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開催日時: 2018年9月21日 15:00 ~ 16:00
開催場所: 2号館小講義室
講師: Ann Leen
所属: Associate professor, Center for Cell and Gene Therapy, Baylor College of Medicine
演題: T cell therapy for viruses and cancer

In recent years T cell therapy has emerged as a promising therapeutic for the treatment of both viral infections and cancer. Over the course of this lecture I will report on the outcomes of 75 patients infused with either donor-derived (n=21) or third party “off the shelf” (n=54) virus-specific T cells (VSTs) with activity against 5 common post-transplant viruses (EBV, CMV, Adv, BK, HHV6) that cause significant morbidity and mortality in immunocompromised hematopoietic stem cell transplant recipients. Furthermore, I will provide an update on our ongoing clinical trials using T cells targeting a wide spectrum of tumor-associated antigens [e.g. Survivin, MAGEA4, Synovial sarcoma X (SSX2), WT1, PRAME] expressed by hematological malignancies including Hodgkin and non-Hodgkin Lymphoma, multiple myeloma and leukemia.

世話人: 〇高橋 聡 (分子療法分野)
 四柳 宏 (感染症分野)
開催日時: 2018年10月16日 16:15 ~ 17:00
開催場所: 総合研究棟8階大セミナー室 (入口にて内線75615, 75617 or 75618に電話下さい。)
講師: Claus Thorn Ekstrøm
所属: Professor, Section of Biostatistics, Department of Public Health, University of Copenhagen
演題: Sequential rank agreement methods for comparison of ranked lists

Ranked lists of predictors are common occurrences as part of statistical analyses and they frequently appear in the results section of scientific publications. Often, the aim is to find a consensus set of shared predictors from the top of these ranked lists. Similarly, for large scale *omics-data it can be necessary to identify the importance of SNPs, metabolites, proteins, or expression using different methods of analysis and find the consensus set of predictors across the different analysis methods or under slightly different conditions.
In this work we show how sequential rank agreement can be used to gauge the similarity among ranked lists such that it is possible to make inference about how far the lists agree on the ranking which enables the investigator to make an improved decision on the consensus set of predictors that are of interest. Our method provides both an intuitive interpretation and can be applied to any number of lists even if these are censored. To demonstrate the performance of our approach we show results from a both a simulation study and an application to genomics data, and we illustrate how sequential rank agreement can combine and improve results from both parametric and
non-parametric statistical learning algorithms.

世話人: 〇宮野 悟  (DNA情報解析分野)
 井元 清哉  (健康医療データサイエンス分野)
開催日時: 2018年9月27日 15:30 ~ 16:15
開催場所: 総合研究棟8階大セミナー室 (入口にて内線75615, 75617 or 75618に電話下さい。)
講師: Jiangning Song
所属: Monash Biomedicine Discovery Institute (BDI), Monash University・  
Senior Research Fellow and Group Leader
演題: Harnessing the power of machine-learning techniques to addresssequence classification problems in the era of biomedical big data

Recent advances in high-throughput sequencing have significantly contributed to an ever-increasing gap between the number of gene products (‘proteins’) whose function is well characterized and those for which there is no functional annotation at all. Experimental techniques to determine the protein function are often expensive and time-consuming. Recently, machine-learning (ML) techniques based on statistical learning have provided efficient solutions to challenging problems of sequence classification or functional annotation that were previously considered difficult to address. In this talk, by combining our recent research progress, I will highlight some important developments in the prediction of two representative sequence labeling problems in computational biology, i.e. ‘target substrate labeling’ and ‘active site labeling’, based on the high-dimensional, noisy and redundant information derived from sequences and the 3D structure. I will illustrate how ML methods can extract the predictive power from a variety of features that are derived from different aspects of the data can contribute to the model performance.

世話人: 〇山口 類  (DNA情報解析分野)
 井元 清哉  (健康医療データサイエンス分野)
開催日時: 2018年9月20日 16:00 ~ 17:00
開催場所: 2号館小講義室
講師: 佐々木 敦朗
所属: シンシナティ大学医学部 准教授
演題: GTP代謝リプログラムによる癌の同化作用亢進のメカニズム


世話人: 〇山梨 裕司 (腫瘍抑制分野)
 中西 真 (癌防御シグナル分野)
開催日時: 2018年10月16日 15:30 ~ 16:15
開催場所: 総合研究棟8階大セミナー室 (入口にて内線75615, 75617 or 75618に電話下さい。)
講師: Xiang Zhou
所属: Assistant Professor, Department of Biostatistics, University of Michigan
演題: Variability-Preserving Imputation for Accurate Gene Expression Recovery in Single Cell RNA Sequencing Studies

We develop a method to impute the zero values in single cell RNA sequencing studies to facilitate accurate transcriptome quantification at the single cell level. Our method is based on nonnegative sparse regression models and it is capable of progressively inferring a sparse set of local neighborhood cells that are most predictive of the expression levels of the cell of interest for imputation. A key feature of our method is its ability to preserve gene expression variability across cells after imputation. We illustrate the advantages of our method through several well-designed real data-based analytical experiments.

世話人: 〇宮野 悟 (DNA情報解析分野)
 井元 清哉 (健康医療データサイエンス分野)
開催日時: 2018年9月19日 17:00 ~ 18 : 00
開催場所: 1号館 講堂
講師: Thomas P. Zwaka
所属: Professor, Developmental and Regenerative Biology,
Director, Huffington Foundation Center for Cell-Based Research in Parkinson’s Disease, Icahn School of Medicine at Mount Sinai
演題: Ronin’s role in creating regulatory DNA structures: blurring the distinction between enhancers and promoters

Differences in transcription factor binding to enhancers are generally thought to influence development and therefore could bear on the most fundamental questions in biology. How do cells differentiate to form new tissues? And how do these tissues function in a coordinated and flexible manner? Here I will present an entirely novel gene regulatory paradigm in which promoters (and their enhancers) interact with other promoters to potentially regulate key gene expression programs. Adding to this novelty is our choice of a candidate genomic organizer, Ronin, which emerged when a fossil transposon was co-opted by ancient metazoans to achieve a level of functional diversity not seen before in evolution.

世話人: 〇山田 泰広 (先進病態モデル分野)
 武川 陸寛 (分子シグナル制御分野)
開催日時: 2018年10月3日 17:00 ~ 18:00
開催場所: 病院棟8階北会議室
講師: 久保田 義顕
所属: 慶應義塾大学 医学部 解剖学教室・教授
演題: 網膜血管パターニングの制御機構


世話人: 〇渡辺 すみ子 (再生基礎医科学)
 三宅 健介 (感染遺伝学分野)
開催日時: 2018年9月21日 16:00 ~ 17:00
開催場所: 1号館 講堂
講師: Arie Meir
所属: Google Inc. Product Manager of Google Cloud
演題: Cloud and AI as drivers of technology innovation in the healthcare industry

Electronic health records, medical imaging, genomics are examples of healthcare data sources which can be processed to drive clinical and operational decisions on individual and population levels. However, in a world where data is growing at unprecedented rates, a new strategy and a new set of tools is required to capture the promises of Big Data in the healthcare space.
Cloud computing is rapidly changing the way that healthcare data is handled and creating novel opportunities to leverage large scale data aggregation to train machine learning models which enable operational efficiency and diagnostic assistance tools.
Google has been pushing the boundary of research in the machine learning space. In this talk, Arie Meir, PhD, a product manager with Google Cloud team will present his team’s strategy and perspective on data management, analytics and machine learning with specific focus on medical imaging. Arie will share his observations on some of the challenges seen in the space as well as the opportunities for innovation that these challenges introduce.
Join this talk to hear about the role of artificial intelligence in the future of radiology, find out the truth about big (dirty) data and what does it take to really de-identify healthcare data. Questions from the audience are encouraged and appreciated.

世話人: 〇井元 清哉(健康医療データサイエンス分野)
 宮野 悟(DNA情報解析分野)
開催日時: 2018年9月10日 16:00 ~ 17:00
開催場所: 1号館 講堂
講師: 広田 亨
所属: 公益財団法人がん研究会がん研究所 実験病理部・部長
演題: がん細胞における染色体動態制御システムの破綻--- 明かされつつある染色体不安定性の分子背景

染色体不安定性は、がん組織を構成する細胞の多様性を大きくする要因であり、病期の進行したがんに共通して出現する性質である。その主たる原因は、M期における染色体分配過程のエラーであると考えられているが、その一方で、M期制御分子の遺伝子変異は少なく、何故がん細胞は染色体分配を失敗するのか、その病理機構の解明が待たれている。わたくしたちの研究室では、ヒト細胞が染色体の構築から分配までを滞りなく進める細胞機能を追究し、それを基盤として染色体不安定性の病理機構の解明を目指している。具体的には、これまでの研究で、M期染色体の構築、特にセントロメアの構築、動原体と微小管の結合、M期チェックポイントとその解除が導くセパレースの活性化、といったプロセスの研究を通じて、細胞分裂を支える細胞機能の解明に貢献してきた。他方でこれらのプロセスががん細胞でどのように変化しているのか、未だによくわかっていない。本講演では、Aurora Bキナーゼとプロテアーゼであるseparaseという2つのM期制御分子を基軸として見えてきた染色体動態制御システムをご紹介し、それぞれの制御システムの破綻とがん細胞の染色体不安定性の関連性について議論したい。こうした細胞病態の理解を深めることにより、がん細胞が抱えている脆弱性に着眼した新たな制御法の導出にも挑戦したいと考えている。

世話人: 〇中西 真 (癌防御シグナル分野)
 山田 泰広 (先進病態モデル研究分野)
開催日時: 2018年8月29日 16:00 〜 17:00
開催場所: 病院棟8階南会議室
講師: 谷澤 健太郎
所属: 東京大学医科学研究所附属病院 外科・助教
演題: 大腸憩室の基礎と疾患~患者さんに大腸憩室を語れるように~


世話人: 〇東條 有伸 (分子療法分野)
 四柳 宏 (感染症分野) 篠崎 大 (外科)