scATAnno: Automated Cell Type Annotation for single-cell ATAC Sequencing Data.

Recent advances in single-cell epigenomic techniques have created a growing demand for scATAC-seq analysis. One key analysis task is to determine cell type identity based on the epigenetic data. We introduce scATAnno, a python package designed to automatically annotate scATAC-seq data using large-scale scATAC-seq reference atlases. This workflow generates the reference atlases from publicly available datasets enabling accurate cell type annotation by integrating query data with reference atlases, without the use of scRNA-seq data. To enhance annotation accuracy, we have incorporated KNN-based and weighted distance-based uncertainty scores to effectively detect cell populations within the query data that are distinct from all cell types in the reference data. We compare and benchmark scATAnno against 7 other published approaches for cell annotation and show superior performance in multiple data sets and metrics. We showcase the utility of scATAnno across multiple datasets, including peripheral blood mononuclear cell (PBMC), Triple Negative Breast Cancer (TNBC), and basal cell carcinoma (BCC), and demonstrate that scATAnno accurately annotates cell types across conditions. Overall, scATAnno is a useful tool for scATAC-seq reference building and cell type annotation in scATAC-seq data and can aid in the interpretation of new scATAC-seq datasets in complex biological systems.

Clonal tracing reveals diverse patterns of response to immune checkpoint blockade.

BACKGROUND: Immune checkpoint blockade (ICB) therapy has improved patient survival in a variety of cancers, but only a minority of cancer patients respond. Multiple studies have sought to identify general biomarkers of ICB response, but elucidating the molecular and cellular drivers of resistance for individual tumors remains challenging. We sought to determine whether a tumor with defined genetic background exhibits a stereotypic or heterogeneous response to ICB treatment. RESULTS: We establish a unique mouse system that utilizes clonal tracing and mathematical modeling to monitor the growth of each cancer clone, as well as the bulk tumor, in response to ICB. We find that tumors derived from the same clonal populations showed heterogeneous ICB response and diverse response patterns. Primary response is associated with higher immune infiltration and leads to enrichment of pre-existing ICB-resistant cancer clones. We further identify several cancer cell-intrinsic gene expression signatures associated with ICB resistance, including increased interferon response genes and glucocorticoid response genes. These findings are supported by clinical data from ICB treatment cohorts. CONCLUSIONS: Our study demonstrates diverse response patterns from the same ancestor cancer cells in response to ICB. This suggests the value of monitoring clonal constitution and tumor microenvironment over time to optimize ICB response and to design new combination therapies. Furthermore, as ICB response may enrich for cancer cell-intrinsic resistance signatures, this can affect interpretations of tumor RNA-seq data for response-signature association studies.

Network analysis of gene essentiality in functional genomics experiments.

Many genomic techniques have been developed to study gene essentiality genome-wide, such as CRISPR and shRNA screens. Our analyses of public CRISPR screens suggest protein interaction networks, when integrated with gene expression or histone marks, are highly predictive of gene essentiality. Meanwhile, the quality of CRISPR and shRNA screen results can be significantly enhanced through network neighbor information. We also found network neighbor information to be very informative on prioritizing ChIP-seq target genes and survival indicator genes from tumor profiling. Thus, our study provides a general method for gene essentiality analysis in functional genomic experiments ( http://nest.dfci.harvard.edu ).

CaSNP: a database for interrogating copy number alterations of cancer genome from SNP array data.

Cancer is known to have abundant copy number alterations (CNAs) that greatly contribute to its pathogenesis and progression. Investigation of CNA regions could potentially help identify oncogenes and tumor suppressor genes and infer cancer mechanisms. Although single-nucleotide polymorphism (SNP) arrays have strengthened our ability to identify CNAs with unprecedented resolution, a comprehensive collection of CNA information from SNP array data is still lacking. We developed a web-based CaSNP (http://cistrome.dfci.harvard.edu/CaSNP/) database for storing and interrogating quantitative CNA data, which curated ∼11,500 SNP arrays on 34 different cancer types in 104 studies. With a user input of region or gene of interest, CaSNP will return the CNA information summarizing the frequencies of gain/loss and averaged copy number for each study, and provide links to download the data or visualize it in UCSC Genome Browser. CaSNP also displays the heatmap showing copy numbers estimated at each SNP marker around the query region across all studies for a more comprehensive visualization. Finally, we used CaSNP to study the CNA of protein-coding genes as well as LincRNA genes across all cancer SNP arrays, and found putative regions harboring novel oncogenes and tumor suppressors. In summary, CaSNP is a useful tool for cancer CNA association studies, with the potential to facilitate both basic science and translational research on cancer.