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Budennyy S, Kazakov A, Kovtun E, Zhukov L. New drugs and stock market: a machine learning framework for predicting pharma market reaction to clinical trial announcements. Sci Rep 2023; 13:12817. [PMID: 37550410 PMCID: PMC10406841 DOI: 10.1038/s41598-023-39301-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 07/23/2023] [Indexed: 08/09/2023] Open
Abstract
Pharmaceutical companies operate in a strictly regulated and highly risky environment in which a single slip can lead to serious financial implications. Accordingly, the announcements of clinical trial results tend to determine the future course of events, hence being closely monitored by the public. Most works focus on retrospective analysis of announcement impact on company stock prices, bypassing the consideration of the problem in the predictive paradigm. In this work, we aim to close this gap by proposing a framework that allows predicting the numerical values of announcement-induced changes in stock prices. In fact, it is a problem of the impact prediction of the specific event on the corresponding time series. Our framework includes a BERT model for extracting the sentiment polarity of announcements, a Temporal Fusion Transformer for forecasting the expected return, a graph convolution network for capturing event relationships, and gradient boosting for predicting the price change. We operate with one of the biggest FDA (the Food and Drug Administration) datasets, consisting of 5436 clinical trial announcements from 681 companies for the years 2018-2022. During the study, we get several significant outcomes and domain-specific insights. Firstly, we obtain statistical evidence for the clinical result promulgation influence on the public pharma market value. Secondly, we witness inherently different patterns of responses to positive and negative announcements, reflected in a stronger and more pronounced reaction to negative clinical news. Thirdly, we discover two factors that play a crucial role in a predictive framework: (1) the drug portfolio size of the company, indicating the greater susceptibility to an announcement in the case of low diversification among drug products and (2) the announcement network effect, manifesting through an increase in predictive power when exploiting interdependencies of events belonging to the same company or nosology. Finally, we prove the viability of the forecast setting by getting ROC AUC scores predominantly greater than 0.7 for the classification of price change on historical data. We emphasize the transferability and generalizability of the developed framework on other datasets and domains but on the condition of the presence of two key entities: events and the associated time series.
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Affiliation(s)
- Semen Budennyy
- Sber AI Lab, Moscow, Russia.
- Artificial Intelligence Research Institute (AIRI), Moscow, Russia.
| | | | | | - Leonid Zhukov
- Higher School of Economics University, Moscow, Russia
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Neagu AN, Whitham D, Bruno P, Morrissiey H, Darie CA, Darie CC. Omics-Based Investigations of Breast Cancer. Molecules 2023; 28:4768. [PMID: 37375323 DOI: 10.3390/molecules28124768] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 06/08/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
Breast cancer (BC) is characterized by an extensive genotypic and phenotypic heterogeneity. In-depth investigations into the molecular bases of BC phenotypes, carcinogenesis, progression, and metastasis are necessary for accurate diagnoses, prognoses, and therapy assessments in predictive, precision, and personalized oncology. This review discusses both classic as well as several novel omics fields that are involved or should be used in modern BC investigations, which may be integrated as a holistic term, onco-breastomics. Rapid and recent advances in molecular profiling strategies and analytical techniques based on high-throughput sequencing and mass spectrometry (MS) development have generated large-scale multi-omics datasets, mainly emerging from the three "big omics", based on the central dogma of molecular biology: genomics, transcriptomics, and proteomics. Metabolomics-based approaches also reflect the dynamic response of BC cells to genetic modifications. Interactomics promotes a holistic view in BC research by constructing and characterizing protein-protein interaction (PPI) networks that provide a novel hypothesis for the pathophysiological processes involved in BC progression and subtyping. The emergence of new omics- and epiomics-based multidimensional approaches provide opportunities to gain insights into BC heterogeneity and its underlying mechanisms. The three main epiomics fields (epigenomics, epitranscriptomics, and epiproteomics) are focused on the epigenetic DNA changes, RNAs modifications, and posttranslational modifications (PTMs) affecting protein functions for an in-depth understanding of cancer cell proliferation, migration, and invasion. Novel omics fields, such as epichaperomics or epimetabolomics, could investigate the modifications in the interactome induced by stressors and provide PPI changes, as well as in metabolites, as drivers of BC-causing phenotypes. Over the last years, several proteomics-derived omics, such as matrisomics, exosomics, secretomics, kinomics, phosphoproteomics, or immunomics, provided valuable data for a deep understanding of dysregulated pathways in BC cells and their tumor microenvironment (TME) or tumor immune microenvironment (TIMW). Most of these omics datasets are still assessed individually using distinct approches and do not generate the desired and expected global-integrative knowledge with applications in clinical diagnostics. However, several hyphenated omics approaches, such as proteo-genomics, proteo-transcriptomics, and phosphoproteomics-exosomics are useful for the identification of putative BC biomarkers and therapeutic targets. To develop non-invasive diagnostic tests and to discover new biomarkers for BC, classic and novel omics-based strategies allow for significant advances in blood/plasma-based omics. Salivaomics, urinomics, and milkomics appear as integrative omics that may develop a high potential for early and non-invasive diagnoses in BC. Thus, the analysis of the tumor circulome is considered a novel frontier in liquid biopsy. Omics-based investigations have applications in BC modeling, as well as accurate BC classification and subtype characterization. The future in omics-based investigations of BC may be also focused on multi-omics single-cell analyses.
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Affiliation(s)
- Anca-Narcisa Neagu
- Laboratory of Animal Histology, Faculty of Biology, "Alexandru Ioan Cuza" University of Iasi, Carol I Bvd, No. 20A, 700505 Iasi, Romania
| | - Danielle Whitham
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699, USA
| | - Pathea Bruno
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699, USA
| | - Hailey Morrissiey
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699, USA
| | - Celeste A Darie
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699, USA
| | - Costel C Darie
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699, USA
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Shen L, Zhai Y, Pan AX, Zhao Q, Zhou M, Liu J. Development of an integrated and comprehensive clinical trial process management system. BMC Med Inform Decis Mak 2023; 23:61. [PMID: 37024877 PMCID: PMC10078087 DOI: 10.1186/s12911-023-02158-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 03/17/2023] [Indexed: 04/08/2023] Open
Abstract
BACKGROUND The process of initiating and completing clinical drug trials in hospital settings is highly complex, with numerous institutional, technical, and record-keeping barriers. In this study, we independently developed an integrated clinical trial management system (CTMS) designed to comprehensively optimize the process management of clinical trials. The CTMS includes system development methods, efficient integration with external business systems, terminology, and standardization protocols, as well as data security and privacy protection. METHODS The development process proceeded through four stages, including demand analysis and problem collection, system design, system development and testing, system trial operation, and training the whole hospital to operate the system. The integrated CTMS comprises three modules: project approval and review management, clinical trial operations management, and background management modules. These are divided into seven subsystems and 59 internal processes, realizing all the functions necessary to comprehensively perform the process management of clinical trials. Efficient data integration is realized through extract-transform-load, message queue, and remote procedure call services with external systems such as the hospital information system (HIS), laboratory information system (LIS), electronic medical record (EMR), and clinical data repository (CDR). Data security is ensured by adopting corresponding policies for data storage and data access. Privacy protection complies with laws and regulations and de-identifies sensitive patient information. RESULTS The integrated CTMS was successfully developed in September 2015 and updated to version 4.2.5 in March 2021. During this period, 1388 study projects were accepted, 43,051 electronic data stored, and 12,144 subjects recruited in the First Affiliated Hospital, Zhejiang University School of Medicine. CONCLUSION The developed integrated CTMS realizes the data management of the entire clinical trials process, providing basic conditions for the efficient, high-quality, and standardized operation of clinical trials.
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Affiliation(s)
- Liang Shen
- Department of Information Technology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
| | - You Zhai
- Research Center for Clinical Pharmacy, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang Provincial Key Laboratory for Drug Evaluation and Clinical Research, Hangzhou, 310003, China
| | - AXiang Pan
- Department of Information Technology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
| | - Qingwei Zhao
- Research Center for Clinical Pharmacy, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang Provincial Key Laboratory for Drug Evaluation and Clinical Research, Hangzhou, 310003, China
| | - Min Zhou
- Department of Information Technology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China.
| | - Jian Liu
- Research Center for Clinical Pharmacy, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang Provincial Key Laboratory for Drug Evaluation and Clinical Research, Hangzhou, 310003, China.
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Roach JC, Hara J, Fridman D, Lovejoy JC, Jade K, Heim L, Romansik R, Swietlikowski A, Phillips S, Rapozo MK, Shay MA, Fischer D, Funk C, Dill L, Brant‐Zawadzki M, Hood L, Shankle WR. The Coaching for Cognition in Alzheimer's (COCOA) trial: Study design. A&D Transl Res & Clin Interv 2022; 8:e12318. [PMID: 35910672 PMCID: PMC9322829 DOI: 10.1002/trc2.12318] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 05/07/2022] [Accepted: 05/13/2022] [Indexed: 11/15/2022]
Abstract
Comprehensive treatment of Alzheimer's disease (AD) requires not only pharmacologic treatment but also management of existing medical conditions and lifestyle modifications including diet, cognitive training, and exercise. We present the design and methodology for the Coaching for Cognition in Alzheimer's (COCOA) trial. AD and other dementias result from the interplay of multiple interacting dysfunctional biological systems. Monotherapies have had limited success. More interventional studies are needed to test the effectiveness of multimodal multi‐domain therapies for dementia prevention and treatment. Multimodal therapies use multiple interventions to address multiple systemic causes and potentiators of cognitive decline and functional loss; they can be personalized, as different sets of etiologies and systems responsive to therapy may be present in different individuals. COCOA is designed to test the hypothesis that coached multimodal interventions beneficially alter the trajectory of cognitive decline for individuals on the spectrum of AD and related dementias (ADRD). COCOA is a two‐arm prospective randomized controlled trial (RCT). COCOA collects psychometric, clinical, lifestyle, genomic, proteomic, metabolomic, and microbiome data at multiple timepoints across 2 years for each participant. These data enable systems biology analyses. One arm receives standard of care and generic healthy aging recommendations. The other arm receives standard of care and personalized data‐driven remote coaching. The primary outcome measure is the Memory Performance Index (MPI), a measure of cognition. The MPI is a summary statistic of the MCI Screen (MCIS). Secondary outcome measures include the Functional Assessment Staging Test (FAST), a measure of function. COCOA began enrollment in January 2018. We hypothesize that multimodal interventions will ameliorate cognitive decline and that data‐driven health coaching will increase compliance, assist in personalizing multimodal interventions, and improve outcomes for patients, particularly for those in the early stages of the AD spectrum.
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Affiliation(s)
| | - Junko Hara
- Pickup Family Neurosciences Institute Hoag Memorial Hospital Presbyterian Newport Beach California USA
| | - Deborah Fridman
- Hoag Center for Research and Education Hoag Memorial Hospital Presbyterian Newport Beach California USA
| | | | - Kathleen Jade
- Institute for Systems Biology Seattle Washington USA
| | - Laura Heim
- Hoag Center for Research and Education Hoag Memorial Hospital Presbyterian Newport Beach California USA
| | - Rachel Romansik
- Hoag Center for Research and Education Hoag Memorial Hospital Presbyterian Newport Beach California USA
| | - Adrienne Swietlikowski
- Hoag Center for Research and Education Hoag Memorial Hospital Presbyterian Newport Beach California USA
| | - Sheree Phillips
- Hoag Center for Research and Education Hoag Memorial Hospital Presbyterian Newport Beach California USA
| | | | - Maria A. Shay
- Institute for Systems Biology Seattle Washington USA
| | - Dan Fischer
- Institute for Systems Biology Seattle Washington USA
- Oregon Health & Science University Portland Oregon USA
| | - Cory Funk
- Institute for Systems Biology Seattle Washington USA
| | - Lauren Dill
- Pickup Family Neurosciences Institute Hoag Memorial Hospital Presbyterian Newport Beach California USA
- VA Long Beach Healthcare System Long Beach California USA
| | - Michael Brant‐Zawadzki
- Pickup Family Neurosciences Institute Hoag Memorial Hospital Presbyterian Newport Beach California USA
| | - Leroy Hood
- Institute for Systems Biology Seattle Washington USA
- Providence St. Joseph Health Renton Washington USA
| | - William R. Shankle
- Pickup Family Neurosciences Institute Hoag Memorial Hospital Presbyterian Newport Beach California USA
- Department of Cognitive Sciences University of California Irvine California USA
- Shankle Clinic Newport Beach California USA
- EMBIC Corporation Newport Beach California USA
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Ajnakina O, Das T, Lally J, Di Forti M, Pariante CM, Marques TR, Mondelli V, David AS, Murray RM, Palaniyappan L, Dazzan P. Structural Covariance of Cortical Gyrification at Illness Onset in Treatment Resistance: A Longitudinal Study of First-Episode Psychoses. Schizophr Bull 2021; 47:1729-1739. [PMID: 33851203 PMCID: PMC8530394 DOI: 10.1093/schbul/sbab035] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Treatment resistance (TR) in patients with first-episode psychosis (FEP) is a major cause of disability and functional impairment, yet mechanisms underlying this severe disorder are poorly understood. As one view is that TR has neurodevelopmental roots, we investigated whether its emergence relates to disruptions in synchronized cortical maturation quantified using gyrification-based connectomes. Seventy patients with FEP evaluated at their first presentation to psychiatric services were followed up using clinical records for 4 years; of these, 17 (24.3%) met the definition of TR and 53 (75.7%) remained non-TR at 4 years. Structural MRI images were obtained within 5 weeks from first exposure to antipsychotics. Local gyrification indices were computed for 148 contiguous cortical regions using FreeSurfer; each subject's contribution to group-based structural covariance was quantified using a jack-knife procedure, providing a single deviation matrix for each subject. The latter was used to derive topological properties that were compared between TR and non-TR patients using a Functional Data Analysis approach. Compared to the non-TR patients, TR patients showed a significant reduction in small-worldness (Hedges's g = 2.09, P < .001) and a reduced clustering coefficient (Hedges's g = 1.07, P < .001) with increased length (Hedges's g = -2.17, P < .001), indicating a disruption in the organizing principles of cortical folding. The positive symptom burden was higher in patients with more pronounced small-worldness (r = .41, P = .001) across the entire sample. The trajectory of synchronized cortical development inferred from baseline MRI-based structural covariance highlights the possibility of identifying patients at high-risk of TR prospectively, based on individualized gyrification-based connectomes.
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Affiliation(s)
- Olesya Ajnakina
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, London, UK
| | - Tushar Das
- Departments of Psychiatry & Medical Biophysics, Robarts Research Institute & Lawson Health Research Institute, University of Western Ontario, London, Ontario, Canada
| | - John Lally
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
- Department of Psychiatry, St Vincent’s Hospital Fairview, Dublin, Ireland
- Department of Psychiatry, Mater Misericordiae University Hospital, Dublin, Ireland
| | - Marta Di Forti
- MRC Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Carmine M Pariante
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
| | - Tiago Reis Marques
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Psychiatric Imaging Group, MRC London Institute of Medical Sciences (LMS), Hammersmith Hospital, Imperial College London, London, UK
| | - Valeria Mondelli
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
| | - Anthony S David
- Institute of Mental Health, University College London, London, UK
| | - Robin M Murray
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Department of Psychiatry, Experimental Biomedicine and Clinical Neuroscience, University of Palermo, Palermo, Italy
| | - Lena Palaniyappan
- Departments of Psychiatry & Medical Biophysics, Robarts Research Institute & Lawson Health Research Institute, University of Western Ontario, London, Ontario, Canada
| | - Paola Dazzan
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, UK
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Nussinov R, Jang H, Nir G, Tsai CJ, Cheng F. A new precision medicine initiative at the dawn of exascale computing. Signal Transduct Target Ther 2021; 6:3. [PMID: 33402669 PMCID: PMC7785737 DOI: 10.1038/s41392-020-00420-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 10/27/2020] [Accepted: 10/30/2020] [Indexed: 12/14/2022] Open
Abstract
Which signaling pathway and protein to select to mitigate the patient's expected drug resistance? The number of possibilities facing the physician is massive, and the drug combination should fit the patient status. Here, we briefly review current approaches and data and map an innovative patient-specific strategy to forecast drug resistance targets that centers on parallel (or redundant) proliferation pathways in specialized cells. It considers the availability of each protein in each pathway in the specific cell, its activating mutations, and the chromatin accessibility of its encoding gene. The construction of the resulting Proliferation Pathway Network Atlas will harness the emerging exascale computing and advanced artificial intelligence (AI) methods for therapeutic development. Merging the resulting set of targets, pathways, and proteins, with current strategies will augment the choice for the attending physicians to thwart resistance.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD, 21702, USA.
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel.
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD, 21702, USA
| | - Guy Nir
- Department of Genetics, Harvard Medical School, Boston, MA, 02115, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA
- Department of Biochemistry & Molecular Biology, Department of Neuroscience, Cell Biology and Anatomy, Sealy Center for Structural Biology and Molecular Biophysics, University of Texas Medical Branch, Galveston, TX, 77555, USA
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD, 21702, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44106, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA
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Abstract
Breast cancer incidence is increasing worldwide with more than 600,000 deaths reported in 2018 alone. In current practice treatment options for breast cancer patients consists of surgery, chemotherapy, radiotherapy or targeting of classical markers of breast cancer subtype: estrogen receptor (ER) and HER2. However, these treatments fail to prevent recurrence and metastasis. Improved understanding of breast cancer and metastasis biology will help uncover novel biomarkers and therapeutic opportunities to improve patient stratification and treatment. We will first provide an overview of current methods and models used to study breast cancer biology, focusing on 2D and 3D cell culture, including organoids, and on in vivo models such as the MMTV mouse model and patient-derived xenografts (PDX). Next, genomic, transcriptomic, and proteomic approaches and their integration will be considered in the context of breast cancer susceptibility, breast cancer drivers, and therapeutic response and resistance to treatment. Finally, we will discuss how 'Omics datasets in combination with traditional breast cancer models are useful for generating insights into breast cancer biology, for suggesting individual treatments in precision oncology, and for creating data repositories to undergo further meta-analysis. System biology has the potential to catalyze the next great leap forward in treatment options for breast cancer patients.
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Affiliation(s)
- Joseph Parsons
- Division of Molecular and Cellular Function, School of Biological Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Manchester, United Kingdom
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Manchester, United Kingdom
| | - Chiara Francavilla
- Division of Molecular and Cellular Function, School of Biological Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Manchester, United Kingdom
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Nussinov R, Jang H, Tsai CJ, Cheng F. Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers. PLoS Comput Biol 2019; 15:e1006658. [PMID: 30921324 DOI: 10.1371/journal.pcbi.1006658] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor’s genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses—all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.
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Hernandez-martinez J, Sánchez-reyes R, De la Garza-salazar JG, Arrieta O. Onco-omics Approaches and Applications in Clinical Trials for Cancer Patients. Translational Research and Onco-Omics Applications in the Era of Cancer Personal Genomics 2019. [DOI: 10.1007/978-3-030-24100-1_5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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