NLM DIR Seminar Schedule
UPCOMING SEMINARS
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Feb. 18, 2025 Samuel Lee
Efficient predictions of alternative protein conformations by AlphaFold2-based sequence association -
Feb. 25, 2025 Zhizheng Wang
GeneAgent: Self-verification Language Agent for Gene Set Analysis using Domain Databases -
March 4, 2025 Sofya Garushyants
TBD -
March 11, 2025 Sanasar Babajanyan
TBD -
March 18, 2025 MG Hirsch
TBD
RECENT SEMINARS
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Feb. 11, 2025 Po-Ting Lai
Enhancing Biomedical Relation Extraction with Directionality -
Feb. 4, 2025 Victor Tobiasson
On the dominance of Asgard contributions to Eukaryogenesis -
Jan. 28, 2025 Kaleb Abram
Leveraging metagenomics to investigate the co-occurrence of virome and defensome elements at large scale -
Jan. 21, 2025 Qiao Jin
Artificial Intelligence for Evidence-based Medicine -
Jan. 17, 2025 Xuegong Zhang
Using Large Cellular Models to Understand Cell Transcriptomics Language
Scheduled Seminars on March 19, 2024
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
Abstract:
Cancer progression is an evolutionary process driven by the selection of cells adapted to gain growth advantage. We present the first formal study on the adaptation of gene expression in subclonal evolution. We model evolutionary changes in gene expression as stochastic Ornstein–Uhlenbeck processes, jointly leveraging the evolutionary history of subclones and single-cell expression data. Applying our model to sublines derived from single cells of a mouse melanoma revealed that sublines with distinct phenotypes are underlined by different patterns of gene expression adaptation, indicating non-genetic mechanisms of cancer evolution.
Interestingly, sublines previously observed to be resistant to anti-CTLA-4 treatment showed adaptive expression of genes related to invasion and non-canonical Wnt signaling, whereas sublines that responded to treatment showed adaptive expression of genes related to proliferation and canonical Wnt signalling. Our results suggest that clonal phenotypes emerge as the result of specific adaptivity patterns of gene expression.