Differential chromatin accessibility and transcriptional dynamics define breast cancer subtypes and their lineages.

Breast cancer is a heterogeneous disease, and treatment is guided by biomarker profiles representing distinct molecular subtypes. Breast cancer arises from the breast ductal epithelium, and experimental data suggests breast cancer subtypes have different cells of origin within that lineage. The precise cells of origin for each subtype and the transcriptional networks that characterize these tumor-normal lineages are not established. In this work, we applied bulk, single-cell (sc), and single-nucleus (sn) multi-omic techniques as well as spatial transcriptomics and multiplex imaging on 61 samples from 37 breast cancer patients to show characteristic links in gene expression and chromatin accessibility between breast cancer subtypes and their putative cells of origin. We applied the PAM50 subtyping algorithm in tandem with bulk RNA-seq and snRNA-seq to reliably subtype even low-purity tumor samples and confirm promoter accessibility using snATAC. Trajectory analysis of chromatin accessibility and differentially accessible motifs clearly connected progenitor populations with breast cancer subtypes supporting the cell of origin for basal-like and luminal A and B tumors. Regulatory network analysis of transcription factors underscored the importance of BHLHE40 in luminal breast cancer and luminal mature cells, and KLF5 in basal-like tumors and luminal progenitor cells. Furthermore, we identify key genes defining the basal-like ( PRKCA , SOX6 , RGS6 , KCNQ3 ) and luminal A/B ( FAM155A , LRP1B ) lineages, with expression in both precursor and cancer cells and further upregulation in tumors. Exhausted CTLA4-expressing CD8+ T cells were enriched in basal-like breast cancer, suggesting altered means of immune dysfunction among breast cancer subtypes. We used spatial transcriptomics and multiplex imaging to provide spatial detail for key markers of benign and malignant cell types and immune cell colocation. These findings demonstrate analysis of paired transcription and chromatin accessibility at the single cell level is a powerful tool for investigating breast cancer lineage development and highlight transcriptional networks that define basal and luminal breast cancer lineages.

Epigenetic and transcriptomic characterization reveals progression markers and essential pathways in clear cell renal cell carcinoma.

Identifying tumor-cell-specific markers and elucidating their epigenetic regulation and spatial heterogeneity provides mechanistic insights into cancer etiology. Here, we perform snRNA-seq and snATAC-seq in 34 and 28 human clear cell renal cell carcinoma (ccRCC) specimens, respectively, with matched bulk proteogenomics data. By identifying 20 tumor-specific markers through a multi-omics tiered approach, we reveal an association between higher ceruloplasmin (CP) expression and reduced survival. CP knockdown, combined with spatial transcriptomics, suggests a role for CP in regulating hyalinized stroma and tumor-stroma interactions in ccRCC. Intratumoral heterogeneity analysis portrays tumor cell-intrinsic inflammation and epithelial-mesenchymal transition (EMT) as two distinguishing features of tumor subpopulations. Finally, BAP1 mutations are associated with widespread reduction of chromatin accessibility, while PBRM1 mutations generally increase accessibility, with the former affecting five times more accessible peaks than the latter. These integrated analyses reveal the cellular architecture of ccRCC, providing insights into key markers and pathways in ccRCC tumorigenesis.

Spatially restricted drivers and transitional cell populations cooperate with the microenvironment in untreated and chemo-resistant pancreatic cancer.

Pancreatic ductal adenocarcinoma is a lethal disease with limited treatment options and poor survival. We studied 83 spatial samples from 31 patients (11 treatment-naïve and 20 treated) using single-cell/nucleus RNA sequencing, bulk-proteogenomics, spatial transcriptomics and cellular imaging. Subpopulations of tumor cells exhibited signatures of proliferation, KRAS signaling, cell stress and epithelial-to-mesenchymal transition. Mapping mutations and copy number events distinguished tumor populations from normal and transitional cells, including acinar-to-ductal metaplasia and pancreatic intraepithelial neoplasia. Pathology-assisted deconvolution of spatial transcriptomic data identified tumor and transitional subpopulations with distinct histological features. We showed coordinated expression of TIGIT in exhausted and regulatory T cells and Nectin in tumor cells. Chemo-resistant samples contain a threefold enrichment of inflammatory cancer-associated fibroblasts that upregulate metallothioneins. Our study reveals a deeper understanding of the intricate substructure of pancreatic ductal adenocarcinoma tumors that could help improve therapy for patients with this disease.

Pollock: fishing for cell states.

Motivation: The use of single-cell methods is expanding at an ever-increasing rate. While there are established algorithms that address cell classification, they are limited in terms of cross platform compatibility, reliance on the availability of a reference dataset and classification interpretability. Here, we introduce Pollock, a suite of algorithms for cell type identification that is compatible with popular single-cell methods and analysis platforms, provides a set of pretrained human cancer reference models, and reports interpretability scores that identify the genes that drive cell type classifications. Results: Pollock performs comparably to existing classification methods, while offering easily deployable pretrained classification models across a wide variety of tissue and data types. Additionally, it demonstrates utility in immune pan-cancer analysis. Availability and implementation: Source code and documentation are available at https://github.com/ding-lab/pollock. Pretrained models and datasets are available for download at https://zenodo.org/record/5895221. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Proteogenomic characterization of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications. In addition, whole-genome sequencing, whole-exome sequencing, methylation, RNA sequencing (RNA-seq), and microRNA sequencing (miRNA-seq) were performed on the same tissues to facilitate an integrated proteogenomic analysis and determine the impact of genomic alterations on protein expression, signaling pathways, and post-translational modifications. To ensure robust downstream analyses, tumor neoplastic cellularity was assessed via multiple orthogonal strategies using molecular features and verified via pathological estimation of tumor cellularity based on histological review. This integrated proteogenomic characterization of PDAC will serve as a valuable resource for the community, paving the way for early detection and identification of novel therapeutic targets.

A proteogenomic portrait of lung squamous cell carcinoma.

Lung squamous cell carcinoma (LSCC) remains a leading cause of cancer death with few therapeutic options. We characterized the proteogenomic landscape of LSCC, providing a deeper exposition of LSCC biology with potential therapeutic implications. We identify NSD3 as an alternative driver in FGFR1-amplified tumors and low-p63 tumors overexpressing the therapeutic target survivin. SOX2 is considered undruggable, but our analyses provide rationale for exploring chromatin modifiers such as LSD1 and EZH2 to target SOX2-overexpressing tumors. Our data support complex regulation of metabolic pathways by crosstalk between post-translational modifications including ubiquitylation. Numerous immune-related proteogenomic observations suggest directions for further investigation. Proteogenomic dissection of CDKN2A mutations argue for more nuanced assessment of RB1 protein expression and phosphorylation before declaring CDK4/6 inhibition unsuccessful. Finally, triangulation between LSCC, LUAD, and HNSCC identified both unique and common therapeutic vulnerabilities. These observations and proteogenomics data resources may guide research into the biology and treatment of LSCC.

Proteogenomic and metabolomic characterization of human glioblastoma.

Glioblastoma (GBM) is the most aggressive nervous system cancer. Understanding its molecular pathogenesis is crucial to improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs provides insights to GBM biology. We identify key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation, as well as potential targets for EGFR-, TP53-, and RB1-altered tumors. Immune subtypes with distinct immune cell types are discovered using bulk omics methodologies, validated by snRNA-seq, and correlated with specific expression and histone acetylation patterns. Histone H2B acetylation in classical-like and immune-low GBM is driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identify specific lipid distributions across subtypes and distinct global metabolic changes in IDH-mutated tumors. This work highlights biological relationships that could contribute to stratification of GBM patients for more effective treatment.

CRISPR-DT: designing gRNAs for the CRISPR-Cpf1 system with improved target efficiency and specificity.

MOTIVATION: The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cpf1 system has been successfully applied in genome editing. However, target efficiency of the CRISPR-Cpf1 system varies among different guide RNA (gRNA) sequences. RESULTS: In this study, we reanalyzed the published CRISPR-Cpf1 gRNAs data and found many sequence and structural features related to their target efficiency. With the aid of Random Forest in feature selection, a support vector machine model was created to predict target efficiency for any given gRNAs. We have developed the first CRISPR-Cpf1 web service application, CRISPR-DT (CRISPR DNA Targeting), to help users design optimal gRNAs for the CRISPR-Cpf1 system by considering both target efficiency and specificity. CRISPR-DT will empower researchers in genome editing. AVAILABILITY AND IMPLEMENTATION: CRISPR-DT, mainly implemented in Perl, PHP and JavaScript, is freely available at http://bioinfolab.miamioh.edu/CRISPR-DT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CRISPR-RT: a web application for designing CRISPR-C2c2 crRNA with improved target specificity.

Summary: CRISPR-Cas systems have been successfully applied in genome editing. Recently, the CRISPR-C2c2 system has been reported as a tool for RNA editing. Here we describe CRISPR-RT (CRISPR RNA-Targeting), the first web application to help biologists design crRNAs with improved target specificity for the CRISPR-C2c2 system. CRISPR-RT allows users to set up a wide range of parameters, making it highly flexible for current and future research in CRISPR-based RNA editing. CRISPR-RT covers major model organisms and can be easily extended to cover other species. CRISPR-RT will empower researchers in RNA editing. Availability and implementation: Freely available at http://bioinfolab.miamioh.edu/CRISPR-RT. Contact: liangc@miamioh.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

CT-Finder: A Web Service for CRISPR Optimal Target Prediction and Visualization.

The CRISPR system holds much promise for successful genome engineering, but therapeutic, industrial, and research applications will place high demand on improving the specificity and efficiency of this tool. CT-Finder (http://bioinfolab.miamioh.edu/ct-finder) is a web service to help users design guide RNAs (gRNAs) optimized for specificity. CT-Finder accommodates the original single-gRNA Cas9 system and two specificity-enhancing paired-gRNA systems: Cas9 D10A nickases (Cas9n) and dimeric RNA-guided FokI nucleases (RFNs). Optimal target candidates can be chosen based on the minimization of predicted off-target effects. Graphical visualization of on-target and off-target sites in the genome is provided for target validation. Major model organisms are covered by this web service.