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Roshani F, Ahvar M, Ebrahimi A. Network analysis to identify driver genes and combination drugs in brain cancer. Sci Rep 2024; 14:18666. [PMID: 39134610 PMCID: PMC11319350 DOI: 10.1038/s41598-024-69705-9] [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: 04/25/2024] [Accepted: 08/07/2024] [Indexed: 08/15/2024] Open
Abstract
Brain cancer is one of the deadliest diseases, although many efforts have been made to treat it, there is no comprehensive and effective treatment approach yet. In recent years, the use of network-based analysis to identify important biological genes and pathways involved in various complex diseases, including brain cancer, has attracted the attention of researchers. The goal of this manuscript is to perform a comprehensive analysis of the various results presented related to brain cancer. For this purpose, firstly, based on the CORMINE medical database, collected all the genes related to brain cancer with a valid P-value. Then the structural and functional relationships between the above gene sets have been identified based on the STRING database. Next, in the PPI network, hub centrality analysis was performed to determine the proteins that have many connections with other proteins. After the modularization of the network, the module with the most hub vertices is considered as the most relevant module to the formation and progression of brain cancer. Since the driver vertices play an important role in biological systems, the edges of the selected module were oriented, and by analyzing the controllability of complex networks, a set of five proteins with the highest control power has been identified. Finally, based on the drug-gene interaction, a set of drugs effective on each of the driver genes has been obtained, which can potentially be used as new combination drugs. Validation of the hub and driver proteins shows that they are mainly essential proteins in the biological processes related to the various cancers and therefore the drugs that affect them can be considered as new combination therapy. The presented procedure can be used for any other complex disease.
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Affiliation(s)
| | - Mobina Ahvar
- Department of Physics, Alzahra University, Tehran, Iran
| | - Ali Ebrahimi
- Department of Physics, Alzahra University, Tehran, Iran.
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
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2
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Zhang W, Huang RS. Computer-aided drug discovery strategies for novel therapeutics for prostate cancer leveraging next-generating sequencing data. Expert Opin Drug Discov 2024; 19:841-853. [PMID: 38860709 DOI: 10.1080/17460441.2024.2365370] [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: 03/14/2024] [Accepted: 06/04/2024] [Indexed: 06/12/2024]
Abstract
INTRODUCTION Prostate cancer (PC) is the most common malignancy and accounts for a significant proportion of cancer deaths among men. Although initial therapy success can often be observed in patients diagnosed with localized PC, many patients eventually develop disease recurrence and metastasis. Without effective treatments, patients with aggressive PC display very poor survival. To curb the current high mortality rate, many investigations have been carried out to identify efficacious therapeutics. Compared to de novo drug designs, computational methods have been widely employed to offer actionable drug predictions in a fast and cost-efficient way. Particularly, powered by an increasing availability of next-generation sequencing molecular profiles from PC patients, computer-aided approaches can be tailored to screen for candidate drugs. AREAS COVERED Herein, the authors review the recent advances in computational methods for drug discovery utilizing molecular profiles from PC patients. Given the uniqueness in PC therapeutic needs, they discuss in detail the drug discovery goals of these studies, highlighting their translational values for clinically impactful drug nomination. EXPERT OPINION Evolving molecular profiling techniques may enable new perspectives for computer-aided approaches to offer drug candidates for different tumor microenvironments. With ongoing efforts to incorporate new compounds into large-scale high-throughput screens, the authors envision continued expansion of drug candidate pools.
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Affiliation(s)
- Weijie Zhang
- Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, USA
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, USA
| | - R Stephanie Huang
- Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, USA
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, USA
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3
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Hu LZ, Douglass E, Turunen M, Pampou S, Grunn A, Realubit R, Antolin AA, Wang ALE, Li H, Subramaniam P, Mundi PS, Karan C, Alvarez M, Califano A. Elucidating Compound Mechanism of Action and Polypharmacology with a Large-scale Perturbational Profile Compendium. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.08.561457. [PMID: 37873470 PMCID: PMC10592689 DOI: 10.1101/2023.10.08.561457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
The Mechanism of Action (MoA) of a drug is generally represented as a small, non-tissue-specific repertoire of high-affinity binding targets. Yet, drug activity and polypharmacology are increasingly associated with a broad range of off-target and tissue-specific effector proteins. To address this challenge, we have leveraged a microfluidics-based Plate-Seq technology to survey drug perturbational profiles representing >700 FDA-approved and experimental oncology drugs, in cell lines selected as high-fidelity models of 23 aggressive tumor subtypes. Built on this dataset, we implemented an efficient computational framework to define a tissue-specific protein activity landscape of these drugs and reported almost 50 million differential protein activities derived from drug perturbations vs. vehicle controls. These analyses revealed thousands of highly reproducible and novel, drug-mediated modulation of tissue-specific targets, leading to generation of a proteome-wide drug functional network, characterization of MoA-related drug clusters and off-target effects, dramatical expansion of druggable human proteome, and identification and experimental validation of novel, tissue-specific inhibitors of undruggable oncoproteins, most never reported before. The drug perturbation profile resource described here represents the first, large-scale, whole-genome-wide, RNA-Seq based dataset assembled to date, with the proposed framework, which is easily extended to elucidating the MoA of novel small-molecule libraries, facilitates mechanistic exploration of drug functions, supports systematic and quantitative approaches to precision oncology, and serves as a rich data foundation for drug discovery.
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Kalla J, Pfneissl J, Mair T, Tran L, Egger G. A systematic review on the culture methods and applications of 3D tumoroids for cancer research and personalized medicine. Cell Oncol (Dordr) 2024:10.1007/s13402-024-00960-8. [PMID: 38806997 DOI: 10.1007/s13402-024-00960-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/11/2024] [Indexed: 05/30/2024] Open
Abstract
Cancer is a highly heterogeneous disease, and thus treatment responses vary greatly between patients. To improve therapy efficacy and outcome for cancer patients, more representative and patient-specific preclinical models are needed. Organoids and tumoroids are 3D cell culture models that typically retain the genetic and epigenetic characteristics, as well as the morphology, of their tissue of origin. Thus, they can be used to understand the underlying mechanisms of cancer initiation, progression, and metastasis in a more physiological setting. Additionally, co-culture methods of tumoroids and cancer-associated cells can help to understand the interplay between a tumor and its tumor microenvironment. In recent years, tumoroids have already helped to refine treatments and to identify new targets for cancer therapy. Advanced culturing systems such as chip-based fluidic devices and bioprinting methods in combination with tumoroids have been used for high-throughput applications for personalized medicine. Even though organoid and tumoroid models are complex in vitro systems, validation of results in vivo is still the common practice. Here, we describe how both animal- and human-derived tumoroids have helped to identify novel vulnerabilities for cancer treatment in recent years, and how they are currently used for precision medicine.
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Affiliation(s)
- Jessica Kalla
- Department of Pathology, Medical University of Vienna, Vienna, Austria
| | - Janette Pfneissl
- Department of Pathology, Medical University of Vienna, Vienna, Austria
| | - Theresia Mair
- Department of Pathology, Medical University of Vienna, Vienna, Austria
| | - Loan Tran
- Department of Pathology, Medical University of Vienna, Vienna, Austria
- Ludwig Boltzmann Institute Applied Diagnostics, Vienna, Austria
| | - Gerda Egger
- Department of Pathology, Medical University of Vienna, Vienna, Austria.
- Ludwig Boltzmann Institute Applied Diagnostics, Vienna, Austria.
- Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria.
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5
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Worley J, Noh H, You D, Turunen MM, Ding H, Paull E, Griffin AT, Grunn A, Zhang M, Guillan K, Bush EC, Brosius SJ, Hibshoosh H, Mundi PS, Sims P, Dalerba P, Dela Cruz FS, Kung AL, Califano A. Identification and Pharmacological Targeting of Treatment-Resistant, Stem-like Breast Cancer Cells for Combination Therapy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.08.562798. [PMID: 38798673 PMCID: PMC11118419 DOI: 10.1101/2023.11.08.562798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Tumors frequently harbor isogenic yet epigenetically distinct subpopulations of multi-potent cells with high tumor-initiating potential-often called Cancer Stem-Like Cells (CSLCs). These can display preferential resistance to standard-of-care chemotherapy. Single-cell analyses can help elucidate Master Regulator (MR) proteins responsible for governing the transcriptional state of these cells, thus revealing complementary dependencies that may be leveraged via combination therapy. Interrogation of single-cell RNA sequencing profiles from seven metastatic breast cancer patients, using perturbational profiles of clinically relevant drugs, identified drugs predicted to invert the activity of MR proteins governing the transcriptional state of chemoresistant CSLCs, which were then validated by CROP-seq assays. The top drug, the anthelmintic albendazole, depleted this subpopulation in vivo without noticeable cytotoxicity. Moreover, sequential cycles of albendazole and paclitaxel-a commonly used chemotherapeutic -displayed significant synergy in a patient-derived xenograft (PDX) from a TNBC patient, suggesting that network-based approaches can help develop mechanism-based combinatorial therapies targeting complementary subpopulations.
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Affiliation(s)
- Jeremy Worley
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY USA 10032
| | - Heeju Noh
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Daoqi You
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Mikko M Turunen
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Hongxu Ding
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- Department of Pharmacy Practice & Science, College of Pharmacy, University of Arizona, Tucson, Arizona, USA 85721
| | - Evan Paull
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Aaron T Griffin
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Adina Grunn
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Mingxuan Zhang
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Kristina Guillan
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Erin C Bush
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Samantha J Brosius
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Hanina Hibshoosh
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, USA 10032
- Department of Pathology & Cell Biology, Columbia University Irving Medical Center, New York, USA 10032
| | - Prabhjot S Mundi
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, USA 10032
| | - Peter Sims
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Piero Dalerba
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, USA 10032
- Department of Pathology & Cell Biology, Columbia University Irving Medical Center, New York, USA 10032
- Columbia Stem Cell Initiative, Columbia University Irving Medical Center, New York, USA 10032
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Filemon S Dela Cruz
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Andrew L Kung
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Andrea Califano
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, USA 10032
- Department of Biochemistry & Molecular Biophysics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- Department of Biomedical Informatics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY USA 10032
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Rosenberger G, Li W, Turunen M, He J, Subramaniam PS, Pampou S, Griffin AT, Karan C, Kerwin P, Murray D, Honig B, Liu Y, Califano A. Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis. Nat Commun 2024; 15:3909. [PMID: 38724493 PMCID: PMC11082183 DOI: 10.1038/s41467-024-47957-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 04/16/2024] [Indexed: 05/12/2024] Open
Abstract
Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.
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Affiliation(s)
- George Rosenberger
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Wenxue Li
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA
| | - Mikko Turunen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Jing He
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Prem S Subramaniam
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Sergey Pampou
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Aaron T Griffin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Medical Scientist Training Program, Columbia University Irving Medical Center, New York, NY, USA
| | - Charles Karan
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Patrick Kerwin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Diana Murray
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Barry Honig
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Yansheng Liu
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA.
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA.
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA.
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
- Chan Zuckerberg Biohub New York, New York, NY, USA.
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Stokes ME, Vasciaveo A, Small JC, Zask A, Reznik E, Smith N, Wang Q, Daniels J, Forouhar F, Rajbhandari P, Califano A, Stockwell BR. Subtype-selective prenylated isoflavonoids disrupt regulatory drivers of MYCN-amplified cancers. Cell Chem Biol 2024; 31:805-819.e9. [PMID: 38061356 PMCID: PMC11031350 DOI: 10.1016/j.chembiol.2023.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 07/18/2023] [Accepted: 11/13/2023] [Indexed: 01/05/2024]
Abstract
Transcription factors have proven difficult to target with small molecules because they lack pockets necessary for potent binding. Disruption of protein expression can suppress targets and enable therapeutic intervention. To this end, we developed a drug discovery workflow that incorporates cell-line-selective screening and high-throughput expression profiling followed by regulatory network analysis to identify compounds that suppress regulatory drivers of disease. Applying this approach to neuroblastoma (NBL), we screened bioactive molecules in cell lines representing its MYC-dependent (MYCNA) and mesenchymal (MES) subtypes to identify selective compounds, followed by PLATESeq profiling of treated cells. This revealed compounds that disrupt a sub-network of MYCNA-specific regulatory proteins, resulting in MYCN degradation in vivo. The top hit was isopomiferin, a prenylated isoflavonoid that inhibited casein kinase 2 (CK2) in cells. Isopomiferin and its structural analogs inhibited MYC and MYCN in NBL and lung cancer cells, highlighting the general MYC-inhibiting potential of this unique scaffold.
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Affiliation(s)
- Michael E Stokes
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA
| | - Alessandro Vasciaveo
- Department of Systems Biology, Columbia University Medical Center, New York City, NY 10032, USA
| | - Jonnell Candice Small
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA
| | - Arie Zask
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA
| | - Eduard Reznik
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA
| | - Nailah Smith
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA
| | - Qian Wang
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA
| | - Jacob Daniels
- Department of Pharmacology, Columbia University Medical Center, New York City, NY 10032, USA
| | - Farhad Forouhar
- Proteomics and Macromolecular Crystallography Shared Resource (PMCSR), Columbia University Medical Center, New York City, NY 10032, USA
| | - Presha Rajbhandari
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA
| | - Andrea Califano
- Department of Systems Biology, Columbia University Medical Center, New York City, NY 10032, USA.
| | - Brent R Stockwell
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA; Department of Chemistry, Columbia University, New York City, NY 10027, USA; Department of Pathology and Cell Biology and Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA.
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8
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Feng W, Ladewig E, Salsabeel N, Zhao H, Lee YS, Gopalan A, Lange M, Luo H, Kang W, Fan N, Rosiek E, de Stanchina E, Chen Y, Carver BS, Leslie CS, Sawyers CL. ERG activates a stem-like proliferation-differentiation program in prostate epithelial cells with mixed basal-luminal identity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.15.540839. [PMID: 38585869 PMCID: PMC10996491 DOI: 10.1101/2023.05.15.540839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
To gain insight into how ERG translocations cause prostate cancer, we performed single cell transcriptional profiling of an autochthonous mouse model at an early stage of disease initiation. Despite broad expression of ERG in all prostate epithelial cells, proliferation was enriched in a small, stem-like population with mixed-luminal basal identity (called intermediate cells). Through a series of lineage tracing and primary prostate tissue transplantation experiments, we find that tumor initiating activity resides in a subpopulation of basal cells that co-express the luminal genes Tmprss2 and Nkx3.1 (called BasalLum) but not in the larger population of classical Krt8+ luminal cells. Upon ERG activation, BasalLum cells give rise to the highly proliferative intermediate state, which subsequently transitions to the larger population of Krt8+ luminal cells characteristic of ERG-positive human cancers. Furthermore, this proliferative population is characterized by an ERG-specific chromatin state enriched for NFkB, AP-1, STAT and NFAT binding, with implications for TF cooperativity. The fact that the proliferative potential of ERG is enriched in a small stem-like population implicates the chromatin context of these cells as a critical variable for unmasking its oncogenic activity.
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Affiliation(s)
- Weiran Feng
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Erik Ladewig
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Nazifa Salsabeel
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Huiyong Zhao
- Antitumor Assessment Core Facility, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Young Sun Lee
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Anuradha Gopalan
- Department of Pathology, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Matthew Lange
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Hanzhi Luo
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Wenfei Kang
- Molecular Cytology Core Facility, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Ning Fan
- Molecular Cytology Core Facility, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Eric Rosiek
- Molecular Cytology Core Facility, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Elisa de Stanchina
- Antitumor Assessment Core Facility, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Yu Chen
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Brett S. Carver
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
- Department of Surgery, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
- Division of Urology, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Christina S. Leslie
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Charles L. Sawyers
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
- Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
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9
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Murphy KC, Ruscetti M. Advances in Making Cancer Mouse Models More Accessible and Informative through Non-Germline Genetic Engineering. Cold Spring Harb Perspect Med 2024; 14:a041348. [PMID: 37277206 PMCID: PMC10982712 DOI: 10.1101/cshperspect.a041348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Genetically engineered mouse models (GEMMs) allow for modeling of spontaneous tumorigenesis within its native microenvironment in mice and have provided invaluable insights into mechanisms of tumorigenesis and therapeutic strategies to treat human disease. However, as their generation requires germline manipulation and extensive animal breeding that is time-, labor-, and cost-intensive, traditional GEMMs are not accessible to most researchers, and fail to model the full breadth of cancer-associated genetic alterations and therapeutic targets. Recent advances in genome-editing technologies and their implementation in somatic tissues of mice have ushered in a new class of mouse models: non-germline GEMMs (nGEMMs). nGEMM approaches can be leveraged to generate somatic tumors de novo harboring virtually any individual or group of genetic alterations found in human cancer in a mouse through simple procedures that do not require breeding, greatly increasing the accessibility and speed and scale on which GEMMs can be produced. Here we describe the technologies and delivery systems used to create nGEMMs and highlight new biological insights derived from these models that have rapidly informed functional cancer genomics, precision medicine, and immune oncology.
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Affiliation(s)
- Katherine C Murphy
- Department of Molecular, Cell, and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, Massachusetts 01605, USA
| | - Marcus Ruscetti
- Department of Molecular, Cell, and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, Massachusetts 01605, USA;
- Immunology and Microbiology Program, University of Massachusetts Chan Medical School, Worcester, Massachusetts 01605, USA
- Cancer Center, University of Massachusetts Chan Medical School, Worcester, Massachusetts 01605, USA
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10
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Fernández EC, Tomassoni L, Zhang X, Wang J, Obradovic A, Laise P, Griffin AT, Vlahos L, Minns HE, Morales DV, Simmons C, Gallitto M, Wei HJ, Martins TJ, Becker PS, Crawford JR, Tzaridis T, Wechsler-Reya RJ, Garvin J, Gartrell RD, Szalontay L, Zacharoulis S, Wu CC, Zhang Z, Califano A, Pavisic J. Elucidation and Pharmacologic Targeting of Master Regulator Dependencies in Coexisting Diffuse Midline Glioma Subpopulations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.17.585370. [PMID: 38559080 PMCID: PMC10979998 DOI: 10.1101/2024.03.17.585370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Diffuse Midline Gliomas (DMGs) are universally fatal, primarily pediatric malignancies affecting the midline structures of the central nervous system. Despite decades of clinical trials, treatment remains limited to palliative radiation therapy. A major challenge is the coexistence of molecularly distinct malignant cell states with potentially orthogonal drug sensitivities. To address this challenge, we leveraged established network-based methodologies to elucidate Master Regulator (MR) proteins representing mechanistic, non-oncogene dependencies of seven coexisting subpopulations identified by single-cell analysis-whose enrichment in essential genes was validated by pooled CRISPR/Cas9 screens. Perturbational profiles of 372 clinically relevant drugs helped identify those able to invert the activity of subpopulation-specific MRs for follow-up in vivo validation. While individual drugs predicted to target individual subpopulations-including avapritinib, larotrectinib, and ruxolitinib-produced only modest tumor growth reduction in orthotopic models, systemic co-administration induced significant survival extension, making this approach a valuable contribution to the rational design of combination therapy.
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11
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Yu Z, Wu Z, Wang Z, Wang Y, Zhou M, Li W, Liu G, Tang Y. Network-Based Methods and Their Applications in Drug Discovery. J Chem Inf Model 2024; 64:57-75. [PMID: 38150548 DOI: 10.1021/acs.jcim.3c01613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.
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Affiliation(s)
- Zhuohang Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Ze Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yimeng Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Moran Zhou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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12
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Mundi PS, Dela Cruz FS, Grunn A, Diolaiti D, Mauguen A, Rainey AR, Guillan K, Siddiquee A, You D, Realubit R, Karan C, Ortiz MV, Douglass EF, Accordino M, Mistretta S, Brogan F, Bruce JN, Caescu CI, Carvajal RD, Crew KD, Decastro G, Heaney M, Henick BS, Hershman DL, Hou JY, Iwamoto FM, Jurcic JG, Kiran RP, Kluger MD, Kreisl T, Lamanna N, Lassman AB, Lim EA, Manji GA, McKhann GM, McKiernan JM, Neugut AI, Olive KP, Rosenblat T, Schwartz GK, Shu CA, Sisti MB, Tergas A, Vattakalam RM, Welch M, Wenske S, Wright JD, Hibshoosh H, Kalinsky K, Aburi M, Sims PA, Alvarez MJ, Kung AL, Califano A. A Transcriptome-Based Precision Oncology Platform for Patient-Therapy Alignment in a Diverse Set of Treatment-Resistant Malignancies. Cancer Discov 2023; 13:1386-1407. [PMID: 37061969 PMCID: PMC10239356 DOI: 10.1158/2159-8290.cd-22-1020] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 01/14/2023] [Accepted: 03/14/2023] [Indexed: 04/17/2023]
Abstract
Predicting in vivo response to antineoplastics remains an elusive challenge. We performed a first-of-kind evaluation of two transcriptome-based precision cancer medicine methodologies to predict tumor sensitivity to a comprehensive repertoire of clinically relevant oncology drugs, whose mechanism of action we experimentally assessed in cognate cell lines. We enrolled patients with histologically distinct, poor-prognosis malignancies who had progressed on multiple therapies, and developed low-passage, patient-derived xenograft models that were used to validate 35 patient-specific drug predictions. Both OncoTarget, which identifies high-affinity inhibitors of individual master regulator (MR) proteins, and OncoTreat, which identifies drugs that invert the transcriptional activity of hyperconnected MR modules, produced highly significant 30-day disease control rates (68% and 91%, respectively). Moreover, of 18 OncoTreat-predicted drugs, 15 induced the predicted MR-module activity inversion in vivo. Predicted drugs significantly outperformed antineoplastic drugs selected as unpredicted controls, suggesting these methods may substantively complement existing precision cancer medicine approaches, as also illustrated by a case study. SIGNIFICANCE Complementary precision cancer medicine paradigms are needed to broaden the clinical benefit realized through genetic profiling and immunotherapy. In this first-in-class application, we introduce two transcriptome-based tumor-agnostic systems biology tools to predict drug response in vivo. OncoTarget and OncoTreat are scalable for the design of basket and umbrella clinical trials. This article is highlighted in the In This Issue feature, p. 1275.
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Affiliation(s)
- Prabhjot S. Mundi
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
| | - Filemon S. Dela Cruz
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY USA 10065
| | - Adina Grunn
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
| | - Daniel Diolaiti
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY USA 10065
| | - Audrey Mauguen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY USA 10065
| | - Allison R. Rainey
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY USA 10065
| | - Kristina Guillan
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY USA 10065
| | - Armaan Siddiquee
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY USA 10065
| | - Daoqi You
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY USA 10065
| | - Ronald Realubit
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
| | - Charles Karan
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
| | - Michael V. Ortiz
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY USA 10065
| | - Eugene F. Douglass
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
| | - Melissa Accordino
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Suzanne Mistretta
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
| | - Frances Brogan
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
| | - Jeffrey N. Bruce
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Neurological Surgery, Columbia University Irving Medical Center, 710 W 168th Street, New York, NY USA 10032
| | - Cristina I. Caescu
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
| | - Richard D. Carvajal
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Katherine D Crew
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Guarionex Decastro
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Urology, Columbia University Irving Medical Center, 160 Fort Washington Ave, New York, NY USA 10032
| | - Mark Heaney
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Brian S Henick
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Dawn L Hershman
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
- Department of Epidemiology, Columbia University Mailman School of Public Health, 722 West 168th St. NY, NY 10032
| | - June Y. Hou
- Department of Obstetrics & Gynecology, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY USA 10032
| | - Fabio M. Iwamoto
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Neurology, Columbia University Irving Medical Center, 710 W 168th Street, New York, NY USA 10032
| | - Joseph G. Jurcic
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Ravi P. Kiran
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Surgery, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY USA 10032
| | - Michael D Kluger
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Surgery, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY USA 10032
| | - Teri Kreisl
- Novartis Five Cambridge, MA 02142, United States
| | - Nicole Lamanna
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Andrew B. Lassman
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Neurology, Columbia University Irving Medical Center, 710 W 168th Street, New York, NY USA 10032
| | - Emerson A. Lim
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Gulam A. Manji
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Guy M McKhann
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Neurological Surgery, Columbia University Irving Medical Center, 710 W 168th Street, New York, NY USA 10032
| | - James M. McKiernan
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Urology, Columbia University Irving Medical Center, 160 Fort Washington Ave, New York, NY USA 10032
| | - Alfred I Neugut
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
- Department of Epidemiology, Columbia University Mailman School of Public Health, 722 West 168th St. NY, NY 10032
| | - Kenneth P. Olive
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Todd Rosenblat
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Gary K. Schwartz
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Catherine A Shu
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Michael B. Sisti
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Neurological Surgery, Columbia University Irving Medical Center, 710 W 168th Street, New York, NY USA 10032
- Department of Otolaryngology Head and Neck Surgery, Columbia University Irving Medical Center, 710 W 168th Street, New York, NY USA 10032
- Department of Radiation Oncology, Columbia University Irving Medical Center, 161 Fort Washington Avenue, New York, NY 10032, United States
| | - Ana Tergas
- Department of Obstetrics & Gynecology, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY USA 10032
| | - Reena M Vattakalam
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Obstetrics & Gynecology, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY USA 10032
| | - Mary Welch
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Neurology, Columbia University Irving Medical Center, 710 W 168th Street, New York, NY USA 10032
| | - Sven Wenske
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Urology, Columbia University Irving Medical Center, 160 Fort Washington Ave, New York, NY USA 10032
| | - Jason D. Wright
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Obstetrics & Gynecology, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY USA 10032
| | - Hanina Hibshoosh
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
| | - Kevin Kalinsky
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Winship Cancer Institute of Emory University and Department of Hematology and Medical Oncology, Emory University School of Medicine, 1365-C Clifton Road NE, Atlanta, GA 30322, United States
| | - Mahalaxmi Aburi
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
| | - Peter A. Sims
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, 701 W 168th Street, New York, NY USA 10032
| | - Mariano J. Alvarez
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- DarwinHealth Inc. New York
| | - Andrew L. Kung
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY USA 10065
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave, New York, NY USA 10032
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY USA 10032
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, 701 W 168th Street, New York, NY USA 10032
- Department of Biomedical Informatics, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY USA 10032
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY USA 10032
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13
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Tagore S, Tsang S, Mills GB, Califano A. Systematic Pan-cancer Functional Inference and Validation of Hyper, Hypo and Neomorphic Mutations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.29.538640. [PMID: 37205498 PMCID: PMC10187182 DOI: 10.1101/2023.04.29.538640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
While the functional effects of many recurrent cancer mutations have been characterized, the TCGA repository comprises more than 10M non-recurrent events, whose function is unknown. We propose that the context specific activity of transcription factor (TF) proteins-as measured by expression of their transcriptional targets-provides a sensitive and accurate reporter assay to assess the functional role of oncoprotein mutations. Analysis of differentially active TFs in samples harboring mutations of unknown significance-compared to established gain (GOF/hypermorph) or loss (LOF/hypomorph) of function-helped functionally characterize 577,866 individual mutational events across TCGA cohorts, including identification of mutations that are either neomorphic (gain of novel function) or phenocopy other mutations ( mutational mimicry ). Validation using mutation knock-in assays confirmed 15 out of 15 predicted gain and loss of function mutations and 15 of 20 predicted neomorphic mutations. This could help determine targeted therapy in patients with mutations of unknown significance in established oncoproteins.
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