1
|
Zhang Y, Zhou Y, Zhou Y, Yu X, Shen X, Hong Y, Zhang Y, Wang S, Mou M, Zhang J, Tao L, Gao J, Qiu Y, Chen Y, Zhu F. TheMarker: a comprehensive database of therapeutic biomarkers. Nucleic Acids Res 2024; 52:D1450-D1464. [PMID: 37850638 PMCID: PMC10767989 DOI: 10.1093/nar/gkad862] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/21/2023] [Accepted: 09/29/2023] [Indexed: 10/19/2023] Open
Abstract
Distinct from the traditional diagnostic/prognostic biomarker (adopted as the indicator of disease state/process), the therapeutic biomarker (ThMAR) has emerged to be very crucial in the clinical development and clinical practice of all therapies. There are five types of ThMAR that have been found to play indispensable roles in various stages of drug discovery, such as: Pharmacodynamic Biomarker essential for guaranteeing the pharmacological effects of a therapy, Safety Biomarker critical for assessing the extent or likelihood of therapy-induced toxicity, Monitoring Biomarker indispensable for guiding clinical management by serially measuring patients' status, Predictive Biomarker crucial for maximizing the clinical outcome of a therapy for specific individuals, and Surrogate Endpoint fundamental for accelerating the approval of a therapy. However, these data of ThMARs has not been comprehensively described by any of the existing databases. Herein, a database, named 'TheMarker', was therefore constructed to (a) systematically offer all five types of ThMAR used at different stages of drug development, (b) comprehensively describe ThMAR information for the largest number of drugs among available databases, (c) extensively cover the widest disease classes by not just focusing on anticancer therapies. These data in TheMarker are expected to have great implication and significant impact on drug discovery and clinical practice, and it is freely accessible without any login requirement at: https://idrblab.org/themarker.
Collapse
Affiliation(s)
- Yintao Zhang
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Ying Zhou
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
| | - Yuan Zhou
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyuan Yu
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyi Shen
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven 06510, USA
| | - Yanfeng Hong
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Yuxin Zhang
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shanshan Wang
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jinsong Zhang
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yunqing Qiu
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
| | - Yuzong Chen
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
- Institute of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen 518000, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| |
Collapse
|
2
|
Park JD, Jang HJ, Choi SH, Jo GH, Choi JH, Hwang S, Park W, Park KS. The ELK3-DRP1 axis determines the chemosensitivity of triple-negative breast cancer cells to CDDP by regulating mitochondrial dynamics. Cell Death Discov 2023; 9:237. [PMID: 37422450 DOI: 10.1038/s41420-023-01536-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/20/2023] [Accepted: 06/28/2023] [Indexed: 07/10/2023] Open
Abstract
Triple-negative breast cancer (TNBC) is the most lethal form of breast cancer. TNBC patients have higher rates of metastasis and restricted therapy options. Although chemotherapy is the conventional treatment for TNBC, the frequent occurrence of chemoresistance significantly lowers the efficacy of treatment. Here, we demonstrated that ELK3, an oncogenic transcriptional repressor that is highly expressed in TNBC, determined the chemosensitivity of two representative TNBC cell lines (MDA-MB231 and Hs578T) to cisplatin (CDDP) by regulating mitochondrial dynamics. We observed that the knockdown of ELK3 in MDA-MB231 and Hs578T rendered these cell lines more susceptible to the effects of CDDP. We further demonstrated that the chemosensitivity of TNBC cells was caused by the CDDP-mediated acceleration of mitochondrial fission, excessive mitochondrial reactive oxygen species production, and subsequent DNA damage. In addition, we identified DNM1L, a gene encoding the dynamin-related protein 1 (a major regulator of mitochondrial fission), as a direct downstream target of ELK3. Based on these results, we propose that the suppression of ELK3 expression could be used as a potential therapeutic strategy for overcoming the chemoresistance or inducing the chemosensitivity of TNBC.
Collapse
Affiliation(s)
- Joo Dong Park
- Department of Biomedical Science, CHA University, Seongnam, Republic of Korea
| | - Hye Jung Jang
- Department of Biomedical Science, CHA University, Seongnam, Republic of Korea
| | - Seung Hee Choi
- Department of Biomedical Science, CHA University, Seongnam, Republic of Korea
| | - Gae Hoon Jo
- Department of Biomedical Science, CHA University, Seongnam, Republic of Korea
| | - Jin-Ho Choi
- Department of Biomedical Science, CHA University, Seongnam, Republic of Korea
| | - Sohyun Hwang
- Department of Biomedical Science, CHA University, Seongnam, Republic of Korea
| | - Wooram Park
- Department of Integrative Biotechnology, Sungkyunkwan University, Suwon, Republic of Korea
| | - Kyung-Soon Park
- Department of Biomedical Science, CHA University, Seongnam, Republic of Korea.
| |
Collapse
|
3
|
Shahzad M, Tahir MA, Alhussein M, Mobin A, Shams Malick RA, Anwar MS. NeuPD-A Neural Network-Based Approach to Predict Antineoplastic Drug Response. Diagnostics (Basel) 2023; 13:2043. [PMID: 37370938 DOI: 10.3390/diagnostics13122043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 06/01/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023] Open
Abstract
With the beginning of the high-throughput screening, in silico-based drug response analysis has opened lots of research avenues in the field of personalized medicine. For a decade, many different predicting techniques have been recommended for the antineoplastic (anti-cancer) drug response, but still, there is a need for improvements in drug sensitivity prediction. The intent of this research study is to propose a framework, namely NeuPD, to validate the potential anti-cancer drugs against a panel of cancer cell lines in publicly available datasets. The datasets used in this work are Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). As not all drugs are effective on cancer cell lines, we have worked on 10 essential drugs from the GDSC dataset that have achieved the best modeling results in previous studies. We also extracted 1610 essential oncogene expressions from 983 cell lines from the same dataset. Whereas, from the CCLE dataset, 16,383 gene expressions from 1037 cell lines and 24 drugs have been used in our experiments. For dimensionality reduction, Pearson correlation is applied to best fit the model. We integrate the genomic features of cell lines and drugs' fingerprints to fit the neural network model. For evaluation of the proposed NeuPD framework, we have used repeated K-fold cross-validation with 5 times repeats where K = 10 to demonstrate the performance in terms of root mean square error (RMSE) and coefficient determination (R2). The results obtained on the GDSC dataset that were measured using these cost functions show that our proposed NeuPD framework has outperformed existing approaches with an RMSE of 0.490 and R2 of 0.929.
Collapse
Affiliation(s)
- Muhammad Shahzad
- FAST School of Computing, National University of Computer and Emerging Sciences (NUCES-FAST), Karachi 75030, Pakistan
| | - Muhammad Atif Tahir
- FAST School of Computing, National University of Computer and Emerging Sciences (NUCES-FAST), Karachi 75030, Pakistan
| | - Musaed Alhussein
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia
| | - Ansharah Mobin
- FAST School of Computing, National University of Computer and Emerging Sciences (NUCES-FAST), Karachi 75030, Pakistan
| | - Rauf Ahmed Shams Malick
- FAST School of Computing, National University of Computer and Emerging Sciences (NUCES-FAST), Karachi 75030, Pakistan
| | - Muhammad Shahid Anwar
- Department of AI and Software, Gachon University, Seongnam-si 13120, Republic of Korea
| |
Collapse
|
4
|
Sales de Queiroz A, Sales Santa Cruz G, Jean-Marie A, Mazauric D, Roux J, Cazals F. Gene prioritization based on random walks with restarts and absorbing states, to define gene sets regulating drug pharmacodynamics from single-cell analyses. PLoS One 2022; 17:e0268956. [PMID: 36342924 PMCID: PMC9639845 DOI: 10.1371/journal.pone.0268956] [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: 08/27/2021] [Accepted: 05/12/2022] [Indexed: 11/09/2022] Open
Abstract
Prioritizing genes for their role in drug sensitivity, is an important step in understanding drugs mechanisms of action and discovering new molecular targets for co-treatment. To formalize this problem, we consider two sets of genes X and P respectively composing the gene signature of cell sensitivity at the drug IC50 and the genes involved in its mechanism of action, as well as a protein interaction network (PPIN) containing the products of X and P as nodes. We introduce Genetrank, a method to prioritize the genes in X for their likelihood to regulate the genes in P. Genetrank uses asymmetric random walks with restarts, absorbing states, and a suitable renormalization scheme. Using novel so-called saturation indices, we show that the conjunction of absorbing states and renormalization yields an exploration of the PPIN which is much more progressive than that afforded by random walks with restarts only. Using MINT as underlying network, we apply Genetrank to a predictive gene signature of cancer cells sensitivity to tumor-necrosis-factor-related apoptosis-inducing ligand (TRAIL), performed in single-cells. Our ranking provides biological insights on drug sensitivity and a gene set considerably enriched in genes regulating TRAIL pharmacodynamics when compared to the most significant differentially expressed genes obtained from a statistical analysis framework alone. We also introduce gene expression radars, a visualization tool embedded in MA plots to assess all pairwise interactions at a glance on graphical representations of transcriptomics data. Genetrank is made available in the Structural Bioinformatics Library (https://sbl.inria.fr/doc/Genetrank-user-manual.html). It should prove useful for mining gene sets in conjunction with a signaling pathway, whenever other approaches yield relatively large sets of genes.
Collapse
Affiliation(s)
| | | | | | | | - Jérémie Roux
- CNRS UMR 7284, Inserm U 1081, Institut de Recherche sur le Cancer et le Vieillissement de Nice, Centre Antoine Lacassagne, Universite Côte d’Azur, Nice, France
- * E-mail: (FC); (JR)
| | - Frédéric Cazals
- Inria, Université Côte d’Azur, Nice, France
- * E-mail: (FC); (JR)
| |
Collapse
|
5
|
Packeiser EM, Taher L, Kong W, Ernst M, Beck J, Hewicker-Trautwein M, Brenig B, Schütz E, Murua Escobar H, Nolte I. RNA-seq of nine canine prostate cancer cell lines reveals diverse therapeutic target signatures. Cancer Cell Int 2022; 22:54. [PMID: 35109825 PMCID: PMC8812184 DOI: 10.1186/s12935-021-02422-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 12/21/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Canine prostate adenocarcinoma (PAC) and transitional cell carcinoma (TCC) are typically characterized by metastasis and chemoresistance. Cell lines are important model systems for developing new therapeutic strategies. However, as they adapt to culturing conditions and undergo clonal selection, they can diverge from the tissue from which they were originally derived. Therefore, a comprehensive characterization of cell lines and their original tissues is paramount. METHODS This study compared the transcriptomes of nine canine cell lines derived from PAC, PAC metastasis and TCC to their respective original primary tumor or metastasis tissues. Special interests were laid on cell culture-related differences, epithelial to mesenchymal transition (EMT), the prostate and bladder cancer pathways, therapeutic targets in the PI3K-AKT signaling pathway and genes correlated with chemoresistance towards doxorubicin and carboplatin. RESULTS Independent analyses for PAC, PAC metastasis and TCC revealed 1743, 3941 and 463 genes, respectively, differentially expressed in the cell lines relative to their original tissues (DEGs). While genes associated with tumor microenvironment were mostly downregulated in the cell lines, patient-specific EMT features were conserved. Furthermore, examination of the prostate and bladder cancer pathways revealed extensive concordance between cell lines and tissues. Interestingly, all cell lines preserved downstream PI3K-AKT signaling, but each featured a unique therapeutic target signature. Additionally, resistance towards doxorubicin was associated with G2/M cell cycle transition and cell membrane biosynthesis, while carboplatin resistance correlated with histone, m- and tRNA processing. CONCLUSION Comparative whole-transcriptome profiling of cell lines and their original tissues identifies models with conserved therapeutic target expression. Moreover, it is useful for selecting suitable negative controls, i.e., cell lines lacking therapeutic target expression, increasing the transfer efficiency from in vitro to primary neoplasias for new therapeutic protocols. In summary, the dataset presented here constitutes a rich resource for canine prostate and bladder cancer research.
Collapse
Affiliation(s)
- Eva-Maria Packeiser
- Small Animal Clinic, University of Veterinary Medicine Hannover, Hannover, Germany
- Department of Medicine, Clinic III, Hematology, Oncology and Palliative Medicine, University Medical Center Rostock, Rostock, Germany
| | - Leila Taher
- Institute of Biomedical Informatics, Graz University of Technology, Graz, Austria
- Division of Bioinformatics, Department of Biology, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, 18057, Rostock, Germany
| | - Weibo Kong
- Small Animal Clinic, University of Veterinary Medicine Hannover, Hannover, Germany
- Department of Medicine, Clinic III, Hematology, Oncology and Palliative Medicine, University Medical Center Rostock, Rostock, Germany
- Institute of Muscle Biology and Growth, Research Institute for Farm Animal Biology (FBN), 18196, Dummerstorf, Germany
| | - Mathias Ernst
- Division of Bioinformatics, Department of Biology, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | | | | | - Bertram Brenig
- University of Göttingen, Institute of Veterinary Medicine, Göttingen, Germany
| | | | - Hugo Murua Escobar
- Small Animal Clinic, University of Veterinary Medicine Hannover, Hannover, Germany.
- Department of Medicine, Clinic III, Hematology, Oncology and Palliative Medicine, University Medical Center Rostock, Rostock, Germany.
- Comprehensive Cancer Center Mecklenburg-Vorpommern (CCC-MV), Campus Rostock, University of Rostock, 18057, Rostock, Germany.
| | - Ingo Nolte
- Small Animal Clinic, University of Veterinary Medicine Hannover, Hannover, Germany.
| |
Collapse
|
6
|
Yang H, Qi C, Li B, Cheng L. Non-coding RNAs as Novel Biomarkers in Cancer Drug Resistance. Curr Med Chem 2021; 29:837-848. [PMID: 34348605 DOI: 10.2174/0929867328666210804090644] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 06/09/2021] [Accepted: 06/15/2021] [Indexed: 11/22/2022]
Abstract
Chemotherapy is often the primary and most effective anticancer treatment; however, drug resistance remains a major obstacle to it being curative. Recent studies have demonstrated that non-coding RNAs (ncRNAs), especially microRNAs and long non-coding RNAs, are involved in drug resistance of tumor cells in many ways, such as modulation of apoptosis, drug efflux and metabolism, epithelial-to-mesenchymal transition, DNA repair, and cell cycle progression. Exploring the relationships between ncRNAs and drug resistance will not only contribute to our understanding of the mechanisms of drug resistance and provide ncRNA biomarkers of chemoresistance, but will also help realize personalized anticancer treatment regimens. Due to the high cost and low efficiency of biological experimentation, many researchers have opted to use computational methods to identify ncRNA biomarkers associated with drug resistance. In this review, we summarize recent discoveries related to ncRNA-mediated drug resistance and highlight the computational methods and resources available for ncRNA biomarkers involved in chemoresistance.
Collapse
Affiliation(s)
- Haixiu Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081. China
| | - Changlu Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081. China
| | - Boyan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081. China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081. China
| |
Collapse
|
7
|
Feng F, Shen B, Mou X, Li Y, Li H. Large-scale pharmacogenomic studies and drug response prediction for personalized cancer medicine. J Genet Genomics 2021; 48:540-551. [PMID: 34023295 DOI: 10.1016/j.jgg.2021.03.007] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 03/26/2021] [Accepted: 03/28/2021] [Indexed: 12/26/2022]
Abstract
The response rate of most anti-cancer drugs is limited because of the high heterogeneity of cancer and the complex mechanism of drug action. Personalized treatment that stratifies patients into subgroups using molecular biomarkers is promising to improve clinical benefit. With the accumulation of preclinical models and advances in computational approaches of drug response prediction, pharmacogenomics has made great success over the last 20 years and is increasingly used in the clinical practice of personalized cancer medicine. In this article, we first summarize FDA-approved pharmacogenomic biomarkers and large-scale pharmacogenomic studies of preclinical cancer models such as patient-derived cell lines, organoids, and xenografts. Furthermore, we comprehensively review the recent developments of computational methods in drug response prediction, covering network, machine learning, and deep learning technologies and strategies to evaluate immunotherapy response. In the end, we discuss challenges and propose possible solutions for further improvement.
Collapse
Affiliation(s)
- Fangyoumin Feng
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Bihan Shen
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xiaoqin Mou
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yixue Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 330106, China
| | - Hong Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
| |
Collapse
|
8
|
Ding J, Hostallero DE, El Khili MR, Fonseca GJ, Milette S, Noorah N, Guay-Belzile M, Spicer J, Daneshtalab N, Sirois M, Tremblay K, Emad A, Rousseau S. A network-informed analysis of SARS-CoV-2 and hemophagocytic lymphohistiocytosis genes' interactions points to Neutrophil extracellular traps as mediators of thrombosis in COVID-19. PLoS Comput Biol 2021; 17:e1008810. [PMID: 33684134 PMCID: PMC7971900 DOI: 10.1371/journal.pcbi.1008810] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 03/18/2021] [Accepted: 02/17/2021] [Indexed: 01/10/2023] Open
Abstract
Abnormal coagulation and an increased risk of thrombosis are features of severe COVID-19, with parallels proposed with hemophagocytic lymphohistiocytosis (HLH), a life-threating condition associated with hyperinflammation. The presence of HLH was described in severely ill patients during the H1N1 influenza epidemic, presenting with pulmonary vascular thrombosis. We tested the hypothesis that genes causing primary HLH regulate pathways linking pulmonary thromboembolism to the presence of SARS-CoV-2 using novel network-informed computational algorithms. This approach led to the identification of Neutrophils Extracellular Traps (NETs) as plausible mediators of vascular thrombosis in severe COVID-19 in children and adults. Taken together, the network-informed analysis led us to propose the following model: the release of NETs in response to inflammatory signals acting in concert with SARS-CoV-2 damage the endothelium and direct platelet-activation promoting abnormal coagulation leading to serious complications of COVID-19. The underlying hypothesis is that genetic and/or environmental conditions that favor the release of NETs may predispose individuals to thrombotic complications of COVID-19 due to an increase risk of abnormal coagulation. This would be a common pathogenic mechanism in conditions including autoimmune/infectious diseases, hematologic and metabolic disorders.
Collapse
Affiliation(s)
- Jun Ding
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- The Meakins-Christie Laboratories at the Research Institute of the McGill University Heath Centre Research Institute, Montréal, Canada
| | - David Earl Hostallero
- Department of Electrical and Computer Engineering, McGill University, Montréal, Canada
| | - Mohamed Reda El Khili
- Department of Electrical and Computer Engineering, McGill University, Montréal, Canada
| | - Gregory Joseph Fonseca
- The Meakins-Christie Laboratories at the Research Institute of the McGill University Heath Centre Research Institute, Montréal, Canada
| | - Simon Milette
- Goodman Cancer Research Centre, McGill University, Montréal, Canada
| | - Nuzha Noorah
- The Meakins-Christie Laboratories at the Research Institute of the McGill University Heath Centre Research Institute, Montréal, Canada
| | - Myriam Guay-Belzile
- The Meakins-Christie Laboratories at the Research Institute of the McGill University Heath Centre Research Institute, Montréal, Canada
| | - Jonathan Spicer
- Division of Thoracic and Upper Gastrointestinal Surgery, McGill University Health Centre Research Institute, Montréal, Canada
| | - Noriko Daneshtalab
- School of Pharmacy, Memorial University of Newfoundland, St. John’s, Newfoundland, Canada
| | - Martin Sirois
- Montreal Heart Institute and Department of pharmacology and physiology, Faculty of medicine, Université de Montréal, Montréal, Canada
| | - Karine Tremblay
- Pharmacology-physiology Department, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Centre intégré universitaire de santé et de services sociaux du Saguenay-Lac-Saint-Jean (Chicoutimi University Hospital) Research Center, Saguenay, Canada
| | - Amin Emad
- Department of Electrical and Computer Engineering, McGill University, Montréal, Canada
| | - Simon Rousseau
- The Meakins-Christie Laboratories at the Research Institute of the McGill University Heath Centre Research Institute, Montréal, Canada
| |
Collapse
|
9
|
Emad A, Sinha S. Inference of phenotype-relevant transcriptional regulatory networks elucidates cancer type-specific regulatory mechanisms in a pan-cancer study. NPJ Syst Biol Appl 2021; 7:9. [PMID: 33558504 PMCID: PMC7870953 DOI: 10.1038/s41540-021-00169-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Accepted: 01/05/2021] [Indexed: 01/30/2023] Open
Abstract
Reconstruction of transcriptional regulatory networks (TRNs) is a powerful approach to unravel the gene expression programs involved in healthy and disease states of a cell. However, these networks are usually reconstructed independent of the phenotypic (or clinical) properties of the samples. Therefore, they may confound regulatory mechanisms that are specifically related to a phenotypic property with more general mechanisms underlying the full complement of the analyzed samples. In this study, we develop a method called InPheRNo to identify "phenotype-relevant" TRNs. This method is based on a probabilistic graphical model that models the simultaneous effects of multiple transcription factors (TFs) on their target genes and the statistical relationship between the target genes' expression and the phenotype. Extensive comparison of InPheRNo with related approaches using primary tumor samples of 18 cancer types from The Cancer Genome Atlas reveals that InPheRNo can accurately reconstruct cancer type-relevant TRNs and identify cancer driver TFs. In addition, survival analysis reveals that the activity level of TFs with many target genes could distinguish patients with poor prognosis from those with better prognosis.
Collapse
Affiliation(s)
- Amin Emad
- Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada.
| | - Saurabh Sinha
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
| |
Collapse
|
10
|
Li K, Du Y, Li L, Wei DQ. Bioinformatics Approaches for Anti-cancer Drug Discovery. Curr Drug Targets 2021; 21:3-17. [PMID: 31549592 DOI: 10.2174/1389450120666190923162203] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Revised: 07/17/2019] [Accepted: 07/26/2019] [Indexed: 12/23/2022]
Abstract
Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers' identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.
Collapse
Affiliation(s)
- Kening Li
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yuxin Du
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lu Li
- Department of Bioinformatics, Nanjing Medical University, Nanjing 211166, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| |
Collapse
|
11
|
Yang H, Xu Y, Shang D, Shi H, Zhang C, Dong Q, Zhang Y, Bai Z, Cheng S, Li X. ncDRMarker: a computational method for identifying non-coding RNA signatures of drug resistance based on heterogeneous network. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:1395. [PMID: 33313140 PMCID: PMC7723624 DOI: 10.21037/atm-20-603] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background Drug resistance is the primary cause of failure in the treatment of cancer. Identifying signatures of chemoresistance will help to overcome this problem. Current drug resistance studies focus on protein-coding genes and ignore non-coding RNAs (ncRNAs), rendering it a challenging task to systematically identify ncRNAs involved in drug resistance. Methods In this study, protein-protein, miRNA-target gene, miRNA-lncRNA interactions were integrated to construct a mRNA-miRNA-lncRNA network. Then, the random walk with restart (RWR) method was extended to the network for identifying ncRNA signatures of drug resistance. The leave-one-out cross validation (LOOCV) and receiver operating characteristic curve (ROC) were used to estimate the performance of ncDRMarker. Wilcoxon rank-sum test was used to validate the identified ncRNAs in NCI-60 cancer cell lines. KEGG pathway enrichment analysis was implemented to characterize the biological function of some identified ncRNAs. Results We performed this method on ten common clinical chemotherapy drugs and analyzed the results in detail. The region beneath the ROC was up to 0.881–0.951, which did not change significantly in the incomplete network, indicating the high performance and robustness of the method. Further, we confirmed the role of the identified ncRNAs in drug resistance, i.e., miR-92a-3p, a candidate chemoresistance ncRNA of tamoxifen and paclitaxel, can significantly classify cancer cell lines into sensitive or resistant to tamoxifen (or paclitaxel). We also dissected the mRNA-miRNA-lncRNA composite network and found that some hub ncRNAs, such as miR-124-3p, were involved in resistance of multiple drugs and engaged in many significant cancer-related pathways. Lastly, we have provided a ncDRMarker platform for users to identify candidate ncRNAs of drug resistance, which is available at http://bio-bigdata.hrbmu.edu.cn/ncDRMarker/index. Conclusions Our findings suggest that ncDRMarker is an effective computational technique for prioritizing candidate ncRNAs of drug resistance. Additionally, the identified ncRNAs could be targeted to overcome drug resistance and help realize individualized treatment.
Collapse
Affiliation(s)
- Haixiu Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yanjun Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Desi Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hongbo Shi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chunlong Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Qun Dong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yizheng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ziyi Bai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shujun Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, Cancer Institute and Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| |
Collapse
|
12
|
A recursive framework for predicting the time-course of drug sensitivity. Sci Rep 2020; 10:17682. [PMID: 33077880 PMCID: PMC7573611 DOI: 10.1038/s41598-020-74725-2] [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: 03/26/2020] [Accepted: 10/05/2020] [Indexed: 11/08/2022] Open
Abstract
The biological processes involved in a drug’s mechanisms of action are oftentimes dynamic, complex and difficult to discern. Time-course gene expression data is a rich source of information that can be used to unravel these complex processes, identify biomarkers of drug sensitivity and predict the response to a drug. However, the majority of previous work has not fully utilized this temporal dimension. In these studies, the gene expression data is either considered at one time-point (before the administration of the drug) or two time-points (before and after the administration of the drug). This is clearly inadequate in modeling dynamic gene–drug interactions, especially for applications such as long-term drug therapy. In this work, we present a novel REcursive Prediction (REP) framework for drug response prediction by taking advantage of time-course gene expression data. Our goal is to predict drug response values at every stage of a long-term treatment, given the expression levels of genes collected in the previous time-points. To this end, REP employs a built-in recursive structure that exploits the intrinsic time-course nature of the data and integrates past values of drug responses for subsequent predictions. It also incorporates tensor completion that can not only alleviate the impact of noise and missing data, but also predict unseen gene expression levels (GEXs). These advantages enable REP to estimate drug response at any stage of a given treatment from some GEXs measured in the beginning of the treatment. Extensive experiments on two datasets corresponding to multiple sclerosis patients treated with interferon are included to showcase the effectiveness of REP.
Collapse
|
13
|
Kim YA, Sarto Basso R, Wojtowicz D, Liu AS, Hochbaum DS, Vandin F, Przytycka TM. Identifying Drug Sensitivity Subnetworks with NETPHIX. iScience 2020; 23:101619. [PMID: 33089107 PMCID: PMC7566085 DOI: 10.1016/j.isci.2020.101619] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 09/08/2020] [Accepted: 09/24/2020] [Indexed: 12/29/2022] Open
Abstract
Phenotypic heterogeneity in cancer is often caused by different patterns of genetic alterations. Understanding such phenotype-genotype relationships is fundamental for the advance of personalized medicine. We develop a computational method, named NETPHIX (NETwork-to-PHenotype association with eXclusivity) to identify subnetworks of genes whose genetic alterations are associated with drug response or other continuous cancer phenotypes. Leveraging interaction information among genes and properties of cancer mutations such as mutual exclusivity, we formulate the problem as an integer linear program and solve it optimally to obtain a subnetwork of associated genes. Applied to a large-scale drug screening dataset, NETPHIX uncovered gene modules significantly associated with drug responses. Utilizing interaction information, NETPHIX modules are functionally coherent and can thus provide important insights into drug action. In addition, we show that modules identified by NETPHIX together with their association patterns can be leveraged to suggest drug combinations.
Collapse
Affiliation(s)
- Yoo-Ah Kim
- National Center of Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD 20894, USA
| | - Rebecca Sarto Basso
- Department of Industrial Engineering and Operations Research, University of California at Berkeley, Berkeley, CA 94709, USA
| | - Damian Wojtowicz
- National Center of Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD 20894, USA
| | - Amanda S Liu
- Montgomery Blair High School, Silver Spring, MD 20901, USA
| | - Dorit S Hochbaum
- Department of Industrial Engineering and Operations Research, University of California at Berkeley, Berkeley, CA 94709, USA
| | - Fabio Vandin
- Department of Information Engineering, University of Padova, Padova 35131, Italy
| | - Teresa M Przytycka
- National Center of Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD 20894, USA
| |
Collapse
|
14
|
Emad A, Ray T, Jensen TW, Parat M, Natrajan R, Sinha S, Ray PS. Superior breast cancer metastasis risk stratification using an epithelial-mesenchymal-amoeboid transition gene signature. Breast Cancer Res 2020; 22:74. [PMID: 32641077 PMCID: PMC7341640 DOI: 10.1186/s13058-020-01304-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 06/01/2020] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Cancer cells are known to display varying degrees of metastatic propensity, but the molecular basis underlying such heterogeneity remains unclear. Our aims in this study were to (i) elucidate prognostic subtypes in primary tumors based on an epithelial-to-mesenchymal-to-amoeboid transition (EMAT) continuum that captures the heterogeneity of metastatic propensity and (ii) to more comprehensively define biologically informed subtypes predictive of breast cancer metastasis and survival in lymph node-negative (LNN) patients. METHODS We constructed a novel metastasis biology-based gene signature (EMAT) derived exclusively from cancer cells induced to undergo either epithelial-to-mesenchymal transition (EMT) or mesenchymal-to-amoeboid transition (MAT) to gauge their metastatic potential. Genome-wide gene expression data obtained from 913 primary tumors of lymph node-negative breast cancer (LNNBC) patients were analyzed. EMAT gene signature-based prognostic stratification of patients was performed to identify biologically relevant subtypes associated with distinct metastatic propensity. RESULTS Delineated EMAT subtypes display a biologic range from less stem-like to more stem-like cell states and from less invasive to more invasive modes of cancer progression. Consideration of EMAT subtypes in combination with standard clinical parameters significantly improved survival prediction. EMAT subtypes outperformed prognosis accuracy of receptor or PAM50-based BC intrinsic subtypes even after adjusting for treatment variables in 3 independent, LNNBC cohorts including a treatment-naïve patient cohort. CONCLUSIONS EMAT classification is a biologically informed method that provides prognostic information beyond that which can be provided by traditional cancer staging or PAM50 molecular subtype status and may improve metastasis risk assessment in early stage, LNNBC patients, who may otherwise be perceived to be at low metastasis risk.
Collapse
Affiliation(s)
- Amin Emad
- Department of Electrical and Computer Engineering, McGill University, Montreal, Quebec, Canada
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
- Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Tania Ray
- Onconostic Technologies Inc., Champaign, Illinois, USA
| | - Tor W Jensen
- Illinois Health Sciences Institute, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Meera Parat
- Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Rachael Natrajan
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Saurabh Sinha
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.
- Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.
| | - Partha S Ray
- Onconostic Technologies Inc., Champaign, Illinois, USA.
| |
Collapse
|
15
|
Blatti C, Emad A, Berry MJ, Gatzke L, Epstein M, Lanier D, Rizal P, Ge J, Liao X, Sobh O, Lambert M, Post CS, Xiao J, Groves P, Epstein AT, Chen X, Srinivasan S, Lehnert E, Kalari KR, Wang L, Weinshilboum RM, Song JS, Jongeneel CV, Han J, Ravaioli U, Sobh N, Bushell CB, Sinha S. Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform. PLoS Biol 2020; 18:e3000583. [PMID: 31971940 PMCID: PMC6977717 DOI: 10.1371/journal.pbio.3000583] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 12/19/2019] [Indexed: 12/19/2022] Open
Abstract
We present Knowledge Engine for Genomics (KnowEnG), a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene prioritization, sample clustering, gene set analysis, and expression signature analysis. The system specializes in "knowledge-guided" data mining and machine learning algorithms, in which user-provided data are analyzed in light of prior information about genes, aggregated from numerous knowledge bases and encoded in a massive "Knowledge Network." KnowEnG adheres to "FAIR" principles (findable, accessible, interoperable, and reuseable): its tools are easily portable to diverse computing environments, run on the cloud for scalable and cost-effective execution, and are interoperable with other computing platforms. The analysis tools are made available through multiple access modes, including a web portal with specialized visualization modules. We demonstrate the KnowEnG system's potential value in democratization of advanced tools for the modern genomics era through several case studies that use its tools to recreate and expand upon the published analysis of cancer data sets.
Collapse
Affiliation(s)
- Charles Blatti
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Amin Emad
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- Department of Electrical and Computer Engineering, McGill University, Montreal, Canada
| | - Matthew J. Berry
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Lisa Gatzke
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Milt Epstein
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Daniel Lanier
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Pramod Rizal
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Jing Ge
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Xiaoxia Liao
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Omar Sobh
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Mike Lambert
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Corey S. Post
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Jinfeng Xiao
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Peter Groves
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Aidan T. Epstein
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Xi Chen
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Subhashini Srinivasan
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Erik Lehnert
- Seven Bridges Genomics, Charlestown, Massachusetts, United States of America
| | - Krishna R. Kalari
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Richard M. Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Jun S. Song
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - C. Victor Jongeneel
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Jiawei Han
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Umberto Ravaioli
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Nahil Sobh
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Colleen B. Bushell
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Saurabh Sinha
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- * E-mail:
| |
Collapse
|
16
|
Sonnenblick A, Salmon-Divon M, Salgado R, Dvash E, Pondé N, Zahavi T, Salmon A, Loibl S, Denkert C, Joensuu H, Ameye L, Van den Eynden G, Kellokumpu-Lehtinen PL, Azaria A, Loi S, Michiels S, Richard F, Sotiriou C. Reactive stroma and trastuzumab resistance in HER2-positive early breast cancer. Int J Cancer 2020; 147:266-276. [PMID: 31904863 DOI: 10.1002/ijc.32859] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 11/25/2019] [Accepted: 11/26/2019] [Indexed: 12/21/2022]
Abstract
We investigated the value of reactive stroma as a predictor for trastuzumab resistance in patients with early HER2-positive breast cancer receiving adjuvant therapy. The pathological reactive stroma and the mRNA gene signatures that reflect reactive stroma in 209 HER2-positive breast cancer samples from the FinHer adjuvant trial were evaluated. Levels of stromal gene signatures were determined as a continuous parameter, and pathological reactive stromal findings were defined as stromal predominant breast cancer (SPBC; ≥50% stromal) and correlated with distant disease-free survival. Gene signatures associated with reactive stroma in HER2-positive early breast cancer (N = 209) were significantly associated with trastuzumab resistance in estrogen receptor (ER)-negative tumors (hazard ratio [HR] = 1.27 p interaction = 0.014 [DCN], HR = 1.58, p interaction = 0.027 [PLAU], HR = 1.71, p interaction = 0.019 [HER2STROMA, novel HER2 stromal signature]), but not in ER-positive tumors (HR = 0.73 p interaction = 0.47 [DCN], HR = 0.71, p interaction = 0.73 [PLAU], HR = 0.84; p interaction = 0.36 [HER2STROMA]). Pathological evaluation of HER2-positive/ER-negative tumors suggested an association between SPBC and trastuzumab resistance. Reactive stroma did not correlate with tumor-infiltrating lymphocytes (TILs), and the expected benefit from trastuzumab in patients with high levels of TILs was pronounced only in tumors with low stromal reactivity (SPBC <50%). In conclusion, reactive stroma in HER2-positive/ER-negative early breast cancer tumors may predict resistance to adjuvant trastuzumab therapy.
Collapse
Affiliation(s)
- Amir Sonnenblick
- Institute of Oncology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Mali Salmon-Divon
- Department of Molecular Biology, Adelson School of Medicine, Ariel University, Ariel, Israel
| | - Roberto Salgado
- Department of Pathology, GZA-ZNA, Antwerp, Belgium.,Division of Research, Peter Mac Callum Cancer Center, Melbourne, Australia
| | - Efrat Dvash
- Institute of Oncology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Noam Pondé
- Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium.,Medical Oncology Department, AC Camargo Cancer Center, São Paulo, Brazil
| | - Tamar Zahavi
- Sharett Institute of Oncology, Hadassah Hebrew University Medical Center, Jerusalem, Israel
| | - Asher Salmon
- Sharett Institute of Oncology, Hadassah Hebrew University Medical Center, Jerusalem, Israel
| | - Sibylle Loibl
- German Breast Group, Neu-Isenburg and Goethe University Frankfurt and Centre for Haematology and Oncology, Bethanien, Frankfurt, Germany
| | - Carsten Denkert
- Institute of Pathology, Philipps-University Marburg and UKGM Marburg, Marburg, Germany
| | - Heikki Joensuu
- Department of Oncology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Lieveke Ameye
- Data Management Unit, Institut Jules Bordet, Université Libre de Bruxelles, Belgium
| | - Gert Van den Eynden
- Molecular Immunology Lab, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | | | - Amos Azaria
- Department of Computer Science, Ariel University, Ariel, Israel
| | - Sherene Loi
- Peter MacCallum Cancer Centre, University of Melbourne, Parkville, Victoria, Australia
| | - Stefan Michiels
- Service de Biostatistique et d'Epidémiologie, Gustave Roussy, CESP U108, University Paris-Sud, University Paris-Saclay, Villejuif, France
| | - François Richard
- Laboratory for Translational Breast Cancer Research, Department of Oncology, KU Leuven, Leuven, Belgium
| | | |
Collapse
|
17
|
Huang EW, Bhope A, Lim J, Sinha S, Emad A. Tissue-guided LASSO for prediction of clinical drug response using preclinical samples. PLoS Comput Biol 2020; 16:e1007607. [PMID: 31967990 PMCID: PMC6975549 DOI: 10.1371/journal.pcbi.1007607] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 12/15/2019] [Indexed: 12/12/2022] Open
Abstract
Prediction of clinical drug response (CDR) of cancer patients, based on their clinical and molecular profiles obtained prior to administration of the drug, can play a significant role in individualized medicine. Machine learning models have the potential to address this issue but training them requires data from a large number of patients treated with each drug, limiting their feasibility. While large databases of drug response and molecular profiles of preclinical in-vitro cancer cell lines (CCLs) exist for many drugs, it is unclear whether preclinical samples can be used to predict CDR of real patients. We designed a systematic approach to evaluate how well different algorithms, trained on gene expression and drug response of CCLs, can predict CDR of patients. Using data from two large databases, we evaluated various linear and non-linear algorithms, some of which utilized information on gene interactions. Then, we developed a new algorithm called TG-LASSO that explicitly integrates information on samples' tissue of origin with gene expression profiles to improve prediction performance. Our results showed that regularized regression methods provide better prediction performance. However, including the network information or common methods of including information on the tissue of origin did not improve the results. On the other hand, TG-LASSO improved the predictions and distinguished resistant and sensitive patients for 7 out of 13 drugs. Additionally, TG-LASSO identified genes associated with the drug response, including known targets and pathways involved in the drugs' mechanism of action. Moreover, genes identified by TG-LASSO for multiple drugs in a tissue were associated with patient survival. In summary, our analysis suggests that preclinical samples can be used to predict CDR of patients and identify biomarkers of drug sensitivity and survival.
Collapse
Affiliation(s)
- Edward W. Huang
- Department of Computer Science, University of Illinois at Urbana-Champaign, Illinois, United States of America
| | - Ameya Bhope
- Department of Electrical and Computer Engineering, McGill University, Canada
| | - Jing Lim
- Department of Computer Science, University of Illinois at Urbana-Champaign, Illinois, United States of America
| | - Saurabh Sinha
- Department of Computer Science, University of Illinois at Urbana-Champaign, Illinois, United States of America
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Illinois, United States of America
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Illinois, United States of America
| | - Amin Emad
- Department of Electrical and Computer Engineering, McGill University, Canada
| |
Collapse
|
18
|
Zolotareva O, Kleine M. A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases. J Integr Bioinform 2019; 16:/j/jib.ahead-of-print/jib-2018-0069/jib-2018-0069.xml. [PMID: 31494632 PMCID: PMC7074139 DOI: 10.1515/jib-2018-0069] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Accepted: 07/12/2019] [Indexed: 12/16/2022] Open
Abstract
Modern high-throughput experiments provide us with numerous potential associations between genes and diseases. Experimental validation of all the discovered associations, let alone all the possible interactions between them, is time-consuming and expensive. To facilitate the discovery of causative genes, various approaches for prioritization of genes according to their relevance for a given disease have been developed. In this article, we explain the gene prioritization problem and provide an overview of computational tools for gene prioritization. Among about a hundred of published gene prioritization tools, we select and briefly describe 14 most up-to-date and user-friendly. Also, we discuss the advantages and disadvantages of existing tools, challenges of their validation, and the directions for future research.
Collapse
Affiliation(s)
- Olga Zolotareva
- Bielefeld University, Faculty of Technology and Center for Biotechnology, International Research Training Group "Computational Methods for the Analysis of the Diversity and Dynamics of Genomes" and Genome Informatics, Universitätsstraße 25, Bielefeld, Germany
| | - Maren Kleine
- Bielefeld University, Faculty of Technology, Bioinformatics/Medical Informatics Department, Universitätsstraße 25, Bielefeld, Germany
| |
Collapse
|
19
|
Genetic markers in methotrexate treatments. THE PHARMACOGENOMICS JOURNAL 2018; 18:689-703. [DOI: 10.1038/s41397-018-0047-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 07/06/2018] [Accepted: 08/10/2018] [Indexed: 12/20/2022]
|
20
|
Hanson C, Cairns J, Wang L, Sinha S. Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation. Genome Res 2018; 28:1207-1216. [PMID: 29898900 PMCID: PMC6071639 DOI: 10.1101/gr.227066.117] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Accepted: 05/31/2018] [Indexed: 12/12/2022]
Abstract
Recent studies have analyzed large-scale data sets of gene expression to identify genes associated with interindividual variation in phenotypes ranging from cancer subtypes to drug sensitivity, promising new avenues of research in personalized medicine. However, gene expression data alone is limited in its ability to reveal cis-regulatory mechanisms underlying phenotypic differences. In this study, we develop a new probabilistic model, called pGENMi, that integrates multi-omic data to investigate the transcriptional regulatory mechanisms underlying interindividual variation of a specific phenotype—that of cell line response to cytotoxic treatment. In particular, pGENMi simultaneously analyzes genotype, DNA methylation, gene expression, and transcription factor (TF)-DNA binding data, along with phenotypic measurements, to identify TFs regulating the phenotype. It does so by combining statistical information about expression quantitative trait loci (eQTLs) and expression-correlated methylation marks (eQTMs) located within TF binding sites, as well as observed correlations between gene expression and phenotype variation. Application of pGENMi to data from a panel of lymphoblastoid cell lines treated with 24 drugs, in conjunction with ENCODE TF ChIP data, yielded a number of known as well as novel (TF, Drug) associations. Experimental validations by TF knockdown confirmed 41% of the predicted and tested associations, compared to a 12% confirmation rate of tested nonassociations (controls). An extensive literature survey also corroborated 62% of the predicted associations above a stringent threshold. Moreover, associations predicted only when combining eQTL and eQTM data showed higher precision compared to an eQTL-only or eQTM-only analysis using pGENMi, further demonstrating the value of multi-omic integrative analysis.
Collapse
Affiliation(s)
- Casey Hanson
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Junmei Cairns
- Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota 55905, USA
| | - Liewei Wang
- Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota 55905, USA
| | - Saurabh Sinha
- Department of Computer Science and Institute of Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| |
Collapse
|
21
|
|