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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Student Exit Seminar: A scalable computing framework for whole-body mouse cell lineage reconstruction
March 28, 2024 @ 2:00 pm - 3:00 pm PDT
Student Exit Seminar: A scalable computing framework for whole-body mouse cell lineage reconstruction
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Seminar abstract:
The advent of CRISPR-Cas9 genome editing technologies has spurred the development of high-resolution cell lineage tracing systems. These experimental systems utilize genome editing tools to introduce random mutations to chromosome-embedded, synthetic DNA barcode arrays. As these barcodes are continuously mutated and inherited from mother to daughter cells, the lineage of cells sacrificed at the time of observation can be reconstructed from the mutation patterns in their barcodes, akin to phylogeny estimation in evolutionary biology. Recent advances in single-cell sequencing technologies has facilitated large-scale applications of cell lineage tracing systems, which has highlighted major limitations with respect to information decoding capacity. To that end, the Yachie lab recently proposed a theoretical framework capable of reconstructing the phylogeny of hundreds of millions of sequences—a task that far surpasses the capabilities of existing software. A major remaining challenge is to overcome technical biases, noise, and sparsity typically associated with barcode sequence readouts derived from large-scale sequencing experiments. My project focused on the development of a rejection sampling method along with a bootstrapping strategy based on orthogonal tree agreement. Together, they provided a relatively unbiased estimate of confidence for proposed tree branches, which could be leveraged to significantly improve tree reconstruction accuracy. Developing a scalable lineage reconstruction method is paramount to realizing the overarching goal of the Yachie lab to resolve the whole-body mapping of the mouse developmental lineage, a resource that would revolutionize our understanding of mammalian development.
Speaker: Brett Kiyota
