Biology subjects

Shahamatdar, S.

Publications and source records attributed to Shahamatdar, S..

3 recordsLinked to original sources

bioRxiv2022

Deceptive learning in histopathology

Deep learning holds immense potential for histopathology, automating tasks that are simple for expert pathologists, and revealing novel biology for tasks that were previously considered difficult or impossible to solve by eye alone. However, the extent to which the visual strategies learned by deep learning models in histopathological analysis are trustworthy or not has yet to be systematically analyzed. In this work, we address this problem and discover new limits on the histopathological tasks for which deep learning models learn trustworthy versus deceptive solutions. While tasks that have been extensively studied in the field like tumor detection are reliable and trustworthy, recent advances demonstrating the ability to learn molecular profiling from hematoxylin and eosin (H&E) stained slides do not hold up to closer scrutiny. Our analysis framework represents a new approach in understanding the capabilities of deep learning models, which should be incorporated into the computational pathologists toolkit.

pathology↗

bioRxiv2021

Redefining replication in multi-ancestry genome-wide association studies

Since 2005, genome-wide association (GWA) datasets have been largely biased toward sampling European ancestry individuals, and recent studies have shown that GWA results estimated from self-identified European individuals are not transferable to non-European individuals due to various confounding challenges. Here, we demonstrate that enrichment analyses which aggregate SNP-level association statistics at multiple genomic scales--from genes to genomic regions and pathways--have been underutilized in the GWA era and can generate biologically interpretable hypotheses regarding the genetic basis of complex trait architecture. We illustrate examples of the robust associations generated by enrichment analyses while studying 25 continuous traits assayed in 566,786 individuals from seven diverse self-identified human ancestries in the UK Biobank and the Biobank Japan, as well as 44,348 admixed individuals from the PAGE consortium including cohorts of African-American, Hispanic and Latin American, Native Hawaiian, and American Indian/Alaska Native individuals. We identify 1,000 gene-level associations that are genome-wide significant in at least two ancestry cohorts across these 25 traits, as well as highly conserved pathway associations with triglyceride levels in European, East Asian, and Native Hawaiian cohorts.

genomics↗

bioRxiv2019

Germline features associated with immune infiltration in solid tumors

Given the clinical success of immune checkpoint blockade (ICB) across a diverse set of solid tumors, and the emerging role for different immune infiltrates in contributing to response to ICB, a comprehensive assessment of the properties that dictate immune infiltrations may reveal new biological insights and inform the development of new effective therapies. Multiple studies have examined somatic and functional immune properties associated with different tumor infiltrates; however, germline features that associate with specific immune infiltrates in cancers have been incompletely characterized. Here, we analyzed over 7 million autosomal germline variants in the TCGA cohort (5788 European-ancestry samples across 30 cancer types) and tested for pan-cancer association with established immune-related phenotypes that describe the tumor immune microenvironment. We identified: one SNP associated with the fraction of follicular helper T cells in bulk tumor; 77 unique candidate genes, some of which are involved in cytokine-mediated signaling (e.g. CNTF and TRIM34) and cancer pathogenesis (e.g. ATR and AKAP9); and subnetworks with genes that are part of DNA repair (RAD51 and XPC) and transcription elongation (CCNT2) pathways. We found a positive association between polygenic risk for rheumatoid arthritis and absolute fraction of infiltrating CD8 T cells. Overall, we identified multiple germline genetic features associated with specific tumor-immune phenotypes across cancer, and developed a framework for probing inherited features that contribute to variation in immune infiltration.

genetics↗