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Swanson, E. M.

Publications and source records attributed to Swanson, E. M..

2 recordsLinked to original sources

Dynamic inositol pyrophosphate synthesis is a targetable therapeutic opportunity in ovarian cancer.

We previously reported that the phosphate exporter XPR1 is required to prevent toxic phosphate accumulation in ovarian cancer cells. To guide therapeutic development, we sought to systematically compare potential strategies to inhibit XPR1: directly targeting the phosphate efflux channel, targeting its partner protein KIDINS220, or inhibiting the synthesis of inositol pyrophosphates (PP-InsPs), metabolites which activate XPR1. We evaluated functional domains in XPR1 and KIDINS220 using mutational scanning and found that loss of function mutations in XPR1 clustered in distinct regions throughout the protein, with the most deleterious mutations in the PP-InsP-binding domain. In contrast, loss of function mutations in KIDINS220 were infrequent and altered the localization of XPR1, consistent with a scaffolding role for KIDINS220. These data highlight the functional relevance of PP-InsPs, which we confirmed by inhibiting their synthesis using IP6K inhibitors. We demonstrate that IP6K inhibition phenocopies XPR1 inhibition across hundreds of cancer cell lines, with the mechanism of sensitivity solely due to inhibition of cellular phosphate efflux. Finally, we show that IP6K inhibitors decrease tumor burden in xenograft models of ovarian cancer, but that the rapid resynthesis of PP-InsPs requires high exposures to achieve efficacy. This study comprehensively evaluates the XPR1-dependent phosphate efflux network and reinforces the concept of directly targeting XPR1 as a precision medicine strategy to benefit patients with ovarian cancer.

cancer biology↗

cspray: Distributed Single Cell Transcriptome Analysis

The size of individual single cell samples continues to grow with advancing technologies, as do the number of samples included in individual experiments and across organizations. This presents challenges for processing this data at scale, both in terms of computational throughput and the required size of the machines that must process this data. We present a single cell RNA processing method that is fully distributed, capable of processing arbitrarily large files, and numbers of files, without requiring per-file based compute sizing. Our method, cspray, includes data ingestion, preprocessing, highly variable gene annotation, PCA, and clustering. We also show that this processing at scale permits LLM based reference-free cluster annotation on low resolution clusters, which demonstrates these techniques can be used to build single cell data discovery platforms at scale.

bioinformatics↗