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DTSTART;TZID=America/Los_Angeles:20231117T130000
DTEND;TZID=America/Los_Angeles:20231117T133000
DTSTAMP:20231026T193016Z
CREATED:20231026T193016Z
LAST-MODIFIED:20231026T193016Z
UID:25962-1700226000-1700227800@qcb.ucla.edu
SUMMARY:Research-in-Progress (RIP) Seminar: Mao Tian (Boutros)\, Junior Bioinformatician in JCCC Cancer Data Science
DESCRIPTION:TITLE: “Characterization of Genomics Landscape and Natural History of Anaplastic Thyroid Cancer using High Depth WGS and Subclonal Reconstruction.” \nABSTRACT: Anaplastic thyroid cancer (ATC) is among the most lethal cancer types\, with a median survival rate of approximately 12 weeks. ATC is resistant to both chemo- and radiotherapy\, a characteristic attributed to its surrounding tissues and rapid progression. Although ATC is less common than other thyroid cancers\, such as papillary thyroid carcinoma (PTC) and differentiated thyroid carcinoma (DTC)\, it frequently co-occurs with both PTC and DTC. In this study\, we assembled a 108-sample cohort consisting of 46 ATC patients and 5 thyroid cancer cell lines. We conducted high-depth (x90) whole genome sequencing on ATC samples\, as well as on adjacent PTC or DTC tissues\, and included blood controls when available. Our analysis identified both germline and somatic variants in ATC\, revealing a moderate mutation burden compared to C-type tumors and a higher incidence of recurrent SNVs and CNVs than in PTC or DTC. Notably\, ATC samples exhibited more active mutational signatures\, such as SBS2 and SBS13\, than DTC or PTC samples. Additionally\, we employed subclonal reconstruction to model the natural history of ATC in relation to cooccurring DTC and PTC.
URL:https://qcb.ucla.edu/event/research-in-progress-rip-seminar-mao-tian-boutros-junior-bioinformatician-in-jccc-cancer-data-science/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:QCBio Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://qcb.ucla.edu/wp-content/uploads/sites/14/2023/10/MaoTian_Headshot_2019.jpg
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DTSTART;TZID=America/Los_Angeles:20231117T133000
DTEND;TZID=America/Los_Angeles:20231117T140000
DTSTAMP:20231118T002605Z
CREATED:20231107T155230Z
LAST-MODIFIED:20231118T002605Z
UID:25997-1700227800-1700229600@qcb.ucla.edu
SUMMARY:Research-in-Progress (RIP) Seminar: Michael Cheng (Yang)\, Graduate Student in Bioinformatics
DESCRIPTION:TITLE: “scGRNdb: A Cell Type Gene Regulatory Network Atlas for Human and Mouse.” \nABSTRACT: Gene regulatory networks (GRNs) elucidate the complex regulatory landscape in cells and tissues\, making them powerful tools for understanding mechanisms in disease pathophysiology and identifying therapeutic targets. The advent of single cell RNA-sequencing (scRNAseq) enables a more granular study of disease mechanisms using cell type-specific GRNs\, but most existing GRN methods are not optimized for scRNAseq and robust network resources are scarce. We recently developed SCING\, which improves GRN performance on scRNAseq and spatial transcriptomics data compared to existing methods. Here we present scGRNdb: a GRN atlas of 1\,000+ SCING GRNs for cell types across 12 human and mouse single cell data atlases. Functional annotation of these networks revealed subnetworks that recapitulate known cell type specific pathways and gene mechanisms for neurological and cardiovascular diseases. Furthermore\, we will host scGRNdb and GRN analysis tools on a public web server to facilitate single cell research and biomedical discovery. \n\nhttps://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2023/11/Michael-Cheng-111723.mp4
URL:https://qcb.ucla.edu/event/research-in-progress-rip-seminar-michael-cheng-yang-graduate-student-in-bioinformatics/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:QCBio Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://qcb.ucla.edu/wp-content/uploads/sites/14/2023/11/michaelcheng_headshot.jpeg
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