Kunes, R. Z. | Search bioRxiv

bioRxiv2023

Leptomeningeal anti-tumor immunity follows unique signaling principles

Metastasis to the cerebrospinal fluid (CSF)-filled leptomeninges, or leptomeningeal metastasis (LM), represents a fatal complication of cancer. Proteomic and transcriptomic analyses of human CSF reveal a substantial inflammatory infiltrate in LM. We find the solute and immune composition of CSF in the setting of LM changes dramatically, with notable enrichment in IFN-{gamma} signaling. To investigate the mechanistic relationships between immune cell signaling and cancer cells within the leptomeninges, we developed syngeneic lung, breast, and melanoma LM mouse models. Here we show that transgenic host mice, lacking IFN-{gamma} or its receptor, fail to control LM growth. Overexpression of Ifng through a targeted AAV system controls cancer cell growth independent of adaptive immunity. Instead, leptomeningeal IFN-{gamma} actively recruits and activates peripheral myeloid cells, generating a diverse spectrum of dendritic cell subsets. These migratory, CCR7+ dendritic cells orchestrate the influx, proliferation, and cytotoxic action of natural killer cells to control cancer cell growth in the leptomeninges. This work uncovers leptomeningeal-specific IFN-{gamma} signaling and suggests a novel immune-therapeutic approach against tumors within this space.

cancer biology↗

bioRxiv2022

Supervised discovery of interpretable gene programs from single-cell data

Factor analysis can drive biological discovery by decomposing single-cell gene expression data into a minimal set of gene programs that correspond to processes executed by cells in a sample. However, matrix factorization methods are prone to technical artifacts and poor factor interpretability. We have developed Spectra, an algorithm that identifies user-provided gene programs, modifies them to dataset context as needed, and detects novel programs that together best explain expression covariation. Spectra overcomes the dominance of cell-type signals by modeling cell-type-specific programs, and can characterize interpretable cell states along a continuum. We show that it outperforms existing approaches in challenging tumor immune contexts; Spectra finds factors that change under immune checkpoint therapy, disentangles the highly correlated features of CD8+ T-cell tumor reactivity and exhaustion, finds a novel program that explains continuous macrophage state changes under therapy, and identifies cell-type-specific immune metabolic programs.

bioinformatics↗