czauderna
- Department of Surgery and Urology for Children and Adolescents, Medical University of Gdansk, Gdansk, Poland
| | - Bruce Morland
- Department of Oncology, Birmingham Women’s and Children’s Hospital, Birmingham, United Kingdom
| | | | - Stefano Cairo
- XenTech, Evry, France
- Champions Oncology, Rockville, Maryland, USA
| | - Roland Kappler
- Department of Pediatric Surgery, Dr. von Hauner Children’s Hospital, University Hospital, LMU Munich, Germany
| | - Carolina Armengol
- Childhood Liver Oncology Group (c-LOG), Health Sciences Research Institute Germans Trias i Pujol (IGTP), Badalona, Catalonia, Spain
- Liver and Digestive Diseases Networking Biomedical Research Centre (CIBEREHD), Madrid, Spain
| | - Diego di Bernardo
- Telethon Institute of Genetics and Medicine, Pozzuoli, Naples, Italy
- Department of Chemical, Materials and Industrial Production Engineering, University of Naples “Federico II”, Naples, Italy
| |
Collapse
|
2
|
Cappelletti L, Rekerle L, Fontana T, Hansen P, Casiraghi E, Ravanmehr V, Mungall CJ, Yang JJ, Spranger L, Karlebach G, Caufield JH, Carmody L, Coleman B, Oprea TI, Reese J, Valentini G, Robinson PN. Node-degree aware edge sampling mitigates inflated classification performance in biomedical random walk-based graph representation learning. BIOINFORMATICS ADVANCES 2024; 4:vbae036. [PMID: 38577542 PMCID: PMC10994718 DOI: 10.1093/bioadv/vbae036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 01/31/2024] [Accepted: 02/29/2024] [Indexed: 04/06/2024]
Abstract
Motivation Graph representation learning is a family of related approaches that learn low-dimensional vector representations of nodes and other graph elements called embeddings. Embeddings approximate characteristics of the graph and can be used for a variety of machine-learning tasks such as novel edge prediction. For many biomedical applications, partial knowledge exists about positive edges that represent relationships between pairs of entities, but little to no knowledge is available about negative edges that represent the explicit lack of a relationship between two nodes. For this reason, classification procedures are forced to assume that the vast majority of unlabeled edges are negative. Existing approaches to sampling negative edges for training and evaluating classifiers do so by uniformly sampling pairs of nodes. Results We show here that this sampling strategy typically leads to sets of positive and negative examples with imbalanced node degree distributions. Using representative heterogeneous biomedical knowledge graph and random walk-based graph machine learning, we show that this strategy substantially impacts classification performance. If users of graph machine-learning models apply the models to prioritize examples that are drawn from approximately the same distribution as the positive examples are, then performance of models as estimated in the validation phase may be artificially inflated. We present a degree-aware node sampling approach that mitigates this effect and is simple to implement. Availability and implementation Our code and data are publicly available at https://github.com/monarch-initiative/negativeExampleSelection.
Collapse
Affiliation(s)
- Luca Cappelletti
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Milano 20133, Italy
| | - Lauren Rekerle
- The Jackson Laboratory for Genomic Medicine, CT 06032, United States
| | - Tommaso Fontana
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Milano 20133, Italy
| | - Peter Hansen
- The Jackson Laboratory for Genomic Medicine, CT 06032, United States
| | - Elena Casiraghi
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Milano 20133, Italy
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, United States
| | - Vida Ravanmehr
- The Jackson Laboratory for Genomic Medicine, CT 06032, United States
| | - Christopher J Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, United States
| | - Jeremy J Yang
- Department of Internal Medicine and UNM Comprehensive Cancer Center, UNM School of Medicine, Albuquerque, NM 87102, United States
| | - Leonard Spranger
- Institute of Bioinformatics, Freie Universität Berlin, Berlin, 14195, Germany
| | - Guy Karlebach
- The Jackson Laboratory for Genomic Medicine, CT 06032, United States
| | - J Harry Caufield
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, United States
| | - Leigh Carmody
- The Jackson Laboratory for Genomic Medicine, CT 06032, United States
| | - Ben Coleman
- The Jackson Laboratory for Genomic Medicine, CT 06032, United States
- Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, United States
| | - Tudor I Oprea
- Department of Internal Medicine and UNM Comprehensive Cancer Center, UNM School of Medicine, Albuquerque, NM 87102, United States
| | - Justin Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, United States
| | - Giorgio Valentini
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Milano 20133, Italy
- ELLIS—European Laboratory for Learning and Intelligent Systems
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, CT 06032, United States
- Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, United States
- ELLIS—European Laboratory for Learning and Intelligent Systems
- Berlin Institute of Health, Charité – Universitätsmedizin Berlin, Berlin, 10117, Germany
| |
Collapse
|
3
|
Dou Y, Ren Y, Zhao X, Jin J, Xiong S, Luo L, Xu X, Yang X, Yu J, Guo L, Liang T. CSSLdb: Discovery of cancer-specific synthetic lethal interactions based on machine learning and statistic inference. Comput Biol Med 2024; 170:108066. [PMID: 38310806 DOI: 10.1016/j.compbiomed.2024.108066] [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] [Received: 11/04/2023] [Revised: 12/22/2023] [Accepted: 01/27/2024] [Indexed: 02/06/2024]
Abstract
Synthetic lethality (SL) occurs when the inactivation of two genes results in cell death while the inactivation of either gene alone is non-lethal. SL-based therapy has become a promising anti-cancer treatment option with the increasing researches and applications in clinical practice, while the specific therapeutic opportunities for various cancers have not yet been comprehensively investigated. Herein, we described a computational approach based on machine learning and statistical inference to discover the cancer-specific synthetic lethal interactions. First, Random Forest and One-Class SVM were used to perform cancer unbiased prediction of synthetic lethality. Then, two strategies, including mutual exclusivity and differential expression, were used to screen cancer-specific synthetic lethal interactions, resulting in 14,582 SL gene pairs in 33 cancer types. Finally, we developed a freely available database of CSSLdb (Cancer Specific Synthetic Lethality Database, http://www.tmliang.cn/CSSL/) to present cancer-specific synthetic lethal genetic interactions, which would enrich the relevant research and contribute to underlying therapy strategies based on synthetic lethality.
Collapse
Affiliation(s)
- Yuyang Dou
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Yujie Ren
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Xinmiao Zhao
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Jiaming Jin
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Shizheng Xiong
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Lulu Luo
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, School of Life Science, Nanjing Normal University, Nanjing, 210023, China
| | - Xinru Xu
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, School of Life Science, Nanjing Normal University, Nanjing, 210023, China
| | - Xueni Yang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Jiafeng Yu
- Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, 253023, China
| | - Li Guo
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.
| | - Tingming Liang
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, School of Life Science, Nanjing Normal University, Nanjing, 210023, China.
| |
Collapse
|
4
|
Schäffer AA, Chung Y, Kammula AV, Ruppin E, Lee JS. A systematic analysis of the landscape of synthetic lethality-driven precision oncology. MED 2024; 5:73-89.e9. [PMID: 38218178 DOI: 10.1016/j.medj.2023.12.009] [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] [Received: 07/07/2023] [Revised: 09/10/2023] [Accepted: 12/13/2023] [Indexed: 01/15/2024]
Abstract
BACKGROUND Synthetic lethality (SL) denotes a genetic interaction between two genes whose co-inactivation is detrimental to cells. Because more than 25 years have passed since SL was proposed as a promising way to selectively target cancer vulnerabilities, it is timely to comprehensively assess its impact so far and discuss its future. METHODS We systematically analyzed the literature and clinical trial data from the PubMed and Trialtrove databases to portray the preclinical and clinical landscape of SL oncology. FINDINGS We identified 235 preclinically validated SL pairs and found 1,207 pertinent clinical trials, and the number keeps increasing over time. About one-third of these SL clinical trials go beyond the typically studied DNA damage response (DDR) pathway, testifying to the recently broadening scope of SL applications in clinical oncology. We find that SL oncology trials have a greater success rate than non-SL-based trials. However, about 75% of the preclinically validated SL interactions have not yet been tested in clinical trials. CONCLUSIONS Dissecting the recent efforts harnessing SL to identify predictive biomarkers, novel therapeutic targets, and effective combination therapy, our systematic analysis reinforces the hope that SL may serve as a key driver of precision oncology going forward. FUNDING Funded by the Samsung Research Funding & Incubation Center of Samsung Electronics, the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Republic of Korea government (MSIT), the Kwanjeong Educational Foundation, the Intramural Research Program of the National Institutes of Health (NIH), National Cancer Institute (NCI), and Center for Cancer Research (CCR).
Collapse
Affiliation(s)
- Alejandro A Schäffer
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Youngmin Chung
- Department of Artificial Intelligence, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Ashwin V Kammula
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Joo Sang Lee
- Department of Artificial Intelligence, Sungkyunkwan University, Suwon 16419, Republic of Korea; Department of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea; Department of Digital Health & Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology, Samsung Medical Center, Sungkyunkwan University, Seoul 06351, Republic of Korea.
| |
Collapse
|
5
|
Tepeli YI, Seale C, Gonçalves JP. ELISL: early-late integrated synthetic lethality prediction in cancer. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btad764. [PMID: 38113447 DOI: 10.1093/bioinformatics/btad764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 11/06/2023] [Accepted: 12/18/2023] [Indexed: 12/21/2023]
Abstract
MOTIVATION Anti-cancer therapies based on synthetic lethality (SL) exploit tumour vulnerabilities for treatment with reduced side effects, by targeting a gene that is jointly essential with another whose function is lost. Computational prediction is key to expedite SL screening, yet existing methods are vulnerable to prevalent selection bias in SL data and reliant on cancer or tissue type-specific omics, which can be scarce. Notably, sequence similarity remains underexplored as a proxy for related gene function and joint essentiality. RESULTS We propose ELISL, Early-Late Integrated SL prediction with forest ensembles, using context-free protein sequence embeddings and context-specific omics from cell lines and tissue. Across eight cancer types, ELISL showed superior robustness to selection bias and recovery of known SL genes, as well as promising cross-cancer predictions. Co-occurring mutations in a BRCA gene and ELISL-predicted pairs from the HH, FGF, WNT, or NEIL gene families were associated with longer patient survival times, revealing therapeutic potential. AVAILABILITY AND IMPLEMENTATION Data: 10.6084/m9.figshare.23607558 & Code: github.com/joanagoncalveslab/ELISL.
Collapse
Affiliation(s)
- Yasin I Tepeli
- Pattern Recognition & Bioinformatics, Department of Intelligent Systems, Faculty EEMCS, Delft University of Technology, Delft, The Netherlands
| | - Colm Seale
- Pattern Recognition & Bioinformatics, Department of Intelligent Systems, Faculty EEMCS, Delft University of Technology, Delft, The Netherlands
- Holland Proton Therapy Center (HollandPTC), Delft, The Netherlands
| | - Joana P Gonçalves
- Pattern Recognition & Bioinformatics, Department of Intelligent Systems, Faculty EEMCS, Delft University of Technology, Delft, The Netherlands
| |
Collapse
|
6
|
Karimpour M, Totonchi M, Behmanesh M, Montazeri H. Pathway-driven analysis of synthetic lethal interactions in cancer using perturbation screens. Life Sci Alliance 2024; 7:e202302268. [PMID: 37863651 PMCID: PMC10589366 DOI: 10.26508/lsa.202302268] [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: 07/11/2023] [Revised: 10/10/2023] [Accepted: 10/10/2023] [Indexed: 10/22/2023] Open
Abstract
Synthetic lethality offers a promising approach for developing effective therapeutic interventions in cancer when direct targeting of driver genes is impractical. In this study, we comprehensively analyzed large-scale CRISPR, shRNA, and PRISM screens to identify potential synthetic lethal (SL) interactions in pan-cancer and 12 individual cancer types, using a new computational framework that leverages the biological function and signaling pathway information of key driver genes to mitigate the confounding effects of background genetic alterations in different cancer cell lines. This approach has successfully identified several putative SL interactions, including KRAS-MAP3K2 and APC-TCF7L2 in pan cancer, and CCND1-METTL1, TP53-FRS3, SMO-MDM2, and CCNE1-MTOR in liver, blood, skin, and gastric cancers, respectively. In addition, we proposed several FDA-approved cancer-targeted drugs for various cancer types through PRISM drug screens, such as cabazitaxel for VHL-mutated kidney cancer and alectinib for lung cancer with NRAS or KRAS mutations. Leveraging pathway information can enhance the concordance of shRNA and CRISPR screens and provide clinically relevant findings such as the potential efficacy of dasatinib, an inhibitor of SRC, for colorectal cancer patients with mutations in the WNT signaling pathway. These analyses revealed that taking signaling pathway information into account results in the identification of more promising SL interactions.
Collapse
Affiliation(s)
- Mina Karimpour
- Department of Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mehdi Totonchi
- Department of Genetics, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehrdad Behmanesh
- Department of Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Hesam Montazeri
- https://ror.org/05vf56z40 Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| |
Collapse
|
7
|
Wang X, Yang L, Yu C, Ling X, Guo C, Chen R, Li D, Liu Z. An integrated computational strategy to predict personalized cancer drug combinations by reversing drug resistance signatures. Comput Biol Med 2023; 163:107230. [PMID: 37418899 DOI: 10.1016/j.compbiomed.2023.107230] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 06/22/2023] [Accepted: 07/01/2023] [Indexed: 07/09/2023]
Abstract
Drug resistance currently poses the greatest barrier to cancer treatments. To overcome drug resistance, drug combination therapy has been proposed as a promising treatment strategy. Herein, we present Re-Sensitizing Drug Prediction (RSDP), a novel computational strategy, for predicting the personalized cancer drug combination A + B by reversing the resistance signature of drug A. The process integrates multiple biological features using a robust rank aggregation algorithm, including Connectivity Map, synthetic lethality, synthetic rescue, pathway, and drug target. Bioinformatics assessments revealed that RSDP achieved a relatively accurate prediction performance for identifying personalized combinational re-sensitizing drug B against cell line-specific intrinsic resistance, cell line-specific acquired resistance, and patient-specific intrinsic resistance to drug A. In addition, we developed the largest resource of cell line-specific cancer drug resistance signatures, including intrinsic and acquired resistance, as a byproduct of the proposed strategy. The findings indicate that personalized drug resistance signature reversal is a promising strategy for identifying personalized drug combinations, which may guide future clinical decisions regarding personalized medicine.
Collapse
Affiliation(s)
- Xun Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Lele Yang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China; College of Chemistry and Environmental Science, Hebei University, Baoding, 071002, China
| | - Chuang Yu
- China Astronaut Research and Training Center, Beijing, 100094, China
| | - Xinping Ling
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Congcong Guo
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Ruzhen Chen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Dong Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China; College of Chemistry and Environmental Science, Hebei University, Baoding, 071002, China.
| | - Zhongyang Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China; College of Chemistry and Environmental Science, Hebei University, Baoding, 071002, China.
| |
Collapse
|
8
|
Staheli JP, Neal ML, Navare A, Mast FD, Aitchison JD. Predicting host-based, synthetic lethal antiviral targets from omics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.15.553430. [PMID: 37645861 PMCID: PMC10462099 DOI: 10.1101/2023.08.15.553430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Traditional antiviral therapies often have limited effectiveness due to toxicity and development of drug resistance. Host-based antivirals, while an alternative, may lead to non-specific effects. Recent evidence shows that virus-infected cells can be selectively eliminated by targeting synthetic lethal (SL) partners of proteins disrupted by viral infection. Thus, we hypothesized that genes depleted in CRISPR KO screens of virus-infected cells may be enriched in SL partners of proteins altered by infection. To investigate this, we established a computational pipeline predicting SL drug targets of viral infections. First, we identified SARS-CoV-2-induced changes in gene products via a large compendium of omics data. Second, we identified SL partners for each altered gene product. Last, we screened CRISPR KO data for SL partners required for cell viability in infected cells. Despite differences in virus-induced alterations detected by various omics data, they share many predicted SL targets, with significant enrichment in CRISPR KO-depleted datasets. Comparing data from SARS-CoV-2 and influenza infections, we found possible broad-spectrum, host-based antiviral SL targets. This suggests that CRISPR KO data are replete with common antiviral targets due to their SL relationship with virus-altered states and that such targets can be revealed from analysis of omics datasets and SL predictions.
Collapse
Affiliation(s)
- Jeannette P. Staheli
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington, 98101, USA
| | - Maxwell L. Neal
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington, 98101, USA
| | - Arti Navare
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington, 98101, USA
| | - Fred D. Mast
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington, 98101, USA
| | - John D. Aitchison
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington, 98101, USA
| |
Collapse
|
9
|
Barrena N, Valcárcel LV, Olaverri-Mendizabal D, Apaolaza I, Planes FJ. Synthetic lethality in large-scale integrated metabolic and regulatory network models of human cells. NPJ Syst Biol Appl 2023; 9:32. [PMID: 37454223 DOI: 10.1038/s41540-023-00296-3] [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: 01/16/2023] [Accepted: 07/04/2023] [Indexed: 07/18/2023] Open
Abstract
Synthetic lethality (SL) is a promising concept in cancer research. A wide array of computational tools has been developed to predict and exploit synthetic lethality for the identification of tumour-specific vulnerabilities. Previously, we introduced the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to SL developed for genome-scale metabolic networks. The major challenge in our gMCS framework is to go beyond metabolic networks and extend existing algorithms to more complex protein-protein interactions. In this article, we take a step further and incorporate linear regulatory pathways into our gMCS approach. Extensive algorithmic modifications to compute gMCSs in integrated metabolic and regulatory models are presented in detail. Our extended approach is applied to calculate gMCSs in integrated models of human cells. In particular, we integrate the most recent genome-scale metabolic network, Human1, with 3 different regulatory network databases: Omnipath, Dorothea and TRRUST. Based on the computed gMCSs and transcriptomic data, we discovered new essential genes and their associated synthetic lethal for different cancer cell lines. The performance of the different integrated models is assessed with available large-scale in-vitro gene silencing data. Finally, we discuss the most relevant gene essentiality predictions based on published literature in cancer research.
Collapse
Affiliation(s)
- Naroa Barrena
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Luis V Valcárcel
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
- University of Navarra, Biomedical Engineering Center, Campus Universitario, 31009, Pamplona, Navarra, Spain
- University of Navarra, Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, 31080, Pamplona, Spain
| | - Danel Olaverri-Mendizabal
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Iñigo Apaolaza
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
- University of Navarra, Biomedical Engineering Center, Campus Universitario, 31009, Pamplona, Navarra, Spain
- University of Navarra, Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, 31080, Pamplona, Spain
| | - Francisco J Planes
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain.
- University of Navarra, Biomedical Engineering Center, Campus Universitario, 31009, Pamplona, Navarra, Spain.
- University of Navarra, Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, 31080, Pamplona, Spain.
| |
Collapse
|
10
|
Bod L, Kye YC, Shi J, Torlai Triglia E, Schnell A, Fessler J, Ostrowski SM, Von-Franque MY, Kuchroo JR, Barilla RM, Zaghouani S, Christian E, Delorey TM, Mohib K, Xiao S, Slingerland N, Giuliano CJ, Ashenberg O, Li Z, Rothstein DM, Fisher DE, Rozenblatt-Rosen O, Sharpe AH, Quintana FJ, Apetoh L, Regev A, Kuchroo VK. B-cell-specific checkpoint molecules that regulate anti-tumour immunity. Nature 2023; 619:348-356. [PMID: 37344597 PMCID: PMC10795478 DOI: 10.1038/s41586-023-06231-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 05/17/2023] [Indexed: 06/23/2023]
Abstract
The role of B cells in anti-tumour immunity is still debated and, accordingly, immunotherapies have focused on targeting T and natural killer cells to inhibit tumour growth1,2. Here, using high-throughput flow cytometry as well as bulk and single-cell RNA-sequencing and B-cell-receptor-sequencing analysis of B cells temporally during B16F10 melanoma growth, we identified a subset of B cells that expands specifically in the draining lymph node over time in tumour-bearing mice. The expanding B cell subset expresses the cell surface molecule T cell immunoglobulin and mucin domain 1 (TIM-1, encoded by Havcr1) and a unique transcriptional signature, including multiple co-inhibitory molecules such as PD-1, TIM-3, TIGIT and LAG-3. Although conditional deletion of these co-inhibitory molecules on B cells had little or no effect on tumour burden, selective deletion of Havcr1 in B cells both substantially inhibited tumour growth and enhanced effector T cell responses. Loss of TIM-1 enhanced the type 1 interferon response in B cells, which augmented B cell activation and increased antigen presentation and co-stimulation, resulting in increased expansion of tumour-specific effector T cells. Our results demonstrate that manipulation of TIM-1-expressing B cells enables engagement of the second arm of adaptive immunity to promote anti-tumour immunity and inhibit tumour growth.
Collapse
Affiliation(s)
- Lloyd Bod
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Massachusetts General Hospital Cancer Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yoon-Chul Kye
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jingwen Shi
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- BeiGene, Beijing, China
| | - Elena Torlai Triglia
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alexandra Schnell
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Johannes Fessler
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Division of Immunology and Pathophysiology, Medical University of Graz, Graz, Austria
| | | | - Max Y Von-Franque
- Department of Dermatology, Massachusetts General Hospital, Boston, MA, USA
| | - Juhi R Kuchroo
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA, USA
| | - Rocky M Barilla
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sarah Zaghouani
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
| | - Elena Christian
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Toni Marie Delorey
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kanishka Mohib
- Thomas E. Starzl Transplantation Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Sheng Xiao
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
| | - Nadine Slingerland
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Orr Ashenberg
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Zhaorong Li
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - David M Rothstein
- Thomas E. Starzl Transplantation Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - David E Fisher
- Department of Dermatology, Massachusetts General Hospital, Boston, MA, USA
| | - Orit Rozenblatt-Rosen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Howard Hughes Medical Institute, Department of Biology and Koch Institute of Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Arlene H Sharpe
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA, USA
| | - Francisco J Quintana
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Howard Hughes Medical Institute, Department of Biology and Koch Institute of Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lionel Apetoh
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- INSERM, Tours, France
- Faculté de Médecine, Université de Tours, Tours, France
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Howard Hughes Medical Institute, Department of Biology and Koch Institute of Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Genentech, San Francisco, CA, USA.
| | - Vijay K Kuchroo
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA.
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
11
|
Hanover G, Vizeacoumar FS, Banerjee SL, Nair R, Dahiya R, Osornio-Hernandez AI, Morales AM, Freywald T, Himanen JP, Toosi BM, Bisson N, Vizeacoumar FJ, Freywald A. Integration of cancer-related genetic landscape of Eph receptors and ephrins with proteomics identifies a crosstalk between EPHB6 and EGFR. Cell Rep 2023; 42:112670. [PMID: 37392382 DOI: 10.1016/j.celrep.2023.112670] [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: 12/07/2022] [Revised: 05/05/2023] [Accepted: 06/05/2023] [Indexed: 07/03/2023] Open
Abstract
Eph receptors and their ephrin ligands are viewed as promising targets for cancer treatment; however, targeting them is hindered by their context-dependent functionalities. To circumvent this, we explore molecular landscapes underlying their pro- and anti-malignant activities. Using unbiased bioinformatics approaches, we construct a cancer-related network of genetic interactions (GIs) of all Ephs and ephrins to assist in their therapeutic manipulation. We also apply genetic screening and BioID proteomics and integrate them with machine learning approaches to select the most relevant GIs of one Eph receptor, EPHB6. This identifies a crosstalk between EPHB6 and EGFR, and further experiments confirm the ability of EPHB6 to modulate EGFR signaling, enhancing the proliferation of cancer cells and tumor development. Taken together, our observations show EPHB6 involvement in EGFR action, suggesting its targeting might be beneficial in EGFR-dependent tumors, and confirm that the Eph family genetic interactome presented here can be effectively exploited in developing cancer treatment approaches.
Collapse
Affiliation(s)
- Glinton Hanover
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Royal University Hospital, Room 2841, 103 Hospital Drive, Saskatoon, SK S7N 0W8, Canada; Department of Biochemistry, Microbiology and Immunology, University of Saskatchewan, GA20 Health Sciences, 107 Wiggins Road, Saskatoon, SK S7N 5E5, Canada
| | - Frederick S Vizeacoumar
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Royal University Hospital, Room 2841, 103 Hospital Drive, Saskatoon, SK S7N 0W8, Canada
| | - Sara L Banerjee
- Department of Molecular Biology, Medical Biochemistry and Pathology, PROTEO and Centre de Recherche du Centre Hospitalier Universitaire (CHU) de Quebec-Université Laval, Division Oncologie, 9 Rue McMahon, Québec, QC G1R 3S3, Canada
| | - Raveena Nair
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Royal University Hospital, Room 2841, 103 Hospital Drive, Saskatoon, SK S7N 0W8, Canada; Department of Biochemistry, Microbiology and Immunology, University of Saskatchewan, GA20 Health Sciences, 107 Wiggins Road, Saskatoon, SK S7N 5E5, Canada
| | - Renuka Dahiya
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Royal University Hospital, Room 2841, 103 Hospital Drive, Saskatoon, SK S7N 0W8, Canada
| | - Ana I Osornio-Hernandez
- Department of Molecular Biology, Medical Biochemistry and Pathology, PROTEO and Centre de Recherche du Centre Hospitalier Universitaire (CHU) de Quebec-Université Laval, Division Oncologie, 9 Rue McMahon, Québec, QC G1R 3S3, Canada
| | - Alain Morejon Morales
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Royal University Hospital, Room 2841, 103 Hospital Drive, Saskatoon, SK S7N 0W8, Canada; Department of Biochemistry, Microbiology and Immunology, University of Saskatchewan, GA20 Health Sciences, 107 Wiggins Road, Saskatoon, SK S7N 5E5, Canada
| | - Tanya Freywald
- Cancer Research, Saskatchewan Cancer Agency and Division of Oncology, University of Saskatchewan, 4D30.2 Health Sciences Building, 107 Wiggins Road, Saskatoon, SK S7N 5E5, Canada
| | - Juha P Himanen
- Department of Biochemistry, University of Turku, 20500 Turku, Finland
| | - Behzad M Toosi
- Department of Small Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, 52 Campus Drive, Saskatoon, SK S7N 5B4, Canada
| | - Nicolas Bisson
- Department of Molecular Biology, Medical Biochemistry and Pathology, PROTEO and Centre de Recherche du Centre Hospitalier Universitaire (CHU) de Quebec-Université Laval, Division Oncologie, 9 Rue McMahon, Québec, QC G1R 3S3, Canada.
| | - Franco J Vizeacoumar
- Cancer Research, Saskatchewan Cancer Agency and Division of Oncology, University of Saskatchewan, 4D30.2 Health Sciences Building, 107 Wiggins Road, Saskatoon, SK S7N 5E5, Canada.
| | - Andrew Freywald
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Royal University Hospital, Room 2841, 103 Hospital Drive, Saskatoon, SK S7N 0W8, Canada.
| |
Collapse
|
12
|
Nair NU, Greninger P, Zhang X, Friedman AA, Amzallag A, Cortez E, Sahu AD, Lee JS, Dastur A, Egan RK, Murchie E, Ceribelli M, Crowther GS, Beck E, McClanaghan J, Klump-Thomas C, Boisvert JL, Damon LJ, Wilson KM, Ho J, Tam A, McKnight C, Michael S, Itkin Z, Garnett MJ, Engelman JA, Haber DA, Thomas CJ, Ruppin E, Benes CH. A landscape of response to drug combinations in non-small cell lung cancer. Nat Commun 2023; 14:3830. [PMID: 37380628 PMCID: PMC10307832 DOI: 10.1038/s41467-023-39528-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 06/14/2023] [Indexed: 06/30/2023] Open
Abstract
Combination of anti-cancer drugs is broadly seen as way to overcome the often-limited efficacy of single agents. The design and testing of combinations are however very challenging. Here we present a uniquely large dataset screening over 5000 targeted agent combinations across 81 non-small cell lung cancer cell lines. Our analysis reveals a profound heterogeneity of response across the tumor models. Notably, combinations very rarely result in a strong gain in efficacy over the range of response observable with single agents. Importantly, gain of activity over single agents is more often seen when co-targeting functionally proximal genes, offering a strategy for designing more efficient combinations. Because combinatorial effect is strongly context specific, tumor specificity should be achievable. The resource provided, together with an additional validation screen sheds light on major challenges and opportunities in building efficacious combinations against cancer and provides an opportunity for training computational models for synergy prediction.
Collapse
Affiliation(s)
- Nishanth Ulhas Nair
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Xiaohu Zhang
- Howard Hughes Medical Institute, Bethesda, MD, USA
| | - Adam A Friedman
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Arnaud Amzallag
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Eliane Cortez
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Avinash Das Sahu
- University of New Mexico, Comprehensive Cancer Center, Albuquerque, NM, USA
| | - Joo Sang Lee
- Samsung Medical Center, Sungkyunkwan University School of Medicine, Suwon, 16419, Republic of Korea
| | - Anahita Dastur
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Regina K Egan
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ellen Murchie
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | | | - Erin Beck
- Howard Hughes Medical Institute, Bethesda, MD, USA
| | | | | | | | - Leah J Damon
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Jeffrey Ho
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Angela Tam
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Sam Michael
- Howard Hughes Medical Institute, Bethesda, MD, USA
| | - Zina Itkin
- Howard Hughes Medical Institute, Bethesda, MD, USA
| | - Mathew J Garnett
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK
| | | | - Daniel A Haber
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Howard Hughes Medical Institute, Bethesda, MD, USA
| | - Craig J Thomas
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institute of Health, Rockville, MD, 20850, USA
- Lymphoid Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Cyril H Benes
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
13
|
Gomari D, Achkar IW, Benedetti E, Tabling J, Halama A, Krumsiek J. piTracer - Automatic reconstruction of molecular cascades for the identification of synergistic drug targets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.08.535933. [PMID: 37066188 PMCID: PMC10104160 DOI: 10.1101/2023.04.08.535933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Cancer cells frequently undergo metabolic reprogramming as a mechanism of resistance against chemotherapeutic drugs. Metabolomic profiling provides a direct readout of metabolic changes and can thus be used to identify these tumor escape mechanisms. Here, we introduce piTracer, a computational tool that uses multi-scale molecular networks to identify potential combination therapies from pre- and post-treatment metabolomics data. We first demonstrate piTracer’s core ability to reconstruct cellular cascades by inspecting well-characterized molecular pathways and previously studied associations between genetic variants and metabolite levels. We then apply a new gene ranking algorithm on differential metabolomic profiles from human breast cancer cells after glutaminase inhibition. Four of the automatically identified gene targets were experimentally tested by simultaneous inhibition of the respective targets and glutaminase. Of these combination treatments, two were be confirmed to induce synthetic lethality in the cell line. In summary, piTracer integrates the molecular monitoring of escape mechanisms into comprehensive pathway networks to accelerate drug target identification. The tool is open source and can be accessed at https://github.com/krumsieklab/pitracer .
Collapse
|
14
|
Weng S, Ruan H. Multi-omics characterization of synthetic lethality-related molecular features: implications for SL-based therapeutic target screening. FEBS J 2023; 290:1477-1480. [PMID: 36461713 DOI: 10.1111/febs.16692] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Accepted: 11/25/2022] [Indexed: 12/05/2022]
Abstract
Synthetic lethality (SL) represents the co-occurrence of two or more non-lethal disordered genes that could lead to cell death. SL-based anticancer therapeutics could specifically kill the cancer cells carrying the targeted mutated gene while leaving normal cells alive. Recent large-scale computational and experimental screenings provide rich resources of SL information while lacking systematic research on molecular features of SL genes. Combined with comprehensive multi-omics data analysis and experimental validation of one SL gene pair, Guo et al. portrayed a systematic layout of cancer-specific SL interactions that could improve understanding of carcinogenesis and potentially assist the subsequent screening of anticancer therapeutic targets.
Collapse
Affiliation(s)
- Shenghui Weng
- Institutes of Biology and Medical Sciences, Soochow University, China
| | - Hang Ruan
- Institutes of Biology and Medical Sciences, Soochow University, China
| |
Collapse
|
15
|
Dinstag G, Shulman ED, Elis E, Ben-Zvi DS, Tirosh O, Maimon E, Meilijson I, Elalouf E, Temkin B, Vitkovsky P, Schiff E, Hoang DT, Sinha S, Nair NU, Lee JS, Schäffer AA, Ronai Z, Juric D, Apolo AB, Dahut WL, Lipkowitz S, Berger R, Kurzrock R, Papanicolau-Sengos A, Karzai F, Gilbert MR, Aldape K, Rajagopal PS, Beker T, Ruppin E, Aharonov R. Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome. MED 2023; 4:15-30.e8. [PMID: 36513065 PMCID: PMC10029756 DOI: 10.1016/j.medj.2022.11.001] [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: 04/11/2022] [Revised: 08/30/2022] [Accepted: 10/31/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. METHODS We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient's response to a variety of therapies in multiple cancer types without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: personalized oncology (PO), aimed at prioritizing treatments for a single patient, and clinical trial design (CTD), selecting the most likely responders in a patient cohort. FINDINGS Evaluating ENLIGHT's performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patient's treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable with that of supervised predictors developed for specific indications and drugs. In combination with the interferon-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy. CONCLUSIONS ENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome. FUNDING This research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Isaac Meilijson
- Pangea Biomed Ltd., Tel Aviv, Israel; Tel Aviv University, Tel Aviv, Israel
| | | | | | | | | | - Danh-Tai Hoang
- Biological Data Science Institute, College of Science, The Australian National University, Canberra, ACT, Australia
| | - Sanju Sinha
- Cancer Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nishanth Ulhas Nair
- Cancer Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Joo Sang Lee
- Department of Precision Medicine, School of Medicine & Department of Artificial Intelligence, Sungkyunkwan University, Suwon, Republic of Korea
| | - Alejandro A Schäffer
- Cancer Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ze'ev Ronai
- Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Dejan Juric
- Department of Medicine, Massachusetts General Hospital Cancer Center, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Andrea B Apolo
- Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - William L Dahut
- Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stanley Lipkowitz
- Women's Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Raanan Berger
- Cancer Center, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Razelle Kurzrock
- Worldwide Innovative Network (WIN) for Personalized Cancer Therapy, Chevilly-Larue, France
| | | | - Fatima Karzai
- Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mark R Gilbert
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Kenneth Aldape
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Padma S Rajagopal
- Cancer Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Women's Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Eytan Ruppin
- Cancer Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | | |
Collapse
|
16
|
Fan K, Tang S, Gökbağ B, Cheng L, Li L. Multi-view graph convolutional network for cancer cell-specific synthetic lethality prediction. Front Genet 2023; 13:1103092. [PMID: 36699450 PMCID: PMC9868610 DOI: 10.3389/fgene.2022.1103092] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 12/22/2022] [Indexed: 01/11/2023] Open
Abstract
Synthetic lethal (SL) genetic interactions have been regarded as a promising focus for investigating potential targeted therapeutics to tackle cancer. However, the costly investment of time and labor associated with wet-lab experimental screenings to discover potential SL relationships motivates the development of computational methods. Although graph neural network (GNN) models have performed well in the prediction of SL gene pairs, existing GNN-based models are not designed for predicting cancer cell-specific SL interactions that are more relevant to experimental validation in vitro. Besides, neither have existing methods fully utilized diverse graph representations of biological features to improve prediction performance. In this work, we propose MVGCN-iSL, a novel multi-view graph convolutional network (GCN) model to predict cancer cell-specific SL gene pairs, by incorporating five biological graph features and multi-omics data. Max pooling operation is applied to integrate five graph-specific representations obtained from GCN models. Afterwards, a deep neural network (DNN) model serves as the prediction module to predict the SL interactions in individual cancer cells (iSL). Extensive experiments have validated the model's successful integration of the multiple graph features and state-of-the-art performance in the prediction of potential SL gene pairs as well as generalization ability to novel genes.
Collapse
Affiliation(s)
- Kunjie Fan
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Shan Tang
- College of Pharmacy, The Ohio State University, Columbus, OH, United States
| | - Birkan Gökbağ
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Lijun Cheng
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Lang Li
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States,College of Pharmacy, The Ohio State University, Columbus, OH, United States,*Correspondence: Lang Li,
| |
Collapse
|
17
|
Discovery of synthetic lethal interactions from large-scale pan-cancer perturbation screens. Nat Commun 2022; 13:7748. [PMID: 36517508 PMCID: PMC9751287 DOI: 10.1038/s41467-022-35378-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 11/29/2022] [Indexed: 12/15/2022] Open
Abstract
The development of cancer therapies is limited by the availability of suitable drug targets. Potential candidate drug targets can be identified based on the concept of synthetic lethality (SL), which refers to pairs of genes for which an aberration in either gene alone is non-lethal, but co-occurrence of the aberrations is lethal to the cell. Here, we present SLIdR (Synthetic Lethal Identification in R), a statistical framework for identifying SL pairs from large-scale perturbation screens. SLIdR successfully predicts SL pairs even with small sample sizes while minimizing the number of false positive targets. We apply SLIdR to Project DRIVE data and find both established and potential pan-cancer and cancer type-specific SL pairs consistent with findings from literature and drug response screening data. We experimentally validate two predicted SL interactions (ARID1A-TEAD1 and AXIN1-URI1) in hepatocellular carcinoma, thus corroborating the ability of SLIdR to identify potential drug targets.
Collapse
|
18
|
Gervits A, Sharan R. Predicting genetic interactions, cell line dependencies and drug sensitivities with variational graph auto-encoder. FRONTIERS IN BIOINFORMATICS 2022; 2:1025783. [DOI: 10.3389/fbinf.2022.1025783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 11/21/2022] [Indexed: 12/03/2022] Open
Abstract
Large scale cancer genomics data provide crucial information about the disease and reveal points of intervention. However, systematic data have been collected in specific cell lines and their collection is laborious and costly. Hence, there is a need to develop computational models that can predict such data for any genomic context of interest. Here we develop novel models that build on variational graph auto-encoders and can integrate diverse types of data to provide high quality predictions of genetic interactions, cell line dependencies and drug sensitivities, outperforming previous methods. Our models, data and implementation are available at: https://github.com/aijag/drugGraphNet.
Collapse
|
19
|
Tang S, Gökbağ B, Fan K, Shao S, Huo Y, Wu X, Cheng L, Li L. Synthetic lethal gene pairs: Experimental approaches and predictive models. Front Genet 2022; 13:961611. [PMID: 36531238 PMCID: PMC9751344 DOI: 10.3389/fgene.2022.961611] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 11/07/2022] [Indexed: 03/27/2024] Open
Abstract
Synthetic lethality (SL) refers to a genetic interaction in which the simultaneous perturbation of two genes leads to cell or organism death, whereas viability is maintained when only one of the pair is altered. The experimental exploration of these pairs and predictive modeling in computational biology contribute to our understanding of cancer biology and the development of cancer therapies. We extensively reviewed experimental technologies, public data sources, and predictive models in the study of synthetic lethal gene pairs and herein detail biological assumptions, experimental data, statistical models, and computational schemes of various predictive models, speculate regarding their influence on individual sample- and population-based synthetic lethal interactions, discuss the pros and cons of existing SL data and models, and highlight potential research directions in SL discovery.
Collapse
Affiliation(s)
- Shan Tang
- College of Pharmacy, The Ohio State University, Columbus, OH, United States
| | - Birkan Gökbağ
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States
| | - Kunjie Fan
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States
| | - Shuai Shao
- College of Pharmacy, The Ohio State University, Columbus, OH, United States
| | - Yang Huo
- Indiana University, Bloomington, IN, United States
| | - Xue Wu
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States
| | - Lijun Cheng
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States
| |
Collapse
|
20
|
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.
Collapse
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.
| |
Collapse
|
21
|
Seale C, Tepeli Y, Gonçalves JP. Overcoming selection bias in synthetic lethality prediction. Bioinformatics 2022; 38:4360-4368. [PMID: 35876858 PMCID: PMC9477536 DOI: 10.1093/bioinformatics/btac523] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 07/13/2022] [Accepted: 07/22/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Synthetic lethality (SL) between two genes occurs when simultaneous loss of function leads to cell death. This holds great promise for developing anti-cancer therapeutics that target synthetic lethal pairs of endogenously disrupted genes. Identifying novel SL relationships through exhaustive experimental screens is challenging, due to the vast number of candidate pairs. Computational SL prediction is therefore sought to identify promising SL gene pairs for further experimentation. However, current SL prediction methods lack consideration for generalizability in the presence of selection bias in SL data. RESULTS We show that SL data exhibit considerable gene selection bias. Our experiments designed to assess the robustness of SL prediction reveal that models driven by the topology of known SL interactions (e.g. graph, matrix factorization) are especially sensitive to selection bias. We introduce selection bias-resilient synthetic lethality (SBSL) prediction using regularized logistic regression or random forests. Each gene pair is described by 27 molecular features derived from cancer cell line, cancer patient tissue and healthy donor tissue samples. SBSL models are built and tested using approximately 8000 experimentally derived SL pairs across breast, colon, lung and ovarian cancers. Compared to other SL prediction methods, SBSL showed higher predictive performance, better generalizability and robustness to selection bias. Gene dependency, quantifying the essentiality of a gene for cell survival, contributed most to SBSL predictions. Random forests were superior to linear models in the absence of dependency features, highlighting the relevance of mutual exclusivity of somatic mutations, co-expression in healthy tissue and differential expression in tumour samples. AVAILABILITY AND IMPLEMENTATION https://github.com/joanagoncalveslab/sbsl. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Colm Seale
- Pattern Recognition & Bioinformatics, Department of Intelligent Systems, Faculty EEMCS, Delft University of Technology, Delft 2628 XE, The Netherlands
- Holland Proton Therapy Center (HollandPTC), Delft 2600 AC, The Netherlands
| | - Yasin Tepeli
- Pattern Recognition & Bioinformatics, Department of Intelligent Systems, Faculty EEMCS, Delft University of Technology, Delft 2628 XE, The Netherlands
| | - Joana P Gonçalves
- Pattern Recognition & Bioinformatics, Department of Intelligent Systems, Faculty EEMCS, Delft University of Technology, Delft 2628 XE, The Netherlands
| |
Collapse
|
22
|
Guo L, Dou Y, Xia D, Yin Z, Xiang Y, Luo L, Zhang Y, Wang J, Liang T. SLOAD: a comprehensive database of cancer-specific synthetic lethal interactions for precision cancer therapy via multi-omics analysis. Database (Oxford) 2022; 2022:6677988. [PMID: 36029479 PMCID: PMC9419874 DOI: 10.1093/database/baac075] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/27/2022] [Accepted: 08/20/2022] [Indexed: 11/14/2022]
Abstract
Abstract
Synthetic lethality has been widely concerned because of its potential role in cancer treatment, which can be harnessed to selectively kill cancer cells via identifying inactive genes in a specific cancer type and further targeting the corresponding synthetic lethal partners. Herein, to obtain cancer-specific synthetic lethal interactions, we aimed to predict genetic interactions via a pan-cancer analysis from multiple molecular levels using random forest and then develop a user-friendly database. First, based on collected public gene pairs with synthetic lethal interactions, candidate gene pairs were analyzed via integrating multi-omics data, mainly including DNA mutation, copy number variation, methylation and mRNA expression data. Then, integrated features were used to predict cancer-specific synthetic lethal interactions using random forest. Finally, SLOAD (http://www.tmliang.cn/SLOAD) was constructed via integrating these findings, which was a user-friendly database for data searching, browsing, downloading and analyzing. These results can provide candidate cancer-specific synthetic lethal interactions, which will contribute to drug designing in cancer treatment that can promote therapy strategies based on the principle of synthetic lethality.
Database URL http://www.tmliang.cn/SLOAD/
Collapse
Affiliation(s)
- Li Guo
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications , No. 9, Wenyuan Road, Qixia District, Nanjing, Jiangsu 210023, China
| | - Yuyang Dou
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications , No. 9, Wenyuan Road, Qixia District, Nanjing, Jiangsu 210023, China
| | - Daoliang Xia
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications , No. 9, Wenyuan Road, Qixia District, Nanjing, Jiangsu 210023, China
| | - Zibo Yin
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications , No. 9, Wenyuan Road, Qixia District, Nanjing, Jiangsu 210023, China
| | - Yangyang Xiang
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications , No. 9, Wenyuan Road, Qixia District, Nanjing, Jiangsu 210023, China
| | - Lulu Luo
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, School of Life Science, Nanjing Normal University , No. 1, Wenyuan Road, Qixia District, Nanjing, Jiangsu 210023, China
| | - Yuting Zhang
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications , No. 9, Wenyuan Road, Qixia District, Nanjing, Jiangsu 210023, China
| | - Jun Wang
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications , No. 9, Wenyuan Road, Qixia District, Nanjing, Jiangsu 210023, China
| | - Tingming Liang
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, School of Life Science, Nanjing Normal University , No. 1, Wenyuan Road, Qixia District, Nanjing, Jiangsu 210023, China
| |
Collapse
|
23
|
EpICC: A Bayesian neural network model with uncertainty correction for a more accurate classification of cancer. Sci Rep 2022; 12:14628. [PMID: 36028643 PMCID: PMC9418241 DOI: 10.1038/s41598-022-18874-6] [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: 05/27/2022] [Accepted: 08/22/2022] [Indexed: 11/09/2022] Open
Abstract
Accurate classification of cancers into their types and subtypes holds the key for choosing the right treatment strategy and can greatly impact patient well-being. However, existence of large-scale variations in the molecular processes driving even a single type of cancer can make accurate classification a challenging problem. Therefore, improved and robust methods for classification are absolutely critical. Although deep learning-based methods for cancer classification have been proposed earlier, they all provide point estimates for predictions without any measure of confidence and thus, can fall short in real-world applications where key decisions are to be made based on the predictions of the classifier. Here we report a Bayesian neural network-based model for classification of cancer types as well as sub-types from transcriptomic data. This model reported a measure of confidence with each prediction through analysis of epistemic uncertainty. We incorporated an uncertainty correction step with the Bayesian network-based model to greatly enhance prediction accuracy of cancer types (> 97% accuracy) and sub-types (> 80%). Our work suggests that reporting uncertainty measure with each classification can enable more accurate and informed decision-making that can be highly valuable in clinical settings.
Collapse
|
24
|
Liu Z, Lin D, Zhou Y, Zhang L, Yang C, Guo B, Xia F, Li Y, Chen D, Wang C, Chen Z, Leng C, Xiao Z. Exploring synthetic lethal network for the precision treatment of clear cell renal cell carcinoma. Sci Rep 2022; 12:13222. [PMID: 35918352 PMCID: PMC9345903 DOI: 10.1038/s41598-022-16657-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 07/13/2022] [Indexed: 11/29/2022] Open
Abstract
The emerging targeted therapies have revolutionized the treatment of advanced clear cell renal cell carcinoma (ccRCC) over the past 15 years. Nevertheless, lack of personalized treatment limits the development of effective clinical guidelines and improvement of patient prognosis. In this study, large-scale genomic profiles from ccRCC cohorts were explored for integrative analysis. A credible method was developed to identify synthetic lethality (SL) pairs and a list of 72 candidate pairs was determined, which might be utilized to selectively eliminate tumors with genetic aberrations using SL partners of specific mutations. Further analysis identified BRD4 and PRKDC as novel medical targets for patients with BAP1 mutations. After mapping these target genes to the comprehensive drug datasets, two agents (BI-2536 and PI-103) were found to have considerable therapeutic potentials in the BAP1 mutant tumors. Overall, our findings provided insight into the overview of ccRCC mutation patterns and offered novel opportunities for improving individualized cancer treatment.
Collapse
Affiliation(s)
- Zhicheng Liu
- Department of Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Dongxu Lin
- Department and Institute of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Yi Zhou
- Department of Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Linmeng Zhang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Chen Yang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Bin Guo
- Department of Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Feng Xia
- Department of Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Yan Li
- Department of Immunology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, Guangdong, China
| | - Danyang Chen
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Cun Wang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Zhong Chen
- Department and Institute of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
| | - Chao Leng
- Department of Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
| | - Zhenyu Xiao
- Department of Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
| |
Collapse
|
25
|
Monfort-Lanzas P, Gronauer R, Madersbacher L, Schatz C, Rieder D, Hackl H. MIO: MicroRNA target analysis system for Immuno-Oncology. Bioinformatics 2022; 38:3665-3667. [PMID: 35642895 PMCID: PMC9272810 DOI: 10.1093/bioinformatics/btac366] [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: 01/13/2022] [Revised: 04/25/2022] [Indexed: 11/13/2022] Open
Abstract
Summary MicroRNAs have been shown to be able to modulate the tumor microenvironment and the immune response and hence could be interesting biomarkers and therapeutic targets in immuno-oncology; however, dedicated analysis tools are missing. Here, we present a user-friendly web platform MIO and a Python toolkit miopy integrating various methods for visualization and analysis of provided or custom bulk microRNA and gene expression data. We include regularized regression and survival analysis and provide information of 40 microRNA target prediction tools as well as a collection of curated immune related gene and microRNA signatures and processed TCGA data including estimations of infiltrated immune cells and the immunophenoscore. The integration of several machine learning methods enables the selection of prognostic and predictive microRNAs and gene interaction network biomarkers. Availability and implementation https://mio.icbi.at, https://github.com/icbi-lab/mio and https://github.com/icbi-lab/miopy. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Pablo Monfort-Lanzas
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innrain 80, Innsbruck, 6020, Austria.,Institute of Medical Biochemistry, Biocenter, Medical University of Innsbruck, Innrain 80, 6020, Austria Innsbruck
| | - Raphael Gronauer
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innrain 80, Innsbruck, 6020, Austria
| | - Leonie Madersbacher
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innrain 80, Innsbruck, 6020, Austria
| | - Christoph Schatz
- Institute of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, Müllerstraße 44, Innsbruck, 6020, Austria
| | - Dietmar Rieder
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innrain 80, Innsbruck, 6020, Austria
| | - Hubert Hackl
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innrain 80, Innsbruck, 6020, Austria
| |
Collapse
|
26
|
Pal LR, Cheng K, Nair NU, Martin-Sancho L, Sinha S, Pu Y, Riva L, Yin X, Schischlik F, Lee JS, Chanda SK, Ruppin E. Synthetic lethality-based prediction of anti-SARS-CoV-2 targets. iScience 2022; 25:104311. [PMID: 35502318 PMCID: PMC9044693 DOI: 10.1016/j.isci.2022.104311] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 10/19/2021] [Accepted: 04/04/2022] [Indexed: 02/07/2023] Open
Abstract
Novel strategies are needed to identify drug targets and treatments for the COVID-19 pandemic. The altered gene expression of virus-infected host cells provides an opportunity to specifically inhibit viral propagation via targeting the synthetic lethal and synthetic dosage lethal (SL/SDL) partners of such altered host genes. Pursuing this disparate antiviral strategy, here we comprehensively analyzed multiple in vitro and in vivo bulk and single-cell RNA-sequencing datasets of SARS-CoV-2 infection to predict clinically relevant candidate antiviral targets that are SL/SDL with altered host genes. The predicted SL/SDL-based targets are highly enriched for infected cell inhibiting genes reported in four SARS-CoV-2 CRISPR-Cas9 genome-wide genetic screens. We further selected a focused subset of 26 genes that we experimentally tested in a targeted siRNA screen using human Caco-2 cells. Notably, as predicted, knocking down these targets reduced viral replication and cell viability only under the infected condition without harming noninfected healthy cells.
Collapse
Affiliation(s)
- Lipika R. Pal
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Kuoyuan Cheng
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
| | - Nishanth Ulhas Nair
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Laura Martin-Sancho
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Sanju Sinha
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
| | - Yuan Pu
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Laura Riva
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Xin Yin
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Fiorella Schischlik
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Joo Sang Lee
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
- Samsung Medical Center, Sungkyunkwan University School of Medicine, Suwon 16419, Republic of Korea
| | - Sumit K. Chanda
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
- Department of Computer Science, University of Maryland, College Park, MD, USA
| |
Collapse
|
27
|
Wang J, Wu M, Huang X, Wang L, Zhang S, Liu H, Zheng J. SynLethDB 2.0: a web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery. Database (Oxford) 2022; 2022:6585691. [PMID: 35562840 PMCID: PMC9216587 DOI: 10.1093/database/baac030] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 04/04/2022] [Accepted: 04/24/2022] [Indexed: 11/30/2022]
Abstract
Two genes are synthetic lethal if mutations in both genes result in impaired cell viability, while mutation of either gene does not affect the cell survival. The potential usage of synthetic lethality (SL) in anticancer therapeutics has attracted many researchers to identify synthetic lethal gene pairs. To include newly identified SLs and more related knowledge, we present a new version of the SynLethDB database to facilitate the discovery of clinically relevant SLs. We extended the first version of SynLethDB database significantly by including new SLs identified through Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening, a knowledge graph about human SLs, a new web interface, etc. Over 16 000 new SLs and 26 types of other relationships have been added, encompassing relationships among 14 100 genes, 53 cancers, 1898 drugs, etc. Moreover, a brand-new web interface has been developed to include modules such as SL query by disease or compound, SL partner gene set enrichment analysis and knowledge graph browsing through a dynamic graph viewer. The data can be downloaded directly from the website or through the RESTful Application Programming Interfaces (APIs). Database URL: https://synlethdb.sist.shanghaitech.edu.cn/v2.
Collapse
Affiliation(s)
- Jie Wang
- School of Information Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Min Wu
- Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, Singapore 138632, Singapore
| | - Xuhui Huang
- School of Computing, National University of Singapore, Computing 1, 13 Computing Drive, Singapore 117417, Singapore
| | - Li Wang
- School of Information Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Sophia Zhang
- College of Agriculture and Life Sciences, Cornell University, 260 Roberts Hall, Ithaca, NY 14853, USA
| | - Hui Liu
- School of Computer Science and Technology, Nanjing Tech University, 30 Puzhu Road, Nanjing 211816, China
| | - Jie Zheng
- School of Information Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China.,Shanghai Engineering Research Center of Intelligent Vision and Imaging, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| |
Collapse
|
28
|
Tercan B, Qin G, Kim TK, Aguilar B, Phan J, Longabaugh W, Pot D, Kemp CJ, Chambwe N, Shmulevich I. SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery. F1000Res 2022; 11:493. [PMID: 36761837 PMCID: PMC9880341 DOI: 10.12688/f1000research.110903.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/13/2022] [Indexed: 12/24/2022] Open
Abstract
Synthetic lethal interactions (SLIs), genetic interactions in which the simultaneous inactivation of two genes leads to a lethal phenotype, are promising targets for therapeutic intervention in cancer, as exemplified by the recent success of PARP inhibitors in treating BRCA1/2-deficient tumors. We present SL-Cloud, a new component of the Institute for Systems Biology Cancer Gateway in the Cloud (ISB-CGC), that provides an integrated framework of cloud-hosted data resources and curated workflows to enable facile prediction of SLIs. This resource addresses two main challenges related to SLI inference: the need to wrangle and preprocess large multi-omic datasets and the availability of multiple comparable prediction approaches. SL-Cloud enables customizable computational inference of SLIs and testing of prediction approaches across multiple datasets. We anticipate that cancer researchers will find utility in this tool for discovery of SLIs to support further investigation into potential drug targets for anticancer therapies.
Collapse
Affiliation(s)
- Bahar Tercan
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Guangrong Qin
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Taek-Kyun Kim
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Boris Aguilar
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - John Phan
- General Dynamics Information Technology, Rockville, MD, 20852, USA
| | | | - David Pot
- General Dynamics Information Technology, Rockville, MD, 20852, USA
| | - Christopher J. Kemp
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Nyasha Chambwe
- Institute for Systems Biology, Seattle, WA, 98109, USA
- Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA
| | | |
Collapse
|
29
|
Tercan B, Qin G, Kim TK, Aguilar B, Phan J, Longabaugh W, Pot D, Kemp CJ, Chambwe N, Shmulevich I. SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery. F1000Res 2022; 11:493. [PMID: 36761837 PMCID: PMC9880341 DOI: 10.12688/f1000research.110903.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/04/2022] [Indexed: 12/22/2023] Open
Abstract
Synthetic lethal interactions (SLIs), genetic interactions in which the simultaneous inactivation of two genes leads to a lethal phenotype, are promising targets for therapeutic intervention in cancer, as exemplified by the recent success of PARP inhibitors in treating BRCA1/2-deficient tumors. We present SL-Cloud, a new component of the Institute for Systems Biology Cancer Gateway in the Cloud (ISB-CGC), that provides an integrated framework of cloud-hosted data resources and curated workflows to enable facile prediction of SLIs. This resource addresses two main challenges related to SLI inference: the need to wrangle and preprocess large multi-omic datasets and the availability of multiple comparable prediction approaches. SL-Cloud enables customizable computational inference of SLIs and testing of prediction approaches across multiple datasets. We anticipate that cancer researchers will find utility in this tool for discovery of SLIs to support further investigation into potential drug targets for anticancer therapies.
Collapse
Affiliation(s)
- Bahar Tercan
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Guangrong Qin
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Taek-Kyun Kim
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Boris Aguilar
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - John Phan
- General Dynamics Information Technology, Rockville, MD, 20852, USA
| | | | - David Pot
- General Dynamics Information Technology, Rockville, MD, 20852, USA
| | - Christopher J. Kemp
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Nyasha Chambwe
- Institute for Systems Biology, Seattle, WA, 98109, USA
- Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA
| | | |
Collapse
|
30
|
Ouyang J, Qin G, Liu Z, Jian X, Shi T, Xie L. ToPP: Tumor online prognostic analysis platform for prognostic feature selection and clinical patient subgroup selection. iScience 2022; 25:104190. [PMID: 35479398 PMCID: PMC9035726 DOI: 10.1016/j.isci.2022.104190] [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: 08/05/2021] [Revised: 01/19/2022] [Accepted: 03/30/2022] [Indexed: 11/19/2022] Open
Abstract
Patients with cancer with different molecular characterization and subtypes result in different response to anticancer therapeutics and survival. To identify features that are associated with prognosis is essential to precision medicine by providing clues for target identification, drug discovery. Here, we developed a tumor online prognostic analysis platform (ToPP) which integrated eight multi-omics features and clinical data from 68 cancer projects. It provides multiple approaches for customized prognostic studies, including 1) Prognostic analysis based on multi-omics features and clinical characteristics; 2) Automatic construction of prognostic model; 3) Pancancer prognostic analysis in multi-omics data; 4) Explore the impact of different levels of feature combinations on patient prognosis; 5) More sophisticated prognostic analysis according to regulatory network. ToPP provides a comprehensive source and easy-to-use interface for tumor prognosis research, with one-stop service of multi-omics, subtyping, and online prognostic modeling. The web server is freely available at http://www.biostatistics.online/topp/index.php. ToPP platform integrated eight multi-omics and clinical data from 68 cancer projects ToPP provides multi-omics combination and subgroup selection for prognostic analysis ToPP provides automatic construction of prognostic model for public and custom data Users can perform prognostic analysis based on regulatory network or pathways in ToPP
Collapse
Affiliation(s)
- Jian Ouyang
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute for Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Guangrong Qin
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Zhenhao Liu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute for Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Xingxing Jian
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute for Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
- Bioinformatics Center, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410083, China
| | - Tieliu Shi
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
- Big Data and Engineering Research Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 100045, China
- Corresponding author
| | - Lu Xie
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute for Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
- Bioinformatics Center, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410083, China
- Corresponding author
| |
Collapse
|
31
|
Shreve JT, Khanani SA, Haddad TC. Artificial Intelligence in Oncology: Current Capabilities, Future Opportunities, and Ethical Considerations. Am Soc Clin Oncol Educ Book 2022; 42:1-10. [PMID: 35687826 DOI: 10.1200/edbk_350652] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
The promise of highly personalized oncology care using artificial intelligence (AI) technologies has been forecasted since the emergence of the field. Cumulative advances across the science are bringing this promise to realization, including refinement of machine learning- and deep learning algorithms; expansion in the depth and variety of databases, including multiomics; and the decreased cost of massively parallelized computational power. Examples of successful clinical applications of AI can be found throughout the cancer continuum and in multidisciplinary practice, with computer vision-assisted image analysis in particular having several U.S. Food and Drug Administration-approved uses. Techniques with emerging clinical utility include whole blood multicancer detection from deep sequencing, virtual biopsies, natural language processing to infer health trajectories from medical notes, and advanced clinical decision support systems that combine genomics and clinomics. Substantial issues have delayed broad adoption, with data transparency and interpretability suffering from AI's "black box" mechanism, and intrinsic bias against underrepresented persons limiting the reproducibility of AI models and perpetuating health care disparities. Midfuture projections of AI maturation involve increasing a model's complexity by using multimodal data elements to better approximate an organic system. Far-future positing includes living databases that accumulate all aspects of a person's health into discrete data elements; this will fuel highly convoluted modeling that can tailor treatment selection, dose determination, surveillance modality and schedule, and more. The field of AI has had a historical dichotomy between its proponents and detractors. The successful development of recent applications, and continued investment in prospective validation that defines their impact on multilevel outcomes, has established a momentum of accelerated progress.
Collapse
Affiliation(s)
| | | | - Tufia C Haddad
- Department of Oncology, Mayo Clinic, Rochester, MN.,Center for Digital Health, Mayo Clinic, Rochester, MN
| |
Collapse
|
32
|
Wang J, Zhang Q, Han J, Zhao Y, Zhao C, Yan B, Dai C, Wu L, Wen Y, Zhang Y, Leng D, Wang Z, Yang X, He S, Bo X. Computational methods, databases and tools for synthetic lethality prediction. Brief Bioinform 2022; 23:6555403. [PMID: 35352098 PMCID: PMC9116379 DOI: 10.1093/bib/bbac106] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/15/2022] [Accepted: 03/02/2022] [Indexed: 12/17/2022] Open
Abstract
Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.
Collapse
Affiliation(s)
- Jing Wang
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Qinglong Zhang
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Junshan Han
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Yanpeng Zhao
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Caiyun Zhao
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Bowei Yan
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Chong Dai
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Lianlian Wu
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Yuqi Wen
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Yixin Zhang
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Dongjin Leng
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Zhongming Wang
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Xiaoxi Yang
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Song He
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Xiaochen Bo
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| |
Collapse
|
33
|
Occhipinti A, Hamadi Y, Kugler H, Wintersteiger CM, Yordanov B, Angione C. Discovering Essential Multiple Gene Effects Through Large Scale Optimization: An Application to Human Cancer Metabolism. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2339-2352. [PMID: 32248120 DOI: 10.1109/tcbb.2020.2973386] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Computational modelling of metabolic processes has proven to be a useful approach to formulate our knowledge and improve our understanding of core biochemical systems that are crucial to maintaining cellular functions. Towards understanding the broader role of metabolism on cellular decision-making in health and disease conditions, it is important to integrate the study of metabolism with other core regulatory systems and omics within the cell, including gene expression patterns. After quantitatively integrating gene expression profiles with a genome-scale reconstruction of human metabolism, we propose a set of combinatorial methods to reverse engineer gene expression profiles and to find pairs and higher-order combinations of genetic modifications that simultaneously optimize multi-objective cellular goals. This enables us to suggest classes of transcriptomic profiles that are most suitable to achieve given metabolic phenotypes. We demonstrate how our techniques are able to compute beneficial, neutral or "toxic" combinations of gene expression levels. We test our methods on nine tissue-specific cancer models, comparing our outcomes with the corresponding normal cells, identifying genes as targets for potential therapies. Our methods open the way to a broad class of applications that require an understanding of the interplay among genotype, metabolism, and cellular behaviour, at scale.
Collapse
|
34
|
Precise Characterization of Genetic Interactions in Cancer via Molecular Network Refining Processes. Int J Mol Sci 2021; 22:ijms222011114. [PMID: 34681774 PMCID: PMC8540220 DOI: 10.3390/ijms222011114] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 10/05/2021] [Accepted: 10/13/2021] [Indexed: 12/13/2022] Open
Abstract
Genetic interactions (GIs), such as the synthetic lethal interaction, are promising therapeutic targets in precision medicine. However, despite extensive efforts to characterize GIs by large-scale perturbation screening, considerable false positives have been reported in multiple studies. We propose a new computational approach for improved precision in GI identification by applying constraints that consider actual biological phenomena. In this study, GIs were characterized by assessing mutation, loss of function, and expression profiles in the DEPMAP database. The expression profiles were used to exclude loss-of-function data for nonexpressed genes in GI characterization. More importantly, the characterized GIs were refined based on Kyoto Encyclopedia of Genes and Genomes (KEGG) or protein–protein interaction (PPI) networks, under the assumption that genes genetically interacting with a certain mutated gene are adjacent in the networks. As a result, the initial GIs characterized with CRISPR and RNAi screenings were refined to 65 and 23 GIs based on KEGG networks and to 183 and 142 GIs based on PPI networks. The evaluation of refined GIs showed improved precision with respect to known synthetic lethal interactions. The refining process also yielded a synthetic partner network (SPN) for each mutated gene, which provides insight into therapeutic strategies for the mutated genes; specifically, exploring the SPN of mutated BRAF revealed ELAVL1 as a potential target for treating BRAF-mutated cancer, as validated by previous research. We expect that this work will advance cancer therapeutic research.
Collapse
|
35
|
Johnson KL, Qi Z, Yan Z, Wen X, Nguyen TC, Zaleta-Rivera K, Chen CJ, Fan X, Sriram K, Wan X, Chen ZB, Zhong S. Revealing protein-protein interactions at the transcriptome scale by sequencing. Mol Cell 2021; 81:4091-4103.e9. [PMID: 34348091 PMCID: PMC8500946 DOI: 10.1016/j.molcel.2021.07.006] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 06/12/2021] [Accepted: 07/08/2021] [Indexed: 02/07/2023]
Abstract
We describe PROPER-seq (protein-protein interaction sequencing) to map protein-protein interactions (PPIs) en masse. PROPER-seq first converts transcriptomes of input cells into RNA-barcoded protein libraries, in which all interacting protein pairs are captured through nucleotide barcode ligation, recorded as chimeric DNA sequences, and decoded at once by sequencing and mapping. We applied PROPER-seq to human embryonic kidney cells, T lymphocytes, and endothelial cells and identified 210,518 human PPIs (collected in the PROPER v.1.0 database). Among these, 1,365 and 2,480 PPIs are supported by published co-immunoprecipitation (coIP) and affinity purification-mass spectrometry (AP-MS) data, 17,638 PPIs are predicted by the prePPI algorithm without previous experimental validation, and 100 PPIs overlap human synthetic lethal gene pairs. In addition, four previously uncharacterized interaction partners with poly(ADP-ribose) polymerase 1 (PARP1) (a critical protein in DNA repair) known as XPO1, MATR3, IPO5, and LEO1 are validated in vivo. PROPER-seq presents a time-effective technology to map PPIs at the transcriptome scale, and PROPER v.1.0 provides a rich resource for studying PPIs.
Collapse
Affiliation(s)
- Kara L Johnson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zhijie Qi
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zhangming Yan
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Xingzhao Wen
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Tri C Nguyen
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Kathia Zaleta-Rivera
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Chien-Ju Chen
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Xiaochen Fan
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Kiran Sriram
- Department of Diabetes Complications and Metabolism, Beckman Research Institute, City of Hope, Duarte, CA 91010, USA
| | - Xueyi Wan
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zhen Bouman Chen
- Department of Diabetes Complications and Metabolism, Beckman Research Institute, City of Hope, Duarte, CA 91010, USA
| | - Sheng Zhong
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.
| |
Collapse
|
36
|
Abstract
Michaelis constants (Km) are essential to predict the catalytic rate of enzymes, but are not widely available. A new study in PLOS Biology uses artificial intelligence (AI) to accurately predict Km on a proteome-wide scale, paving the way for dynamic, genome-wide modeling of metabolism.
Collapse
Affiliation(s)
- Albert A. Antolin
- Department of Data Science, The Institute of Cancer Research, London, United Kingdom
- Division of Cancer Therapeutics, The Institute of Cancer Research, London, United Kingdom
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine & Institute of Biomedicine of Universitat de Barcelona, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) and Metabolomics node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| |
Collapse
|
37
|
INCISOR: An Algorithm to Identify Synthetic Rescue Mediators of Resistance to Targeted and Immunotherapy. Methods Mol Biol 2021. [PMID: 34590278 DOI: 10.1007/978-1-0716-1740-3_11] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Despite the success of targeted therapies including immunotherapies in cancer treatments, tumor resistance to targeted therapies remains a fundamental challenge. Tumors can evolve resistance to a therapy that targets one gene by acquiring compensatory alterations in another gene, such compensatory interaction between two genes is referred to as synthetic rescue (SR) interactions. To identify SRs, here we describe an algorithm, INCISOR, that leverages tumor transcriptomics and clinical information from 10,000 patients as well as data from experimental screens. INCISOR can identify SRs that are common across several cancer-types in genome-wide fashion by sifting through half a billion possible gene-gene combinations and provide a framework to design therapies to tackle resistance.
Collapse
|
38
|
Pal LR, Cheng K, Nair NU, Martin-Sancho L, Sinha S, Pu Y, Riva L, Yin X, Schischlik F, Lee JS, Chanda SK, Ruppin E. Synthetic lethality-based prediction of anti-SARS-CoV-2 targets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.09.14.460408. [PMID: 34545363 PMCID: PMC8452092 DOI: 10.1101/2021.09.14.460408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Novel strategies are needed to identify drug targets and treatments for the COVID-19 pandemic. The altered gene expression of virus-infected host cells provides an opportunity to specifically inhibit viral propagation via targeting the synthetic lethal (SL) partners of such altered host genes. Pursuing this antiviral strategy, here we comprehensively analyzed multiple in vitro and in vivo bulk and single-cell RNA-sequencing datasets of SARS-CoV-2 infection to predict clinically relevant candidate antiviral targets that are SL with altered host genes. The predicted SL-based targets are highly enriched for infected cell inhibiting genes reported in four SARS-CoV-2 CRISPR-Cas9 genome-wide genetic screens. Integrating our predictions with the results of these screens, we further selected a focused subset of 26 genes that we experimentally tested in a targeted siRNA screen using human Caco-2 cells. Notably, as predicted, knocking down these targets reduced viral replication and cell viability only under the infected condition without harming non-infected cells. Our results are made publicly available, to facilitate their in vivo testing and further validation.
Collapse
Affiliation(s)
- Lipika R. Pal
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Kuoyuan Cheng
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
| | - Nishanth Ulhas Nair
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Laura Martin-Sancho
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Sanju Sinha
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
| | - Yuan Pu
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Laura Riva
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Xin Yin
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Fiorella Schischlik
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Joo Sang Lee
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
- Samsung Medical Center, Sungkyunkwan University School of Medicine, Suwon 16419, Republic of Korea
| | - Sumit K. Chanda
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
- Department of Computer Science, University of Maryland, College Park, MD, USA
| |
Collapse
|
39
|
Khateb A, Deshpande A, Feng Y, Finlay D, Lee JS, Lazar I, Fabre B, Li Y, Fujita Y, Zhang T, Yin J, Pass I, Livneh I, Jeremias I, Burian C, Mason JR, Almog R, Horesh N, Ofran Y, Brown K, Vuori K, Jackson M, Ruppin E, Deshpande AJ, Ronai ZA. The ubiquitin ligase RNF5 determines acute myeloid leukemia growth and susceptibility to histone deacetylase inhibitors. Nat Commun 2021; 12:5397. [PMID: 34518534 PMCID: PMC8437979 DOI: 10.1038/s41467-021-25664-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 08/18/2021] [Indexed: 12/12/2022] Open
Abstract
Acute myeloid leukemia (AML) remains incurable, largely due to its resistance to conventional treatments. Here, we find that increased abundance of the ubiquitin ligase RNF5 contributes to AML development and survival. High RNF5 expression in AML patient specimens correlates with poor prognosis. RNF5 inhibition decreases AML cell growth in culture, in patient-derived xenograft (PDX) samples and in vivo, and delays development of MLL-AF9-driven leukemogenesis in mice, prolonging their survival. RNF5 inhibition causes transcriptional changes that overlap with those seen upon histone deacetylase (HDAC)1 inhibition. RNF5 induces the formation of K29 ubiquitin chains on the histone-binding protein RBBP4, promoting its recruitment to and subsequent epigenetic regulation of genes involved in AML maintenance. Correspondingly, RNF5 or RBBP4 knockdown enhances AML cell sensitivity to HDAC inhibitors. Notably, low expression of both RNF5 and HDAC coincides with a favorable prognosis. Our studies identify an ERAD-independent role for RNF5, demonstrating that its control of RBBP4 constitutes an epigenetic pathway that drives AML, and highlight RNF5/RBBP4 as markers useful to stratify patients for treatment with HDAC inhibitors.
Collapse
Affiliation(s)
- Ali Khateb
- Technion Integrated Cancer Center, Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel
- Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Anagha Deshpande
- Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Yongmei Feng
- Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Darren Finlay
- Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Joo Sang Lee
- Cancer Data Science Lab (CDSL), National Cancer Institute, National Institute of Health, Bethesda, MD, USA
| | - Ikrame Lazar
- Technion Integrated Cancer Center, Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel
- Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Bertrand Fabre
- Technion Integrated Cancer Center, Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel
- Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Yan Li
- Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Yu Fujita
- Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
- Division of Respiratory Medicine, Department of Internal Medicine, Jikei University School of Medicine, Tokyo, Japan
| | - Tongwu Zhang
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Jun Yin
- Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Ian Pass
- Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Ido Livneh
- Technion Integrated Cancer Center, Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel
| | - Irmela Jeremias
- Research Unit Apoptosis in Hematopoietic Stem Cells, Helmholtz Center Munich, German Center for Environmental Health, Munich, Germany
| | - Carol Burian
- Scripps MD Anderson Cancer Center, La Jolla, CA, USA
| | - James R Mason
- Scripps MD Anderson Cancer Center, La Jolla, CA, USA
| | - Ronit Almog
- Rambam Health Care Campus, Epidemiology Department and Biobank, Haifa, Israel
| | - Nurit Horesh
- Rambam Health Care Campus, Hematology and Bone marrow Transplantation Department, Haifa, Israel
| | - Yishai Ofran
- Technion Integrated Cancer Center, Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel
- Rambam Health Care Campus, Hematology and Bone marrow Transplantation Department, Haifa, Israel
| | - Kevin Brown
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Kristiina Vuori
- Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Michael Jackson
- Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Eytan Ruppin
- Cancer Data Science Lab (CDSL), National Cancer Institute, National Institute of Health, Bethesda, MD, USA
| | - Aniruddha J Deshpande
- Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Ze'ev A Ronai
- Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA.
| |
Collapse
|
40
|
Comprehensive prediction of robust synthetic lethality between paralog pairs in cancer cell lines. Cell Syst 2021; 12:1144-1159.e6. [PMID: 34529928 DOI: 10.1016/j.cels.2021.08.006] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 07/08/2021] [Accepted: 08/18/2021] [Indexed: 12/15/2022]
Abstract
Pairs of paralogs may share common functionality and, hence, display synthetic lethal interactions. As the majority of human genes have an identifiable paralog, exploiting synthetic lethality between paralogs may be a broadly applicable approach for targeting gene loss in cancer. However, only a biased subset of human paralog pairs has been tested for synthetic lethality to date. Here, by analyzing genome-wide CRISPR screens and molecular profiles of over 700 cancer cell lines, we identify features predictive of synthetic lethality between paralogs, including shared protein-protein interactions and evolutionary conservation. We develop a machine-learning classifier based on these features to predict which paralog pairs are most likely to be synthetic lethal and to explain why. We show that our classifier accurately predicts the results of combinatorial CRISPR screens in cancer cell lines and furthermore can distinguish pairs that are synthetic lethal in multiple cell lines from those that are cell-line specific. A record of this paper's transparent peer review process is included in the supplemental information.
Collapse
|
41
|
Benfatto S, Serçin Ö, Dejure FR, Abdollahi A, Zenke FT, Mardin BR. Uncovering cancer vulnerabilities by machine learning prediction of synthetic lethality. Mol Cancer 2021; 20:111. [PMID: 34454516 PMCID: PMC8401190 DOI: 10.1186/s12943-021-01405-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 08/10/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Synthetic lethality describes a genetic interaction between two perturbations, leading to cell death, whereas neither event alone has a significant effect on cell viability. This concept can be exploited to specifically target tumor cells. CRISPR viability screens have been widely employed to identify cancer vulnerabilities. However, an approach to systematically infer genetic interactions from viability screens is missing. METHODS Here we describe PAn-canceR Inferred Synthetic lethalities (PARIS), a machine learning approach to identify cancer vulnerabilities. PARIS predicts synthetic lethal (SL) interactions by combining CRISPR viability screens with genomics and transcriptomics data across hundreds of cancer cell lines profiled within the Cancer Dependency Map. RESULTS Using PARIS, we predicted 15 high confidence SL interactions within 549 DNA damage repair (DDR) genes. We show experimental validation of an SL interaction between the tumor suppressor CDKN2A, thymidine phosphorylase (TYMP) and the thymidylate synthase (TYMS), which may allow stratifying patients for treatment with TYMS inhibitors. Using genome-wide mapping of SL interactions for DDR genes, we unraveled a dependency between the aldehyde dehydrogenase ALDH2 and the BRCA-interacting protein BRIP1. Our results suggest BRIP1 as a potential therapeutic target in ~ 30% of all tumors, which express low levels of ALDH2. CONCLUSIONS PARIS is an unbiased, scalable and easy to adapt platform to identify SL interactions that should aid in improving cancer therapy with increased availability of cancer genomics data.
Collapse
Affiliation(s)
- Salvatore Benfatto
- BioMed X Institute (GmbH), Im Neuenheimer Feld 583, 69120, Heidelberg, Germany
| | - Özdemirhan Serçin
- BioMed X Institute (GmbH), Im Neuenheimer Feld 583, 69120, Heidelberg, Germany
| | - Francesca R Dejure
- BioMed X Institute (GmbH), Im Neuenheimer Feld 583, 69120, Heidelberg, Germany
| | - Amir Abdollahi
- Division of Molecular and Translational Radiation Oncology, National Centre for Tumour Diseases (NCT), Heidelberg University Hospital, 69120, Heidelberg, Germany
| | - Frank T Zenke
- Translational Innovation Platform Oncology & Immuno-Oncology, Merck KGaA, Frankfurter Str. 250, 64293, Darmstadt, Germany
| | - Balca R Mardin
- BioMed X Institute (GmbH), Im Neuenheimer Feld 583, 69120, Heidelberg, Germany.
| |
Collapse
|
42
|
Yang C, Guo Y, Qian R, Huang Y, Zhang L, Wang J, Huang X, Liu Z, Qin W, Wang C, Chen H, Ma X, Zhang D. Mapping the landscape of synthetic lethal interactions in liver cancer. Theranostics 2021; 11:9038-9053. [PMID: 34522226 PMCID: PMC8419043 DOI: 10.7150/thno.63416] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 08/14/2021] [Indexed: 12/11/2022] Open
Abstract
Almost all the current therapies against liver cancer are based on the "one size fits all" principle and offer only limited survival benefit. Fortunately, synthetic lethality (SL) may provide an alternate route towards individualized therapy in liver cancer. The concept that simultaneous losses of two genes are lethal to a cell while a single loss is non-lethal can be utilized to selectively eliminate tumors with genetic aberrations. Methods: To infer liver cancer-specific SL interactions, we propose a computational pipeline termed SiLi (statistical inference-based synthetic lethality identification) that incorporates five inference procedures. Based on large-scale sequencing datasets, SiLi analysis was performed to identify SL interactions in liver cancer. Results: By SiLi analysis, a total of 272 SL pairs were discerned, which included 209 unique target candidates. Among these, polo-like kinase 1 (PLK1) was considered to have considerable therapeutic potential. Further computational and experimental validation of the SL pair TP53-PLK1 demonstrated that inhibition of PLK1 could be a novel therapeutic strategy specifically targeting those patients with TP53-mutant liver tumors. Conclusions: In this study, we report a comprehensive analysis of synthetic lethal interactions of liver cancer. Our findings may open new possibilities for patient-tailored therapeutic interventions in liver cancer.
Collapse
Affiliation(s)
- Chen Yang
- Department of Clinical Medicine, School of Medicine, Zhejiang University City College, Hangzhou, China
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuchen Guo
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ruolan Qian
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yiwen Huang
- Department of Clinical Medicine, Fujian Medical University, Fuzhou, China
| | - Linmeng Zhang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Wang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaowen Huang
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zhicheng Liu
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenxin Qin
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Cun Wang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huimin Chen
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xuhui Ma
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dayong Zhang
- Department of Clinical Medicine, School of Medicine, Zhejiang University City College, Hangzhou, China
| |
Collapse
|
43
|
Park Y, Heider D, Hauschild AC. Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence. Cancers (Basel) 2021; 13:3148. [PMID: 34202427 PMCID: PMC8269018 DOI: 10.3390/cancers13133148] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 06/16/2021] [Accepted: 06/21/2021] [Indexed: 12/18/2022] Open
Abstract
The rapid improvement of next-generation sequencing (NGS) technologies and their application in large-scale cohorts in cancer research led to common challenges of big data. It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. In addition, NGS data have been integrated with systems biology to build better predictive models to determine the characteristics of tumors and tumor subtypes. Therefore, various machine learning algorithms were introduced to identify underlying biological mechanisms. In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. Subsequently, we discuss how deep neural networks outperform other approaches, the potential of graph neural networks (GNN) in systems biology, and the limitations in NGS biomedical research. To reflect on the various challenges and corresponding computational solutions, we will discuss the following three topics: (i) molecular characteristics, (ii) tumor heterogeneity, and (iii) drug discovery. We conclude that machine learning and network-based approaches can add valuable insights and build highly accurate models. However, a well-informed choice of learning algorithm and biological network information is crucial for the success of each specific research question.
Collapse
Affiliation(s)
- Youngjun Park
- Department of Mathematics and Computer Science, Philipps-University of Marburg, 35032 Marburg, Germany; (Y.P.); (D.H.)
| | - Dominik Heider
- Department of Mathematics and Computer Science, Philipps-University of Marburg, 35032 Marburg, Germany; (Y.P.); (D.H.)
| | - Anne-Christin Hauschild
- Department of Mathematics and Computer Science, Philipps-University of Marburg, 35032 Marburg, Germany; (Y.P.); (D.H.)
- Department of Medical Informatics, University Medical Center Göttingen, 37075 Göttingen, Germany
| |
Collapse
|
44
|
Paradis JS, Acosta M, Saddawi-Konefka R, Kishore A, Gomes F, Arang N, Tiago M, Coma S, Lubrano S, Wu X, Ford K, Day CP, Merlino G, Mali P, Pachter JA, Sato T, Aplin AE, Gutkind JS. Synthetic Lethal Screens Reveal Cotargeting FAK and MEK as a Multimodal Precision Therapy for GNAQ-Driven Uveal Melanoma. Clin Cancer Res 2021; 27:3190-3200. [PMID: 33568347 PMCID: PMC8895627 DOI: 10.1158/1078-0432.ccr-20-3363] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 01/17/2021] [Accepted: 02/05/2021] [Indexed: 11/16/2022]
Abstract
PURPOSE Uveal melanoma is the most common eye cancer in adults. Approximately 50% of patients with uveal melanoma develop metastatic uveal melanoma (mUM) in the liver, even after successful treatment of the primary lesions. mUM is refractory to current chemo- and immune-therapies, and most mUM patients die within a year. Uveal melanoma is characterized by gain-of-function mutations in GNAQ/GNA11, encoding Gαq proteins. We have recently shown that the Gαq-oncogenic signaling circuitry involves a noncanonical pathway distinct from the classical activation of PLCβ and MEK-ERK. GNAQ promotes the activation of YAP1, a key oncogenic driver, through focal adhesion kinase (FAK), thereby identifying FAK as a druggable signaling hub downstream from GNAQ. However, targeted therapies often activate compensatory resistance mechanisms leading to cancer relapse and treatment failure. EXPERIMENTAL DESIGN We performed a kinome-wide CRISPR-Cas9 sgRNA screen to identify synthetic lethal gene interactions that can be exploited therapeutically. Candidate adaptive resistance mechanisms were investigated by cotargeting strategies in uveal melanoma and mUM in vitro and in vivo experimental systems. RESULTS sgRNAs targeting the PKC and MEK-ERK signaling pathways were significantly depleted after FAK inhibition, with ERK activation representing a predominant resistance mechanism. Pharmacologic inhibition of MEK and FAK showed remarkable synergistic growth-inhibitory effects in uveal melanoma cells and exerted cytotoxic effects, leading to tumor collapse in uveal melanoma xenograft and liver mUM models in vivo. CONCLUSIONS Coupling the unique genetic landscape of uveal melanoma with the power of unbiased genetic screens, our studies reveal that FAK and MEK-ERK cotargeting may provide a new network-based precision therapeutic strategy for mUM treatment.See related commentary by Harbour, p. 2967.
Collapse
Affiliation(s)
- Justine S Paradis
- Moores Cancer Center, University of California San Diego, La Jolla, California
| | - Monica Acosta
- Moores Cancer Center, University of California San Diego, La Jolla, California
| | - Robert Saddawi-Konefka
- Moores Cancer Center, University of California San Diego, La Jolla, California
- Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, University of California San Diego, La Jolla, California
| | - Ayush Kishore
- Moores Cancer Center, University of California San Diego, La Jolla, California
| | - Frederico Gomes
- Moores Cancer Center, University of California San Diego, La Jolla, California
| | - Nadia Arang
- Moores Cancer Center, University of California San Diego, La Jolla, California
- Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, California
| | - Manoela Tiago
- Department of Cancer Biology, Thomas Jefferson University, Philadelphia, Pennsylvania
| | | | - Simone Lubrano
- Moores Cancer Center, University of California San Diego, La Jolla, California
| | - Xingyu Wu
- Moores Cancer Center, University of California San Diego, La Jolla, California
| | - Kyle Ford
- Department of Bioengineering, University of California San Diego, San Diego, California
| | - Chi-Ping Day
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, NCI, NIH, Maryland
| | - Glenn Merlino
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, NCI, NIH, Maryland
| | - Prashant Mali
- Department of Bioengineering, University of California San Diego, San Diego, California
| | | | - Takami Sato
- Department of Medical Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania
- Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Andrew E Aplin
- Department of Cancer Biology, Thomas Jefferson University, Philadelphia, Pennsylvania
- Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - J Silvio Gutkind
- Moores Cancer Center, University of California San Diego, La Jolla, California.
- Department of Pharmacology, University of California San Diego, La Jolla, California
| |
Collapse
|
45
|
Wang HC, Chou MC, Wu CC, Chan LP, Moi SH, Pan MR, Liu TC, Yang CH. Application of the Interaction between Tissue Immunohistochemistry Staining and Clinicopathological Factors for Evaluating the Risk of Oral Cancer Progression by Hierarchical Clustering Analysis: A Case-Control Study in a Taiwanese Population. Diagnostics (Basel) 2021; 11:diagnostics11060925. [PMID: 34063938 PMCID: PMC8224004 DOI: 10.3390/diagnostics11060925] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 05/12/2021] [Accepted: 05/17/2021] [Indexed: 01/31/2023] Open
Abstract
The aim of this single-center case-control study is to investigate the feasibility and accuracy of oral cancer protein risk stratification (OCPRS) to analyze the risk of cancer progression. All patients diagnosed with oral cancer in Taiwan, between 2012 and 2014, and who underwent surgical intervention were selected for the study. The tissue was further processed for immunohistochemistry (IHC) for 21 target proteins. Analyses were performed using the results of IHC staining, clinicopathological characteristics, and survival outcomes. Novel stratifications with a hierarchical clustering approach and combinations were applied using the Cox proportional hazard regression model. Of the 163 participants recruited, 102 patients were analyzed, and OCPRS successfully identified patients with different progression-free survival (PFS) profiles in high-risk (53 subjects) versus low-risk (49 subjects) groups (p = 0.012). OCPRS was composed of cytoplasmic PLK1, phosphoMet, and SGK2 IHC staining. After controlling for the influence of clinicopathological features, high-risk patients were 2.33 times more likely to experience cancer progression than low-risk patients (p = 0.020). In the multivariate model, patients with extranodal extension (HR = 2.66, p = 0.045) demonstrated a significantly increased risk for disease progression. Risk stratification with OCPRS provided distinct PFS groups for patients with oral cancer after surgical intervention. OCPRS appears suitable for routine clinical use for progression and prognosis estimation.
Collapse
Affiliation(s)
- Hui-Ching Wang
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan;
- Division of Hematology and Oncology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan;
- Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung 807, Taiwan;
| | - Meng-Chun Chou
- Department of Nursing, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan;
| | - Chun-Chieh Wu
- Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung 807, Taiwan;
- Department of Pathology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Leong-Perng Chan
- Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan;
- Department of Otolaryngology-Head and Neck Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Department of Otorhinolaryngology-Head and Neck Surgery, Kaohsiung Municipal Ta-Tung Hospital and Kaohsiung Medical University Hospital, Kaohsiung 807, Taiwan
| | - Sin-Hua Moi
- Center of Cancer Program Development, E-Da Cancer Hospital, I-Shou University, Kaohsiung 824, Taiwan;
| | - Mei-Ren Pan
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan;
- Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung 807, Taiwan;
- Correspondence: (M.-R.P.); (T.-C.L.); (C.-H.Y.); Tel.: +886-7-3121101-5092-34 (M.-R.P.); +886-4-781-3888 (T.-C.L.); +886-7-381-4526 (C.-H.Y.); Fax: +886-7-3218309 (M.-R.P.)
| | - Ta-Chih Liu
- Department of Hematology-Oncology, Chang Bing Show Chwan Memorial Hospital, Changhua 505, Taiwan
- Correspondence: (M.-R.P.); (T.-C.L.); (C.-H.Y.); Tel.: +886-7-3121101-5092-34 (M.-R.P.); +886-4-781-3888 (T.-C.L.); +886-7-381-4526 (C.-H.Y.); Fax: +886-7-3218309 (M.-R.P.)
| | - Cheng-Hong Yang
- Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807, Taiwan
- Ph. D. Program in Biomedical Engineering, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Correspondence: (M.-R.P.); (T.-C.L.); (C.-H.Y.); Tel.: +886-7-3121101-5092-34 (M.-R.P.); +886-4-781-3888 (T.-C.L.); +886-7-381-4526 (C.-H.Y.); Fax: +886-7-3218309 (M.-R.P.)
| |
Collapse
|
46
|
Synthetic lethality-mediated precision oncology via the tumor transcriptome. Cell 2021; 184:2487-2502.e13. [PMID: 33857424 DOI: 10.1016/j.cell.2021.03.030] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 10/29/2020] [Accepted: 03/12/2021] [Indexed: 01/27/2023]
Abstract
Precision oncology has made significant advances, mainly by targeting actionable mutations in cancer driver genes. Aiming to expand treatment opportunities, recent studies have begun to explore the utility of tumor transcriptome to guide patient treatment. Here, we introduce SELECT (synthetic lethality and rescue-mediated precision oncology via the transcriptome), a precision oncology framework harnessing genetic interactions to predict patient response to cancer therapy from the tumor transcriptome. SELECT is tested on a broad collection of 35 published targeted and immunotherapy clinical trials from 10 different cancer types. It is predictive of patients' response in 80% of these clinical trials and in the recent multi-arm WINTHER trial. The predictive signatures and the code are made publicly available for academic use, laying a basis for future prospective clinical studies.
Collapse
|
47
|
Abstract
Glioblastoma remains incurable despite advances in surgery, radiation, and chemotherapy, underscoring the need for new therapies. The genetic heterogenicity, presence of redundant molecular pathways, and the blood-brain barrier have limited the applicability of molecularly targeted agents. The therapeutic benefit seen with a small subset of patients suggests, however, that patient selection is critical. Recent investigations show that molecularly targeted synthetic lethality is a promising complementary approach. The article provides an overview of the challenges of molecularly targeted therapy in adults with glioblastoma, including current trials and future therapeutic directions.
Collapse
Affiliation(s)
- Matthew A Smith-Cohn
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, 37 Convent Drive, Building 37, Room 1016, Bethesda, MD 20892, USA; Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Orieta Celiku
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, 37 Convent Drive, Building 37, Room 1142, Bethesda, MD 20892, USA
| | - Mark R Gilbert
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
| |
Collapse
|
48
|
Parkhitko AA, Singh A, Hsieh S, Hu Y, Binari R, Lord CJ, Hannenhalli S, Ryan CJ, Perrimon N. Cross-species identification of PIP5K1-, splicing- and ubiquitin-related pathways as potential targets for RB1-deficient cells. PLoS Genet 2021; 17:e1009354. [PMID: 33591981 PMCID: PMC7909629 DOI: 10.1371/journal.pgen.1009354] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 02/26/2021] [Accepted: 01/11/2021] [Indexed: 01/02/2023] Open
Abstract
The RB1 tumor suppressor is recurrently mutated in a variety of cancers including retinoblastomas, small cell lung cancers, triple-negative breast cancers, prostate cancers, and osteosarcomas. Finding new synthetic lethal (SL) interactions with RB1 could lead to new approaches to treating cancers with inactivated RB1. We identified 95 SL partners of RB1 based on a Drosophila screen for genetic modifiers of the eye phenotype caused by defects in the RB1 ortholog, Rbf1. We validated 38 mammalian orthologs of Rbf1 modifiers as RB1 SL partners in human cancer cell lines with defective RB1 alleles. We further show that for many of the RB1 SL genes validated in human cancer cell lines, low activity of the SL gene in human tumors, when concurrent with low levels of RB1 was associated with improved patient survival. We investigated higher order combinatorial gene interactions by creating a novel Drosophila cancer model with co-occurring Rbf1, Pten and Ras mutations, and found that targeting RB1 SL genes in this background suppressed the dramatic tumor growth and rescued fly survival whilst having minimal effects on wild-type cells. Finally, we found that drugs targeting the identified RB1 interacting genes/pathways, such as UNC3230, PYR-41, TAK-243, isoginkgetin, madrasin, and celastrol also elicit SL in human cancer cell lines. In summary, we identified several high confidence, evolutionarily conserved, novel targets for RB1-deficient cells that may be further adapted for the treatment of human cancer.
Collapse
Affiliation(s)
- Andrey A. Parkhitko
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, Massachusetts, United States of America
- Aging Institute of UPMC and the University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Arashdeep Singh
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Sharon Hsieh
- Department of Biology, Boston University, Boston, Massachusetts, United States of America
| | - Yanhui Hu
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Richard Binari
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, Massachusetts, United States of America
- Howard Hughes Medical Institute, Boston, Massachusetts, United States of America
| | - Christopher J. Lord
- CRUK Gene Function Laboratory, The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, United Kingdom
| | - Sridhar Hannenhalli
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Colm J. Ryan
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
- School of Computer Science, University College Dublin, Dublin, Ireland
| | - Norbert Perrimon
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, Massachusetts, United States of America
- Howard Hughes Medical Institute, Boston, Massachusetts, United States of America
| |
Collapse
|
49
|
In Silico Inference of Synthetic Cytotoxic Interactions from Paclitaxel Responses. Int J Mol Sci 2021; 22:ijms22031097. [PMID: 33499282 PMCID: PMC7865701 DOI: 10.3390/ijms22031097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 01/18/2021] [Accepted: 01/19/2021] [Indexed: 11/16/2022] Open
Abstract
To exploit negatively interacting pairs of cancer somatic mutations in chemotherapy responses or synthetic cytotoxicity (SC), we systematically determined mutational pairs that had significantly lower paclitaxel half maximal inhibitory concentration (IC50) values. We evaluated 407 cell lines with somatic mutation profiles and estimated their copy number and drug-inhibitory concentrations in Genomics of Drug Sensitivity in Cancer (GDSC) database. The SC effect of 142 mutated gene pairs on response to paclitaxel was successfully cross-validated using human cancer datasets for urogenital cancers available in The Cancer Genome Atlas (TCGA) database. We further analyzed the cumulative effect of increasing SC pair numbers on the TP53 tumor suppressor gene. Patients with TCGA bladder and urogenital cancer exhibited improved cancer survival rates as the number of disrupted SC partners (i.e., SYNE2, SON, and/or PRY) of TP53 increased. The prognostic effect of SC burden on response to paclitaxel treatment could be differentiated from response to other cytotoxic drugs. Thus, the concept of pairwise SC may aid the identification of novel therapeutic and prognostic targets.
Collapse
|
50
|
Harnessing the Co-vulnerabilities of Amino Acid-Restricted Cancers. Cell Metab 2021; 33:9-20. [PMID: 33406406 PMCID: PMC7837405 DOI: 10.1016/j.cmet.2020.12.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 12/10/2020] [Accepted: 12/13/2020] [Indexed: 12/18/2022]
Abstract
Sustained proliferative potential of cancer cells creates heightened energetic and biosynthetic demands. The resulting overt dependence of cancer cells on unperturbed nutrient supply has prompted a widespread interest in amino acid restriction strategies as potential cancer therapeutics. However, owing to rapid signaling and metabolic reprogramming in cancer cells, the prospects for success of amino acid restriction approaches remain unclear. We thus recognize that the identification of co-vulnerabilities of amino acid-restricted cancers may inform actionable targets for effective combined interventions. In this perspective, we outline the current state of key cellular mechanisms underlying adaptation to amino acid restriction and discuss the role of signal transduction pathways governing cancer cell resistance to amino acid restriction, with potential ramifications for the design of future therapeutic efforts.
Collapse
|