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 Research Seminar: Interpreting rules of gene regulation learned by deep learning – Dr. Peter Koo
November 28, 2024 @ 11:00 am - 12:00 pm PST
SBME Research Seminar: Interpreting rules of gene regulation learned by deep learning – Dr. Peter Koo
Seminar Abstract:
Deep neural networks (DNNs) have become a widely used tool to analyze high-throughput functional genomics data. While demonstrating remarkable predictive capabilities, a DNN’s low interpretability makes it challenging to translate their improved predictions into discoveries in biology and healthcare. While progress has been made to explain a DNN’s predictions through feature attributions and counterfactual explanations, existing interpretability methods sacrifice deeper insights in favor of simpler explanations that can work generically across models and domains. Here, we introduce two domain-specific model interpretability methods for regulatory genomics. First, we leverage domain knowledge based on multiplex assays of variant effects (MAVEs) to characterize biological mechanisms within a genomic locus learned by a DNN. Our approach, called SQUID, approximates a DNN about a custom region of sequence space using in silico MAVEs and biophysics-inspired surrogate models, which provide parameters with direct interpretations as cis-regulatory mechanisms. We show that SQUID leads to more consistent representations of transcription factor binding motifs and yields improved single-nucleotide variant effect predictions compared to existing attribution methods. Second, we leverage CRISPR-inspired perturbations to address targeted biological questions of gene regulation learned by a DNN. Our approach, called CRÈME, employs multiscale in silico perturbations to identify CREs and characterize their enhancing or silencing effect on DNN predictions. By interpreting Enformer, a state-of-the-art sequence-based DNN for gene expression prediction, CRÈME reveals that Enformer’s predictions integrate the effects of numerous enhancers and silencers in an additive manner, though for some genes, the CREs interact through complex rules, such as cooperativity or redundancy. Together, our work demonstrates that domain knowledge is essential for interpreting deeper biological insights from genomic DNNs.
Dr. Peter Koo Biography
Dr. Peter Koo is an Assistant Professor at the Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory. He leads a research group focused on developing robust and trustworthy deep learning solutions for genomics, with the broader aim of advancing precision medicine for complex diseases, including cancer. The Koo lab has made notable contributions in several areas, including designing deep learning architectures that incorporate biophysical priors, creating methods to train and evaluate robust deep learning models, and developing techniques to interpret deep learning models and uncover biological mechanisms to accelerate scientific discoveries. Prior to joining CSHL, Dr. Koo was a Postdoctoral Fellow with Dr. Sean Eddy at Harvard University, where he developed deep neural networks to investigate the cis-recognition rules of RNA-binding proteins. Dr. Koo obtained a Ph.D. in Physics from Yale University, working under the guidance of Dr. Simon Mochrie, where he developed machine learning methods rooted in statistical physics to characterize the dynamic interactions of diffusing proteins.
Location:
LSC 1002 (LT2)