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Biology subjects

Cillo, A.

Publications and source records attributed to Cillo, A..

2 recordsLinked to original sources

All-trans retinoic acid induces durable tumor immunity in IDH-mutant gliomas by rescuing transcriptional repression of the CRBP1-retinoic acid axis

Diffuse gliomas are epigenetically dysregulated, immunologically cold, and fatal tumors characterized by mutations in isocitrate dehydrogenase (IDH). Although IDH mutations yield a uniquely immunosuppressive tumor microenvironment, the regulatory mechanisms that drive the immune landscape of IDH mutant (IDHm) gliomas remain unknown. Here, we reveal that transcriptional repression of retinoic acid (RA) pathway signaling impairs both innate and adaptive immune surveillance in IDHm glioma through epigenetic silencing of retinol binding protein 1 (RBP1) and induces a profound anti-inflammatory landscape marked by loss of inflammatory cell states and infiltration of suppressive myeloid phenotypes. Restorative retinoic acid therapy in murine glioma models promotes clonal CD4+ T cell expansion and induces tumor regression in IDHm, but not IDH wildtype (IDHwt), gliomas. Our findings provide a mechanistic rationale for RA immunotherapy in IDHm glioma and is the basis for an ongoing investigator-initiated, single-center clinical trial investigating all-trans retinoic acid (ATRA) in recurrent IDHm human subjects.

cancer biology↗

BAMM-SC: A Bayesian mixture model for clustering droplet-based single cell transcriptomic data from population studies

The recently developed droplet-based single cell transcriptome sequencing (scRNA-seq) technology makes it feasible to perform a population-scale scRNA-seq study, in which the transcriptome is measured for tens of thousands of single cells from multiple individuals. Despite the advances of many clustering methods, there are few tailored methods for population-scale scRNA-seq studies. Here, we have developed a BAyesiany Mixture Model for Single Cell sequencing (BAMM-SC) method to cluster scRNA-seq data from multiple individuals simultaneously. Specifically, BAMM-SC takes raw data as input and can account for data heterogeneity and batch effect among multiple individuals in a unified Bayesian hierarchical model framework. Results from extensive simulations and application of BAMM-SC to in-house scRNA-seq datasets using blood, lung and skin cells from humans or mice demonstrated that BAMM-SC outperformed existing clustering methods with improved clustering accuracy and reduced impact from batch effects. BAMM-SC has been implemented in a user-friendly R package with a detailed tutorial available on www.pitt.edu/~Cwec47/singlecell.html.

bioinformatics↗