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DTSTART;TZID=America/New_York:20230509T130000
DTEND;TZID=America/New_York:20230509T140000
DTSTAMP:20260408T230918
CREATED:20230410T125338Z
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SUMMARY:Model-robust and Efficient Covariate Adjustment for Cluster-randomized Experiments
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday May 9\, 2023\n1:00PM Eastern Time \nJoin the Zoom. \nFan Li\, PhD\nAssistant Professor of Biostatistics\, Yale School of Public Health \nCluster-randomized experiments are increasingly used to evaluate interventions in routine practice conditions\, and researchers often adopt model-based methods with covariate adjustment in the statistical analyses. However\, the validity of model-based covariate adjustment is unclear when the working models are misspecified\, leading to ambiguity of estimands and risk of bias. In this article\, we first adapt two conventional model-based methods\, generalized estimating equations and linear mixed models\, with weighted g-computation to achieve robust inference for cluster-average and individual-average treatment effects. Furthermore\, we propose an efficient estimator for each estimand that allows for flexible covariate adjustment and additionally addresses cluster size variation dependent on treatment assignment and other cluster characteristics. Such cluster size variations often occur post-randomization and\, if ignored\, can lead to bias of model-based estimators. For our proposed estimator\, we prove that when the nuisance functions are consistently estimated by machine learning algorithms\, the estimator is consistent\, asymptotically normal\, and efficient. When the nuisance functions are estimated via parametric working models\, the estimator is triply-robust. Simulation studies and analyses of three real-world cluster-randomized experiments demonstrate that the proposed methods are superior to existing alternatives.
URL:https://ds.dfci.harvard.edu/event/model-robust-and-efficient-covariate-adjustment-for-cluster-randomized-experiments/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2023/04/fanli-scaled-e1681131195600.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230329T130000
DTEND;TZID=America/New_York:20230329T140000
DTSTAMP:20260408T230918
CREATED:20230120T174649Z
LAST-MODIFIED:20230329T184122Z
UID:3949-1680094800-1680098400@ds.dfci.harvard.edu
SUMMARY:Dimension Reduction of Longitudinal Microbiome Data
DESCRIPTION:Frontiers in Biostatistics Seminar \nWednesday March 29\, 2023\n1:00PM Eastern Time \nPixu Shi\, PhD\nAssistant Professor of Biostatistics & Bioinformatics\nDivision of Integrative Genomics\nDepartment of Biostatistics & Bioinformatics\nDuke University School of Medicine \nAbstract:\nThe analysis of longitudinal microbiome is crucial to the understanding of how microbiome changes over time. It often requires careful maneuver of high dimensional microbial features\, missing samples and varying time points across subjects. Longitudinal microbiome data can often be formatted into a high-dimensional order-3 tensor with three modes representing the subject\, time\, and bacteria respectively. In this talk\, we present functional tensor SVD\, a dimension reduction tool that can uncover sub-population structures in subjects\, compress high-dimensional features into low-dimensional trajectories\, and extract shared temporal patterns among features\, all without imputation of missing samples or rounding of time points. We will demonstrate the robust performance of our method through simulations and multiple case studies.
URL:https://ds.dfci.harvard.edu/event/dimension-reduction-of-longitudinal-microbiome-data/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2023/01/Pixu_profile_square.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230302T130000
DTEND;TZID=America/New_York:20230302T140000
DTSTAMP:20260408T230918
CREATED:20230224T194353Z
LAST-MODIFIED:20230308T124839Z
UID:4088-1677762000-1677765600@ds.dfci.harvard.edu
SUMMARY:Computation of High-Dimensional Penalized Generalized Linear Mixed Models
DESCRIPTION:Thursday March 2\, 2023\n1:00pm ET \nHillary Heiling\nBiostatistics PhD candidate\nUniversity of North Carolina Chapel Hill. \nAdd to calendar \nHillary’s statistical focus has primarily been in cancer-related research\, both through her graduate research assistant role in the Lineberger Comprehensive Cancer Center as well as her personal research applications. In cancer research as well as other biomedical areas\, there are issues with the replicability of results between studies. Hillary will present her work on improving the replicable selection of gene signatures for the prediction of cancer subtypes by combining data across studies and performing variable selection on generalized linear mixed models. Hillary has been able to extend the feasible dimensionality of the application to hundreds of predictors by using a factor model decomposition on the random effects\, which behaves as a dimension reduction technique. Hillary has developed software to perform this task and has published her ‘glmmPen’ R package on CRAN.
URL:https://ds.dfci.harvard.edu/event/computation-of-high-dimensional-penalized-generalized-linear-mixed-models/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2023/02/hillary-e1677267404626.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230214T130000
DTEND;TZID=America/New_York:20230214T140000
DTSTAMP:20260408T230918
CREATED:20221128T194757Z
LAST-MODIFIED:20230307T152329Z
UID:3899-1676379600-1676383200@ds.dfci.harvard.edu
SUMMARY:Using the Case Study of Atezolizumab Development to Rethink Early Phase Oncology Trial Design
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday February 14\, 2023\n1:00PM Eastern Time\nYouTube video \nEmily Zabor\, DrPH\nAssistant Staff Biostatistician\nDepartment of Quantitative Health Sciences at the Cleveland Clinic\, with a joint appointment in the Taussig Cancer Institute\nAssistant Professor of Medicine\, the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-emily-zabor/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2022/11/emily-touched-8-crop-scaled-e1669664843585.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230124T130000
DTEND;TZID=America/New_York:20230124T140000
DTSTAMP:20260408T230918
CREATED:20221128T192916Z
LAST-MODIFIED:20230203T145004Z
UID:3888-1674565200-1674568800@ds.dfci.harvard.edu
SUMMARY:A Nonparametric Bayesian Approach to Use RWD in Clinical Trial Design
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday January 24\, 2023\n1:00PM Eastern Time\nClick to watch the YouTube video. \nPeter Mueller\, PhD\nProfessor\nDepartment of Statistics and Data Sciences\nDepartment of Mathematics\nUniversity of Texas at Austin \n 
URL:https://ds.dfci.harvard.edu/event/a-nonparametric-bayesian-approach-to-use-rwd-in-clinical-trial-design/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2022/11/mueller.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221206T130000
DTEND;TZID=America/New_York:20221206T140000
DTSTAMP:20260408T230918
CREATED:20221013T170456Z
LAST-MODIFIED:20221207T142413Z
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SUMMARY:COVID Data-Driven Policy: NC DHHS's Use of Data for Pandemic Response
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday December 6\, 2022\n1:00PM Eastern Time \nRegister for Zoom link. \nJessie Tenenbaum\, PhD\nshe / her / hers\nChief Data Officer\nNC Department of Health and Human Services
URL:https://ds.dfci.harvard.edu/event/covid-data-driven-policy-nc-dhhss-use-of-data-for-pandemic-response/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2022/10/Tenenbaum-square-copy.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221101T130000
DTEND;TZID=America/New_York:20221101T140000
DTSTAMP:20260408T230918
CREATED:20221013T144710Z
LAST-MODIFIED:20221101T194024Z
UID:3821-1667307600-1667311200@ds.dfci.harvard.edu
SUMMARY:Design and Implementation of Bayesian Adaptive Phase I Trials in Oncology using the DEDUCE Application
DESCRIPTION:Frontiers in Biostatistics Seminar Series \nTuesday November 1\, 2022\n1:00PM Eastern Time \nRegister for in-person or virtual attendance. \nWendy London\, PhD\nAssociate Professor\, Harvard Medical School\nDirector of Biostatistics\, Boston Children’s Hospital \nClement Ma\, PhD Assistant Professor\, University of Toronto\nIndependent Scientist\, CAMH
URL:https://ds.dfci.harvard.edu/event/design-and-implementation-of-bayesian-adaptive-phase-i-trials-in-oncology-using-the-deduce-application/
LOCATION:Center of Life Sciences\, Room 11081\, 3 Blackfan Circle\, Boston\, MA\, 02215\, United States
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220913T130000
DTEND;TZID=America/New_York:20220913T140000
DTSTAMP:20260408T230918
CREATED:20220822T141425Z
LAST-MODIFIED:20220916T155058Z
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SUMMARY:It’s All Relative: Testing Differential Abundance in Compositional Microbiome Data
DESCRIPTION:Frontiers in Biostatistics Seminar Series \nTuesday September 13\, 2022\n1:00PM Eastern Time \nYijuan Hu\, Ph.D.\nAssociate Professor\nDepartment of Biostatistics and Bioinformatics\nRollins School of Public Health\nEmory University \nRegister for Zoom link \nAbstract: Studies on the human microbiome have revealed that differences in microbial communities are associated with many human disorders such as inflammatory bowel disease\, type II diabetes\, and even Alzheimer’s disease and some cancers. The microbiome is a particularly attractive target for establishing new biomarkers for disease diagnosis and prognosis\, and for developing low-cost\, low-risk interventions. Microbiome data have two unique features. First\, they are compositional\, i.e.\, the total number of sequencing reads per sample is an experimental artifact and only the relative abundance of taxa can be measured. Second\, they are subject to a wide variety of experimental biases (e.g.\, in the process of DNA extraction and PCR amplification) that plague most analyses that directly analyze relative abundance data. These features call for analyses that are based on log-ratio transformation of the relative abundance data. Existing methods often ignore experimental biases\, do not handle the extensive (50-90%) zero count data adequately\, and do not accommodate other complexities in microbiome data (e.g.\, high-dimensionality\, confounding covariates\, and continuous covariates of interest). In this talk\, we present a new logistic-regression-based method that takes into account all of these features of microbiome data for robust testing of differential abundance. Our simulation studies indicate that our method is the only one that universally controls the FDR while at the same time maintaining good power. We illustrate our method by the analysis of a throat microbiome dataset. \n 
URL:https://ds.dfci.harvard.edu/event/its-all-relative-testing-differential-abundance-in-compositional-microbiome-data/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2022/08/hu-photo-small.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220426T130000
DTEND;TZID=America/New_York:20220426T140000
DTSTAMP:20260408T230918
CREATED:20220105T135143Z
LAST-MODIFIED:20220429T134618Z
UID:3248-1650978000-1650981600@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Tree-based Ensembling Strategies for Handling Heterogeneous Data
DESCRIPTION:Maya Ramchandran\nData Scientist\, ZephyrAI \nAbstract: Adapting machine learning algorithms to better handle clustering or other partition structure within training data sets is important across a wide variety of biological applications. We first consider the task of learning prediction models when multiple training studies are available. We present a novel weighting approach  for constructing tree-based ensemble learners in this setting\, showing that incorporating multiple layers of ensembling in the training process by weighting trees increases the robustness of the resulting predictor and achieves superior performance to Random Forest. Next\, we broaden the scope of the problem to consider the effect of ensembling forest-based learners trained on clusters within a single data set with heterogeneity in the distribution of the features. We show that constructing ensembles of forests trained on estimated clusters determined by algorithms such as k-means results in significant improvements in accuracy and generalizability over the traditional Random Forest algorithm. We denote our novel approach as the Cross-Cluster Weighted Forest\, and display its robustness and accuracy across simulations and on cancer molecular profiling and gene expression data sets that are naturally divisible into clusters. Finally\, we provide theoretical support to these empirical observations by asymptotically analyzing linear least squares and random forest regressions under a linear model. In particular\, for random forest regression under fixed dimensional linear models\, our bounds imply a strict benefit of our ensembling strategy over classic Random Forest. \nYouTube Video. \nMaya Ramchandran recently completed her PhD at the Harvard Biostatistics department under the supervision of Dr. Giovanni Parmigiani\, where she developed machine learning ensembling strategies with applications to cancer prediction problems. She holds a BS in Applied Mathematics-Biology from Brown University and a Masters of Music in Violin Performance from the New England Conservatory. She currently works as a data scientist at ZephyrAI\, a biotechnology startup that develops novel drug combination and repurposing treatments for oncology.
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-tree-based-ensembling-strategies-for-handling-heterogeneous-data/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2022/01/1595653826867.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220307T130000
DTEND;TZID=America/New_York:20220307T140000
DTSTAMP:20260408T230918
CREATED:20220301T215910Z
LAST-MODIFIED:20220307T192958Z
UID:3390-1646658000-1646661600@ds.dfci.harvard.edu
SUMMARY:Data Science Seminar: Engineering Protease Activity Sensors For Personalized Detection and Profiling of Cancer
DESCRIPTION:Monday March 7th\, 2022\n1:00PM Eastern Time \nAva Soleimany\, PhD\nSenior Researcher\, Biomedical Machine Learning Group at Microsoft Research\, New England \nAbstract: Precision cancer medicine envisions a world where diagnostic and therapeutic opportunities are intelligently tailored to individual patient needs. Achieving this vision necessitates access to high quality\, accurate\, and individualized information about disease state. Engineered probes that sense disease activity — dynamically and directly within the body — could provide this information by generating signals that functionally measure the state of one’s disease. \nIn this talk\, I will discuss my work in engineering a novel class of nanoscale sensors that directly query tumor microenvironments by measuring the activity of proteases\, enzymes directly involved in all functional hallmarks of cancer. I will share how we can leverage the rich\, functional data generated by these sensors to design and deploy novel\, expressive\, and high-fidelity machine learning models to power individualized diagnostic decision-making and noninvasive monitoring. Finally\, I show how we can engineer sensors to spatially profile protease activity in situ\, demonstrating their ability to discover personalized insights into mechanisms of tumor progression and treatment response and encouraging their translation for precision cancer medicine.
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-engineering-protease-activity-sensors-for-personalized-detection-and-profiling-of-cancer/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2022/03/Ava_Soleimany_Background_small-e1646171916155.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220304T120000
DTEND;TZID=America/New_York:20220304T130000
DTSTAMP:20260408T230918
CREATED:20220223T191012Z
LAST-MODIFIED:20220304T195647Z
UID:3365-1646395200-1646398800@ds.dfci.harvard.edu
SUMMARY:Data Science Seminar: Radiomics for Feature Extraction from Radiological Images
DESCRIPTION:Friday\, March 4\, 2022\n12:00PM Eastern Time \nAni Eloyan\, PhD\nAssistant Professor\nDepartment of Biostatistics\, Brown University \nRegister. \nAbstract: Cancer patients routinely undergo radiological evaluations where images of various modalities including computed tomography\, positron emission tomography\, and magnetic resonance images are collected for diagnosis and for evaluation of disease progression. Tumor characteristics\, often referred to as measures of tumor heterogeneity\, can be computed using these clinical images and used as predictors of disease progression and patient survival. Several approaches to quantifying tumor heterogeneity have been proposed including simple intensity histogram-based measures\, metrics attempting to quantify average distance from a homogeneous surface\, and texture analysis-based methods. I will present a statistical framework for principal manifold estimation for obtaining a smooth estimate of tumor surface. I will describe an estimation procedure for tumor heterogeneity using clustering methods considering the neighborhood topology of the tumors. The proposed approach incorporates the spatial structure of the tumor image using neighborhood summary measures. The results of using the proposed methods to estimate cancer survival using machine learning algorithms will be discussed.
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-radiomics-for-feature-extraction-from-radiological-images/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2022/02/aeloyan_photo.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220301T130000
DTEND;TZID=America/New_York:20220301T140000
DTSTAMP:20260408T230918
CREATED:20220105T134746Z
LAST-MODIFIED:20220304T192258Z
UID:3245-1646139600-1646143200@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Considerations for Extracting Real-World Evidence from Real-World Data
DESCRIPTION:Tuesday\, March 1\, 2022\n1:00pm Eastern Time \nRebecca A. Hubbard\, PhD\nProfessor of Biostatistics\nUniversity of Pennsylvania Perlman School of Medicine \nYouTube Video \nAbstract: Opportunities to use real-world data (RWD)\, including electronic health records (EHR) and medical claims data\, have exploded over the past decade. The Covid-19 pandemic has provided a particularly dramatic illustration of the potential value of RWD for advancing medical research as well as the high-risk of bias inherent to many such studies. Using data sources that were not collected for research purposes comes at a cost\, and naïve use of these data without considering their complexity and imperfect quality can lead to biased inference. While RWD offer the opportunity to generate timely evidence grounded in real-world populations and clinical practice\, issues of information bias and confounding create serious threats to the validity of these studies. The statistician is faced with a quandary: how to effectively utilize RWD to advance research without compromising best practices for principled data analysis. In this talk I will use examples from my research on methods for the analysis of EHR derived-data to illustrate how an understanding of the EHR data generating mechanism can inform selection of appropriate study questions and development and application of statistical methods that minimize the risk of bias. The overarching goal of this presentation is to raise awareness of challenges associated with the analysis of RWD and demonstrate that valid evidence generation can be grounded in an understanding of the scientific context and data generating process. \n  \nDr. Hubbard’s research focuses on the development and application of methods to improve analyses using real world data sources including electronic health records (EHR) and claims data. The data science era demands novel analytic methods to transform the wealth of data created as a byproduct of our digital interactions into valid and generalizable knowledge. Dr. Hubbard’s research emphasizes statistical methods designed to meet this challenge by addressing the messiness and complexity of real world data including informative observation schemes\, phenotyping error\, and error and missingness in confounders. Her methods have been applied to support the advancement of a broad range of research areas through use of EHR and claims data including health services research\, cancer epidemiology\, aging and dementia\, and pharmacoepidemiology. \n 
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-using-ehr-to-accelerate-cancer-outcomes-research-without-leaving-valid-inference-behind/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2022/01/1154_3.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220228T130000
DTEND;TZID=America/New_York:20220228T140000
DTSTAMP:20260408T230918
CREATED:20220217T180736Z
LAST-MODIFIED:20220218T174651Z
UID:3347-1646053200-1646056800@ds.dfci.harvard.edu
SUMMARY:Data Science Seminar: From descriptive to predictive biology via single-cell multiomics
DESCRIPTION:Monday February 28\, 2022\n1:00PM Eastern Time \nGenevieve Stein-O’Brien\nInstructor\, Johns Hopkins University\, School of Medicine\nDepartment of Oncology\, Division of Biostatistics and Bioinformatics;\nDepartment of Neuroscience; and McKusick-Nathans Department of Genomic Medicine\nAssistant Director\, Johns Hopkins University Single Cell Consortium \nRegister. \nAbstract: As the single-cell field races to characterize each cell type\, state\, and behavior\, the complexity of the computational analysis approaches the complexity of the biological systems. Single cell and imaging technologies now enable unprecedented measurements of state transitions in biological systems\, providing high-throughput data that capture tens-of-thousands of measurements on hundreds-of-thousands of samples. Thus\, the definition of cell type and state is evolving to encompass the broad range of biological questions now attainable. To answer these questions requires the development of computational tools for integrated multiomics analysis. Merged with statistical and mathematical models\, these algorithms will be able to forecast future states of biological systems\, going from statistical inferences of phenotypes to time course predictions of the biological systems with dynamic maps. Thus\, systems biology for forecasting biological system dynamics from multiomic data represents the future of cell biology empowering a new generation of technology-driven predictive medicine.
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-from-descriptive-to-predictive-biology-via-single-cell-multiomics/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2022/02/1609946354584.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220224T130000
DTEND;TZID=America/New_York:20220224T140000
DTSTAMP:20260408T230918
CREATED:20220218T134643Z
LAST-MODIFIED:20220218T175729Z
UID:3355-1645707600-1645711200@ds.dfci.harvard.edu
SUMMARY:Data Science Seminar: Spatial meshing for general Bayesian multivariate models
DESCRIPTION:Thursday February 24\, 2022\n1:00PM Eastern Time \nMichele Peruzzi\, PhD\nPostdoctoral Associate\, Department of Statistical Science\, Duke University \nRegister. \nAbstract: In this talk\, I will consider the problem of fitting Bayesian models with spatial random effects to large scale multivariate multi-type data from satellite imaging\, land-based weather and air quality sensors\, and citizen science\, with diverse applications in the environmental sciences\, ecology\, and public health. \nIn these contexts\, the goal of quantifying spatial associations via random effects in Bayesian hierarchical models can be achieved by letting a Gaussian process (GP) characterize dependence in space\, time\, and across outcomes. GPs have desirable properties and lead to extremely flexible models\, which are able to accurately quantify uncertainties. Unfortunately\, GPs are notoriously poor performers in large data settings. While the literature on scalable GPs has primarily focused on their well-known bottlenecks in models of univariate continuous outcomes\, I consider the more challenging hurdles to efficient computations facing latent models of multivariate non-Gaussian outcomes.\nI introduce spatial meshing and manifold preconditioning as tools for efficient computations of multivariate Bayesian models of spatially referenced non-Gaussian data. First\, I outline spatial meshing as a tool for building scalable processes using patterned directed acyclic graphs on partitioned spatial domains. Then\, I present manifold preconditioning as a novel Langevin method for superior sampling performance with non-Gaussian multivariate data that are common in studying species’ communities. \nIn addition to these main topics\, I discuss additional strategies for improving Markov-chain Monte Carlo performance\, concluding with applications showcasing the flexibility of the proposed methodologies. All presented methods are implemented in the high performance R package ‘meshed’\, available on CRAN.
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-spatial-meshing-for-general-bayesian-multivariate-models/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2022/02/thumb_image_1811412.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220215T130000
DTEND;TZID=America/New_York:20220215T140000
DTSTAMP:20260408T230918
CREATED:20220208T194759Z
LAST-MODIFIED:20220215T200828Z
UID:3322-1644930000-1644933600@ds.dfci.harvard.edu
SUMMARY:Data Science Seminar: End-to-end AI for Screening Mammography
DESCRIPTION:Tuesday February 15\, 2022\n1:00PM Eastern Time \nWilliam Lotter\, PhD\nVice President of Machine Learning\, RadNet\, Inc.\nChief Technology Officer & Co-Founder\, DeepHealth\, Inc. \nRegister. \nScreening mammography has been estimated to reduce breast cancer mortality by 20-40%\, but significant opportunities remain for improving access and overall quality. Artificial intelligence (AI) has the potential to deliver these improvements\, but developing clinically-effective AI presents additional challenges spanning development through clinical integration. In this talk\, I will present an AI algorithm that addresses the “needle-in-a-haystack” nature of mammography while efficiently using labeled training data and enabling localization-based explainability. I will then detail the algorithm’s performance\, providing evidence of generalization across populations and an ability to aid in earlier cancer detection. Finally\, I will discuss our efforts in achieving regulatory clearance and large-scale clinical deployment.
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-end-to-end-ai-for-screening-mammography/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2022/02/bill-lotter.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220208T130000
DTEND;TZID=America/New_York:20220208T140000
DTSTAMP:20260408T230918
CREATED:20210614T165138Z
LAST-MODIFIED:20220215T201001Z
UID:2802-1644325200-1644328800@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Early Phase Design Considerations for Oncology Drug Development in the Era of Immunotherapy and Targeted Agents
DESCRIPTION:Tuesday\, February 8\, 2022\n1:00pm Eastern Time \nYouTube Video \nElizabeth Garrett-Mayer\, PhD\, FSCT\nVice President\nCenter for Research and Analytics (CENTRA) \nDr. Garrett-Mayer joined ASCO in 2017 as CENTRA’s Division Director for Biostatistics and Research Data Governance and became CENTRA’s first Vice President in 2022. CENTRA leads ASCO’s research efforts\, including the TAPUR Study\, ASCO’s COVID-19 Registry\, and research projects aimed at increasing minority enrollment and expanding eligibility criteria in clinical trials. Prior to joining ASCO\, she served on the faculty in the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins in the Department of Oncology\, and then joined the faculty of the Medical University of South Carolina (MUSC) and established the Biostatistics Shared Resource at the Hollings Cancer Center (HCC). \nShe earned her bachelor’s degree from Bowdoin College in Math and Economics\, and a PhD in Biostatistics from Johns Hopkins Bloomberg School of Public Health. Her publication record includes more than 280 peer-reviewed publications\, primarily in cancer research and research methods. She has also been a member of numerous NIH grant review committees\, Data Safety Monitoring Boards for NIH-supported clinical trials and has served on the editorial board of three peer-reviewed journals (Clinical Trials\, Cancer\, and the Journal of the National Cancer Institute)\, and as faculty on the ASCO/AACR Methods in Clinical Cancer Research Workshop for over a decade. \n  \nAbstract: \nUncertainty about optimality of doses of anti-cancer agents approved in recent years\, including PD-L1 blockade agents and targeted agents\, has led to a movement in the oncology drug development field to reconsider the traditional approach for drug development.  Following in the path of approvals for nivolumab and pembrolizumab\, trial designs for dose finding trials have ballooned from sample sizes in the range of 12-50 to the hundreds or more.  In addition\, post-marketing trials have demonstrated that lower doses of certain drugs may be as efficacious as approved doses.  The has contributed to a renewed focus on ‘dose optimization\,’ causing statisticians\, researchers\, patient advocates and regulators to realize the profound inadequacies of traditional phase I dose finding designs. Trial approaches need to incorporate the adaptive nature of a seamless toxicity-efficacy paradigm\, while maintaining practical aspects of trial implementation\, statistical properties\, endpoint measurement and safety and well-being of patients. This talk will discuss these aspects and provide recent context of drug approvals in oncology.
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-elizabeth-garrett-meyer/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220204T130000
DTEND;TZID=America/New_York:20220204T140000
DTSTAMP:20260408T230918
CREATED:20220202T172751Z
LAST-MODIFIED:20220202T172751Z
UID:3303-1643979600-1643983200@ds.dfci.harvard.edu
SUMMARY:Data Science Seminar: Deciphering Tissue Microenvironment from Next Generation Sequencing Data
DESCRIPTION:Friday February 4\, 2022\n1:00PM Eastern Time \nRegister. \nJian Hu\nPhD Candidate\, Department of Biostatistics\, Epidemiology and Informatics\nUniversity of Pennsylvania \nABSTRACT: The advent of high-throughput next-generation sequencing (NGS) technologies has transformed our understanding of cell biology and human disease. As NGS has been adopted earliest by the scientific community\, its use has now become widespread\, and the technology has improved rapidly. At present\, it is now common for laboratories to assay genome-wide transcriptomes of thousands of cells in a single scRNA-seq experiment. In addition\, technologies that enable the measurement of new information\, for example\, chromatin accessibility\, protein quantification\, and spatial location\, have been developed. In order to take full advantage of the multi-modality information when analyzing NGS data\, new methods are demanded. This seminar will introduce several machine learning algorithms for NGS data analysis with different aims\, including cell type classification\, spatial domain detection\, and tumor microenvironment annotation. \nParts of the talk are based on the following two papers: \n\nhttps://www.nature.com/articles/s42256-020-00233-7 \n\nhttps://www.nature.com/articles/s41592-021-01255-8 \nKEYWORDS: single cell RNA sequencing (scRNA-seq)\, Spatial transcriptomics (ST)\, tumor microenvironment\, machine learning
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-deciphering-tissue-microenvironment-from-next-generation-sequencing-data/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220111T130000
DTEND;TZID=America/New_York:20220111T140000
DTSTAMP:20260408T230918
CREATED:20211130T233743Z
LAST-MODIFIED:20220111T182225Z
UID:3204-1641906000-1641909600@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Studies on COVID-19 and Cancer using National Real-World VA Data
DESCRIPTION:Nathanael Fillmore is the Associate Director for Machine Learning and Advanced Analytics at the VA Boston Healthcare System’s Cooperative Studies Program Informatics Center. He leads a data science team focused on using machine learning and data science methods\, in combination with the VA’s large clinical\, genomic\, and imaging databases\, to generate knowledge and resources that will improve care for Veterans with cancer. He also holds a faculty position at Harvard Medical School and is site director for the VA/NCI Big Data-Scientist Training Enhancement Program. Previously\, he was a post-doctoral fellow at Dana-Farber Cancer Institute and Harvard School of Public Health under the direction of Giovanni Parmigiani and Nikhil Munshi. \nThis seminar was not recorded. 
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-studies-on-covid-19-and-cancer-using-national-real-world-va-data/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211019T130000
DTEND;TZID=America/New_York:20211019T140000
DTSTAMP:20260408T230918
CREATED:20210928T181448Z
LAST-MODIFIED:20211201T165250Z
UID:3058-1634648400-1634652000@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Treatment-free Survival as a Novel Outcome Measure of Immuno-oncology-based Therapy
DESCRIPTION:Tuesday\, October 19\, 2021\n1:00pm Eastern Time \nMeredith Regan\, ScD\nAssociate Professor\nDepartment of Data Science\, Dana-Farber Cancer Institute\,\nHarvard Medical School \nTreatment-free Survival as a Novel Outcome Measure of Immuno-oncology-based Therapy \nYouTube Link
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-meredith-regan/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210914T130000
DTEND;TZID=America/New_York:20210914T140000
DTSTAMP:20260408T230918
CREATED:20210614T164652Z
LAST-MODIFIED:20211021T134349Z
UID:2797-1631624400-1631628000@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: COVID Vaccine Efficacy Trial Designs\, Open Questions and Statistical Challenges
DESCRIPTION:Tuesday September 14\, 2021\n1:00PM Eastern Time \nHolly Janes\, Ph.D.\nProfessor\, Vaccine and Infectious Disease Division\, Fred Hutchinson Cancer Research Center\nProfessor\, Public Health Sciences Division\, Fred Hutchinson Cancer Research Center \nYouTube video now available.
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-holly-janes/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210608T130000
DTEND;TZID=America/New_York:20210608T140000
DTSTAMP:20260408T230918
CREATED:20210108T131835Z
LAST-MODIFIED:20211021T134536Z
UID:2398-1623157200-1623160800@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: The Use of External Control Data for Predictions and Interim Analyses in Clinical Trials
DESCRIPTION:June 8\, 2021\n1:00PM \nLorenzo Trippa\, PhD\nAssociate Professor\nDepartment of Biostatistics\, Harvard T.H. Chan School of Public Health\nDepartment of Data Science\, Dana-Farber Cancer Institute \nThe Use of External Control Data for Predictions and Interim Analyses in Clinical Trials \nYouTube video now available.
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-lorenzo-trippa/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210511T130000
DTEND;TZID=America/New_York:20210511T140000
DTSTAMP:20260408T230918
CREATED:20200921T180944Z
LAST-MODIFIED:20210511T180948Z
UID:2138-1620738000-1620741600@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Single-Cell RNA-Seq Data Analysis Via a Regularized Zero-Inflated Mixture Model Framework
DESCRIPTION:Frontiers in Biostatistics Seminar\nMay 11\, 2021\n1:00PM \nJianhua Hu\, PhD\nProfessor\, Biostatistics (in Medicine and in the Herbert Irving Comprehensive Cancer Center)\nDirector\, Cancer Biostatistics Program\nColumbia University \nRegister at: http://bit.ly/FIBMay21 \nAbstract: Applications of single-cell RNA sequencing in various biomedical research areas have been blooming. This new technology provides unprecedented opportunities to study disease heterogeneity at the cellular level. However\, unique characteristics of scRNA-seq data\, including large dimensionality\, high dropout rates\, and possibly batch effects\, bring great difficulty into the analysis of such data. Not appropriately addressing these issues obstructs true scientific discovery. Herein\, we propose a unified Regularized Zero-inflated Mixture Model framework designed for scRNA-seq data (RZiMM-scRNA) to simultaneously detect cell subgroups and identify gene differential expression based on a developed importance score\, accounting for both dropouts and batch effects. We conduct extensive empirical investigation to demonstrate the promise of RZiMM-scRNA in comparison to several popular methods\, including K-means and Hierarchical clustering. \nThis seminar was not recorded as the research presented has not been published yet. 
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-jianhua-hu/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210309T130000
DTEND;TZID=America/New_York:20210309T140000
DTSTAMP:20260408T230918
CREATED:20200921T180427Z
LAST-MODIFIED:20210511T181203Z
UID:2133-1615294800-1615298400@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Distributed Statistical Learning and Inference in EHR and Other Healthcare Datasets
DESCRIPTION:Frontiers in Biostatistics Seminar\nMarch 9\, 2021\n1:00PM \nRui Duan\, PhD\nAssistant Professor of Biostatistics\nHarvard TH Chan School of Public Health \nDistributed Statistical Learning and Inference in EHR and Other Healthcare Datasets  \nAbstract: The growth of availability and variety of healthcare data sources has provided unique opportunities for data integration and evidence synthesis\, which can potentially accelerate knowledge discovery and enable better clinical decision making.  However\, many practical and technical challenges\, such as data privacy\, high-dimensionality and heterogeneity across different datasets\, remain to be addressed. In this talk\, I will introduce several methods for effective and efficient integration of electronic health records and other healthcare datasets. Specifically\, we develop communication-efficient distributed algorithms for jointly analyzing multiple datasets without the need of sharing patient-level data. Our algorithms are able to account for heterogeneity across different datasets. We provide theoretical guarantees for the performance of our algorithms\, and examples of implementing the algorithms to real-world clinical research networks. \nYouTube Link: https://www.youtube.com/watch?v=IscQ3ruxl1o
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-rui-duan/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210209T130000
DTEND;TZID=America/New_York:20210209T140000
DTSTAMP:20260408T230918
CREATED:20200921T175912Z
LAST-MODIFIED:20210511T181321Z
UID:2129-1612875600-1612879200@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Group Sequential Design Assuming Delayed Benefit
DESCRIPTION:February 9\, 2021\n1:00PM ET \nKeaven Anderson\, PhD\nScientific AVP\, Methodology Research\, Biostatistics at Merck \nGroup Sequential Design Assuming Delayed Benefit \nAbstract: We consider an asymptotic approach to design of group sequential trials with a potentially delayed effects. Logrank\, weighted logrank tests and combination tests are of primary interest\, but we also consider restricted mean survival. The asymptotic approach allows both quick derivation of study design properties that are also easily verified using simulation. We rely heavily on work done by Tsiatis\, 1981 published while he was at the HSPH. The impact of a potential delay in treatment effect on timing of analyses\, study boundaries and sample size are demonstrated. The value of a robust design that is well powered under a variety of assumptions is emphasized. Open source software is provided for implementation. Given the current regulatory climate\, logrank testing may still be preferred for these trials\, but we hope that efficiencies and Type I error control associated with alternatives may make other options more acceptable for future consideration. \nYouTube Link: https://www.youtube.com/watch?v=DhwZOX5uMKU
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-keaven-anderson/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210112T130000
DTEND;TZID=America/New_York:20210112T140000
DTSTAMP:20260408T230918
CREATED:20200921T175311Z
LAST-MODIFIED:20210112T190625Z
UID:2123-1610456400-1610460000@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Statistical Modeling and Adjustment for Sampling Biases
DESCRIPTION:Frontiers in Biostatistics Seminar\nJanuary 12\, 2021\n1:00PM \nJing Ning\, PhD\nAssociate Professor. Department of Biostatistics\nDivision of Quantitative Sciences\nThe University of Texas M.D. Anderson Cancer Center \nRegister at: https://bit.ly/FIBJan12 \nAbstract: Bias sampling mechanisms are commonly encountered in applications where the subjects in a target population are not given an equal chance to be selected\, either accidentally\, by natural circumstances\, or intentionally by design. Statistical methods not properly accounting for such a challenge often lead to invalid inferences. For example\, evidence combined from published studies may lead to overly optimistic conclusions due to publication bias\, and the well-known length bias can cause the screening to appear to be more successful than it really is. In this talk\, I will present our recent work to adjust the sampling biases in diverse applications such as the survivorship bias in prevalent cohort\, the self-reporting bias in longitudinal analysis and the publication bias in meta-analysis.
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-jing-ning/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201214T150000
DTEND;TZID=America/New_York:20201214T163000
DTSTAMP:20260408T230918
CREATED:20201130T190048Z
LAST-MODIFIED:20201215T114236Z
UID:2341-1607958000-1607963400@ds.dfci.harvard.edu
SUMMARY:DF/HCC Cancer Data Science Program &Harvard Chan Bioinformatics CoreJoint Symposium on scRNAseq Methodology
DESCRIPTION:Monday\, December 14\, 3:00-4:30 PM ET  \nRSVP https://bit.ly/CDSBioDec14 \nSpeakers: \n\nAedin Culhane\, Senior Research Scientist\, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health\nIsabella Grabski\, PhD Student in Biostatistics\, Harvard University\nProbabilistic gene barcodes identify cell-types in single-cell RNA-sequencing data\nShannan Ho Sui\, Senior Research Scientist\, Harvard T.H. Chan School of Public Health\nRadhika S Khetani\, Research Scientist\, Harvard T.H. Chan School of Public Health\nPeter Kharchenko\, Associate Professor of Biomedical Informatics\, Harvard Medical School\nX. Shirley Liu\, Professor\, Harvard T.H. Chan School of Public Health\nAnalysis of Single-Cell Data to Model Tumor and Immune Gene Regulation\nLuca Pinello\, Associate Professor of Pathology\, Harvard Medical School and Mass General Cancer Center\nTrajectory inference and visualization of single cell data
URL:https://ds.dfci.harvard.edu/event/df-hcc-cancer-data-science-program-harvard-chan-bioinformatics-corejoint-symposium-on-scrnaseq-methodology/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201015T130000
DTEND;TZID=America/New_York:20201015T140000
DTSTAMP:20260408T230918
CREATED:20201008T172433Z
LAST-MODIFIED:20201019T155448Z
UID:2174-1602766800-1602770400@ds.dfci.harvard.edu
SUMMARY:Computational Biology of DNA Repair in Cancer
DESCRIPTION:Data Science Seminar \nOctober 15\, 2020\n1:00PM ET \nDominik Glodzik\, PhD\nRepare Therapeutics \nZoom link: https://bit.ly/DSOct15 \nAbstract: Whole genome sequences contain within them signatures of mutational processes. In particular\, some of the mutation signatures relate to impaired DNA-repair in cancer cells. Accurate measurement of mutation signatures reveals the role of DNA-repair deficiencies in etiology and progression of cancer. \nWe extended the computational methods for analysis of mutation signatures in order to describe patterns of chromosomal rearrangements. In particular\, the rearrangement signatures enable the assessment of proficiency of homologous recombination (HR). HRDetect\, an algorithm we developed\, predicts probability of HR-deficiency\, and is based on holistic portrayal of mutational signatures across different classes of somatic mutations. Around 20% of breast cancers contain signatures of HR-deficiency\, and this group is wider than the group of carriers of BRCA1/2 mutations. By contrast to adult cancers\, pediatric cancers with known DNA-repair defects display variation of mutational signatures\, hinting at tissue-specificity of mutational signatures. Finally\, in the chromosomally unstable cancers\, we identified structural rearrangements\, in coding and non-coding regions\, that can act as cancer drivers. Altogether\, these results indicate that computational assessment of DNA-repair capacity of tumor cells is now possible. The methods will be crucial to understanding of the DNA-repair mechanisms and tissue-specificity of mutational processes. \nBio: Dominik Glodzik received his PhD in Computational Biology from the University of Edinburgh\, and held a postdoctoral position at Wellcome Trust Sanger Institute\, before moving to a staff scientist position at Memorial Sloan Kettering Cancer Center. Currently he is a Principal Bioinformatician at Repare Therapeutics in Cambridge.
URL:https://ds.dfci.harvard.edu/event/computational-biology-of-dna-repair-in-cancer/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201013T130000
DTEND;TZID=America/New_York:20201013T140000
DTSTAMP:20260408T230918
CREATED:20200430T165238Z
LAST-MODIFIED:20201109T234833Z
UID:894-1602594000-1602597600@ds.dfci.harvard.edu
SUMMARY:Constructing Confidence Interval for RMST under Group Sequential Setting
DESCRIPTION:Frontiers in Biostatistics Seminar\nOctober 13\, 2020\n1:00PM \nLu Tian\, PhD\nAssociate Professor of Biomedical Data Science in the School of Medicine\nStanford University \nIt is appealing to compared survival distributions based on restricted mean survival time (RMST)\, since it generates a clinically interpretable summary of the treatment effect\, which can be estimated nonparametrically without assuming restrictive model assumptions such as the proportional hazards assumption. However\, there are special challenges in designing and analyzing group sequential study based on RMST\, because the truncation timepoint of the RMST in the interim analysis often differs from that in the final analysis. A valid test controls the unconditional type one error has been developed in the past. However\, there is no appropriate statistical procedure for constructing the confidence interval for the treatment effect measured by a contrast in RMST\, while it is crucial for informative clinical decision making. In this talk\, I will review some important design issues for study based on RMST. I will then discuss how to conduct hypothesis testing and how to construct confidence intervals for the difference RMST in a group sequential setting. Examples and numerical studies will be presented to illustrate the method. \nA recording of this seminar is available on your YouTube Channel.
URL:https://ds.dfci.harvard.edu/event/constructing-confidence-interval-for-rmst-under-group-sequential-setting/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200929T130000
DTEND;TZID=America/New_York:20200929T140000
DTSTAMP:20260408T230918
CREATED:20200922T173720Z
LAST-MODIFIED:20200923T183848Z
UID:2146-1601384400-1601388000@ds.dfci.harvard.edu
SUMMARY:3D Spatial Organization Within Tumors
DESCRIPTION:Data Science Seminar \nSeptember 29\, 2020\n1:00PM ET \nMartin Aryee\, PhD\nAssistant Professor of Pathology\, Harvard Medical School\nAssistant Molecular Pathologist\, Massachusetts General Hospital \nZoom link: https://dfci.zoom.us/j/95524743149?pwd=SzN4cjJZUnhsNVl3dXNmZjZ1N3F4QT09 \nAbstract: The spatial organization of biological systems can impart additional functionality beyond that of the individual components. This is true at a range of scales – from cells in a tissue to individual genes and regulatory elements in a single genome. High-throughput assays that permit spatial measurements have advanced greatly in the past decade and revealed oncogenic architectural alterations in tumors at the tissue\, cellular and chromosome levels. Here I will discuss tumor spatial organization in gastrointestinal malignancies at two different scales. First\, I will describe how we used single-cell RNA-Seq and RNA in situ hybridization in pancreatic cancer to identify cell state and tissue architecture changes induced by contact with stromal cancer-associated fibroblasts (CAFs). We analyzed the spatial architecture of cell states in 195 pancreatic tumors\, mapping over 300\,000 individual cancer cells. We were able to classify different types of tumor glands based on their cell-type composition\, and show that these functional units can be used to characterize tumors in ways not evident from analyses of dissociated single cells. Second\, I will discuss findings from a recent study of 3D genome organization within colon cancer cell nuclei. We found oncogenic changes at the level of regulatory chromatin loops and topologically associated domains (TADs)\, but the most surprising and striking change involves a large-scale repackaging of heterochromatin that appears to restrain tumor progression\, representing a failed anti-tumor epigenetic brake.
URL:https://ds.dfci.harvard.edu/event/3d-spatial-organization-within-tumors/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200922T140000
DTEND;TZID=America/New_York:20200922T150000
DTSTAMP:20260408T230918
CREATED:20200915T212158Z
LAST-MODIFIED:20200917T142852Z
UID:2101-1600783200-1600786800@ds.dfci.harvard.edu
SUMMARY:Cancer Development\, Heterogeneity and Dynamics from Premalignancy to Drug Refractory Disease
DESCRIPTION:Data Science Seminar \nSeptember 22\, 2020\n2:00PM ET \nIgnaty Leshchiner\, PhD\nPostdoctoral Fellow\, Harvard Medical School/Brigham and Women’s Hospital \nZoom link: http://bit.ly/DSSept22 \nAbstract: \nReal-time study of tumor emergence and progression in patients will help predict and ultimately change the course of the patient’s disease. This could be achieved by inferring genotypes of heterogeneous cell populations within the tumor\, their fitness\, growth rates\, corresponding expression patterns and drug tolerance states. We have developed a set of computational methods to infer the order of tumor-initiating events and to follow the dynamics and competition of cancer cell populations during disease progression and treatment. The package\, PhylogicNDT\, uses tumor genomic data to reconstruct the process of tumor formation\, natural growth kinetics\, competition and spread of resistance clones. We applied this package to 2\,658 primary cancers to reconstruct developmental trajectories and history of common tumor types in premalignancy and early malignancy state; reconstruct cancer cell populations and growth rates\, fitness and kinetics of individual clones during natural progression of leukemia in vivo; analyze spatial progression of resistance clones and find new resistance mechanisms in a large cohort of rapid autopsy cases. By integrating blood biopsy (ctDNA)\, solid tissue biopsy and autopsy data we show that resistance often emerges in multiple distant metastatic sites simultaneously\, with evidence of multiple resistance mutations present in the blood’s ctDNA at the same time. Finally\, we combine bulk and single cell sequencing data to help identify genetically distinct clones and explain their phenotypic differences. We envision that treatment decisions will improve with better understanding of tumor development\, clonal structure and microenvironment\, and the path tumor takes to become malignant and progress after treatment.
URL:https://ds.dfci.harvard.edu/event/cancer-development-heterogeneity-and-dynamics-from-premalignancy-to-drug-refractory-disease/
CATEGORIES:Seminar
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END:VEVENT
END:VCALENDAR