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Zhang T, Zhang SW, Xie MY, Li Y. Identifying cooperating cancer driver genes in individual patients through hypergraph random walk. J Biomed Inform 2024:104710. [PMID: 39159864 DOI: 10.1016/j.jbi.2024.104710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 07/30/2024] [Accepted: 08/14/2024] [Indexed: 08/21/2024]
Abstract
OBJECTIVE Identifying cancer driver genes, especially rare or patient-specific cancer driver genes, is a primary goal in cancer therapy. Although researchers have proposed some methods to tackle this problem, these methods mostly identify cancer driver genes at single gene level, overlooking the cooperative relationship among cancer driver genes. Identifying cooperating cancer driver genes in individual patients is pivotal for understanding cancer etiology and advancing the development of personalized therapies. METHODS Here, we propose a novel Personalized Cooperating cancer Driver Genes (PCoDG) method by using hypergraph random walk to identify the cancer driver genes that cooperatively drive individual patient cancer progression. By leveraging the powerful ability of hypergraph in representing multi-way relationships, PCoDG first employs the personalized hypergraph to depict the complex interactions among mutated genes and differentially expressed genes of an individual patient. Then, a hypergraph random walk algorithm based on hyperedge similarity is utilized to calculate the importance scores of mutated genes, integrating these scores with signaling pathway data to identify the cooperating cancer driver genes in individual patients. RESULTS The experimental results on three TCGA cancer datasets (i.e., BRCA, LUAD, and COADREAD) demonstrate the effectiveness of PCoDG in identifying personalized cooperating cancer driver genes. These genes identified by PCoDG not only offer valuable insights into patient stratification correlating with clinical outcomes, but also provide an useful reference resource for tailoring personalized treatments. CONCLUSION We propose a novel method that can effectively identify cooperating cancer driver genes for individual patients, thereby deepening our understanding of the cooperative relationship among personalized cancer driver genes and advancing the development of precision oncology.
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Affiliation(s)
- Tong Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China; School of Electrical and Mechanical Engineering, Pingdingshan University, Pingdingshan 467000, China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
| | - Ming-Yu Xie
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Yan Li
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
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2
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Plum PS, Hess T, Bertrand D, Morgenstern I, Velazquez Camacho O, Jonas C, Alidousty C, Wagner B, Roessler S, Albrecht T, Becker J, Richartz V, Holz B, Hoppe S, Poh HM, Chia BKH, Chan CX, Pathiraja T, Teo AS, Marquardt JU, Khng A, Heise M, Fei Y, Thieme R, Klein S, Hong JH, Dima SO, Popescu I, Hoppe-Lotichius M, Buettner R, Lautem A, Otto G, Quaas A, Nagarajan N, Rozen S, Teh BT, Goeppert B, Drebber U, Lang H, Tan P, Gockel I, Schumacher J, Hillmer AM. Integrative genomic analyses of European intrahepatic cholangiocarcinoma: Novel ROS1 fusion gene and PBX1 as prognostic marker. Clin Transl Med 2024; 14:e1723. [PMID: 38877653 PMCID: PMC11178519 DOI: 10.1002/ctm2.1723] [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: 10/30/2023] [Revised: 04/20/2024] [Accepted: 05/13/2024] [Indexed: 06/16/2024] Open
Abstract
BACKGROUND Cholangiocarcinoma (CCA) is a fatal cancer of the bile duct with a poor prognosis owing to limited therapeutic options. The incidence of intrahepatic CCA (iCCA) is increasing worldwide, and its molecular basis is emerging. Environmental factors may contribute to regional differences in the mutation spectrum of European patients with iCCA, which are underrepresented in systematic genomic and transcriptomic studies of the disease. METHODS We describe an integrated whole-exome sequencing and transcriptomic study of 37 iCCAs patients in Germany. RESULTS We observed as most frequently mutated genes ARID1A (14%), IDH1, BAP1, TP53, KRAS, and ATM in 8% of patients. We identified FGFR2::BICC1 fusions in two tumours, and FGFR2::KCTD1 and TMEM106B::ROS1 as novel fusions with potential therapeutic implications in iCCA and confirmed oncogenic properties of TMEM106B::ROS1 in vitro. Using a data integration framework, we identified PBX1 as a novel central regulatory gene in iCCA. We performed extended screening by targeted sequencing of an additional 40 CCAs. In the joint analysis, IDH1 (13%), BAP1 (10%), TP53 (9%), KRAS (7%), ARID1A (7%), NF1 (5%), and ATM (5%) were the most frequently mutated genes, and we found PBX1 to show copy gain in 20% of the tumours. According to other studies, amplifications of PBX1 tend to occur in European iCCAs in contrast to liver fluke-associated Asian iCCAs. CONCLUSIONS By analyzing an additional European cohort of iCCA patients, we found that PBX1 protein expression was a marker of poor prognosis. Overall, our findings provide insight into key molecular alterations in iCCA, reveal new targetable fusion genes, and suggest that PBX1 is a novel modulator of this disease.
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Affiliation(s)
- Patrick S Plum
- Department of General, Visceral, Cancer and Transplantation Surgery, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
- Institute of Pathology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, Leipzig, Germany
| | - Timo Hess
- Center for Human Genetics, University Hospital of Marburg, Marburg, Germany
| | - Denis Bertrand
- Computational and Systems Biology, Agency for Science, Technology and Research (A*STAR), Genome Institute of Singapore, Singapore, Singapore
| | - Isabelle Morgenstern
- General, Visceral and Transplant Surgery, Johannes Gutenberg University, Mainz, Germany
| | - Oscar Velazquez Camacho
- Institute of Pathology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Christoph Jonas
- Institute of Pathology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Christina Alidousty
- Institute of Pathology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Britta Wagner
- Institute of Pathology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Stephanie Roessler
- Institute of Pathology, University of Heidelberg, Heidelberg, Germany
- Liver Cancer Center Heidelberg (LCCH), Heidelberg, Germany
| | - Thomas Albrecht
- Institute of Pathology, University of Heidelberg, Heidelberg, Germany
- Liver Cancer Center Heidelberg (LCCH), Heidelberg, Germany
| | - Jessica Becker
- Institute of Human Genetics, University Hospital of Bonn, Bonn, Germany
| | - Vanessa Richartz
- Institute of Pathology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Barbara Holz
- Institute of Pathology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Sascha Hoppe
- Institute of Pathology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Huay Mei Poh
- Cancer Therapeutics and Stratified Oncology, Agency for Science, Technology and Research (A*STAR), Genome Institute of Singapore, Singapore, Singapore
| | - Burton Kuan Hui Chia
- Computational and Systems Biology, Agency for Science, Technology and Research (A*STAR), Genome Institute of Singapore, Singapore, Singapore
| | - Cheryl Xueli Chan
- Cancer Therapeutics and Stratified Oncology, Agency for Science, Technology and Research (A*STAR), Genome Institute of Singapore, Singapore, Singapore
| | - Thushangi Pathiraja
- Cancer Therapeutics and Stratified Oncology, Agency for Science, Technology and Research (A*STAR), Genome Institute of Singapore, Singapore, Singapore
| | - Audrey Sm Teo
- Cancer Therapeutics and Stratified Oncology, Agency for Science, Technology and Research (A*STAR), Genome Institute of Singapore, Singapore, Singapore
| | - Jens U Marquardt
- I Department of Medicine, Johannes Gutenberg University, Mainz, Germany
- Department of Medicine, University Hospital Schleswig-Holstein, Lübeck, Germany
| | - Alexis Khng
- Cancer Therapeutics and Stratified Oncology, Agency for Science, Technology and Research (A*STAR), Genome Institute of Singapore, Singapore, Singapore
| | - Michael Heise
- General, Visceral and Transplant Surgery, Johannes Gutenberg University, Mainz, Germany
- Department for General, Visceral and Transplant Surgery, University Hospital Frankfurt, Goethe-University Frankfurt/Main, Frankfurt, Germany
| | - Yao Fei
- Cancer Therapeutics and Stratified Oncology, Agency for Science, Technology and Research (A*STAR), Genome Institute of Singapore, Singapore, Singapore
| | - René Thieme
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, Leipzig, Germany
| | - Sebastian Klein
- Institute of Pathology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Jing Han Hong
- Duke-NUS Medical School, Cancer and Stem Cell Biology, Singapore, Singapore
- Division of Medical Science, Laboratory of Cancer Epigenome, National Cancer Centre Singapore, Singapore, Singapore
| | - Simona O Dima
- Center of Digestive Diseases and Liver Transplantation, Fundeni Clinical Institute, Bucharest, Romania
| | - Irinel Popescu
- Center of Digestive Diseases and Liver Transplantation, Fundeni Clinical Institute, Bucharest, Romania
| | - Maria Hoppe-Lotichius
- General, Visceral and Transplant Surgery, Johannes Gutenberg University, Mainz, Germany
| | - Reinhard Buettner
- Institute of Pathology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Anja Lautem
- General, Visceral and Transplant Surgery, Johannes Gutenberg University, Mainz, Germany
| | - Gerd Otto
- Emeritus of the Division of Transplantation Surgery, University Medical Center, Mainz, Germany
| | - Alexander Quaas
- Institute of Pathology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Niranjan Nagarajan
- Computational and Systems Biology, Agency for Science, Technology and Research (A*STAR), Genome Institute of Singapore, Singapore, Singapore
| | - Steve Rozen
- Duke-NUS Medical School, Cancer and Stem Cell Biology, Singapore, Singapore
| | - Bin Tean Teh
- Duke-NUS Medical School, Cancer and Stem Cell Biology, Singapore, Singapore
| | - Benjamin Goeppert
- RKH Klinikum Ludwigsburg, Institute of Pathology and Neuropathology, Ludwigsburg, Germany
- Institute of Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Uta Drebber
- Institute of Pathology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Hauke Lang
- General, Visceral and Transplant Surgery, Johannes Gutenberg University, Mainz, Germany
| | - Patrick Tan
- Duke-NUS Medical School, Cancer and Stem Cell Biology, Singapore, Singapore
- Agency for Science, Technology and Research (A*STAR), Genome Institute of Singapore, Singapore, Singapore
| | - Ines Gockel
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, Leipzig, Germany
| | | | - Axel M Hillmer
- Institute of Pathology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
- Cancer Therapeutics and Stratified Oncology, Agency for Science, Technology and Research (A*STAR), Genome Institute of Singapore, Singapore, Singapore
- Center for Molecular Medicine Cologne, University of Cologne, Cologne, Germany
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Xu H, Li C, Zhang L, Ding Z, Lu T, Hu H. Immunotherapy efficacy prediction through a feature re-calibrated 2.5D neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 249:108135. [PMID: 38569256 DOI: 10.1016/j.cmpb.2024.108135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 03/11/2024] [Accepted: 03/13/2024] [Indexed: 04/05/2024]
Abstract
BACKGROUND AND OBJECTIVE Lung cancer continues to be a leading cause of cancer-related mortality worldwide, with immunotherapy emerging as a promising therapeutic strategy for advanced non-small cell lung cancer (NSCLC). Despite its potential, not all patients experience benefits from immunotherapy, and the current biomarkers used for treatment selection possess inherent limitations. As a result, the implementation of imaging-based biomarkers to predict the efficacy of lung cancer treatments offers a promising avenue for improving therapeutic outcomes. METHODS This study presents an automatic system for immunotherapy efficacy prediction on the subjects with lung cancer, facilitating significant clinical implications. Our model employs an advanced 2.5D neural network that incorporates 2D intra-slice feature extraction and 3D inter-slice feature aggregation. We further present a lesion-focused prior to guide the re-calibration for intra-slice features, and a attention-based re-calibration for the inter-slice features. Finally, we design an accumulated back-propagation strategy to optimize network parameters in a memory-efficient fashion. RESULTS We demonstrate that the proposed method achieves impressive performance on an in-house clinical dataset, surpassing existing state-of-the-art models. Furthermore, the proposed model exhibits increased efficiency in inference for each subject on average. To further validate the effectiveness of our model and its components, we conducted comprehensive and in-depth ablation experiments and discussions. CONCLUSION The proposed model showcases the potential to enhance physicians' diagnostic performance due to its impressive performance in predicting immunotherapy efficacy, thereby offering significant clinical application value. Moreover, we conduct adequate comparison experiments of the proposed methods and existing advanced models. These findings contribute to our understanding of the proposed model's effectiveness and serve as motivation for future work in immunotherapy efficacy prediction.
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Affiliation(s)
- Haipeng Xu
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fujian 350014, China.
| | - Chenxin Li
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong 999077, SAR, China.
| | - Longfeng Zhang
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fujian 350014, China.
| | - Zhiyuan Ding
- School of Informatics, Xiamen University, Fujian 350014, China.
| | - Tao Lu
- Department of Radiology, Fujian Medical University Cancer Hospital and Fujian Cancer Hospital, Fujian 350014, China.
| | - Huihua Hu
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fujian 350014, China.
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Wei PJ, Zhu AD, Cao R, Zheng C. Personalized Driver Gene Prediction Using Graph Convolutional Networks with Conditional Random Fields. BIOLOGY 2024; 13:184. [PMID: 38534453 DOI: 10.3390/biology13030184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 03/03/2024] [Accepted: 03/10/2024] [Indexed: 03/28/2024]
Abstract
Cancer is a complex and evolutionary disease mainly driven by the accumulation of genetic variations in genes. Identifying cancer driver genes is important. However, most related studies have focused on the population level. Cancer is a disease with high heterogeneity. Thus, the discovery of driver genes at the individual level is becoming more valuable but is a great challenge. Although there have been some computational methods proposed to tackle this challenge, few can cover all patient samples well, and there is still room for performance improvement. In this study, to identify individual-level driver genes more efficiently, we propose the PDGCN method. PDGCN integrates multiple types of data features, including mutation, expression, methylation, copy number data, and system-level gene features, along with network structural features extracted using Node2vec in order to construct a sample-gene interaction network. Prediction is performed using a graphical convolutional neural network model with a conditional random field layer, which is able to better combine the network structural features with biological attribute features. Experiments on the ACC (Adrenocortical Cancer) and KICH (Kidney Chromophobe) datasets from TCGA (The Cancer Genome Atlas) demonstrated that the method performs better compared to other similar methods. It can identify not only frequently mutated driver genes, but also rare candidate driver genes and novel biomarker genes. The results of the survival and enrichment analyses of these detected genes demonstrate that the method can identify important driver genes at the individual level.
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Affiliation(s)
- Pi-Jing Wei
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, 111 Jiulong Road, Hefei 230601, China
| | - An-Dong Zhu
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, 111 Jiulong Road, Hefei 230601, China
| | - Ruifen Cao
- School of Computer Science and Technology, Anhui University, 111 Jiulong Road, Hefei 230601, China
| | - Chunhou Zheng
- School of Artificial Intelligence, Anhui University, 111 Jiulong Road, Hefei 230601, China
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Xu X, Qi Z, Wang L, Zhang M, Geng Z, Han X. Gsw-fi: a GLM model incorporating shrinkage and double-weighted strategies for identifying cancer driver genes with functional impact. BMC Bioinformatics 2024; 25:99. [PMID: 38448819 PMCID: PMC10916024 DOI: 10.1186/s12859-024-05707-8] [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: 12/15/2022] [Accepted: 02/16/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND Cancer, a disease with high morbidity and mortality rates, poses a significant threat to human health. Driver genes, which harbor mutations accountable for the initiation and progression of tumors, play a crucial role in cancer development. Identifying driver genes stands as a paramount objective in cancer research and precision medicine. RESULTS In the present work, we propose a method for identifying driver genes using a Generalized Linear Regression Model (GLM) with Shrinkage and double-Weighted strategies based on Functional Impact, which is named GSW-FI. Firstly, an estimating model is proposed for assessing the background functional impacts of genes based on GLM, utilizing gene features as predictors. Secondly, the shrinkage and double-weighted strategies as two revising approaches are integrated to ensure the rationality of the identified driver genes. Lastly, a statistical method of hypothesis testing is designed to identify driver genes by leveraging the estimated background function impacts. Experimental results conducted on 31 The Cancer Genome Altas datasets demonstrate that GSW-FI outperforms ten other prediction methods in terms of the overlap fraction with well-known databases and consensus predictions among different methods. CONCLUSIONS GSW-FI presents a novel approach that efficiently identifies driver genes with functional impact mutations using computational methods, thereby advancing the development of precision medicine for cancer.
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Affiliation(s)
- Xiaolu Xu
- School of Computer and Artificial Intelligence, Liaoning Normal University, Dalian, China
| | - Zitong Qi
- Department of Statistics, University of Washington, Seattle, USA
| | - Lei Wang
- Center for Reproductive and Genetic Medicine, Dalian Women and Children's Medical Group, Dalian, China.
| | - Meiwei Zhang
- Center for Reproductive and Genetic Medicine, Dalian Women and Children's Medical Group, Dalian, China.
| | - Zhaohong Geng
- Department of Cardiology, Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Xiumei Han
- College of Artificial Intelligence, Dalian Maritime University, Dalian, China
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Nourbakhsh M, Degn K, Saksager A, Tiberti M, Papaleo E. Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks. Brief Bioinform 2024; 25:bbad519. [PMID: 38261338 PMCID: PMC10805075 DOI: 10.1093/bib/bbad519] [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: 06/09/2023] [Revised: 11/27/2023] [Accepted: 12/11/2023] [Indexed: 01/24/2024] Open
Abstract
The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.
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Affiliation(s)
- Mona Nourbakhsh
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Kristine Degn
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Astrid Saksager
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Matteo Tiberti
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
| | - Elena Papaleo
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
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Wang Y, Zhou B, Ru J, Meng X, Wang Y, Liu W. Advances in computational methods for identifying cancer driver genes. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:21643-21669. [PMID: 38124614 DOI: 10.3934/mbe.2023958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance.
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Affiliation(s)
- Ying Wang
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
| | - Bohao Zhou
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
| | - Jidong Ru
- School of Textile Garment and Design, Changshu Institute of Technology, Changshu 215500, China
| | - Xianglian Meng
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China
| | - Yundong Wang
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
| | - Wenjie Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China
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Bai Y, Zhou L, Zhang C, Guo M, Xia L, Tang Z, Liu Y, Deng S. Dual network analysis of transcriptome data for discovery of new therapeutic targets in non-small cell lung cancer. Oncogene 2023; 42:3605-3618. [PMID: 37864031 PMCID: PMC10691970 DOI: 10.1038/s41388-023-02866-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 09/26/2023] [Accepted: 10/04/2023] [Indexed: 10/22/2023]
Abstract
The drug therapy for non-small cell lung cancer (NSCLC) have always been issues of poisonous side effect, acquired drug resistance and narrow applicable population. In this study, we built a novel network analysis method (difference- correlation- enrichment- causality- node), which was based on the difference analysis, Spearman correlation network analysis, biological function analysis and Bayesian causality network analysis to discover new therapeutic target of NSCLC in the sequencing data of BEAS-2B and 7 NSCLC cell lines. Our results showed that, as a proteasome subunit coding gene in the central of cell cycle network, PSMD2 was associated with prognosis and was an independent prognostic factor for NSCLC patients. Knockout of PSMD2 inhibited the proliferation of NSCLC cells by inducing cell cycle arrest, and exhibited marked increase of cell cycle blocking protein p21, p27 and decrease of cell cycle driven protein CDK4, CDK6, CCND1 and CCNE1. IPA and molecular docking suggested bortezomib has stronger affinity to PSMD2 compared with reported targets PSMB1 and PSMB5. In vitro and In vivo experiments demonstrated the inhibitory effect of bortezomib in NSCLC with different driven mutations or with tyrosine kinase inhibitors resistance. Taken together, bortezomib could target PSMD2, PSMB1 and PSMB5 to inhibit the proteasome degradation of cell cycle check points, to block cell proliferation of NSCLC, which was potential optional drug for NSCLC patients.
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Affiliation(s)
- Yuquan Bai
- Institute of Thoracic Oncology and Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Lu Zhou
- Institute of Thoracic Oncology and Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Chuanfen Zhang
- Institute of Thoracic Oncology and Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Minzhang Guo
- Institute of Thoracic Oncology and Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Liang Xia
- Institute of Thoracic Oncology and Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Zhenying Tang
- College of Computer Science, Sichuan University, Chengdu, 610041, China
| | - Yi Liu
- Institute of Thoracic Oncology and Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Senyi Deng
- Institute of Thoracic Oncology and Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610041, China.
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Gillman R, Field MA, Schmitz U, Karamatic R, Hebbard L. Identifying cancer driver genes in individual tumours. Comput Struct Biotechnol J 2023; 21:5028-5038. [PMID: 37867967 PMCID: PMC10589724 DOI: 10.1016/j.csbj.2023.10.019] [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: 07/28/2023] [Revised: 10/10/2023] [Accepted: 10/11/2023] [Indexed: 10/24/2023] Open
Abstract
Cancer is a heterogeneous disease with a strong genetic component making it suitable for precision medicine approaches aimed at identifying the underlying molecular drivers within a tumour. Large scale population-level cancer sequencing consortia have identified many actionable mutations common across both cancer types and sub-types, resulting in an increasing number of successful precision medicine programs. Nonetheless, such approaches fail to consider the effects of mutations unique to an individual patient and may miss rare driver mutations, necessitating personalised approaches to driver-gene prioritisation. One approach is to quantify the functional importance of individual mutations in a single tumour based on how they affect the expression of genes in a gene interaction network (GIN). These GIN-based approaches can be broadly divided into those that utilise an existing reference GIN and those that construct de novo patient-specific GINs. These single-tumour approaches have several limitations that likely influence their results, such as use of reference cohort data, network choice, and approaches to mathematical approximation, and more research is required to evaluate the in vitro and in vivo applicability of their predictions. This review examines the current state of the art methods that identify driver genes in single tumours with a focus on GIN-based driver prioritisation.
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Affiliation(s)
- Rhys Gillman
- Department of Biomedical Sciences and Molecular and Cell Biology, College of Public Health, Medical, and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Cairns, Queensland, Australia
| | - Matt A. Field
- Department of Biomedical Sciences and Molecular and Cell Biology, College of Public Health, Medical, and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Cairns, Queensland, Australia
- Immunogenomics Lab, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
- Menzies School of Health Research, Charles Darwin University, Darwin, Northern Territory, Australia
| | - Ulf Schmitz
- Department of Biomedical Sciences and Molecular and Cell Biology, College of Public Health, Medical, and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Cairns, Queensland, Australia
| | - Rozemary Karamatic
- Gastroenterology and Hepatology, Townsville University Hospital, PO Box 670, Townsville, Queensland 4810, Australia
- College of Medicine and Dentistry, Division of Tropical Health and Medicine, James Cook University, Townsville, Queensland, Australia
| | - Lionel Hebbard
- Department of Biomedical Sciences and Molecular and Cell Biology, College of Public Health, Medical, and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Cairns, Queensland, Australia
- Storr Liver Centre, Westmead Institute for Medical Research, Westmead Hospital and University of Sydney, Sydney, New South Wales, Australia
- Australian Institute for Tropical Health and Medicine, Townsville, Queensland, Australia
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10
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Li Y, Zhang SW, Xie MY, Zhang T. PhenoDriver: interpretable framework for studying personalized phenotype-associated driver genes in breast cancer. Brief Bioinform 2023; 24:bbad291. [PMID: 37738403 DOI: 10.1093/bib/bbad291] [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: 04/02/2023] [Revised: 07/12/2023] [Accepted: 07/27/2023] [Indexed: 09/24/2023] Open
Abstract
Identifying personalized cancer driver genes and further revealing their oncogenic mechanisms is critical for understanding the mechanisms of cell transformation and aiding clinical diagnosis. Almost all existing methods primarily focus on identifying driver genes at the cohort or individual level but fail to further uncover their underlying oncogenic mechanisms. To fill this gap, we present an interpretable framework, PhenoDriver, to identify personalized cancer driver genes, elucidate their roles in cancer development and uncover the association between driver genes and clinical phenotypic alterations. By analyzing 988 breast cancer patients, we demonstrate the outstanding performance of PhenoDriver in identifying breast cancer driver genes at the cohort level compared to other state-of-the-art methods. Otherwise, our PhenoDriver can also effectively identify driver genes with both recurrent and rare mutations in individual patients. We further explore and reveal the oncogenic mechanisms of some known and unknown breast cancer driver genes (e.g. TP53, MAP3K1, HTT, etc.) identified by PhenoDriver, and construct their subnetworks for regulating clinical abnormal phenotypes. Notably, most of our findings are consistent with existing biological knowledge. Based on the personalized driver profiles, we discover two existing and one unreported breast cancer subtypes and uncover their molecular mechanisms. These results intensify our understanding for breast cancer mechanisms, guide therapeutic decisions and assist in the development of targeted anticancer therapies.
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Affiliation(s)
- Yan Li
- School of Automation from Northwestern Polytechnical University, China
| | - Shao-Wu Zhang
- School of Automation from Northwestern Polytechnical University, China
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, China
| | - Ming-Yu Xie
- School of Automation from Northwestern Polytechnical University, China
| | - Tong Zhang
- School of Automation from Northwestern Polytechnical University, China
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11
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Chu X, Guan B, Dai L, Liu JX, Li F, Shang J. Network embedding framework for driver gene discovery by combining functional and structural information. BMC Genomics 2023; 24:426. [PMID: 37516822 PMCID: PMC10386255 DOI: 10.1186/s12864-023-09515-x] [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: 10/02/2022] [Accepted: 07/13/2023] [Indexed: 07/31/2023] Open
Abstract
Comprehensive analysis of multiple data sets can identify potential driver genes for various cancers. In recent years, driver gene discovery based on massive mutation data and gene interaction networks has attracted increasing attention, but there is still a need to explore combining functional and structural information of genes in protein interaction networks to identify driver genes. Therefore, we propose a network embedding framework combining functional and structural information to identify driver genes. Firstly, we combine the mutation data and gene interaction networks to construct mutation integration network using network propagation algorithm. Secondly, the struc2vec model is used for extracting gene features from the mutation integration network, which contains both gene's functional and structural information. Finally, machine learning algorithms are utilized to identify the driver genes. Compared with the previous four excellent methods, our method can find gene pairs that are distant from each other through structural similarities and has better performance in identifying driver genes for 12 cancers in the cancer genome atlas. At the same time, we also conduct a comparative analysis of three gene interaction networks, three gene standard sets, and five machine learning algorithms. Our framework provides a new perspective for feature selection to identify novel driver genes.
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Affiliation(s)
- Xin Chu
- School of Computer Science, Qufu Normal University, Rizhao, 27826, China
| | - Boxin Guan
- School of Computer Science, Qufu Normal University, Rizhao, 27826, China
| | - Lingyun Dai
- School of Computer Science, Qufu Normal University, Rizhao, 27826, China
| | - Jin-Xing Liu
- School of Computer Science, Qufu Normal University, Rizhao, 27826, China
| | - Feng Li
- School of Computer Science, Qufu Normal University, Rizhao, 27826, China.
| | - Junliang Shang
- School of Computer Science, Qufu Normal University, Rizhao, 27826, China.
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12
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Azim R, Wang S, Dipu SA, Islam N, Ala Muid MR, Elahe MF. A patient-specific functional module and path identification technique from RNA-seq data. Comput Biol Med 2023; 158:106871. [PMID: 37030265 DOI: 10.1016/j.compbiomed.2023.106871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 02/12/2023] [Accepted: 03/30/2023] [Indexed: 04/05/2023]
Abstract
With the advancement of new technologies, a huge amount of high dimensional data is being generated which is opening new opportunities and challenges to the study of cancer and diseases. In particular, distinguishing the patient-specific key components and modules which drive tumorigenesis is necessary to analyze. A complex disease generally does not initiate from the dysregulation of a single component but it is the result of the dysfunction of a group of components and networks which differs from patient to patient. However, a patient-specific network is required to understand the disease and its molecular mechanism. We address this requirement by constructing a patient-specific network by sample-specific network theory with integrating cancer-specific differentially expressed genes and elite genes. By elucidating patient-specific networks, it can identify the regulatory modules, driver genes as well as personalized disease networks which can lead to personalized drug design. This method can provide insight into how genes are associating with each other and characterized the patient-specific disease subtypes. The results show that this method can be beneficial for the detection of patient-specific differential modules and interaction between genes. Extensive analysis using existing literature, gene enrichment and survival analysis for three cancer types STAD, PAAD and LUAD shows the effectiveness of this method over other existing methods. In addition, this method can be useful for personalized therapeutics and drug design. This methodology is implemented in the R language and is available at https://github.com/riasatazim/PatientSpecificRNANetwork.
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13
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Identification of Cancer Driver Genes by Integrating Multiomics Data with Graph Neural Networks. Metabolites 2023; 13:metabo13030339. [PMID: 36984779 PMCID: PMC10052551 DOI: 10.3390/metabo13030339] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 02/20/2023] [Accepted: 02/22/2023] [Indexed: 03/02/2023] Open
Abstract
Cancer is a heterogeneous disease that is driven by the accumulation of both genetic and nongenetic alterations, so integrating multiomics data and extracting effective information from them is expected to be an effective way to predict cancer driver genes. In this paper, we first generate comprehensive instructive features for each gene from genomic, epigenomic, transcriptomic levels together with protein–protein interaction (PPI)-networks-derived attributes and then propose a novel semisupervised deep graph learning framework GGraphSAGE to predict cancer driver genes according to the impact of the alterations on a biological system. When applied to eight tumor types, experimental results suggest that GGraphSAGE outperforms several state-of-the-art computational methods for driver genes identification. Moreover, it broadens our current understanding of cancer driver genes from multiomics level and identifies driver genes specific to the tumor type rather than pan-cancer. We expect GGraphSAGE to open new avenues in precision medicine and even further predict drivers for other complex diseases.
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14
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Ren Z, Li Q, Cao K, Li MM, Zhou Y, Wang K. Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data. BMC Bioinformatics 2023; 24:43. [PMID: 36759776 PMCID: PMC9909865 DOI: 10.1186/s12859-023-05141-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 01/05/2023] [Indexed: 02/11/2023] Open
Abstract
BACKGROUND It remains an important challenge to predict the functional consequences or clinical impacts of genetic variants in human diseases, such as cancer. An increasing number of genetic variants in cancer have been discovered and documented in public databases such as COSMIC, but the vast majority of them have no functional or clinical annotations. Some databases, such as CiVIC are available with manual annotation of functional mutations, but the size of the database is small due to the use of human annotation. Since the unlabeled data (millions of variants) typically outnumber labeled data (thousands of variants), computational tools that take advantage of unlabeled data may improve prediction accuracy. RESULT To leverage unlabeled data to predict functional importance of genetic variants, we introduced a method using semi-supervised generative adversarial networks (SGAN), incorporating features from both labeled and unlabeled data. Our SGAN model incorporated features from clinical guidelines and predictive scores from other computational tools. We also performed comparative analysis to study factors that influence prediction accuracy, such as using different algorithms, types of features, and training sample size, to provide more insights into variant prioritization. We found that SGAN can achieve competitive performances with small labeled training samples by incorporating unlabeled samples, which is a unique advantage compared to traditional machine learning methods. We also found that manually curated samples can achieve a more stable predictive performance than publicly available datasets. CONCLUSIONS By incorporating much larger samples of unlabeled data, the SGAN method can improve the ability to detect novel oncogenic variants, compared to other machine-learning algorithms that use only labeled datasets. SGAN can be potentially used to predict the pathogenicity of more complex variants such as structural variants or non-coding variants, with the availability of more training samples and informative features.
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Affiliation(s)
- Zilin Ren
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Quan Li
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON, M5G2C1, Canada
| | - Kajia Cao
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Marilyn M Li
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Yunyun Zhou
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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15
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Azadifar S, Ahmadi A. A novel candidate disease gene prioritization method using deep graph convolutional networks and semi-supervised learning. BMC Bioinformatics 2022; 23:422. [PMID: 36241966 PMCID: PMC9563530 DOI: 10.1186/s12859-022-04954-x] [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: 07/12/2022] [Accepted: 09/20/2022] [Indexed: 11/18/2022] Open
Abstract
Background Selecting and prioritizing candidate disease genes is necessary before conducting laboratory studies as identifying disease genes from a large number of candidate genes using laboratory methods, is a very costly and time-consuming task. There are many machine learning-based gene prioritization methods. These methods differ in various aspects including the feature vectors of genes, the used datasets with different structures, and the learning model. Creating a suitable feature vector for genes and an appropriate learning model on a variety of data with different and non-Euclidean structures, including graphs, as well as the lack of negative data are very important challenges of these methods. The use of graph neural networks has recently emerged in machine learning and other related fields, and they have demonstrated superior performance for a broad range of problems. Methods In this study, a new semi-supervised learning method based on graph convolutional networks is presented using the novel constructing feature vector for each gene. In the proposed method, first, we construct three feature vectors for each gene using terms from the Gene Ontology (GO) database. Then, we train a graph convolution network on these vectors using protein–protein interaction (PPI) network data to identify disease candidate genes. Our model discovers hidden layer representations encoding in both local graph structure as well as features of nodes. This method is characterized by the simultaneous consideration of topological information of the biological network (e.g., PPI) and other sources of evidence. Finally, a validation has been done to demonstrate the efficiency of our method. Results Several experiments are performed on 16 diseases to evaluate the proposed method's performance. The experiments demonstrate that our proposed method achieves the best results, in terms of precision, the area under the ROC curve (AUCs), and F1-score values, when compared with eight state-of-the-art network and machine learning-based disease gene prioritization methods. Conclusion This study shows that the proposed semi-supervised learning method appropriately classifies and ranks candidate disease genes using a graph convolutional network and an innovative method to create three feature vectors for genes based on the molecular function, cellular component, and biological process terms from GO data.
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Affiliation(s)
- Saeid Azadifar
- Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran.
| | - Ali Ahmadi
- Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran
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16
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Somatic variation in normal tissues: friend or foe of cancer early detection? Ann Oncol 2022; 33:1239-1249. [PMID: 36162751 DOI: 10.1016/j.annonc.2022.09.156] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 09/03/2022] [Accepted: 09/10/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Seemingly normal tissues progressively become populated by mutant clones over time. Most of these clones bear mutations in well-known cancer genes but only rarely do they transform into cancer. This poses questions on what triggers cancer initiation and what implications somatic variation has for cancer early detection. DESIGN We analysed recent mutational screens of healthy and cancer-free diseased tissues to compare somatic drivers and the causes of somatic variation across tissues. We then reviewed the mechanisms of clonal expansion and their relationships with age and diseases other than cancer. We finally discussed the relevance of somatic variation for cancer initiation and how it can help or hinder cancer detection and prevention. RESULTS The extent of somatic variation is highly variable across tissues and depends on intrinsic features, such as tissue architecture and turnover, as well as the exposure to endogenous and exogenous insults. Most somatic mutations driving clonal expansion are tissue-specific and inactivate tumor suppressor genes involved in chromatin modification and cell growth signaling. Some of these genes are more frequently mutated in normal tissues than cancer, indicating a context-dependent cancer promoting or protective role. Mutant clones can persist over a long time or disappear rapidly, suggesting that their fitness depends on the dynamic equilibrium with the environment. The disruption of this equilibrium is likely responsible for their transformation into malignant clones and knowing what triggers this process is key for cancer prevention and early detection. Somatic variation should be considered in liquid biopsy, where it may contribute cancer-independent mutations, and in the identification of cancer drivers, since not all mutated genes favoring clonal expansion also drive tumorigenesis. CONCLUSIONS Somatic variation and the factors governing homeostasis of normal tissues should be taken into account when devising strategies for cancer prevention and early detection.
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17
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Zhang SW, Wang ZN, Li Y, Guo WF. Prioritization of cancer driver gene with prize-collecting steiner tree by introducing an edge weighted strategy in the personalized gene interaction network. BMC Bioinformatics 2022; 23:341. [PMID: 35974311 PMCID: PMC9380343 DOI: 10.1186/s12859-022-04802-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 06/13/2022] [Indexed: 11/10/2022] Open
Abstract
Background Cancer is a heterogeneous disease in which tumor genes cooperate as well as adapt and evolve to the changing conditions for individual patients. It is a meaningful task to discover the personalized cancer driver genes that can provide diagnosis and target drug for individual patients. However, most of existing methods mainly ranks potential personalized cancer driver genes by considering the patient-specific nodes information on the gene/protein interaction network. These methods ignore the personalized edge weight information in gene interaction network, leading to false positive results. Results In this work, we presented a novel algorithm (called PDGPCS) to predict the Personalized cancer Driver Genes based on the Prize-Collecting Steiner tree model by considering the personalized edge weight information. PDGPCS first constructs the personalized weighted gene interaction network by integrating the personalized gene expression data and prior known gene/protein interaction network knowledge. Then the gene mutation data and pathway data are integrated to quantify the impact of each mutant gene on every dysregulated pathway with the prize-collecting Steiner tree model. Finally, according to the mutant gene’s aggregated impact score on all dysregulated pathways, the mutant genes are ranked for prioritizing the personalized cancer driver genes. Experimental results on four TCGA cancer datasets show that PDGPCS has better performance than other personalized driver gene prediction methods. In addition, we verified that the personalized edge weight of gene interaction network can improve the prediction performance. Conclusions PDGPCS can more accurately identify the personalized driver genes and takes a step further toward personalized medicine and treatment. The source code of PDGPCS can be freely downloaded from https://github.com/NWPU-903PR/PDGPCS. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04802-y.
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Affiliation(s)
- Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China.
| | - Zhen-Nan Wang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Yan Li
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Wei-Feng Guo
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China.
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18
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Zhang Y, Chang X, Xia J, Huang Y, Sun S, Chen L, Liu X. Identifying network biomarkers of cancer by sample-specific differential network. BMC Bioinformatics 2022; 23:230. [PMID: 35705908 PMCID: PMC9202129 DOI: 10.1186/s12859-022-04772-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 06/02/2022] [Indexed: 02/08/2023] Open
Abstract
Abundant datasets generated from various big science projects on diseases have presented great challenges and opportunities, which contributed to unfolding the complexity of diseases. The discovery of disease-associated molecular networks for each individual plays an important role in personalized therapy and precision treatment of cancer-based on the reference networks. However, there are no effective ways to distinguish the consistency of different reference networks. In this study, we developed a statistical method, i.e. a sample-specific differential network (SSDN), to construct and analyze such networks based on gene expression of a single sample against a reference dataset. We proved that the SSDN is structurally consistent even with different reference datasets if the reference dataset can follow certain conditions. The SSDN also can be used to identify patient-specific disease modules or network biomarkers as well as predict the potential driver genes of a tumor sample.
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Affiliation(s)
- Yu Zhang
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China.,Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou, 310024, China.,School of Mathematics and Statistics, Shandong University, Weihai, 264209, Shandong, China
| | - Xiao Chang
- Institute of Statistics and Applied Mathematics, Anhui University of Finance & Economics, Bengbu, 233030, China.
| | - Jie Xia
- Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Science, Shanghai, 200031, China
| | - Yanhong Huang
- School of Mathematics and Statistics, Shandong University, Weihai, 264209, Shandong, China
| | - Shaoyan Sun
- School of Mathematics and Statistics, Ludong University, Yantai, 264025, China
| | - Luonan Chen
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China. .,Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou, 310024, China. .,Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Science, Shanghai, 200031, China. .,West China Biomedical Big Data Center, Med-X center for informatics, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Xiaoping Liu
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China. .,Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou, 310024, China. .,School of Mathematics and Statistics, Shandong University, Weihai, 264209, Shandong, China.
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19
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Yan J, Hu Z, Li ZW, Sun S, Guo WF. Network Control Models With Personalized Genomics Data for Understanding Tumor Heterogeneity in Cancer. Front Oncol 2022; 12:891676. [PMID: 35712516 PMCID: PMC9195174 DOI: 10.3389/fonc.2022.891676] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 04/12/2022] [Indexed: 11/25/2022] Open
Abstract
Due to rapid development of high-throughput sequencing and biotechnology, it has brought new opportunities and challenges in developing efficient computational methods for exploring personalized genomics data of cancer patients. Because of the high-dimension and small sample size characteristics of these personalized genomics data, it is difficult for excavating effective information by using traditional statistical methods. In the past few years, network control methods have been proposed to solve networked system with high-dimension and small sample size. Researchers have made progress in the design and optimization of network control principles. However, there are few studies comprehensively surveying network control methods to analyze the biomolecular network data of individual patients. To address this problem, here we comprehensively surveyed complex network control methods on personalized omics data for understanding tumor heterogeneity in precision medicine of individual patients with cancer.
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Affiliation(s)
- Jipeng Yan
- Department of Nephrology, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
| | - Zhuo Hu
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| | - Zong-Wei Li
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| | - Shiren Sun
- Department of Nephrology, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
- *Correspondence: Wei-Feng Guo, ; Shiren Sun,
| | - Wei-Feng Guo
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- *Correspondence: Wei-Feng Guo, ; Shiren Sun,
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20
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Petrov I, Alexeyenko A. Individualized discovery of rare cancer drivers in global network context. eLife 2022; 11:74010. [PMID: 35593700 PMCID: PMC9159755 DOI: 10.7554/elife.74010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 05/20/2022] [Indexed: 11/13/2022] Open
Abstract
Late advances in genome sequencing expanded the space of known cancer driver genes several-fold. However, most of this surge was based on computational analysis of somatic mutation frequencies and/or their impact on the protein function. On the contrary, experimental research necessarily accounted for functional context of mutations interacting with other genes and conferring cancer phenotypes. Eventually, just such results become ‘hard currency’ of cancer biology. The new method, NEAdriver employs knowledge accumulated thus far in the form of global interaction network and functionally annotated pathways in order to recover known and predict novel driver genes. The driver discovery was individualized by accounting for mutations’ co-occurrence in each tumour genome – as an alternative to summarizing information over the whole cancer patient cohorts. For each somatic genome change, probabilistic estimates from two lanes of network analysis were combined into joint likelihoods of being a driver. Thus, ability to detect previously unnoticed candidate driver events emerged from combining individual genomic context with network perspective. The procedure was applied to 10 largest cancer cohorts followed by evaluating error rates against previous cancer gene sets. The discovered driver combinations were shown to be informative on cancer outcome. This revealed driver genes with individually sparse mutation patterns that would not be detectable by other computational methods and related to cancer biology domains poorly covered by previous analyses. In particular, recurrent mutations of collagen, laminin, and integrin genes were observed in the adenocarcinoma and glioblastoma cancers. Considering constellation patterns of candidate drivers in individual cancer genomes opens a novel avenue for personalized cancer medicine.
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Affiliation(s)
- Iurii Petrov
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.,Science for Life Laboratory, Solna, Sweden
| | - Andrey Alexeyenko
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.,Science for Life Laboratory, Solna, Sweden.,Evi-networks, enskild konsultföretag, Huddinge, Sweden
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21
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Erten C, Houdjedj A, Kazan H, Taleb Bahmed AA. PersonaDrive: A Method for the Identification and Prioritization of Personalized Cancer Drivers. Bioinformatics 2022; 38:3407-3414. [PMID: 35579340 DOI: 10.1093/bioinformatics/btac329] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 05/06/2022] [Accepted: 05/11/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION A major challenge in cancer genomics is to distinguish the driver mutations that are causally linked to cancer from passenger mutations that do not contribute to cancer development. The majority of existing methods provide a single driver gene list for the entire cohort of patients. However, since mutation profiles of patients from the same cancer type show a high degree of heterogeneity, a more ideal approach is to identify patient-specific drivers. RESULTS We propose a novel method that integrates genomic data, biological pathways, and protein connectivity information for personalized identification of driver genes. The method is formulated on a personalized bipartite graph for each patient. Our approach provides a personalized ranking of the mutated genes of a patient based on the sum of weighted 'pairwise pathway coverage' scores across all the samples, where appropriate pairwise patient similarity scores are used as weights to normalize these coverage scores. We compare our method against three state-of-the-art patient-specific cancer gene prioritization methods. The comparisons are with respect to a novel evaluation method that takes into account the personalized nature of the problem. We show that our approach outperforms the existing alternatives for both the TCGA and the cell line data. Additionally, we show that the KEGG/Reactome pathways enriched in our ranked genes and those that are enriched in cell lines' reference sets overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods. Our findings can provide valuable information towards the development of personalized treatments and therapies. AVAILABILITY All the code and data are available at https://github.com/abu-compbio/PersonaDrive (archived at https://doi.org/10.5281/zenodo.6520187). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Cesim Erten
- Department of Computer Engineering, Antalya Bilim University, Antalya, 07190, Turkey
| | - Aissa Houdjedj
- Department of Computer Engineering, Antalya Bilim University, Antalya, 07190, Turkey.,Department of Computer Engineering, Akdeniz University, Antalya, 07070, Turkey
| | - Hilal Kazan
- Department of Computer Engineering, Antalya Bilim University, Antalya, 07190, Turkey
| | - Ahmed Amine Taleb Bahmed
- Electrical and Computer Engineering Graduate Program, Antalya Bilim University, Antalya, 07190, Turkey
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22
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Chen Z, Lu Y, Cao B, Zhang W, Edwards A, Zhang K. Driver gene detection through Bayesian network integration of mutation and expression profiles. Bioinformatics 2022; 38:2781-2790. [PMID: 35561191 PMCID: PMC9113331 DOI: 10.1093/bioinformatics/btac203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 03/12/2022] [Accepted: 04/06/2022] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION The identification of mutated driver genes and the corresponding pathways is one of the primary goals in understanding tumorigenesis at the patient level. Integration of multi-dimensional genomic data from existing repositories, e.g., The Cancer Genome Atlas (TCGA), offers an effective way to tackle this issue. In this study, we aimed to leverage the complementary genomic information of individuals and create an integrative framework to identify cancer-related driver genes. Specifically, based on pinpointed differentially expressed genes, variants in somatic mutations and a gene interaction network, we proposed an unsupervised Bayesian network integration (BNI) method to detect driver genes and estimate the disease propagation at the patient and/or cohort levels. This new method first captures inherent structural information to construct a functional gene mutation network and then extracts the driver genes and their controlled downstream modules using the minimum cover subset method. RESULTS Using other credible sources (e.g. Cancer Gene Census and Network of Cancer Genes), we validated the driver genes predicted by the BNI method in three TCGA pan-cancer cohorts. The proposed method provides an effective approach to address tumor heterogeneity faced by personalized medicine. The pinpointed drivers warrant further wet laboratory validation. AVAILABILITY AND IMPLEMENTATION The supplementary tables and source code can be obtained from https://xavieruniversityoflouisiana.sharefile.com/d-se6df2c8d0ebe4800a3030311efddafe5. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zhong Chen
- Department of Computer Science, Xavier University of Louisiana, New Orleans, LA 70125, USA
- Bioinformatics Core of Xavier RCMI Center for Cancer Research, Xavier University of Louisiana, New Orleans, LA 70125, USA
| | - You Lu
- Department of Computer Science, Xavier University of Louisiana, New Orleans, LA 70125, USA
- Bioinformatics Core of Xavier RCMI Center for Cancer Research, Xavier University of Louisiana, New Orleans, LA 70125, USA
| | - Bo Cao
- Division of Basic and Pharmaceutical Sciences, College of Pharmacy, Xavier University of Louisiana, New Orleans, LA 70125, USA
| | - Wensheng Zhang
- Department of Computer Science, Xavier University of Louisiana, New Orleans, LA 70125, USA
- Bioinformatics Core of Xavier RCMI Center for Cancer Research, Xavier University of Louisiana, New Orleans, LA 70125, USA
| | - Andrea Edwards
- Department of Computer Science, Xavier University of Louisiana, New Orleans, LA 70125, USA
| | - Kun Zhang
- To whom correspondence should be addressed
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Li Q, Ren Z, Cao K, Li MM, Wang K, Zhou Y. CancerVar: An artificial intelligence-empowered platform for clinical interpretation of somatic mutations in cancer. SCIENCE ADVANCES 2022; 8:eabj1624. [PMID: 35544644 PMCID: PMC9075800 DOI: 10.1126/sciadv.abj1624] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 03/21/2022] [Indexed: 05/12/2023]
Abstract
Several knowledgebases are manually curated to support clinical interpretations of thousands of hotspot somatic mutations in cancer. However, discrepancies or even conflicting interpretations are observed among these databases. Furthermore, many previously undocumented mutations may have clinical or functional impacts on cancer but are not systematically interpreted by existing knowledgebases. To address these challenges, we developed CancerVar to facilitate automated and standardized interpretations for 13 million somatic mutations based on the AMP/ASCO/CAP 2017 guidelines. We further introduced a deep learning framework to predict oncogenicity for these variants using both functional and clinical features. CancerVar achieved satisfactory performance when compared to several independent knowledgebases and, using clinically curated datasets, demonstrated practical utility in classifying somatic variants. In summary, by integrating clinical guidelines with a deep learning framework, CancerVar facilitates clinical interpretation of somatic variants, reduces manual work, improves consistency in variant classification, and promotes implementation of the guidelines.
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Affiliation(s)
- Quan Li
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G2C1, Canada
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Zilin Ren
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Kajia Cao
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Marilyn M. Li
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yunyun Zhou
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
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Effects of Multi-Omics Characteristics on Identification of Driver Genes Using Machine Learning Algorithms. Genes (Basel) 2022; 13:genes13050716. [PMID: 35627101 PMCID: PMC9141966 DOI: 10.3390/genes13050716] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 04/16/2022] [Accepted: 04/18/2022] [Indexed: 12/19/2022] Open
Abstract
Cancer is a complex disease caused by genomic and epigenetic alterations; hence, identifying meaningful cancer drivers is an important and challenging task. Most studies have detected cancer drivers with mutated traits, while few studies consider multiple omics characteristics as important factors. In this study, we present a framework to analyze the effects of multi-omics characteristics on the identification of driver genes. We utilize four machine learning algorithms within this framework to detect cancer driver genes in pan-cancer data, including 75 characteristics among 19,636 genes. The 75 features are divided into four types and analyzed using Kullback–Leibler divergence based on CGC genes and non-CGC genes. We detect cancer driver genes in two different ways. One is to detect driver genes from a single feature type, while the other is from the top N features. The first analysis denotes that the mutational features are the best characteristics. The second analysis reveals that the top 45 features are the most effective feature combinations and superior to the mutational features. The top 45 features not only contain mutational features but also three other types of features. Therefore, our study extends the detection of cancer driver genes and provides a more comprehensive understanding of cancer mechanisms.
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25
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Identification of a Five-Gene Panel to Assess Prognosis for Gastric Cancer. BIOMED RESEARCH INTERNATIONAL 2022; 2022:5593619. [PMID: 35187167 PMCID: PMC8850031 DOI: 10.1155/2022/5593619] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 12/30/2021] [Accepted: 01/04/2022] [Indexed: 11/25/2022]
Abstract
Methods Two datasets were used as training and validation cohorts to establish the predictive model. We used three types of screening criteria: background analysis, pathway analysis, and functional analysis provided by the cBioportal website. Fisher's exact test and multivariable logistic regression were performed to screen out related genes. Furthermore, we performed receiver operating characteristic (ROC) and Kaplan–Meier curve analyses to evaluate the correlation between the selected genes and overall survival. Result We screened five genes (KNL1, NRXN1, C6, CCDC169-SOHLH2, and TTN) that were highly related to recurrence of GC. The area under the receiver operating characteristic (ROC) curve was 0.813, which was much higher than that of the baseline model (AUC = 0.699). This result suggested that the mutation of five selected genes had a significant effect on the prediction of recurrence compared with other factors (age, stages, history, etc.). Furthermore, the Kaplan-Meier estimator also revealed that the mutation of five genes positively correlated with patient survival. Conclusions The patients who have mutations in these five genes may experience longer survival than those who do not have mutations. This five-gene panel will likely be a practical tool for prognostic evaluation and will provide another possible way for clinicians to determine therapy.
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26
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Dressler L, Bortolomeazzi M, Keddar MR, Misetic H, Sartini G, Acha-Sagredo A, Montorsi L, Wijewardhane N, Repana D, Nulsen J, Goldman J, Pollitt M, Davis P, Strange A, Ambrose K, Ciccarelli FD. Comparative assessment of genes driving cancer and somatic evolution in non-cancer tissues: an update of the Network of Cancer Genes (NCG) resource. Genome Biol 2022; 23:35. [PMID: 35078504 PMCID: PMC8790917 DOI: 10.1186/s13059-022-02607-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 01/10/2022] [Indexed: 12/30/2022] Open
Abstract
Background Genetic alterations of somatic cells can drive non-malignant clone formation and promote cancer initiation. However, the link between these processes remains unclear and hampers our understanding of tissue homeostasis and cancer development. Results Here, we collect a literature-based repertoire of 3355 well-known or predicted drivers of cancer and non-cancer somatic evolution in 122 cancer types and 12 non-cancer tissues. Mapping the alterations of these genes in 7953 pan-cancer samples reveals that, despite the large size, the known compendium of drivers is still incomplete and biased towards frequently occurring coding mutations. High overlap exists between drivers of cancer and non-cancer somatic evolution, although significant differences emerge in their recurrence. We confirm and expand the unique properties of drivers and identify a core of evolutionarily conserved and essential genes whose germline variation is strongly counter-selected. Somatic alteration in even one of these genes is sufficient to drive clonal expansion but not malignant transformation. Conclusions Our study offers a comprehensive overview of our current understanding of the genetic events initiating clone expansion and cancer revealing significant gaps and biases that still need to be addressed. The compendium of cancer and non-cancer somatic drivers, their literature support, and properties are accessible in the Network of Cancer Genes and Healthy Drivers resource at http://www.network-cancer-genes.org/. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-022-02607-z.
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27
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Network Approaches for Precision Oncology. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1361:199-213. [DOI: 10.1007/978-3-030-91836-1_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Zeng J, Shufean MA. Molecular-based precision oncology clinical decision making augmented by artificial intelligence. Emerg Top Life Sci 2021; 5:757-764. [PMID: 34874054 PMCID: PMC8786281 DOI: 10.1042/etls20210220] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 11/08/2021] [Accepted: 11/16/2021] [Indexed: 01/03/2023]
Abstract
The rapid growth and decreasing cost of Next-generation sequencing (NGS) technologies have made it possible to conduct routine large panel genomic sequencing in many disease settings, especially in the oncology domain. Furthermore, it is now known that optimal disease management of patients depends on individualized cancer treatment guided by comprehensive molecular testing. However, translating results from molecular sequencing reports into actionable clinical insights remains a challenge to most clinicians. In this review, we discuss about some representative systems that leverage artificial intelligence (AI) to facilitate some processes of clinicians' decision making based upon molecular data, focusing on their application in precision oncology. Some limitations and pitfalls of the current application of AI in clinical decision making are also discussed.
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Affiliation(s)
- Jia Zeng
- Sheikh Khalifa Bin Zayed Al Nahyan Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, U.S.A
| | - Md Abu Shufean
- Sheikh Khalifa Bin Zayed Al Nahyan Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, U.S.A
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29
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Kan Y, Jiang L, Guo Y, Tang J, Guo F. Two-stage-vote ensemble framework based on integration of mutation data and gene interaction network for uncovering driver genes. Brief Bioinform 2021; 23:6426028. [PMID: 34791034 DOI: 10.1093/bib/bbab429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 08/30/2021] [Accepted: 09/18/2021] [Indexed: 11/14/2022] Open
Abstract
Identifying driver genes, exactly from massive genes with mutations, promotes accurate diagnosis and treatment of cancer. In recent years, a lot of works about uncovering driver genes based on integration of mutation data and gene interaction networks is gaining more attention. However, it is in suspense if it is more effective for prioritizing driver genes when integrating various types of mutation information (frequency and functional impact) and gene networks. Hence, we build a two-stage-vote ensemble framework based on somatic mutations and mutual interactions. Specifically, we first represent and combine various kinds of mutation information, which are propagated through networks by an improved iterative framework. The first vote is conducted on iteration results by voting methods, and the second vote is performed to get ensemble results of the first poll for the final driver gene list. Compared with four excellent previous approaches, our method has better performance in identifying driver genes on $33$ types of cancer from The Cancer Genome Atlas. Meanwhile, we also conduct a comparative analysis about two kinds of mutation information, five gene interaction networks and four voting strategies. Our framework offers a new view for data integration and promotes more latent cancer genes to be admitted.
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Affiliation(s)
- Yingxin Kan
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Limin Jiang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.,Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yan Guo
- Comprehensive cancer center, Department of Internal Medicine, University of New Mexico, Albuquerque, U.S
| | - Jijun Tang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,School of Computational Science and Engineering, University of South Carolina, Columbia, U.S
| | - Fei Guo
- School of Computer Science and Engineering, Central South University, Changsha, China
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30
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Lai X, Zhou J, Wessely A, Heppt M, Maier A, Berking C, Vera J, Zhang L. A disease network-based deep learning approach for characterizing melanoma. Int J Cancer 2021; 150:1029-1044. [PMID: 34716589 DOI: 10.1002/ijc.33860] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 10/08/2021] [Accepted: 10/19/2021] [Indexed: 12/12/2022]
Abstract
Multiple types of genomic variations are present in cutaneous melanoma and some of the genomic features may have an impact on the prognosis of the disease. The access to genomics data via public repositories such as The Cancer Genome Atlas (TCGA) allows for a better understanding of melanoma at the molecular level, therefore making characterization of substantial heterogeneity in melanoma patients possible. Here, we proposed an approach that integrates genomics data, a disease network, and a deep learning model to classify melanoma patients for prognosis, assess the impact of genomic features on the classification and provide interpretation to the impactful features. We integrated genomics data into a melanoma network and applied an autoencoder model to identify subgroups in TCGA melanoma patients. The model utilizes communities identified in the network to effectively reduce the dimensionality of genomics data into a patient score profile. Based on the score profile, we identified three patient subtypes that show different survival times. Furthermore, we quantified and ranked the impact of genomic features on the patient score profile using a machine-learning technique. Follow-up analysis of the top-ranking features provided us with the biological interpretation of them at both pathway and molecular levels, such as their mutation and interactome profiles in melanoma and their involvement in pathways associated with signaling transduction, immune system and cell cycle. Taken together, we demonstrated the ability of the approach to identify disease subgroups using a deep learning model that captures the most relevant information of genomics data in the melanoma network.
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Affiliation(s)
- Xin Lai
- Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.,Deutsches Zentrum Immuntherapie, Erlangen, Germany.,Comprehensive Cancer Center Erlangen, Erlangen, Germany
| | - Jinfei Zhou
- College of Computer Science, Sichuan University, Chengdu, China
| | - Anja Wessely
- Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.,Deutsches Zentrum Immuntherapie, Erlangen, Germany.,Comprehensive Cancer Center Erlangen, Erlangen, Germany
| | - Markus Heppt
- Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.,Deutsches Zentrum Immuntherapie, Erlangen, Germany.,Comprehensive Cancer Center Erlangen, Erlangen, Germany
| | - Andreas Maier
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Carola Berking
- Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.,Deutsches Zentrum Immuntherapie, Erlangen, Germany.,Comprehensive Cancer Center Erlangen, Erlangen, Germany
| | - Julio Vera
- Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.,Deutsches Zentrum Immuntherapie, Erlangen, Germany.,Comprehensive Cancer Center Erlangen, Erlangen, Germany
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, China
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Tang YY, Wei PJ, Zhao JP, Xia J, Cao RF, Zheng CH. Identification of driver genes based on gene mutational effects and network centrality. BMC Bioinformatics 2021; 22:457. [PMID: 34560840 PMCID: PMC8461858 DOI: 10.1186/s12859-021-04377-0] [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: 08/15/2021] [Accepted: 08/23/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND As one of the deadliest diseases in the world, cancer is driven by a few somatic mutations that disrupt the normal growth of cells, and leads to abnormal proliferation and tumor development. The vast majority of somatic mutations did not affect the occurrence and development of cancer; thus, identifying the mutations responsible for tumor occurrence and development is one of the main targets of current cancer treatments. RESULTS To effectively identify driver genes, we adopted a semi-local centrality measure and gene mutation effect function to assess the effect of gene mutations on changes in gene expression patterns. Firstly, we calculated the mutation score for each gene. Secondly, we identified differentially expressed genes (DEGs) in the cohort by comparing the expression profiles of tumor samples and normal samples, and then constructed a local network for each mutation gene using DEGs and mutant genes according to the protein-protein interaction network. Finally, we calculated the score of each mutant gene according to the objective function. The top-ranking mutant genes were selected as driver genes. We name the proposed method as mutations effect and network centrality. CONCLUSIONS Four types of cancer data in The Cancer Genome Atlas were tested. The experimental data proved that our method was superior to the existing network-centric method, as it was able to quickly and easily identify driver genes and rare driver factors.
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Affiliation(s)
- Yun-Yun Tang
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China
| | - Pi-Jing Wei
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China
| | - Jian-Ping Zhao
- College of Mathematics and System Sciences, Xinjiang University, Urumqi, China
| | - Junfeng Xia
- Institute of Physical Science and Information Technology, Anhui University, Hefei, China
| | - Rui-Fen Cao
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China.,Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University, Putian, Fujian, China
| | - Chun-Hou Zheng
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China. .,College of Mathematics and System Sciences, Xinjiang University, Urumqi, China.
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32
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Guo WF, Zhang SW, Zeng T, Akutsu T, Chen L. Network control principles for identifying personalized driver genes in cancer. Brief Bioinform 2021; 21:1641-1662. [PMID: 31711128 DOI: 10.1093/bib/bbz089] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 06/26/2019] [Accepted: 06/27/2019] [Indexed: 02/02/2023] Open
Abstract
To understand tumor heterogeneity in cancer, personalized driver genes (PDGs) need to be identified for unraveling the genotype-phenotype associations corresponding to particular patients. However, most of the existing driver-focus methods mainly pay attention on the cohort information rather than on individual information. Recent developing computational approaches based on network control principles are opening a new way to discover driver genes in cancer, particularly at an individual level. To provide comprehensive perspectives of network control methods on this timely topic, we first considered the cancer progression as a network control problem, in which the expected PDGs are altered genes by oncogene activation signals that can change the individual molecular network from one health state to the other disease state. Then, we reviewed the network reconstruction methods on single samples and introduced novel network control methods on single-sample networks to identify PDGs in cancer. Particularly, we gave a performance assessment of the network structure control-based PDGs identification methods on multiple cancer datasets from TCGA, for which the data and evaluation package also are publicly available. Finally, we discussed future directions for the application of network control methods to identify PDGs in cancer and diverse biological processes.
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Affiliation(s)
- Wei-Feng Guo
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Tao Zeng
- Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, 611-0011, Japan
| | - Luonan Chen
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.,Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, 200031, China.,School of Life Science and Technology, ShanghaiTech University, 201210 Shanghai, China.,Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai 201210, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
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Kan Y, Jiang L, Tang J, Guo Y, Guo F. A systematic view of computational methods for identifying driver genes based on somatic mutation data. Brief Funct Genomics 2021; 20:333-343. [PMID: 34312663 DOI: 10.1093/bfgp/elab032] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 06/16/2021] [Accepted: 06/22/2021] [Indexed: 11/13/2022] Open
Abstract
Abnormal changes of driver genes are serious for human health and biomedical research. Identifying driver genes, exactly from enormous genes with mutations, promotes accurate diagnosis and treatment of cancer. A lot of works about uncovering driver genes have been developed over the past decades. By analyzing previous works, we find that computational methods are more efficient than traditional biological experiments when distinguishing driver genes from massive data. In this study, we summarize eight common computational algorithms only using somatic mutation data. We first group these methods into three categories according to mutation features they apply. Then, we conclude a general process of nominating candidate cancer driver genes. Finally, we evaluate three representative methods on 10 kinds of cancer derived from The Cancer Genome Atlas Program and five Chinese projects from the International Cancer Genome Consortium. In addition, we compare results of methods with various parameters. Evaluation is performed from four perspectives, including CGC, OG/TSG, Q-value and QQQuantile-Quantileplot. To sum up, we present algorithms using somatic mutation data in order to offer a systematic view of various mutation features and lay the foundation of methods based on integration of mutation information and other types of data.
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Affiliation(s)
- Yingxin Kan
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Limin Jiang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.,Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jijun Tang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,School of Computational Science and Engineering, University of South Carolina, Columbia, U.S
| | - Yan Guo
- Comprehensive cancer center, Department of Internal Medicine, University of New Mexico, Albuquerque, U.S
| | - Fei Guo
- School of Computer Science and Engineering, Central South University, Changsha, China
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34
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Kosvyra A, Ntzioni E, Chouvarda I. Network analysis with biological data of cancer patients: A scoping review. J Biomed Inform 2021; 120:103873. [PMID: 34298154 DOI: 10.1016/j.jbi.2021.103873] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 06/30/2021] [Accepted: 07/18/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND & OBJECTIVE Network Analysis (NA) is a mathematical method that allows exploring relations between units and representing them as a graph. Although NA was initially related to social sciences, the past two decades was introduced in Bioinformatics. The recent growth of the networks' use in biological data analysis reveals the need to further investigate this area. In this work, we attempt to identify the use of NA with biological data, and specifically: (a) what types of data are used and whether they are integrated or not, (b) what is the purpose of this analysis, predictive or descriptive, and (c) the outcome of such analyses, specifically in cancer diseases. METHODS & MATERIALS The literature review was conducted on two databases, PubMed & IEEE, and was restricted to journal articles of the last decade (January 2010 - December 2019). At a first level, all articles were screened by title and abstract, and at a second level the screening was conducted by reading the full text article, following the predefined inclusion & exclusion criteria leading to 131 articles of interest. A table was created with the information of interest and was used for the classification of the articles. The articles were initially classified to analysis studies and studies that propose a new algorithm or methodology. Each one of these categories was further screened by the following clustering criteria: (a) data used, (b) study purpose, (c) study outcome. Specifically for the studies proposing a new algorithm, the novelty presented in each one was detected. RESULTS & Conclusions: In the past five years researchers are focusing on creating new algorithms and methodologies to enhance this field. The articles' classification revealed that only 25% of the analyses are integrating multi-omics data, although 50% of the new algorithms developed follow this integrative direction. Moreover, only 20% of the analyses and 10% of the newly developed methodologies have a predictive purpose. Regarding the result of the works reviewed, 75% of the studies focus on identifying, prognostic or not, gene signatures. Concluding, this review revealed the need for deploying predictive and multi-omics integrative algorithms and methodologies that can be used to enhance cancer diagnosis, prognosis and treatment.
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Affiliation(s)
- A Kosvyra
- Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
| | - E Ntzioni
- Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - I Chouvarda
- Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Zhang T, Zhang SW, Li Y. Identifying Driver Genes for Individual Patients through Inductive Matrix Completion. Bioinformatics 2021; 37:4477-4484. [PMID: 34175939 DOI: 10.1093/bioinformatics/btab477] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 04/30/2021] [Accepted: 06/25/2021] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION The driver genes play a key role in the evolutionary process of cancer. Effectively identifying these driver genes is crucial to cancer diagnosis and treatment. However, due to the high heterogeneity of cancers, it remains challenging to identify the driver genes for individual patients. Although some computational methods have been proposed to tackle this problem, they seldom consider the fact that the genes functionally similar to the well-established driver genes may likely play similar roles in cancer process, which potentially promotes the driver gene identification. Thus, here we developed a novel approach of IMCDriver to promote the driver gene identification both for cohorts and individual patients. RESULTS IMCDriver first considers the well-established driver genes as prior information, and adopts the using multi-omics data (e.g., somatic mutation, gene expression and protein-protein interaction) to compute the similarity between patients/genes. Then, IMCDriver prioritizes the personalized mutated genes according to their functional similarity to the well-established driver genes via Inductive Matrix Completion. Finally, IMCDriver identifies the highly rank-ordered genes as the personalized driver genes. The results on five cancer datasets from TCGA show that our IMCDriver outperforms other existing state-of-the-art methods both in the cohort and patient-specific driver gene identification. IMCDriver also reveals some novel driver genes that potentially drive cancer development. In addition, even for the driver genes rarely mutated among a population, IMCDriver can still identify them and prioritize them with high priorities. AVAILABILITY Code available at https://github.com/NWPU-903PR/IMCDriver. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Tong Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, China Xi'an.,School of Electrical and Mechanical Engineering, Pingdingshan University, Pingdingshan, China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, China Xi'an
| | - Yan Li
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, China Xi'an
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Zhao W, Yang J, Wu J, Cai G, Zhang Y, Haltom J, Su W, Dong MJ, Chen S, Wu J, Zhou Z, Gu X. CanDriS: posterior profiling of cancer-driving sites based on two-component evolutionary model. Brief Bioinform 2021; 22:6238585. [PMID: 33876217 DOI: 10.1093/bib/bbab131] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 03/17/2021] [Accepted: 03/18/2021] [Indexed: 12/12/2022] Open
Abstract
Current cancer genomics databases have accumulated millions of somatic mutations that remain to be further explored. Due to the over-excess mutations unrelated to cancer, the great challenge is to identify somatic mutations that are cancer-driven. Under the notion that carcinogenesis is a form of somatic-cell evolution, we developed a two-component mixture model: while the ground component corresponds to passenger mutations, the rapidly evolving component corresponds to driver mutations. Then, we implemented an empirical Bayesian procedure to calculate the posterior probability of a site being cancer-driven. Based on these, we developed a software CanDriS (Cancer Driver Sites) to profile the potential cancer-driving sites for thousands of tumor samples from the Cancer Genome Atlas and International Cancer Genome Consortium across tumor types and pan-cancer level. As a result, we identified that approximately 1% of the sites have posterior probabilities larger than 0.90 and listed potential cancer-wide and cancer-specific driver mutations. By comprehensively profiling all potential cancer-driving sites, CanDriS greatly enhances our ability to refine our knowledge of the genetic basis of cancer and might guide clinical medication in the upcoming era of precision medicine. The results were displayed in a database CandrisDB (http://biopharm.zju.edu.cn/candrisdb/).
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Affiliation(s)
- Wenyi Zhao
- College of Pharmaceutical Sciences & College of Computer Science and Technology, Zhejiang University, China
| | - Jingwen Yang
- MOE Key Laboratory of Contemporary Anthropology, Human Phenome Institute, School of Life Sciences, Fudan University, China
| | - Jingcheng Wu
- College of Pharmaceutical Sciences, Zhejiang University, China
| | - Guoxing Cai
- College of Pharmaceutical Sciences, Zhejiang University, China
| | - Yao Zhang
- College of Pharmaceutical Sciences, Zhejiang University, China
| | - Jeffrey Haltom
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, 12 Iowa 50011, USA
| | - Weijia Su
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, 12 Iowa 50011, USA
| | - Michael J Dong
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, 12 Iowa 50011, USA
| | - Shuqing Chen
- College of Pharmaceutical Sciences, Zhejiang University, China
| | - Jian Wu
- College of Computer Science and Technology & School of Medicine, Zhejiang University, China
| | - Zhan Zhou
- College of Pharmaceutical Sciences, Innovation Institute for Artificial Intelligence in Medicine, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Zhejiang University, China
| | - Xun Gu
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, 12 Iowa 50011, USA
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37
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Nulsen J, Misetic H, Yau C, Ciccarelli FD. Pan-cancer detection of driver genes at the single-patient resolution. Genome Med 2021; 13:12. [PMID: 33517897 PMCID: PMC7849133 DOI: 10.1186/s13073-021-00830-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 01/08/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Identifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions. RESULTS We present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways. CONCLUSIONS sysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types ( https://github.com/ciccalab/sysSVM2 ).
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Affiliation(s)
- Joel Nulsen
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London, NW1 1AT, UK
- School of Cancer and Pharmaceutical Sciences, King's College London, London, SE1 1UL, UK
| | - Hrvoje Misetic
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London, NW1 1AT, UK
- School of Cancer and Pharmaceutical Sciences, King's College London, London, SE1 1UL, UK
| | - Christopher Yau
- School of Health Sciences, University of Manchester, Manchester, M13 9PL, UK
- The Alan Turing Institute, London, NW1 2DB, UK
| | - Francesca D Ciccarelli
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London, NW1 1AT, UK.
- School of Cancer and Pharmaceutical Sciences, King's College London, London, SE1 1UL, UK.
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38
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Yu J, Chang X. Topological Data Analysis: A New Method to Identify Genetic Alterations in Cancer. Asia Pac J Oncol Nurs 2021; 8:112-114. [PMID: 33688559 PMCID: PMC7934599 DOI: 10.4103/2347-5625.308301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 11/30/2020] [Indexed: 12/05/2022] Open
Abstract
Cancer is the largest health problem worldwide. A number of targeted therapies are currently employed for the treatment of different cancers. Determining the molecular mechanisms that are necessary for cancer development and progression is the most critical step in targeted therapies. Currently, many studies have identified a large number of frequently mutated cancer-associated genes using recurrence-based methods. However, only the cancer-associated mutations with a mutation frequency >15% can be identified by these methods. In other words, they cannot be used to identify driver genes that have low mutation frequency but play a major role in tumorigenesis and development. Thus, there is an urgent need for a method for identifying cancer-associated genes that are not based on recurrence. In a study, recently published in Nature Communications, research team led by Prof. Raúl Rabadán from the Columbia University successfully devised a novel topological data analysis approach to identify low-prevalence cancer-associated gene mutations using expression data from multiple cancers.
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Affiliation(s)
- Jie Yu
- Foreign Languages College, Tianjin Normal University, Tianjin, China
| | - Xinzhong Chang
- Department of Breast Surgery, Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
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39
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Wei T, Fa B, Luo C, Johnston L, Zhang Y, Yu Z. An Efficient and Easy-to-Use Network-Based Integrative Method of Multi-Omics Data for Cancer Genes Discovery. Front Genet 2021; 11:613033. [PMID: 33488678 PMCID: PMC7820902 DOI: 10.3389/fgene.2020.613033] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 11/25/2020] [Indexed: 12/25/2022] Open
Abstract
Identifying personalized driver genes is essential for discovering critical biomarkers and developing effective personalized therapies of cancers. However, few methods consider weights for different types of mutations and efficiently distinguish driver genes over a larger number of passenger genes. We propose MinNetRank (Minimum used for Network-based Ranking), a new method for prioritizing cancer genes that sets weights for different types of mutations, considers the incoming and outgoing degree of interaction network simultaneously, and uses minimum strategy to integrate multi-omics data. MinNetRank prioritizes cancer genes among multi-omics data for each sample. The sample-specific rankings of genes are then integrated into a population-level ranking. When evaluating the accuracy and robustness of prioritizing driver genes, our method almost always significantly outperforms other methods in terms of precision, F1 score, and partial area under the curve (AUC) on six cancer datasets. Importantly, MinNetRank is efficient in discovering novel driver genes. SP1 is selected as a candidate driver gene only by our method (ranked top three), and SP1 RNA and protein differential expression between tumor and normal samples are statistically significant in liver hepatocellular carcinoma. The top seven genes stratify patients into two subtypes exhibiting statistically significant survival differences in five cancer types. These top seven genes are associated with overall survival, as illustrated by previous researchers. MinNetRank can be very useful for identifying cancer driver genes, and these biologically relevant marker genes are associated with clinical outcome. The R package of MinNetRank is available at https://github.com/weitinging/MinNetRank.
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Affiliation(s)
- Ting Wei
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Botao Fa
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Chengwen Luo
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Luke Johnston
- SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Yue Zhang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Zhangsheng Yu
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
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40
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Zhang L, Karimzadeh M, Welch M, McIntosh C, Wang B. Analytics methods and tools for integration of biomedical data in medicine. Artif Intell Med 2021. [DOI: 10.1016/b978-0-12-821259-2.00007-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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41
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Khalighi S, Singh S, Varadan V. Untangling a complex web: Computational analyses of tumor molecular profiles to decode driver mechanisms. J Genet Genomics 2020; 47:595-609. [PMID: 33423960 PMCID: PMC7902422 DOI: 10.1016/j.jgg.2020.11.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 11/04/2020] [Accepted: 11/14/2020] [Indexed: 12/19/2022]
Abstract
Genome-scale studies focusing on molecular profiling of cancers across tissue types have revealed a plethora of aberrations across the genomic, transcriptomic, and epigenomic scales. The significant molecular heterogeneity across individual tumors even within the same tissue context complicates decoding the key etiologic mechanisms of this disease. Furthermore, it is increasingly likely that biologic mechanisms underlying the pathobiology of cancer involve multiple molecular entities interacting across functional scales. This has motivated the development of computational approaches that integrate molecular measurements with prior biological knowledge in increasingly intricate ways to enable the discovery of driver genomic aberrations across cancers. Here, we review diverse methodological approaches that have powered significant advances in our understanding of the genomic underpinnings of cancer at the cohort and at the individual tumor scales. We outline the key advances and challenges in the computational discovery of cancer mechanisms while motivating the development of systems biology approaches to comprehensively decode the biologic drivers of this complex disease.
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Affiliation(s)
- Sirvan Khalighi
- Division of General Medical Sciences-Oncology, Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Salendra Singh
- Division of General Medical Sciences-Oncology, Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Vinay Varadan
- Division of General Medical Sciences-Oncology, Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA.
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42
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Rabadán R, Mohamedi Y, Rubin U, Chu T, Alghalith AN, Elliott O, Arnés L, Cal S, Obaya ÁJ, Levine AJ, Cámara PG. Identification of relevant genetic alterations in cancer using topological data analysis. Nat Commun 2020; 11:3808. [PMID: 32732999 PMCID: PMC7393176 DOI: 10.1038/s41467-020-17659-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Accepted: 07/09/2020] [Indexed: 01/05/2023] Open
Abstract
Large-scale cancer genomic studies enable the systematic identification of mutations that lead to the genesis and progression of tumors, uncovering the underlying molecular mechanisms and potential therapies. While some such mutations are recurrently found in many tumors, many others exist solely within a few samples, precluding detection by conventional recurrence-based statistical approaches. Integrated analysis of somatic mutations and RNA expression data across 12 tumor types reveals that mutations of cancer genes are usually accompanied by substantial changes in expression. We use topological data analysis to leverage this observation and uncover 38 elusive candidate cancer-associated genes, including inactivating mutations of the metalloproteinase ADAMTS12 in lung adenocarcinoma. We show that ADAMTS12-/- mice have a five-fold increase in the susceptibility to develop lung tumors, confirming the role of ADAMTS12 as a tumor suppressor gene. Our results demonstrate that data integration through topological techniques can increase our ability to identify previously unreported cancer-related alterations.
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Affiliation(s)
- Raúl Rabadán
- Departments of Systems Biology and Biomedical Informatics, Columbia University, 1130 St. Nicholas Ave., New York, NY, 10032, USA.
| | - Yamina Mohamedi
- Departamento de Bioquimica y Biologia Molecular, Universidad de Oviedo, Oviedo, Asturias, Spain
- IUOPA, Instituto Universitario de Oncologia, Oviedo, Asturias, Spain
| | - Udi Rubin
- Departments of Systems Biology and Biomedical Informatics, Columbia University, 1130 St. Nicholas Ave., New York, NY, 10032, USA
- Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Tim Chu
- Departments of Systems Biology and Biomedical Informatics, Columbia University, 1130 St. Nicholas Ave., New York, NY, 10032, USA
| | - Adam N Alghalith
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA, 19104, USA
| | - Oliver Elliott
- Departments of Systems Biology and Biomedical Informatics, Columbia University, 1130 St. Nicholas Ave., New York, NY, 10032, USA
| | - Luis Arnés
- Departments of Systems Biology and Biomedical Informatics, Columbia University, 1130 St. Nicholas Ave., New York, NY, 10032, USA
| | - Santiago Cal
- Departamento de Bioquimica y Biologia Molecular, Universidad de Oviedo, Oviedo, Asturias, Spain
- IUOPA, Instituto Universitario de Oncologia, Oviedo, Asturias, Spain
| | - Álvaro J Obaya
- IUOPA, Instituto Universitario de Oncologia, Oviedo, Asturias, Spain
- Departamento de Biologia Funcional, Universidad de Oviedo, Oviedo, Asturias, Spain
| | - Arnold J Levine
- The Simons Center for Systems Biology, Institute for Advanced Study, Princeton, NJ, 08540, USA.
| | - Pablo G Cámara
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA, 19104, USA.
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43
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John A, Qin B, Kalari KR, Wang L, Yu J. Patient-specific multi-omics models and the application in personalized combination therapy. Future Oncol 2020; 16:1737-1750. [PMID: 32462937 DOI: 10.2217/fon-2020-0119] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
The rapid advancement of high-throughput technologies and sharp decrease in cost have opened up the possibility to generate large amount of multi-omics data on an individual basis. The development of high-throughput -omics, including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiomics, enables the application of multi-omics technologies in the clinical settings. Combination therapy, defined as disease treatment with two or more drugs to achieve efficacy with lower doses or lower drug toxicity, is the basis for the care of diseases like cancer. Patient-specific multi-omics data integration can help the identification and development of combination therapies. In this review, we provide an overview of different -omics platforms, and discuss the methods for multi-omics, high-throughput, data integration, personalized combination therapy.
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Affiliation(s)
- August John
- Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Bo Qin
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA.,Gastroenterology Research Unit, Mayo Clinic, Rochester, MN 55905, USA.,Department of Oncology, Mayo Clinic, Rochester, MN 55905, USA
| | - Krishna R Kalari
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Liewei Wang
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Jia Yu
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
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44
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Richard M, Decamps C, Chuffart F, Brambilla E, Rousseaux S, Khochbin S, Jost D. PenDA, a rank-based method for personalized differential analysis: Application to lung cancer. PLoS Comput Biol 2020; 16:e1007869. [PMID: 32392248 PMCID: PMC7274464 DOI: 10.1371/journal.pcbi.1007869] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 06/05/2020] [Accepted: 04/11/2020] [Indexed: 12/27/2022] Open
Abstract
The hopes of precision medicine rely on our capacity to measure various high-throughput genomic information of a patient and to integrate them for personalized diagnosis and adapted treatment. Reaching these ambitious objectives will require the development of efficient tools for the detection of molecular defects at the individual level. Here, we propose a novel method, PenDA, to perform Personalized Differential Analysis at the scale of a single sample. PenDA is based on the local ordering of gene expressions within individual cases and infers the deregulation status of genes in a sample of interest compared to a reference dataset. Based on realistic simulations of RNA-seq data of tumors, we showed that PenDA outcompetes existing approaches with very high specificity and sensitivity and is robust to normalization effects. Applying the method to lung cancer cohorts, we observed that deregulated genes in tumors exhibit a cancer-type-specific commitment towards up- or down-regulation. Based on the individual information of deregulation given by PenDA, we were able to define two new molecular histologies for lung adenocarcinoma cancers strongly correlated to survival. In particular, we identified 37 biomarkers whose up-regulation lead to bad prognosis and that we validated on two independent cohorts. PenDA provides a robust, generic tool to extract personalized deregulation patterns that can then be used for the discovery of therapeutic targets and for personalized diagnosis. An open-access, user-friendly R package is available at https://github.com/bcm-uga/penda. The hopes of precision medicine rely on our capacity to measure individual molecular information for personalized diagnosis and treatment. These challenging perspectives will be only possible with the development of efficient methodological tools to identify patient-specific molecular defects from the many precise molecular information that one can access at the single-individual, single tissue or even single-cell levels. Such methods will provide a better understanding of disease-specific biological mechanisms and will promote the development of personalized therapeutic strategies. Here we describe a novel method, named PenDA, to perform differential analysis of gene expression at the individual level. Based on a realistic benchmark of simulated tumors, we demonstrated that PenDA reaches very high efficiency in detecting sample-specific deregulated genes. We then applied the method to two large cohorts associated with lung cancer. A detailed statistical analysis of the results allowed to isolate genes with specific deregulation patterns, like genes that are up-regulated in all tumors or genes that are expressed but never deregulated in any tumors. Given their specificities, these genes are likely to be of interest in therapeutic research. In particular, we were able to identified 37 new biomarkers associated to bad prognosis that we validated on two independent cohorts.
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Affiliation(s)
- Magali Richard
- Univ Grenoble Alpes, CNRS, Grenoble INP, TIMC-IMAG, Grenoble, France
- * E-mail: (MR); (DJ)
| | | | - Florent Chuffart
- CNRS UMR 5309, Inserm U1209, Univ Grenoble Alpes, Institute for Advanced Biosciences, Grenoble, France
| | - Elisabeth Brambilla
- CHUGA, Inserm U1209, Univ Grenoble Alpes, Institute for Advanced Biosciences, Grenoble, France
| | - Sophie Rousseaux
- CNRS UMR 5309, Inserm U1209, Univ Grenoble Alpes, Institute for Advanced Biosciences, Grenoble, France
| | - Saadi Khochbin
- CNRS UMR 5309, Inserm U1209, Univ Grenoble Alpes, Institute for Advanced Biosciences, Grenoble, France
| | - Daniel Jost
- Univ Grenoble Alpes, CNRS, Grenoble INP, TIMC-IMAG, Grenoble, France
- University of Lyon, ENS de Lyon, Univ Claude Bernard, CNRS, Laboratory of Biology and Modelling of the Cell, Lyon, France
- * E-mail: (MR); (DJ)
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45
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Wei PJ, Wu FX, Xia J, Su Y, Wang J, Zheng CH. Prioritizing Cancer Genes Based on an Improved Random Walk Method. Front Genet 2020; 11:377. [PMID: 32411180 PMCID: PMC7198854 DOI: 10.3389/fgene.2020.00377] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 03/26/2020] [Indexed: 12/18/2022] Open
Abstract
Identifying driver genes that contribute to cancer progression from numerous passenger genes, although a central goal, is a major challenge. The protein-protein interaction network provides convenient and reasonable assistance for driver gene discovery. Random walk-based methods have been widely used to prioritize nodes in social or biological networks. However, most studies select the next arriving node uniformly from the random walker's neighbors. Few consider transiting preference according to the degree of random walker's neighbors. In this study, based on the random walk method, we propose a novel approach named Driver_IRW (Driver genes discovery with Improved Random Walk method), to prioritize cancer genes in cancer-related network. The key idea of Driver_IRW is to assign different transition probabilities for different edges of a constructed cancer-related network in accordance with the degree of the nodes' neighbors. Furthermore, the global centrality (here is betweenness centrality) and Katz feedback centrality are incorporated into the framework to evaluate the probability to walk to the seed nodes. Experimental results on four cancer types indicate that Driver_IRW performs more efficiently than some previously published methods for uncovering known cancer-related genes. In conclusion, our method can aid in prioritizing cancer-related genes and complement traditional frequency and network-based methods.
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Affiliation(s)
- Pi-Jing Wei
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
- Department of Computer Sciences, University of Saskatchewan, Saskatoon, SK, Canada
- Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
| | - Junfeng Xia
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, China
| | - Yansen Su
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China
| | - Jing Wang
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China
- College of Computer and Information Engineering, Fuyang Normal University, Fuyang, China
| | - Chun-Hou Zheng
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China
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46
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Oh M, Park S, Kim S, Chae H. Machine learning-based analysis of multi-omics data on the cloud for investigating gene regulations. Brief Bioinform 2020; 22:66-76. [PMID: 32227074 DOI: 10.1093/bib/bbaa032] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 02/05/2020] [Accepted: 02/25/2020] [Indexed: 02/06/2023] Open
Abstract
Gene expressions are subtly regulated by quantifiable measures of genetic molecules such as interaction with other genes, methylation, mutations, transcription factor and histone modifications. Integrative analysis of multi-omics data can help scientists understand the condition or patient-specific gene regulation mechanisms. However, analysis of multi-omics data is challenging since it requires not only the analysis of multiple omics data sets but also mining complex relations among different genetic molecules by using state-of-the-art machine learning methods. In addition, analysis of multi-omics data needs quite large computing infrastructure. Moreover, interpretation of the analysis results requires collaboration among many scientists, often requiring reperforming analysis from different perspectives. Many of the aforementioned technical issues can be nicely handled when machine learning tools are deployed on the cloud. In this survey article, we first survey machine learning methods that can be used for gene regulation study, and we categorize them according to five different goals: gene regulatory subnetwork discovery, disease subtype analysis, survival analysis, clinical prediction and visualization. We also summarize the methods in terms of multi-omics input types. Then, we explain why the cloud is potentially a good solution for the analysis of multi-omics data, followed by a survey of two state-of-the-art cloud systems, Galaxy and BioVLAB. Finally, we discuss important issues when the cloud is used for the analysis of multi-omics data for the gene regulation study.
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Affiliation(s)
- Minsik Oh
- Department of Computer Science and Engineering, Seoul National University, Seoul, 08826, Korea
| | - Sungjoon Park
- Department of Computer Science and Engineering, Seoul National University, Seoul, 08826, Korea
| | - Sun Kim
- Department of Computer Science and Engineering, Seoul National University, Seoul, 08826, Korea.,Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, Korea.,Bioinformatics Institute, Seoul National University, Seoul, 08826, Korea
| | - Heejoon Chae
- Division of Computer Science, Sookmyung Women's University, Seoul, 04310,Korea
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Zou J, Wang E. Cancer Biomarker Discovery for Precision Medicine: New Progress. Curr Med Chem 2020; 26:7655-7671. [PMID: 30027846 DOI: 10.2174/0929867325666180718164712] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 06/26/2018] [Accepted: 07/06/2018] [Indexed: 12/30/2022]
Abstract
BACKGROUND Precision medicine puts forward customized healthcare for cancer patients. An important way to accomplish this task is to stratify patients into those who may respond to a treatment and those who may not. For this purpose, diagnostic and prognostic biomarkers have been pursued. OBJECTIVE This review focuses on novel approaches and concepts of exploring biomarker discovery under the circumstances that technologies are developed, and data are accumulated for precision medicine. RESULTS The traditional mechanism-driven functional biomarkers have the advantage of actionable insights, while data-driven computational biomarkers can fulfill more needs, especially with tremendous data on the molecules of different layers (e.g. genetic mutation, mRNA, protein etc.) which are accumulated based on a plenty of technologies. Besides, the technology-driven liquid biopsy biomarker is very promising to improve patients' survival. The developments of biomarker discovery on these aspects are promoting the understanding of cancer, helping the stratification of patients and improving patients' survival. CONCLUSION Current developments on mechanisms-, data- and technology-driven biomarker discovery are achieving the aim of precision medicine and promoting the clinical application of biomarkers. Meanwhile, the complexity of cancer requires more effective biomarkers, which could be accomplished by a comprehensive integration of multiple types of biomarkers together with a deep understanding of cancer.
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Affiliation(s)
- Jinfeng Zou
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, ON, M5G 23C1, Canada
| | - Edwin Wang
- College of Life Science, Tianjin Normal University, Tianjin, China.,Cumming School of Medicine, University of Calgary, Calgary, Alberta AB T2N 1N4, Canada
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Yan H, Deng X, Chen H, Cheng J, He J, Guan Q, Li M, Xie J, Xia J, Gu Y, Guo Z. Identification of Common and Subtype-Specific Mutated Sub-Pathways for a Cancer. Front Genet 2019; 10:1228. [PMID: 31850075 PMCID: PMC6892778 DOI: 10.3389/fgene.2019.01228] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Accepted: 11/06/2019] [Indexed: 01/07/2023] Open
Abstract
The heterogeneity of cancer is a big obstacle for cancer diagnosis and treatment. Prioritizing combinations of driver genes that mutate in most patients of a specific cancer or a subtype of this cancer is a promising way to tackle this problem. Here, we developed an empirical algorithm, named PathMG, to identify common and subtype-specific mutated sub-pathways for a cancer. By analyzing mutation data of 408 samples (Lung-data1) for lung cancer, three sub-pathways each covering at least 90% of samples were identified as the common sub-pathways of lung cancer. These sub-pathways were enriched with mutated cancer genes and drug targets and were validated in two independent datasets (Lung-data2 and Lung-data3). Especially, applying PathMG to analyze two major subtypes of lung cancer, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LSCC), we identified 13 subtype-specific sub-pathways with at least 0.25 mutation frequency difference between LUAD and LSCC samples in Lung-data1, and 12 of the 13 sub-pathways were reproducible in Lung-data2 and Lung-data3. Similar analyses were done for colorectal cancer. Together, PathMG provides us a novel tool to identify potential common and subtype-specific sub-pathways for a cancer, which can provide candidates for cancer diagnoses and sub-pathway targeted treatments.
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Affiliation(s)
- Haidan Yan
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Key Laboratory of Medical Bioinformatics, Fujian Medical University, Fuzhou, China
| | - Xusheng Deng
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Key Laboratory of Medical Bioinformatics, Fujian Medical University, Fuzhou, China
| | - Haifeng Chen
- Department of General Surgery, Fuzhou Second Hospital Affiliated to Xiamen University, Xiamen, China
| | - Jun Cheng
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Key Laboratory of Medical Bioinformatics, Fujian Medical University, Fuzhou, China
| | - Jun He
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Key Laboratory of Medical Bioinformatics, Fujian Medical University, Fuzhou, China
| | - Qingzhou Guan
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Key Laboratory of Medical Bioinformatics, Fujian Medical University, Fuzhou, China
| | - Meifeng Li
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Key Laboratory of Medical Bioinformatics, Fujian Medical University, Fuzhou, China
| | - Jiajing Xie
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Key Laboratory of Medical Bioinformatics, Fujian Medical University, Fuzhou, China
| | - Jie Xia
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Key Laboratory of Medical Bioinformatics, Fujian Medical University, Fuzhou, China
| | - Yunyan Gu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zheng Guo
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Key Laboratory of Medical Bioinformatics, Fujian Medical University, Fuzhou, China
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Kalari KR, Sinnwell JP, Thompson KJ, Tang X, Carlson EE, Yu J, Vedell PT, Ingle JN, Weinshilboum RM, Boughey JC, Wang L, Goetz MP, Suman V. PANOPLY: Omics-Guided Drug Prioritization Method Tailored to an Individual Patient. JCO Clin Cancer Inform 2019; 2:1-11. [PMID: 30652605 DOI: 10.1200/cci.18.00012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
PURPOSE The majority of patients with cancer receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework, PANOPLY (Precision Cancer Genomic Report: Single Sample Inventory), to identify and prioritize drug targets and cancer therapy regimens. MATERIALS AND METHODS The PANOPLY approach integrates clinical data with germline and somatic features obtained from multiomics platforms and applies machine learning and network analysis approaches in the context of the individual patient and matched controls. The PANOPLY workflow uses the following four steps: selection of matched controls to the patient of interest; identification of patient-specific genomic events; identification of suitable drugs using the driver-gene network and random forest analyses; and provision of an integrated multiomics case report of the patient with prioritization of anticancer drugs. RESULTS The PANOPLY workflow can be executed on a stand-alone virtual machine and is also available for download as an R package. We applied the method to an institutional breast cancer neoadjuvant chemotherapy study that collected clinical and genomic data as well as patient-derived xenografts to investigate the prioritization offered by PANOPLY. In a chemotherapy-resistant patient-derived xenograft model, we found that that the prioritized drug, olaparib, was more effective than placebo in treating the tumor ( P < .05). We also applied PANOPLY to in-house and publicly accessible multiomics tumor data sets with therapeutic response or survival data available. CONCLUSION PANOPLY shows promise as a means to prioritize drugs on the basis of clinical and multiomics data for an individual patient with cancer. Additional studies are needed to confirm this approach.
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Affiliation(s)
| | | | | | | | | | - Jia Yu
- All authors: Mayo Clinic, Rochester, MN
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Abstract
Motivation Network propagation has been widely used to aggregate and amplify the effects of tumor mutations using knowledge of molecular interaction networks. However, propagating mutations through interactions irrelevant to cancer leads to erosion of pathway signals and complicates the identification of cancer subtypes. Results To address this problem we introduce a propagation algorithm, Network-Based Supervised Stratification (NBS2), which learns the mutated subnetworks underlying tumor subtypes using a supervised approach. Given an annotated molecular network and reference tumor mutation profiles for which subtypes have been predefined, NBS2 is trained by adjusting the weights on interaction features such that network propagation best recovers the provided subtypes. After training, weights are fixed such that mutation profiles of new tumors can be accurately classified. We evaluate NBS2 on breast and glioblastoma tumors, demonstrating that it outperforms the best network-based approaches in classifying tumors to known subtypes for these diseases. By interpreting the interaction weights, we highlight characteristic molecular pathways driving selected subtypes. Availability and implementation The NBS2 package is freely available at: https://github.com/wzhang1984/NBSS. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Wei Zhang
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Jianzhu Ma
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Trey Ideker
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA.,Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
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