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Heterogeneity of the Treatment Effect with PARP Inhibitors in Metastatic Castration-resistant Prostate cancer: A Living Interactive Systematic Review and Meta-analysis

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European Urology 2025年第6期87卷 626-640页

作者： Naqvi, Syed Arsalan Ahmed Riaz, Irbaz Bin Bibi, Arifa Khan, Muhammad Ali Imran, Manal Khakwani, Kaneez Zahra Rubab Raina, Ammad Anjum, Muhammad Umair Cobran, Ewan K. Warner, Jeremy L. Hussain, Syed A. Singh, Parminder Childs, Daniel S. Baca, Sylvan C. Orme, Jacob J. Mateo, Joaquin Agarwal, Neeraj Gillessen, Silke Murad, Mohammad Hassan Sartor, Oliver Bryce, Alan H. Division of Hematology and Oncology Department of Medicine Mayo Clinic Phoenix AZ United States Department of Internal Medicine University of Oklahoma Oklahoma City OK United States Department of Internal Medicine Dow University of Health Sciences Karachi Pakistan Department of Internal Medicine The University of Arizona Tucson AZ United States Department of Internal Medicine Canyon Vista Medical Center Midwestern University Sierra Vista AZ United States Division of Epidemiology Department of Quantitative Health Sciences Mayo Clinic Scottsdale AZ United States Center for Clinical Cancer Informatics and Data Science Legorreta Cancer Center Brown University Providence RI United States Department of Oncology and Metabolism University of Sheffield Sheffield United Kingdom Department of Oncology Mayo Clinic Rochester MN United States Department of Medical Oncology Dana-Farber Cancer Institute Harvard Medical School Boston MA United States Department of Medical Oncology Vall d'Hebron Institute of Oncology Vall d'Hebron Barcelona Hospital Campus Barcelona Spain Division of Medical Oncology Department of Internal Medicine Huntsman Cancer Institute University of Utah Salt Lake City UT United States Department of Oncology Oncology Institute of Southern Switzerland Ente Ospedaliero Cantonale Bellinzona Switzerland Università della Svizzera Italiana Lugano Switzerland Evidence-Based Practice Center Mayo Clinic Rochester MN United States Department of Oncology City of Hope Cancer Center Goodyear AZ United States

Background and objective: Selection of patients harboring mutations in homologous recombination repair (HRR) genes for treatment with a PARP inhibitor (PARPi) is challenging in metastatic castration-resistant prostate cancer (mCRPC). To gain further insight, we quantitatively assessed the differential efficacy of PARPi therapy among patients with mCRPC and different HRR gene mutations. Methods: This living meta-analysis (LMA) was conducted using the Living Interactive Evidence synthesis framework. We included clinical trials assessing PARPi as monotherapy in pretreated mCRPC or in combination with an androgen receptor pathway inhibitor (ARPI) in treatment-naïve patients. Random-effects meta-analyses were performed for a priori subgroups stratified by HRR status, BRCA status, and each gene. Key findings and limitations: This first report for our LMA includes 13 trials (4278 patients). Among patients with pretreated mCRPC receiving PARPi monotherapy, the tumor response rate per 100 person-months was numerically higher for patients with BRCA2 (50% prostate-specific antigen response [PSA50%] 3.3;objective response rate [ORR] 3.3), BRCA1 (PSA50% 1.2;ORR 2.0), or PALB2 (PSA50% 3.3;ORR 1.4) alterations than for patients with ATM (PSA50% 0.4;ORR 0.3), CDK12 (PSA50% 0.2;ORR 0.2), or CHEK2 (PSA50% 1.0;ORR 0.7) alterations. Among patients receiving PARPi + ARPI, a significant radiographic progression-free survival benefit was observed in those with BRCA (hazard ratio [HR] 0.28, 95% confidence interval [CI] 0.13–0.62) or CDK12 (HR 0.58, 95% CI 0.35–0.95) alterations, but not in patients with PALB2 (HR 0.53, 95% CI 0.21–1.32), ATM (HR 0.93, 95% CI 0.57–1.53), or CHEK2 (HR 0.92, 95% CI 0.53-1.61) alterations. An overall survival benefit was observed for patients with BRCA alterations (HR 0.47, 95% CI 0.31–0.71) after adjustment for crossover and subsequent therapy, but not for patients with PALB2 (HR 0.33, 95% CI 0.10–1.16), ATM (HR 0.97, 95% CI 0.57–1.67), CDK12 (HR 0.80, 95% CI

关键词： Androgen receptor pathway inhibitors Gene-specific therapy Homologous recombination repair Living interactive meta-analysis Metastatic castration-resistant prostate cancer PARP inhibitors

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A SSIM Guided cGAN Architecture For clinically Driven Generative Image Synthesis of Multiplexed Spatial Proteomics Channels

A SSIM Guided cGAN Architecture For Clinically Driven Genera...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Jillur Rahman Saurav Mohammad Sadegh Nasr Helen H Shang Paul Koomey Michael Robben Manfred Huber Jon Weidanz Bríd Ryan Eytan Ruppin Peng Jiang Jacob M. Luber Department of Computer Science University of Texas at Arlington Multi-Interprofessional Center for Health Informatics University of Texas at Arlington Division of Internal Medicine Ronald Reagan University of California Los Angeles Medical Center Department of Bioengineering University of Texas at Arlington Department of Kinesiology University of Texas at Arlington Laboratory of Human Carcinogenesis Center for Cancer Research National Cancer Institute National Institutes of Health Cancer Data Science Laboratory Center for Cancer Research National Cancer Institute National Institutes of Health

Histopathological work in clinical labs often relies on immunostaining of proteins, which can be time-consuming and costly. Multiplexed spatial proteomics imaging can increase interpretive power, but current methods cannot cost-effectively sample the entire proteomic retinue important to diagnostic medicine or drug development. To address this challenge, we developed a conditional generative adversarial network (cGAN) that performs image-to-image (i2i) synthesis to generate accurate biomarker channels in multiplexed spatial proteomics images 1 . We approached this problem as missing biomarker expression generation, where we assumed that a given n-channel multiplexed image has p channels (biomarkers) present and q channels (biomarkers) absent, with p+q=n, and we aimed to generate the missing q channels. To improve accuracy, we selected p and q channels based on their structural similarity, as measured by a structural similarity index measure (SSIM). We demonstrated the effectiveness of our approach using spatial proteomic data from the Human BioMolecular Atlas Program (HuBMAP) 2 , which we used to generate spatial representations of missing proteins through a U-Net based image synthesis pipeline. Channels were hierarchically clustered by SSIM to obtain the minimal set needed to recapitulate the underlying biology represented by the spatial landscape of proteins. We also assessed the scalability of our algorithm using regression slope analysis, which showed that it can generate increasing numbers of missing biomarkers in multiplexed spatial proteomics images. Furthermore, we validated our approach by generating a new spatial proteomics data set from human lung adenocarcinoma tissue sections and showed that our model could accurately synthesize the missing channels from this new data set. Overall, our approach provides a cost-effective and time-efficient alternative to traditional immunostaining methods for generating missing biomarker channels, while also increasing t

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Population simulation modeling of disparities in US breast cancer mortality

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Journal of the National cancer Institute - Monographs 2023年第62期2023卷 178-187页

作者： Mandelblatt, Jeanne S. Schechter, Clyde B. Stout, Natasha K. Huang, Hui Stein, Sarah Hunter Chapman, Christina Trentham-Dietz, Amy Jayasekera, Jinani Gangnon, Ronald E. Hampton, John M. Abraham, Linn O’Meara, Ellen S. Sheppard, Vanessa B. Lee, Sandra J. Department of Oncology Georgetown University Medical Center and Cancer Prevention and Control Program at Georgetown Lombardi Comprehensive Cancer Center WA United States Departments of Family and Social Medicine and of Epidemiology and Population Health Albert Einstein College of Medicine Bronx NY United States Department of Population Sciences Harvard Medical School and Harvard Pilgrim Health Care Institute Boston MA United States Department of Data Science Dana-Farber Cancer Institute and Harvard Medical School Boston MA United States Department of Radiation Oncology Section of Health Services Research Baylor College of Medicine and Health Policy Quality and Informatics Program at the Center for Innovations in Quality Effectiveness and Safety Michael E. DeBakey VA Medical Center Houston TX United States Department of Population Health Sciences and Carbone Cancer Center University of Wisconsin-Madison Madison WI United States Health Equity and Decision Sciences Research Lab National Institute on Minority Health and Health Disparities Intramural Research Program National Institutes of Health Bethesda MD United States Departments of Population Health Sciences and of Biostatistics and Medical Informatics and Carbone Cancer Center University of Wisconsin-Madison Madison WI United States Kaiser Permanente Washington Health Research Institute Seattle WA United States Department of Health Behavior and Policy and Massey Cancer Center Virginia Commonwealth University Richmond VA United States

BACKGROUND: Populations of African American or Black women have persistently higher breast cancer mortality than the overall US population, despite having slightly lower age-adjusted incidence. METHODS: Three cancer Intervention and Surveillance Modeling Network simulation teams modeled cancer mortality disparities between Black female populations and the overall US population. Model inputs used racial group-specific data from clinical trials, national registries, nationally representative surveys, and observational studies. Analyses began with cancer mortality in the overall population and sequentially replaced parameters for Black populations to quantify the percentage of modeled breast cancer morality disparities attributable to differences in demographics, incidence, access to screening and treatment, and variation in tumor biology and response to therapy. RESULTS: Results were similar across the 3 models. In 2019, racial differences in incidence and competing mortality accounted for a net ‒1% of mortality disparities, while tumor subtype and stage distributions accounted for a mean of 20% (range across models = 13%-24%), and screening accounted for a mean of 3% (range = 3%-4%) of the modeled mortality disparities. Treatment parameters accounted for the majority of modeled mortality disparities: mean = 17% (range = 16%-19%) for treatment initiation and mean = 61% (range = 57%-63%) for real-world effectiveness. CONCLUSION: Our model results suggest that changes in policies that target improvements in treatment access could increase breast cancer equity. The findings also highlight that efforts must extend beyond policies targeting equity in treatment initiation to include high-quality treatment completion. This research will facilitate future modeling to test the effects of different specific policy changes on mortality disparities. © The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please email: ***

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powerROC: An Interactive Web Tool for Sample Size Calculation in Assessing Models' Discriminative Abilities

arXiv

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arXiv 2025年

作者： Grolleau, François Tibshirani, Robert Chen, Jonathan H. Stanford Center for Biomedical Informatics Research Stanford University United States Department of Statistics Stanford University United States Department of Biomedical Data Science Stanford University United States Stanford Clinical Excellence Research Center Stanford University United States Division of Hospital Medicine Stanford University United States

Rigorous external validation is crucial for assessing the generalizability of prediction models, particularly by evaluating their discrimination (AUROC) on new data. This often involves comparing a new model's AUROC to that of an established reference model. However, many studies rely on arbitrary rules of thumb for sample size calculations, often resulting in underpowered analyses and unreliable conclusions. This paper reviews crucial concepts for accurate sample size determination in AUROC-based external validation studies, making the theory and practice more accessible to researchers and clinicians. We introduce powerROC, an open-source web tool designed to simplify these calculations, enabling both the evaluation of a single model and the comparison of two models. The tool offers guidance on selecting target precision levels and employs flexible approaches, leveraging either pilot data or user-defined probability distributions. We illustrate powerROC's utility through a case study on hospital mortality prediction using the MIMIC database. Copyright © 2025, The Authors. All rights reserved.

关键词： Prediction models

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Ursodeoxycholic acid inhibits the uptake of cystine through SLC7A11 and impairs de novo synthesis of glutathione

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Journal of Pharmaceutical Analysis 2025年第1期15卷 189-207页

作者： Fu'an Xie Yujia Niu Xiaobing Chen Xu Kong Guangting Yan Aobo Zhuang Xi Li Lanlan Lian Dongmei Qin Quan Zhang Ruyi Zhang Kunrong Yang Xiaogang Xia Kun Chen Mengmeng Xiao Chunkang Yang Ting Wu Ye Shen Chundong Yu Chenghua Luo Shu-Hai Lin Wengang Li Cancer Research Center Xiang'an Hospital of Xiamen UniversitySchool of MedicineXiamen UniversityXiamenFujian361102China State Key Laboratory of Cellular Stress Biology School of Life SciencesFaculty of Medicine and Life SciencesXiamen UniversityXiamenFujian361102China Department of Retroperitoneal Tumor Surgery Peking University International HospitalBeijing102206China School of Public Health Harvard UniversityBostonMA02115USA Department of Laboratory Medicine Xiang'an Hospital of Xiamen UniversityXiamen UniversityXiamenFujian361102China National Institute for Data Science in Health and Medicine Xiamen UniversityXiamenFujian361102China Laboratory of Biochemistry and Molecular Biology Research Department of Clinical LaboratoryFujian Medical University Cancer HospitalFujian Cancer HospitalFuzhou350014China Department of Hepatobiliary Surgery Xiang'an Hospital of Xiamen UniversitySchool of MedicineXiamen UniversityXiamenFujian361102China Department of General Surgery Peking University People's HospitalBeijing100032China Department of Gastrointestinal Surgical Oncology Fujian Medical University Cancer HospitalFujian Cancer HospitalFuzhou350014China Department of Management Jiang Xia Blood Technology Co.Ltd.Shanghai200000China

Ursodeoxycholic acid(UDCA)is a naturally occurring,low-toxicity,and hydrophilic bile acid(BA)in the human body that is converted by intestinal flora using primary *** carrier family 7 member 11(SLC7A11)functions to uptake extracellular cystine in exchange for glutamate,and is highly expressed in a variety of human *** liposarcoma(RLPS)refers to liposarcoma originating from the retroperitoneal *** analysis revealed that UDCA was one of the most significantly downregulated metabolites in sera of RLPS patients compared with healthy *** augmentation of UDCA concentration(≥25 mg/mL)demonstrated a suppressive effect on the proliferation of liposarcoma cells.[15N2]-cystine and[13C5]-glutamine isotope tracing revealed that UDCA impairs cystine uptake and glutathione(GSH)***,UDCA binds to the cystine transporter SLC7A11 to inhibit cystine uptake and impair GSH de novo synthesis,leading to reactive oxygen species(ROS)accumulation and mitochondrial oxidative ***,UDCA can promote the anti-cancer effects of ferroptosis inducers(Erastin,RSL3),the murine double minute 2(MDM2)inhibitors(Nutlin 3a,RG7112),cyclin dependent kinase 4(CDK4)inhibitor(Abemaciclib),and glutaminase inhibitor(CB839).Together,UDCA functions as a cystine exchange factor that binds to SLC7A11 for antitumor activity,and SLC7A11 is not only a new transporter for BA but also a clinically applicable target for *** importantly,in combination with other antitumor chemotherapy or physiotherapy treatments,UDCA may provide effective and promising treatment strategies for RLPS or other types of tumors in a ROS-dependent manner.

关键词： UDCA SLC7A11 Cystine GSH Oxidative stress RLPS

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Immunostimulant nanomodulator boosts antitumor immune response in triple negative breast cancer by synergism of vessel normalization and photothermal therapy

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Nano Research 2023年第8期16卷 11149-11163页

作者： Hao Jiang Yilang He Jia Zhao Ruimin Chang Hailun He Tan Li Xingyu Zhang Bo Shu Wenxuan Zhang Huaiyu Wang Jing Liu Shubing Zhang Yuetao Zhao Department of Biomedical Informatics School of Life SciencesCentral South UniversityChangsha 410013China Department of Biochemistry and Molecular Biology Hunan Province Key Laboratory of Basic and Applied HematologySchool of Life SciencesCentral South UniversityChangsha 410013China Department of Cell Biology School of Life SciencesCentral South UniversityChangsha 410013China The Department of Dermatology Xiangya Clinical Research Center for Cancer ImmunotherapyHunan Key Laboratory of Skin Cancer and PsoriasisHunan Engineering Research Center of Skin Health and DiseaseXiangya HospitalCentral South UniversityNo.87 Xiangya RoadChangsha 410008China Department of General Surgery The Second Xiangya HospitalCentral South UniversityChangsha 410011China State Key Laboratory of Natural and Biomimetic Drugs Department of Chemical BiologySchool of Pharmaceutical SciencesPeking University Health Science CenterBeijing 100191China Institute of Biomedicine and Biotechnology Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Tumor vascular dysfunction and immune suppression predict poor outcomes of tumor *** of photothermal therapy(PTT)and vessel normalization with tumor immunotherapy is promising to augment antitumor ***,we develop a potential immunostimulatory nanomodulator for treatment of triple-negative breast cancer(TNBC)treatment via synergism of PTT,vessel normalization,and priming of tumoral suppressive immune microenvironment by blocking transforming growth factor-β(TGF-β)*** nanomodulator,namely Vac@Apt@BPs,is developed by conjugation of TGF-βinhibitor Vactosertib(Vac)and nucleolin-recognizing aptamer(Apt)on the surface of black phosphorus nanoparticles(BPs).Vac@Apt@BPs show good accumulation in TNBC via aptamer-induced active targeting of *** the blockade of TGF-βsignaling,Vac@Apt@BPs effectively inhibit the formation of tumor neovascular,and normalize the vessels to recover vascular integrity and alleviate the hypoxia *** with the tumor eradication and immunogenic cell death via PTT,robust immune response was boosted by promoted maturation of dendritic cells,suppression of regulatory T cells,and stimulation of effective T *** synergistic therapeutic strategy potentially suppresses the growth of TNBC in mice.

关键词： vessel normalization photothermal therapy black phosphorus triple negative breast cancer transforming growth factor-β

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Using Design science Research to Orchestrate Developing the iCanViz cancer Care data Analytics Research Project

Using Design Science Research to Orchestrate Developing the ...

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Intelligent Computing, Communication, Networking and Services (ICCNS), International Conference on

作者： Reema Abu Al Rub Serin Atiani Mahmoud Mohammed Faten F Kharbat Mohammad Odeh Data Science Department Princess Sumaya University for Technology Amman Jordan College of Engineering Al Ain University Abu Dhabi UAE Cancer Care Informatics King Hussein Cancer Center Jordan University of the West of England UK Global Academy for Digital Health UK

This paper aims to reflect on the extent to which the Design science Research Method (DSRM) is aligned with orchestrating the development of cancer care data analytics research projects using a data strategy-enabled use-case-driven approach. A fit-for-purpose DSRM research framework has been designed to guide the research design and project-manage the iCanViz research project. The iCanViz project is an intelligent cancer data analytics visualizer aligned to multiple cancer sites, cancer incidences, risk correlations, survivals, and deaths in the Arab world within certain years of reported cancer incidences per cancer site. The DSRM approach has been deemed closely aligned with the iCanViz research project. This is primarily due to its ability to articulate and incorporate cancer care data strategy use-cases as agile software development increments within the iCanViz DSRM research framework. Also, the adaptation of the iCanViz DSRM research framework has contributed to enhancing the even distribution and concurrent execution of cancer care data analytics tasks across the software development and research team. Furthermore, the iCanViz DSRM research design has proven effective in designing research frameworks that bridge the gap between the world of cancer care and the creation of cancer care data analytics systems. With a small research team, the iCanViz project achieved this within a relatively short time frame of less than a year.

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Deriving data Modeling Requirements Using Topic Modelling in cancer Care Context: The Case of the iCanViz Project

Deriving Data Modeling Requirements Using Topic Modelling in...

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Intelligent Computing, Communication, Networking and Services (ICCNS), International Conference on

作者： Mahmoud Mohammed Serin Atiani Reema Abu Al Rub Faten F Kharbat Mohammad Odeh Data Science Department Princess Sumaya University for Technology Amman Jordan College of Engineering Al Ain University Abu Dhabi UAE Cancer Care Informatics King Hussein Cancer Center Jordan University of the West of England UK Global Academy for Digital Health UK

The Development of a data model for the iCanViz cancer navigator, a smart cancer incidence analytics project, proved difficult due to the large body of resources to be analyzed. In this paper, we propose an approach to design, develop, and evaluate the data models for projects with large bodies of resources. The proposed approach utilizes topic modeling for data requirement discovery to analyze the semantic structures of thousands of documents and published articles and derive a reference conceptual data model for data strategy use-cases (DS-UCs) for cancer survival and deaths. Using this approach, we are able to process and analyze a great number of resources and map their automatically discovered topics into data model entities. Using the knowledge of a field expert, the derived model was evaluated and adjusted to arrive at a final model that captures the data requirements of the studied DS-UCs. The developed approach proved to be an effective benchmark for data modeling of a complex domain like cancer care, for which the validation of a domain expert is shown to be a critical element.

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Immunophenotype of CAR T cells and apheresis products predicts response in CD22 CAR T cell trial for B cell acute lymphoblastic leukemia

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Molecular Therapy 2025年

作者： Dreyzin, Alexandra Shao, Lipei Cai, Yihua Han, Kyu Lee Prochazkova, Michaela Gertz, Michael Yates, Bonnie Shi, Rongye Martin, Kathryn Taylor, Naomi Highfill, Steven Andresson, Thorkell Stroncek, David Jin, Ping Shah, Nirali N. Center for Cellular Engineering National Institutes of Health Clinical Center Bethesda 20892 MD United States Pediatric Oncology Branch National Cancer Institute National Institutes of Health Bethesda 20892 MD United States Cancer Data Science Laboratory National Cancer Institute National Institutes of Health Bethesda 20892 MD United States Center for Cancer Research Protein Characterization Laboratory National Cancer Institute National Institutes of Health Frederick 21702 MD United States

Although CAR T cell therapy is increasingly used to treat relapsed B cell acute lymphoblastic leukemia (ALL), 20%–30% of patients do not respond, and few clinical predictors of response have been established, especially in the pediatric population. A deeper analysis of CAR T cell infusion products, along with the apheresis product used as the starting material for CAR T cell manufacturing, provides valuable insights for predicting clinical outcomes. We analyzed infusion products and CD4/8-selected T cell starting materials from pediatric and young adult patients on a single-center study with relapsed/refractory B cell ALL who were undergoing treatment with CD22 CAR T cells and evaluated differences between T cells from responders and non-responders (NCT023215612). We found that CAR T cells from non-responders had a more differentiated T cell phenotype and overexpressed genes associated with cytotoxicity and exhaustion compared with those of responders. Furthermore, we found that these differences could be tracked back to the apheresis materials prior to CAR T cell manufacturing. Using flow cytometry-based immunophenotypic markers, we developed a scoring system that distinguished non-responders based on T cell phenotype at the time of apheresis. These findings can help inform outcomes for patients and providers as well as provide insights into targeted manufacturing changes to optimize CAR T cell efficacy. © 2025

关键词： apheresis B cell acute lymphoblastic leukemia biomarkers CAR T cells CD22 cellular therapy leukapheresis pediatric leukemia T cell differentiation T cell exhaustion

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A prediction of mutations in infectious viruses using artificial intelligence

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Genomics and informatics 2024年第1期22卷 1-11页

作者： Choi, Won Jong Park, Jongkeun Seong, Do Young Chung, Dae Sun Hong, Dongwan Department of Precision Medicine and Big Data College of Medicine The Catholic University of Korea Seoul 06591 South Korea Department of Medical Informatics The Catholic University of Korea Seoul 06591 South Korea Department of Medical Sciences Graduate Schoolof College of Medicine The Catholic University of Korea Seoul 06591 South Korea Precision Medicine Research Center College of Medicine The Catholic University of Korea Seoul 06591 South Korea Cancer Evolution Research Center College of Medicine The Catholic University of Korea Seoul 06591 South Korea College of Medicine CMC Institute for Basic Medical Science The Catholic University of Korea Seoul 06591 South Korea

Many subtypes of SARS-CoV-2 have emerged since its early stages, with mutations showing regional and racial differences. These mutations significantly affected the infectivity and severity of the virus. This study aimed to predict the mutations that occur during the evolution of SARS-CoV-2 and identify the key characteristics for making these predictions. We collected and organized data on the lineage, date, clade, and mutations of SARS-CoV-2 from publicly available databases and processed them to predict the mutations. In addition, we utilized various artificial intelligence models to predict newly emerging mutations and created various training sets based on clade information. Using only mutation information resulted in low performance of the learning models, whereas incorporating clade differentiation resulted in high performance in machine learning models, including XGBoost (accuracy: 0.999). However, mutations fixed in the receptor-binding motif (RBM) region of Omicron resulted in decreased predictive performance. Using these models, we predicted potential mutation positions for 24C, following the recently emerged 24A and 24B clades. We identified a mutation at position Q493 in the RBM region. Our study developed effective artificial intelligence models and characteristics for predicting new mutations in continuously evolving infectious viruses. © The Author(s) 2024.

关键词： Clade Deep learning Machine learning Mutation Prediction SARS-CoV-2

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