In vivo CRISPR screens reveal SCAF1 and USP15 as drivers of pancreatic cancer.

Functionally characterizing the genetic alterations that drive pancreatic cancer is a prerequisite for precision medicine. Here, we perform somatic CRISPR/Cas9 mutagenesis screens to assess the transforming potential of 125 recurrently mutated pancreatic cancer genes, which revealed USP15 and SCAF1 as pancreatic tumor suppressors. Mechanistically, we find that USP15 functions in a haploinsufficient manner and that loss of USP15 or SCAF1 leads to reduced inflammatory TNFα, TGF-ß and IL6 responses and increased sensitivity to PARP inhibition and Gemcitabine. Furthermore, we find that loss of SCAF1 leads to the formation of a truncated, inactive USP15 isoform at the expense of full-length USP15, functionally coupling SCAF1 and USP15. Notably, USP15 and SCAF1 alterations are observed in 31% of pancreatic cancer patients. Our results highlight the utility of in vivo CRISPR screens to integrate human cancer genomics and mouse modeling for the discovery of cancer driver genes with potential prognostic and therapeutic implications.

Characterizing the impacts of dataset imbalance on single-cell data integration.

Computational methods for integrating single-cell transcriptomic data from multiple samples and conditions do not generally account for imbalances in the cell types measured in different datasets. In this study, we examined how differences in the cell types present, the number of cells per cell type and the cell type proportions across samples affect downstream analyses after integration. The Iniquitate pipeline assesses the robustness of integration results after perturbing the degree of imbalance between datasets. Benchmarking of five state-of-the-art single-cell RNA sequencing integration techniques in 2,600 integration experiments indicates that sample imbalance has substantial impacts on downstream analyses and the biological interpretation of integration results. Imbalance perturbation led to statistically significant variation in unsupervised clustering, cell type classification, differential expression and marker gene annotation, query-to-reference mapping and trajectory inference. We quantified the impacts of imbalance through newly introduced properties-aggregate cell type support and minimum cell type center distance. To better characterize and mitigate impacts of imbalance, we introduce balanced clustering metrics and imbalanced integration guidelines for integration method users.

The impacts of active and self-supervised learning on efficient annotation of single-cell expression data.

A crucial step in the analysis of single-cell data is annotating cells to cell types and states. While a myriad of approaches has been proposed, manual labeling of cells to create training datasets remains tedious and time-consuming. In the field of machine learning, active and self-supervised learning methods have been proposed to improve the performance of a classifier while reducing both annotation time and label budget. However, the benefits of such strategies for single-cell annotation have yet to be evaluated in realistic settings. Here, we perform a comprehensive benchmarking of active and self-supervised labeling strategies across a range of single-cell technologies and cell type annotation algorithms. We quantify the benefits of active learning and self-supervised strategies in the presence of cell type imbalance and variable similarity. We introduce adaptive reweighting, a heuristic procedure tailored to single-cell data-including a marker-aware version-that shows competitive performance with existing approaches. In addition, we demonstrate that having prior knowledge of cell type markers improves annotation accuracy. Finally, we summarize our findings into a set of recommendations for those implementing cell type annotation procedures or platforms. An R package implementing the heuristic approaches introduced in this work may be found at https://github.com/camlab-bioml/leader .

A noncoding single-nucleotide polymorphism at 8q24 drives IDH1-mutant glioma formation.

Establishing causal links between inherited polymorphisms and cancer risk is challenging. Here, we focus on the single-nucleotide polymorphism rs55705857, which confers a sixfold greater risk of isocitrate dehydrogenase (IDH)-mutant low-grade glioma (LGG). We reveal that rs55705857 itself is the causal variant and is associated with molecular pathways that drive LGG. Mechanistically, we show that rs55705857 resides within a brain-specific enhancer, where the risk allele disrupts OCT2/4 binding, allowing increased interaction with the Myc promoter and increased Myc expression. Mutating the orthologous mouse rs55705857 locus accelerated tumor development in an Idh1R132H-driven LGG mouse model from 472 to 172 days and increased penetrance from 30% to 75%. Our work reveals mechanisms of the heritable predisposition to lethal glioma in ~40% of LGG patients.

Automated assignment of cell identity from single-cell multiplexed imaging and proteomic data.

A major challenge in the analysis of highly multiplexed imaging data is the assignment of cells to a priori known cell types. Existing approaches typically solve this by clustering cells followed by manual annotation. However, these often require several subjective choices and cannot explicitly assign cells to an uncharacterized type. To help address these issues we present Astir, a probabilistic model to assign cells to cell types by integrating prior knowledge of marker proteins. Astir uses deep recognition neural networks for fast inference, allowing for annotations at the million-cell scale in the absence of a previously annotated reference. We apply Astir to over 2.4 million cells from suspension and imaging datasets and demonstrate its scalability, robustness to sample composition, and interpretable uncertainty estimates. We envision deployment of Astir either for a first broad cell type assignment or to accurately annotate cells that may serve as biomarkers in multiple disease contexts. A record of this paper's transparent peer review process is included in the supplemental information.

Loss of MGA repression mediated by an atypical polycomb complex promotes tumor progression and invasiveness.

MGA, a transcription factor and member of the MYC network, is mutated or deleted in a broad spectrum of malignancies. As a critical test of a tumor suppressive role, we inactivated Mga in two mouse models of non-small cell lung cancer using a CRISPR-based approach. MGA loss significantly accelerated tumor growth in both models and led to de-repression of non-canonical Polycomb ncPRC1.6 targets, including genes involved in metastasis and meiosis. Moreover, MGA deletion in human lung adenocarcinoma lines augmented invasive capabilities. We further show that MGA-MAX, E2F6, and L3MBTL2 co-occupy thousands of promoters and that MGA stabilizes these ncPRC1.6 subunits. Lastly, we report that MGA loss also induces a pro-growth effect in human colon organoids. Our studies establish MGA as a bona fide tumor suppressor in vivo and suggest a tumor suppressive mechanism in adenocarcinomas resulting from widespread transcriptional attenuation of MYC and E2F target genes mediated by MGA-MAX associated with a non-canonical Polycomb complex.

MAX Functions as a Tumor Suppressor and Rewires Metabolism in Small Cell Lung Cancer.

Small cell lung cancer (SCLC) is a highly aggressive and lethal neoplasm. To identify candidate tumor suppressors we applied CRISPR/Cas9 gene inactivation screens to a cellular model of early-stage SCLC. Among the top hits was MAX, the obligate heterodimerization partner for MYC family proteins that is mutated in human SCLC. Max deletion increases growth and transformation in cells and dramatically accelerates SCLC progression in an Rb1/Trp53-deleted mouse model. In contrast, deletion of Max abrogates tumorigenesis in MYCL-overexpressing SCLC. Max deletion in SCLC resulted in derepression of metabolic genes involved in serine and one-carbon metabolism. By increasing serine biosynthesis, Max-deleted cells exhibit resistance to serine depletion. Thus, Max loss results in metabolic rewiring and context-specific tumor suppression.