|연사||Dr. Nameeta Shah|
|소속||(Mazumdar Shaw Center for Translational Research, Mazumdar Shaw Medical Foundation, India)|
|일시||2018년 11월 30일(금) 11am|
|장소||양분순 빌딩 계단식 강의실 (207호)|
11/30일(금), KAIST Bio-IT Healthcare Initiative II 사업의 일환으로 바이오및뇌공학 분야의 해외 우수석학을 모시고 아래와 같이 Distinguished Lecture를 개최합니다.
관심있는 여러분들의 많은 참여 바랍니다!
1. 제목 : An anatomic transcriptional atlas of glioblastoma
2. 강연자: Dr. Nameeta Shah(Mazumdar Shaw Center for Translational Research,
3. 일시 : 2018년 11월 30일(금) 11am
4. 장소 : 양분순 빌딩 계단식 강의실 (207호) * 다과가 제공됩니다.
Glioblastoma is a grade IV brain tumor, affecting 3-4/100,000 new patients annually worldwide, with a median survival rate of 12-15 months for patients treated with total resection, radiation treatment, and chemotherapy. Extensive genomic studies of glioblastoma as a whole have provided critical insights into the disease biology but yielded limited treatment options and no cure. The Ivy Glioblastoma Atlas Project offers for the first time data for glioblastoma dissected by its distinct characteristic cell populations. This atlas will allow the worldwide research community to tackle this disease using a holistic approach, to understand its parts to put together a whole.
The molecular and cellular landscape of glioblastoma is highly complex and its relationship to histologic features routinely used for diagnosis is unclear. To investigate this relationship, we generated an anatomic transcriptional atlas of human glioblastoma, adopting a highly-systematized, large-scale, histology-driven approach to the characterization of anatomic features and cancer stem cell niches. The atlas of 42 tumors consists of several data modalities, including 270 transcriptomes, ~11,500 semi-annotated pathology images registered to ~23,000 in situ hybridization gene expression images, ~400 MRI scans, tumor-derived cell lines and xenografts, and supporting longitudinal clinical information. During this talk, I will describe the resource and the technologies used to generate the resource. I’ll also show that gene expression is driven by anatomic location, molecular signatures of anatomic features are highly conserved across tumors, and reflect the cell types and microenvironment of each feature.