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Mukherjee A, Abraham S, Singh A, Balaji S, Mukunthan KS. From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies. Mol Biotechnol 2024:10.1007/s12033-024-01133-6. [PMID: 38565775 DOI: 10.1007/s12033-024-01133-6] [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: 12/27/2023] [Accepted: 02/27/2024] [Indexed: 04/04/2024]
Abstract
In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.
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Affiliation(s)
- Arnab Mukherjee
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Suzanna Abraham
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Akshita Singh
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - S Balaji
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - K S Mukunthan
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
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Buccilli B, Rodriguez Molina MA, Redrovan Palomeque DP, Herrera Sabán CA, C Caliwag FM, Contreras Flores CJS, Abeysiriwardana CWJ, Diarte E, Arruarana VS, Calderon Martinez E. Liquid Biopsies for Monitoring Medulloblastoma: Circulating Tumor DNA as a Biomarker for Disease Progression and Treatment Response. Cureus 2024; 16:e51712. [PMID: 38313884 PMCID: PMC10838584 DOI: 10.7759/cureus.51712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/05/2024] [Indexed: 02/06/2024] Open
Abstract
Pediatric brain tumors, including medulloblastoma (MB), represent a significant challenge in clinical oncology. Early diagnosis, accurate monitoring of therapeutic response, and the detection of minimal residual disease (MRD) are crucial for improving outcomes in these patients. This review aims to explore recent advancements in liquid biopsy techniques for monitoring pediatric brain tumors, with a specific focus on medulloblastoma. The primary research question is how liquid biopsy techniques can be effectively utilized for these purposes. Liquid biopsies, particularly the analysis of circulating tumor DNA (ctDNA) in cerebrospinal fluid (CSF), are investigated as promising noninvasive tools. This comprehensive review examines the components of liquid biopsies, including ctDNA, cell-free DNA (cfDNA), and microRNA (miRNA). Their applications in diagnosis, prognosis, and MRD assessment are critically assessed. The review also discusses the role of liquid biopsies in categorizing medulloblastoma subgroups, risk stratification, and the identification of therapeutic targets. Liquid biopsies have shown promising applications in the pediatric brain tumor field, particularly in medulloblastoma. They offer noninvasive means of diagnosis, monitoring treatment response, and detecting MRD. These biopsies have played a pivotal role in subgroup classification and risk stratification of medulloblastoma patients, aiding in the identification of therapeutic targets. However, challenges related to sensitivity and specificity are noted. In conclusion, this review highlights the growing importance of liquid biopsies, specifically ctDNA analysis in CSF, in pediatric brain tumor management, with a primary focus on medulloblastoma. Liquid biopsies have the potential to revolutionize patient care by enabling early diagnosis, accurate monitoring, and MRD detection. Nevertheless, further research is essential to validate their clinical utility fully. The evolving landscape of liquid biopsy applications underscores their promise in improving outcomes for pediatric brain tumor patients.
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Affiliation(s)
- Barbara Buccilli
- Department of Human Neuroscience, Sapienza University of Rome, Rome, ITA
- Department of Neurosurgery, Mount Sinai Hospital, New York, USA
| | | | | | - Cindy A Herrera Sabán
- Department of General Practice, Facultad de Ciencias Médicas, Universidad de San Carlos de Guatemala, San Carlos, GTM
| | - Fides M C Caliwag
- Department of General Practice, Ateneo School of Medicine and Public Health, Pasig City, PHL
| | | | | | - Edna Diarte
- Department of Medicine, Universidad Autónoma de Sinaloa, Culiacán, MEX
| | - Victor S Arruarana
- Department of Internal Medicine, Brookdale University Hospital Medical Center, New York, USA
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Jiang S, Wang T, Zhang KH. Data-driven decision-making for precision diagnosis of digestive diseases. Biomed Eng Online 2023; 22:87. [PMID: 37658345 PMCID: PMC10472739 DOI: 10.1186/s12938-023-01148-1] [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: 01/14/2023] [Accepted: 08/15/2023] [Indexed: 09/03/2023] Open
Abstract
Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.
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Affiliation(s)
- Song Jiang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
| | - Ting Wang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
| | - Kun-He Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
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Patil N, Howe O, Cahill P, Byrne HJ. Monitoring and modelling the dynamics of the cellular glycolysis pathway: A review and future perspectives. Mol Metab 2022; 66:101635. [PMID: 36379354 PMCID: PMC9703637 DOI: 10.1016/j.molmet.2022.101635] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 10/28/2022] [Accepted: 11/06/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND The dynamics of the cellular glycolysis pathway underpin cellular function and dysfunction, and therefore ultimately health, disease, diagnostic and therapeutic strategies. Evolving our understanding of this fundamental process and its dynamics remains critical. SCOPE OF REVIEW This paper reviews the medical relevance of glycolytic pathway in depth and explores the current state of the art for monitoring and modelling the dynamics of the process. The future perspectives of label free, vibrational microspectroscopic techniques to overcome the limitations of the current approaches are considered. MAJOR CONCLUSIONS Vibrational microspectroscopic techniques can potentially operate in the niche area of limitations of other omics technologies for non-destructive, real-time, in vivo label-free monitoring of glycolysis dynamics at a cellular and subcellular level.
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Affiliation(s)
- Nitin Patil
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland; School of Physics and Optometric & Clinical Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland.
| | - Orla Howe
- School of Biological and Health Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland
| | - Paul Cahill
- School of Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Hugh J Byrne
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland
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Lee B, Mohamad I, Pokhrel R, Murad R, Yuan M, Stapleton S, Bettegowda C, Jallo G, Eberhart CG, Garrett T, Perera RJ. Medulloblastoma cerebrospinal fluid reveals metabolites and lipids indicative of hypoxia and cancer-specific RNAs. Acta Neuropathol Commun 2022; 10:25. [PMID: 35209946 PMCID: PMC8867780 DOI: 10.1186/s40478-022-01326-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 02/04/2022] [Indexed: 02/07/2023] Open
Abstract
Medulloblastoma (MB) is the most common malignant brain tumor in children. There remains an unmet need for diagnostics to sensitively detect the disease, particularly recurrences. Cerebrospinal fluid (CSF) provides a window into the central nervous system, and liquid biopsy of CSF could provide a relatively non-invasive means for disease diagnosis. There has yet to be an integrated analysis of the transcriptomic, metabolomic, and lipidomic changes occurring in the CSF of children with MB. CSF samples from patients with (n = 40) or without (n = 11; no cancer) MB were subjected to RNA-sequencing and high-resolution mass spectrometry to identify RNA, metabolite, and lipid profiles. Differentially expressed transcripts, metabolites, and lipids were identified and their biological significance assessed by pathway analysis. The DIABLO multivariate analysis package (R package mixOmics) was used to integrate the molecular changes characterizing the CSF of MB patients. Differentially expressed transcripts, metabolites, and lipids in CSF were discriminatory for the presence of MB but not the exact molecular subtype. One hundred and ten genes and ten circular RNAs were differentially expressed in MB CSF compared with normal, representing TGF-β signaling, TNF-α signaling via NF-kB, and adipogenesis pathways. Tricarboxylic acid cycle and other metabolites (malate, fumarate, succinate, α-ketoglutarate, hydroxypyruvate, N-acetyl-aspartate) and total triacylglycerols were significantly upregulated in MB CSF compared with normal CSF. Although separating MBs into subgroups using transcriptomic, metabolomic, and lipid signatures in CSF was challenging, we were able to identify a group of omics signatures that could separate cancer from normal CSF. Metabolic and lipidomic profiles both contained indicators of tumor hypoxia. Our approach provides several candidate signatures that deserve further validation, including the novel circular RNA circ_463, and insights into the impact of MB on the CSF microenvironment.
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Affiliation(s)
- Bongyong Lee
- grid.21107.350000 0001 2171 9311Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, 1650 Orleans St, Baltimore, MD 21231 USA ,grid.413611.00000 0004 0467 2330Johns Hopkins All Children’s Hospital, 600 5th St. South, St. Petersburg, FL 33701 USA
| | - Iqbal Mohamad
- grid.15276.370000 0004 1936 8091Department Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida, 1395 Center Drive, Gainesville, FL 32610 USA ,grid.240145.60000 0001 2291 4776Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA
| | - Rudramani Pokhrel
- grid.21107.350000 0001 2171 9311Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, 1650 Orleans St, Baltimore, MD 21231 USA ,grid.413611.00000 0004 0467 2330Johns Hopkins All Children’s Hospital, 600 5th St. South, St. Petersburg, FL 33701 USA
| | - Rabi Murad
- grid.479509.60000 0001 0163 8573Sanford Burnham Prebys Medical Discovery Institute, 10901 N. Torrey Pines Road, La Jolla, CA 92037 USA
| | - Menglang Yuan
- grid.21107.350000 0001 2171 9311Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, 1650 Orleans St, Baltimore, MD 21231 USA ,grid.413611.00000 0004 0467 2330Johns Hopkins All Children’s Hospital, 600 5th St. South, St. Petersburg, FL 33701 USA
| | - Stacie Stapleton
- grid.413611.00000 0004 0467 2330Johns Hopkins All Children’s Hospital, 600 5th St. South, St. Petersburg, FL 33701 USA
| | - Chetan Bettegowda
- grid.21107.350000 0001 2171 9311Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, 1650 Orleans St, Baltimore, MD 21231 USA ,grid.21107.350000 0001 2171 9311Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, USA
| | - George Jallo
- grid.413611.00000 0004 0467 2330Johns Hopkins All Children’s Hospital, 600 5th St. South, St. Petersburg, FL 33701 USA
| | - Charles G. Eberhart
- grid.21107.350000 0001 2171 9311Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, 1650 Orleans St, Baltimore, MD 21231 USA ,grid.21107.350000 0001 2171 9311Department of Pathology, Johns Hopkins University School of Medicine, 720 Rutland Avenue, Baltimore, MD 21205 USA
| | - Timothy Garrett
- Department Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida, 1395 Center Drive, Gainesville, FL, 32610, USA.
| | - Ranjan J. Perera
- grid.21107.350000 0001 2171 9311Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, 1650 Orleans St, Baltimore, MD 21231 USA ,grid.413611.00000 0004 0467 2330Johns Hopkins All Children’s Hospital, 600 5th St. South, St. Petersburg, FL 33701 USA
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Sari B, Isik M, Eylem CC, Kilic C, Okesola BO, Karakaya E, Emregul E, Nemutlu E, Derkus B. Omics Technologies for High-Throughput-Screening of Cell-Biomaterial Interactions. Mol Omics 2022; 18:591-615. [DOI: 10.1039/d2mo00060a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Recent research effort in biomaterial development has largely focused on engineering bio-instructive materials to stimulate specific cell signaling. Assessing the biological performance of these materials using time-consuming and trial-and-error traditional...
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Kwong GA, Ghosh S, Gamboa L, Patriotis C, Srivastava S, Bhatia SN. Synthetic biomarkers: a twenty-first century path to early cancer detection. Nat Rev Cancer 2021; 21:655-668. [PMID: 34489588 PMCID: PMC8791024 DOI: 10.1038/s41568-021-00389-3] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/14/2021] [Indexed: 02/08/2023]
Abstract
Detection of cancer at an early stage when it is still localized improves patient response to medical interventions for most cancer types. The success of screening tools such as cervical cytology to reduce mortality has spurred significant interest in new methods for early detection (for example, using non-invasive blood-based or biofluid-based biomarkers). Yet biomarkers shed from early lesions are limited by fundamental biological and mass transport barriers - such as short circulation times and blood dilution - that limit early detection. To address this issue, synthetic biomarkers are being developed. These represent an emerging class of diagnostics that deploy bioengineered sensors inside the body to query early-stage tumours and amplify disease signals to levels that could potentially exceed those of shed biomarkers. These strategies leverage design principles and advances from chemistry, synthetic biology and cell engineering. In this Review, we discuss the rationale for development of biofluid-based synthetic biomarkers. We examine how these strategies harness dysregulated features of tumours to amplify detection signals, use tumour-selective activation to increase specificity and leverage natural processing of bodily fluids (for example, blood, urine and proximal fluids) for easy detection. Finally, we highlight the challenges that exist for preclinical development and clinical translation of synthetic biomarker diagnostics.
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Affiliation(s)
- Gabriel A Kwong
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory School of Medicine, Atlanta, GA, USA.
- Parker H. Petit Institute of Bioengineering and Bioscience, Atlanta, GA, USA.
- Institute for Electronics and Nanotechnology, Georgia Tech, Atlanta, GA, USA.
- The Georgia Immunoengineering Consortium, Emory University and Georgia Tech, Atlanta, GA, USA.
- Winship Cancer Institute, Emory University, Atlanta, GA, USA.
| | - Sharmistha Ghosh
- Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Lena Gamboa
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory School of Medicine, Atlanta, GA, USA
| | - Christos Patriotis
- Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sudhir Srivastava
- Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Sangeeta N Bhatia
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Howard Hughes Medical Institute, Chevy Chase, MD, USA.
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Malagoli Tagliazucchi G, Taccioli C. GMIEC: a shiny application for the identification of gene-targeted drugs for precision medicine. BMC Genomics 2020; 21:619. [PMID: 32912170 PMCID: PMC7488130 DOI: 10.1186/s12864-020-06996-y] [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: 04/15/2020] [Accepted: 08/17/2020] [Indexed: 11/28/2022] Open
Abstract
Abstract Background Precision medicine is a medical approach that takes into account individual genetic variability and often requires Next Generation Sequencing data in order to predict new treatments. Here we present GMIEC, Genomic Modules Identification et Characterization for genomics medicine, an application that is able to identify specific drugs at the level of single patient integrating multi-omics data such as RNA-sequencing, copy-number variation, methylation, Chromatin Immuno-Precipitation and Exome/Whole Genome sequencing. It is also possible to include clinical data related to each patient. GMIEC has been developed as a web-based R-Shiny platform and gives as output a table easy to use and explore. Results We present GMIEC, a Shiny application for genomics medicine. The tool allows the users the integration of two or more multiple omics datasets (e.g. gene-expression, copy-number), at sample level, to identify groups of genes that share common genomic and corresponding drugs. We demonstrate the characteristics of our application by using it to analyze a prostate cancer data set. Conclusions GMIEC provides a simple interface for genomics medicine. GMIEC was develop with Shiny to provide an application that does not require advanced programming skills. GMIEC consists of three sub-application for the analysis (GMIEC-AN), the visualization (GMIEC-VIS) and the exploration of results (GMIEC-RES). GMIEC is an open source software and is available at https://github.com/guidmt/GMIEC-shiny
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Affiliation(s)
- Guidantonio Malagoli Tagliazucchi
- Division of Cardiology, Azienda Ospedaliero-Universitaria di Parma, 43126, Parma, Italy.,Present address: Department of Genetics, Evolution and Environment, Darwin Building, Gower Street WC1E 6BT, London, UK
| | - Cristian Taccioli
- Department of Animal Medicine, Production and Health, University of Padova, 35020, Legnaro, PD, Italy.
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Chicco D, Heider D, Facchiano A. Editorial: Artificial Intelligence Bioinformatics: Development and Application of Tools for Omics and Inter-Omics Studies. Front Genet 2020; 11:309. [PMID: 32328085 PMCID: PMC7160359 DOI: 10.3389/fgene.2020.00309] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 03/16/2020] [Indexed: 11/13/2022] Open
Affiliation(s)
- Davide Chicco
- Krembil Research Institute, University Health Network, Toronto, ON, Canada
| | - Dominik Heider
- Department of Mathematics and Computer Science, Philipps-University of Marburg, Marburg, Germany
| | - Angelo Facchiano
- Istituto di Scienze dell'Alimentazione, Consiglio Nazionale delle Ricerche (CNR), Avellino, Italy
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Fleck JL, Pavel AB, Cassandras CG. A pan-cancer analysis of progression mechanisms and drug sensitivity in cancer cell lines. Mol Omics 2019; 15:399-405. [PMID: 31570905 DOI: 10.1039/c9mo00119k] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Biomarker discovery involves identifying genetic abnormalities within a tumor. However, one of the main challenges in defining such therapeutic targets is accounting for the molecular heterogeneity of cancer. By integrating somatic mutation and gene expression data from hundreds of heterogeneous cell lines from the Cancer Cell Line Encyclopedia (CCLE), we identify sequences of genetic events that may help explain common patterns of oncogenesis across 22 tumor types, and evaluate the general effect of late-stage mutations on drug sensitivity and resistance mechanisms. Through gene enrichment analysis, we find several cancer-specific and immune pathways that are significantly enriched in each of our three proposed phases of cancer progression. By further analyzing the drug activity area associated with compounds that target the BRAF oncogene, a known predictor of drug sensitivity for several compounds used in cancer treatment, we verify that the acquisition of new driver mutations interferes with the targeted drug mechanism, meaning that cells without late-stage mutations generally respond better to drugs.
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Affiliation(s)
- Julia L Fleck
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rua Marques de Sao Vicente, 225, Rio de Janeiro, Brazil.
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Fan S, Tang J, Tian Q, Wu C. A robust fuzzy rule based integrative feature selection strategy for gene expression data in TCGA. BMC Med Genomics 2019; 12:14. [PMID: 30704464 PMCID: PMC6357346 DOI: 10.1186/s12920-018-0451-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Lots of researches have been conducted in the selection of gene signatures that could distinguish the cancer patients from the normal. However, it is still an open question on how to extract the robust gene features. METHODS In this work, a gene signature selection strategy for TCGA data was proposed by integrating the gene expression data, the methylation data and the prior knowledge about cancer biomarkers. Different from the traditional integration method, the expanded 450 K methylation data were applied instead of the original 450 K array data, and the reported biomarkers were weighted in the feature selection. Fuzzy rule based classification method and cross validation strategy were applied in the model construction for performance evaluation. RESULTS Our selected gene features showed prediction accuracy close to 100% in the cross validation with fuzzy rule based classification model on 6 cancers from TCGA. The cross validation performance of our proposed model is similar to other integrative models or RNA-seq only model, while the prediction performance on independent data is obviously better than other 5 models. The gene signatures extracted with our fuzzy rule based integrative feature selection strategy were more robust, and had the potential to get better prediction results. CONCLUSION The results indicated that the integration of expanded methylation data would cover more genes, and had greater capacity to retrieve the signature genes compared with the original 450 K methylation data. Also, the integration of the reported biomarkers was a promising way to improve the performance. PTCHD3 gene was selected as a discriminating gene in 3 out of the 6 cancers, which suggested that it might play important role in the cancer risk and would be worthy for the intensive investigation.
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Affiliation(s)
- Shicai Fan
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731 Sichuan China
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 611731 Sichuan China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012 China
| | - Jianxiong Tang
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731 Sichuan China
| | - Qi Tian
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731 Sichuan China
| | - Chunguo Wu
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012 China
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Zhang K, Geng W, Zhang S. Network-based logistic regression integration method for biomarker identification. BMC SYSTEMS BIOLOGY 2018; 12:135. [PMID: 30598085 PMCID: PMC6311907 DOI: 10.1186/s12918-018-0657-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Background Many mathematical and statistical models and algorithms have been proposed to do biomarker identification in recent years. However, the biomarkers inferred from different datasets suffer a lack of reproducibilities due to the heterogeneity of the data generated from different platforms or laboratories. This motivates us to develop robust biomarker identification methods by integrating multiple datasets. Methods In this paper, we developed an integrative method for classification based on logistic regression. Different constant terms are set in the logistic regression model to measure the heterogeneity of the samples. By minimizing the differences of the constant terms within the same dataset, both the homogeneity within the same dataset and the heterogeneity in multiple datasets can be kept. The model is formulated as an optimization problem with a network penalty measuring the differences of the constant terms. The L1 penalty, elastic penalty and network related penalties are added to the objective function for the biomarker discovery purpose. Algorithms based on proximal Newton method are proposed to solve the optimization problem. Results We first applied the proposed method to the simulated datasets. Both the AUC of the prediction and the biomarker identification accuracy are improved. We then applied the method to two breast cancer gene expression datasets. By integrating both datasets, the prediction AUC is improved over directly merging the datasets and MetaLasso. And it’s comparable to the best AUC when doing biomarker identification in an individual dataset. The identified biomarkers using network related penalty for variables were further analyzed. Meaningful subnetworks enriched by breast cancer were identified. Conclusion A network-based integrative logistic regression model is proposed in the paper. It improves both the prediction and biomarker identification accuracy. Electronic supplementary material The online version of this article (10.1186/s12918-018-0657-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ke Zhang
- School of Mathematical Sciences, Fudan University, No.220 Handan Road, Shanghai, 200433, China
| | - Wei Geng
- School of Mathematical Sciences, Fudan University, No.220 Handan Road, Shanghai, 200433, China
| | - Shuqin Zhang
- Center for Computational Systems Biology, Shanghai Key Laboratory for Contemporary Applied Mathematics, School of Mathematical Sciences, Fudan University, No.220 Handan Road, Shanghai, 200433, China.
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Eidem HR, Steenwyk JL, Wisecaver JH, Capra JA, Abbot P, Rokas A. integRATE: a desirability-based data integration framework for the prioritization of candidate genes across heterogeneous omics and its application to preterm birth. BMC Med Genomics 2018; 11:107. [PMID: 30453955 PMCID: PMC6245874 DOI: 10.1186/s12920-018-0426-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Accepted: 11/07/2018] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The integration of high-quality, genome-wide analyses offers a robust approach to elucidating genetic factors involved in complex human diseases. Even though several methods exist to integrate heterogeneous omics data, most biologists still manually select candidate genes by examining the intersection of lists of candidates stemming from analyses of different types of omics data that have been generated by imposing hard (strict) thresholds on quantitative variables, such as P-values and fold changes, increasing the chance of missing potentially important candidates. METHODS To better facilitate the unbiased integration of heterogeneous omics data collected from diverse platforms and samples, we propose a desirability function framework for identifying candidate genes with strong evidence across data types as targets for follow-up functional analysis. Our approach is targeted towards disease systems with sparse, heterogeneous omics data, so we tested it on one such pathology: spontaneous preterm birth (sPTB). RESULTS We developed the software integRATE, which uses desirability functions to rank genes both within and across studies, identifying well-supported candidate genes according to the cumulative weight of biological evidence rather than based on imposition of hard thresholds of key variables. Integrating 10 sPTB omics studies identified both genes in pathways previously suspected to be involved in sPTB as well as novel genes never before linked to this syndrome. integRATE is available as an R package on GitHub ( https://github.com/haleyeidem/integRATE ). CONCLUSIONS Desirability-based data integration is a solution most applicable in biological research areas where omics data is especially heterogeneous and sparse, allowing for the prioritization of candidate genes that can be used to inform more targeted downstream functional analyses.
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Affiliation(s)
- Haley R. Eidem
- Department of Biological Sciences, Vanderbilt University, Nashville, TN USA
| | - Jacob L. Steenwyk
- Department of Biological Sciences, Vanderbilt University, Nashville, TN USA
| | - Jennifer H. Wisecaver
- Department of Biological Sciences, Vanderbilt University, Nashville, TN USA
- Department of Biochemistry, Purdue University, West Lafayette, IN USA
| | - John A. Capra
- Department of Biological Sciences, Vanderbilt University, Nashville, TN USA
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN USA
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN USA
| | - Patrick Abbot
- Department of Biological Sciences, Vanderbilt University, Nashville, TN USA
| | - Antonis Rokas
- Department of Biological Sciences, Vanderbilt University, Nashville, TN USA
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN USA
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN USA
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14
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Deng K, Zhang F, Song W, Zhao W, Rong Z, Cai Y, Xu H, Lu M, Wang W, Li A, Hou Y, Li Z, Li K. Identification of pathway-based recurrence-associated signatures in optimally debulked patients with serous ovarian cancer. J Cell Biochem 2018; 119:8564-8573. [PMID: 30126000 DOI: 10.1002/jcb.27098] [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: 12/18/2017] [Accepted: 04/26/2018] [Indexed: 11/06/2022]
Abstract
Serous ovarian cancer (SOC) is the most common form of the histological subtype of epithelial ovarian cancer, with the worst clinical outcome. Despite improvements in surgery and chemotherapy, most patients with SOC experience recurrence within 12-18 months of first-line treatment. Current studies are unable to robustly predict the recurrence of SOC, and more accurate predictive models are urgently required. We have, therefore, developed a novel pathway-structured model to predict the recurrence of SOC. We trained the model on a set of 333 patients and validated it in 3 diversified validation datasets of 403 patients. Genes significantly associated with recurrence within each pathway were identified using a Cox proportional hazards model based on LASSO estimation in the training dataset. Next, a pathway-structured scoring matrix was obtained after computation of the prognostic score for each pathway by fitting to the Cox proportional hazards model. With the pathway-structure scoring matrix as an input, the pathway-based recurrent signatures were identified using the Cox proportional hazards model based on LASSO estimation and the significant pathway-based signatures were externally validated in 3 independent datasets. Meanwhile, our pathway-structured model was compared with a commonly used gene-based model. Our results revealed that our 12 pathway-based signatures successfully predicted the recurrence of SOC with high accuracy in the training dataset and in the 3 validation datasets. Moreover, our pathway-structured model was superior to the gene-based model in 4 datasets. The pathways selected in our study will provide new insights into the pathogenesis and clinical treatments of SOC.
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Affiliation(s)
- Kui Deng
- Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Fan Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Wei Song
- Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Weiwei Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Zhiwei Rong
- Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Yuqing Cai
- Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Huan Xu
- Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Mingliang Lu
- Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Wenjie Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Ang Li
- Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Yan Hou
- Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Zhenzi Li
- Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Kang Li
- Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
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15
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Zeng ISL, Lumley T. Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science). Bioinform Biol Insights 2018; 12:1177932218759292. [PMID: 29497285 PMCID: PMC5824897 DOI: 10.1177/1177932218759292] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2017] [Accepted: 01/24/2018] [Indexed: 12/14/2022] Open
Abstract
Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.
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Affiliation(s)
- Irene Sui Lan Zeng
- Department of Statistics, Faculty of Science, The University of Auckland, Auckland, New Zealand
| | - Thomas Lumley
- Department of Statistics, Faculty of Science, The University of Auckland, Auckland, New Zealand
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16
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Fernandes MS, Sanches JM, Seruca R. Targeting the PI3K Signalling as a Therapeutic Strategy in Colorectal Cancer. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1110:35-53. [PMID: 30623365 DOI: 10.1007/978-3-030-02771-1_4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Colorectal cancer (CRC) remains one of the leading causes of cancer mortality worldwide. Regarded as a heterogeneous disease, a number of biomarkers have been proposed to help in the stratification of CRC patients and to enable the selection of the best therapy for each patient towards personalized therapy. However, although the molecular mechanisms underlying the development of CRC have been elucidated, the therapeutic strategies available for these patients are still quite limited. Thus, over the last few years, a multitude of novel targets and therapeutic strategies have emerged focusing on deregulated molecules and pathways that are implicated in cell growth and survival. Particularly relevant in CRC are the activating mutations in the oncogene PIK3CA that frequently occur in concomitancy with KRAS and BRAF mutations and that lead to deregulation of the major signalling pathways PI3K and MAPK, downstream of EGFR. This review focus on the importance of the PI3K signalling in CRC development, on the current knowledge of PI3K inhibition as a therapeutic approach in CRC and on the implications PI3K signalling molecules may have as potential biomarkers and as new targets for directed therapies in CRC patients.
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Affiliation(s)
- Maria Sofia Fernandes
- Institute for Systems and Robotics (ISR), Instituto Superior Técnico (IST), Lisboa, Portugal
- Instituto de Investigação e Inovação em Saúde (i3S), University of Porto, Porto, Portugal
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Porto, Portugal
| | - João Miguel Sanches
- Institute for Systems and Robotics (ISR), Instituto Superior Técnico (IST), Lisboa, Portugal
| | - Raquel Seruca
- Instituto de Investigação e Inovação em Saúde (i3S), University of Porto, Porto, Portugal.
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Porto, Portugal.
- Faculty of Medicine, University of Porto, Porto, Portugal.
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17
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Genetic load makes cancer cells more sensitive to common drugs: evidence from Cancer Cell Line Encyclopedia. Sci Rep 2017; 7:1938. [PMID: 28512298 PMCID: PMC5434051 DOI: 10.1038/s41598-017-02178-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Accepted: 04/07/2017] [Indexed: 12/16/2022] Open
Abstract
Genetic alterations initiate tumors and enable the evolution of drug resistance. The pro-cancer view of mutations is however incomplete, and several studies show that mutational load can reduce tumor fitness. Given its negative effect, genetic load should make tumors more sensitive to anticancer drugs. Here, we test this hypothesis across all major types of cancer from the Cancer Cell Line Encyclopedia, which provides genetic and expression data of 496 cell lines together with their response to 24 common anticancer drugs. We found that the efficacy of 9 out of 24 drugs showed significant association with genetic load in a pan-cancer analysis. The associations for some tissue-drug combinations were remarkably strong, with genetic load explaining up to 83% of the variance in the drug response. Overall, the role of genetic load depended on both the drug and the tissue type with 10 tissues being particularly vulnerable to genetic load. We also identified changes in gene expression associated with increased genetic load, which included cell-cycle checkpoints, DNA damage and apoptosis. Our results show that genetic load is an important component of tumor fitness and can predict drug sensitivity. Beyond being a biomarker, genetic load might be a new, unexplored vulnerability of cancer.
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18
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Ozerov IV, Lezhnina KV, Izumchenko E, Artemov AV, Medintsev S, Vanhaelen Q, Aliper A, Vijg J, Osipov AN, Labat I, West MD, Buzdin A, Cantor CR, Nikolsky Y, Borisov N, Irincheeva I, Khokhlovich E, Sidransky D, Camargo ML, Zhavoronkov A. In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development. Nat Commun 2016; 7:13427. [PMID: 27848968 PMCID: PMC5116087 DOI: 10.1038/ncomms13427] [Citation(s) in RCA: 90] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2016] [Accepted: 10/03/2016] [Indexed: 01/02/2023] Open
Abstract
Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy.
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Affiliation(s)
- Ivan V Ozerov
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, B301, 1101 33rd Street, Baltimore, Maryland 21218, USA
| | - Ksenia V Lezhnina
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, B301, 1101 33rd Street, Baltimore, Maryland 21218, USA
| | - Evgeny Izumchenko
- The Johns Hopkins University, School of Medicine, Department of Otolaryngology, Head and Neck Cancer Research, 1550 Orleans Street, Baltimore, Maryland 21231, USA
| | - Artem V Artemov
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, B301, 1101 33rd Street, Baltimore, Maryland 21218, USA
| | - Sergey Medintsev
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, B301, 1101 33rd Street, Baltimore, Maryland 21218, USA
| | - Quentin Vanhaelen
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, B301, 1101 33rd Street, Baltimore, Maryland 21218, USA
| | - Alexander Aliper
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, B301, 1101 33rd Street, Baltimore, Maryland 21218, USA.,Laboratory of Bioinformatics, D. Rogachev Federal Research and Clinical Center for Pediatric Hematology, Oncology and Immunology, Samory Mashela 1, Moscow 117997, Russia
| | - Jan Vijg
- Department of Genetics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, New York 10461, USA
| | - Andreyan N Osipov
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, B301, 1101 33rd Street, Baltimore, Maryland 21218, USA.,Laboratory of Bioinformatics, D. Rogachev Federal Research and Clinical Center for Pediatric Hematology, Oncology and Immunology, Samory Mashela 1, Moscow 117997, Russia
| | - Ivan Labat
- BioTime, Inc., 1010 Atlantic Avenue, Alameda, California 94501, USA
| | - Michael D West
- BioTime, Inc., 1010 Atlantic Avenue, Alameda, California 94501, USA
| | - Anton Buzdin
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, B301, 1101 33rd Street, Baltimore, Maryland 21218, USA.,Laboratory of Bioinformatics, D. Rogachev Federal Research and Clinical Center for Pediatric Hematology, Oncology and Immunology, Samory Mashela 1, Moscow 117997, Russia.,National Research Centre 'Kurchatov Institute', Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, 1, Akademika Kurchatova square, Moscow 123182, Russia
| | - Charles R Cantor
- Boston University, Department of Biomedical Engineering, 44 Cummington Street, Boston, Massachusetts 02215, USA
| | - Yuri Nikolsky
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, B301, 1101 33rd Street, Baltimore, Maryland 21218, USA.,Skolkovo Foundation, 5 Nobelya street, Skolkovo Innovation Centre, Mozhajskij region, Moscow 143026, Russia
| | - Nikolay Borisov
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, B301, 1101 33rd Street, Baltimore, Maryland 21218, USA.,Laboratory of Bioinformatics, D. Rogachev Federal Research and Clinical Center for Pediatric Hematology, Oncology and Immunology, Samory Mashela 1, Moscow 117997, Russia.,National Research Centre 'Kurchatov Institute', Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, 1, Akademika Kurchatova square, Moscow 123182, Russia
| | - Irina Irincheeva
- Nutrition and Metabolic Health group, Nestlé Institute of Health Sciences, CH-1015 Lausanne, Switzerland
| | - Edward Khokhlovich
- Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA
| | - David Sidransky
- The Johns Hopkins University, School of Medicine, Department of Otolaryngology, Head and Neck Cancer Research, 1550 Orleans Street, Baltimore, Maryland 21231, USA
| | - Miguel Luiz Camargo
- Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA
| | - Alex Zhavoronkov
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, B301, 1101 33rd Street, Baltimore, Maryland 21218, USA.,Laboratory of Bioinformatics, D. Rogachev Federal Research and Clinical Center for Pediatric Hematology, Oncology and Immunology, Samory Mashela 1, Moscow 117997, Russia.,The Biogerontology Research Foundation, 2354 Chynoweth House, Trevissome Park, Truro TR4 8UN, UK
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