Whole genome doubling (WGD) occurs early in tumorigenesis and generates geneticallyunstable tetraploid cells that fuel tumor development. Cells that undergo WGD (WGD + ) must adapt to accommodate their abnormal tetraploid state; however, the nature of these adaptations, and whether they confer vulnerabilities that can subsequently be exploited therapeutically, is unclear. Using sequencing data from ~10,000 primary human cancer samples and essentiality data from ~600 cancer cell lines, we show that WGD gives rise to common genetic traits that are accompanied by unique vulnerabilities. We reveal that WGD + cells are more dependent on spindle assembly checkpoint signaling, DNA replication factors, and proteasome function than WGDcells. We also identify KIF18A, which encodes for a mitotic kinesin, as being specifically required for the viability of WGD + cells. While loss of KIF18A is largely dispensable for accurate chromosome segregation during mitosis in WGDcells, its loss induces dramatic mitotic errors in WGD + cells, ultimately impairing cell viability. Collectively, our results reveal new strategies to specifically target WGD + cancer cells while sparing the normal, nontransformed WGDcells that comprise human tissue..

Droplet-based microfluidic devices have become widely used to perform single-cell RNA sequencing (scRNA-seq). However, ambient RNA present in the cell suspension can be aberrantly counted along with a cell's native mRNA and result in cross-contamination of transcripts between different cell populations. DecontX is a novel Bayesian method to estimate and remove contamination in individual cells. DecontX accurately predicts contamination levels in a mouse-human mixture dataset and removes aberrant expression of marker genes in PBMC datasets. We also compare the contamination levels between four different scRNA-seq protocols. Overall, DecontX can be incorporated into scRNA-seq workflows to improve downstream analyses.

Purpose: African American (AFR) men have the highest mortality rate from prostate cancer (PCa) compared with men of other racial/ancestral groups. Differences in the spectrum of somatic genome alterations in tumors between AFR men and other populations have not been well-characterized due to a lack of inclusion of significant numbers in genomic studies. Experimental Design: To identify genomic alterations associated with race, we compared the frequencies of somatic alterations in PCa obtained from four publicly available datasets comprising 250 AFR and 611 European American (EUR) men and a targeted sequencing dataset from a commercial platform of 436 AFR and 3018 EUR men. Results: Mutations in ZFHX3 as well as focal deletions in ETV3 were more frequent in tumors from AFR men. TP53 mutations were associated with increasing Gleason score. MYC amplifications were more frequent in tumors from AFR men with metastatic PCa, whereas deletions in PTEN and rearrangements in TMPRSS2-ERG were less frequent in tumors from AFR men. KMT2D truncations and CCND1 amplifications were more frequent in primary PCa from AFR men. Genomic features that could impact clinical decision making were not significantly different between the two groups including tumor mutation burden, MSI status, and genomic alterations in select DNA repair genes, CDK12, and in AR. Conclusions: Although we identified some novel differences in AFR men compared with other populations, the frequencies of genomic alterations in current therapeutic targets for PCa were similar between AFR and EUR men, suggesting that existing precision medicine approaches could be equally beneficial if applied equitably.

Droplet-based microfluidic devices have become widely used to perform single-cell RNA sequencing (scRNAseq) and discover novel cellular heterogeneity in complex biological systems. However, ambient RNA present in the cell suspension can be incorporated into these droplets and aberrantly counted along with a cell's native mRNA. This results in cross-contamination of transcripts between di↵erent cell populations and can potentially decrease the precision of downstream analyses. We developed a novel hierarchical Bayesian method called DecontX to estimate and remove contamination in individual cells from scRNAseq data. DecontX accurately predicted the proportion of contaminated counts in a mixture of mouse and human cells. Decontamination of PBMC datasets removed aberrant expression of cell type specific marker genes from other cell types and improved overall separation of cell clusters. In general, DecontX can be incorporated into scRNA-seq workflows to assess quality of dissociation protocols and improve downstream analyses.

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