SBME Research Seminar - Dr. Hannah Carter
Immune Checkpoint Blockade (ICB) has revolutionized cancer treatment, however mechanisms determining patient response remain poorly understood. We used machine learning to predict ICB response from germline and somatic biomarkers and studied feature usage by the learned model to uncover putative mechanisms driving superior outcomes. Patients with higher T follicular helper infiltrates were robust to defects in the class-I Major Histocompatibility Complex (MHC-I). Further investigation uncovered different ICB responses in MHC-I versus MHC-II neoantigen reliant tumors across patients. Despite similar response rates, MHC-II reliant responses were associated with significantly longer durable clinical benefit (Discovery: Median OS=63.6 vs. 34.5 months P=0.0074; Validation: Median OS=37.5 vs. 33.1 months, P=0.040). Characteristics of the tumor immune microenvironment reflected MHC neoantigen reliance, and analysis of immune checkpoints revealed LAG3 as a potential target in MHC-II but not MHC-I reliant responses. This study highlights the value of interpretable machine learning models in elucidating the biological basis of therapy responses.
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SBME Seminar: Long non-coding RNAs in pluripotency, development and disease
June 25, 2024 @ 11:00 am - 12:00 pm PDT
SBME Seminar: Long non-coding RNAs in pluripotency, development and disease
Seminar’s Abstract:
“Well-regulated gene expression networks are responsible for establishing and maintaining cellular states during development. Of the early cell states present in development, pluripotency is the cellular state that has the potential to derive all cell lineages of the embryo. Mutations in genes associated with pluripotency often lead to abnormal development and embryo lethality, but much of the focus relating to these genes has been on protein-coding genes. However, the last decade has seen the rise of long non-coding RNAs (lncRNAs) as novel players in the control of pluripotency, development, and several diseases including cancer and neurological disorders. As such, our major goal is to understand the molecular mechanisms and functional interactions of lncRNAs in modulating cellular states. In this talk, I will discuss how these lncRNA interactions influence mRNA splicing, gene expression, and ultimately cell fate, but more specifically in the context of pluripotency.”
Dr. Samer Hussein’s Bio:
Dr. Samer Hussein is an associate professor and researcher at Université Laval and its affiliated Cancer Research Center. Dr. Hussein completed his Ph.D. in Neurological Sciences at McGill University, Montréal, Canada, and his post-doctoral training at the University of Helsinki, Finland, and later at the Lunenfeld-Tanenbaum Research Institute in Toronto, Canada. He has published seminal work in the field of reprogramming demonstrating several key findings on how reprogramming to induced pluripotent stem cells (iPSCs) affects the chromatin state, genetic stability, and gene expression of cells undergoing this process of induced cell fate change.
His team now focuses on understanding the molecular underpinnings governing cell fate decision during embryonic stem cell (ESC) differentiation and during the evolution of cancer, more specifically Glioblastoma. They use several bioinformatics and sequencing approaches, such as long read RNA sequencing, and ESC differentiation models, such as human cerebral organoids, to understand the molecular mechanisms and functional interactions of long non-coding RNAs during development and cancer.
Location:
DMCBH 101 LT + Zoom