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Abstract Single-cell transcriptomics has emerged as a powerful tool for understanding how different cells contribute to disease progression by identifying cell types that change across diseases or conditions. However, detecting changing cell types is challenging due to individual-to-individual and cohort-to-cohort variability and naive approaches based on current computational tools lead to false positive findings. To address this, we propose a computational tool, scDist, based on a mixed-effects model that provides a statistically rigorous and computationally efficient approach for detecting transcriptomic differences. By accurately recapitulating known immune cell relationships and mitigating false positives induced by individual and cohort variation, we demonstrate that scDist outperforms current methods in both simulated and real datasets, even with limited sample sizes. Through the analysis of COVID-19 and immunotherapy datasets, scDist uncovers transcriptomic perturbations in dendritic cells, plasmacytoid dendritic cells, and FCER1G+NK cells, that provide new insights into disease mechanisms and treatment responses. As single-cell datasets continue to expand, our faster and statistically rigorous method offers a robust and versatile tool for a wide range of research and clinical applications, enabling the investigation of cellular perturbations with implications for human health and disease.

Abstract Recent advances in single-cell RNA sequencing have shown heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can indicate common cell types and states in the tumor microenvironment (TME). We develop a data driven framework, MetaTiME, to overcome the limitations in resolution and consistency that result from manual labelling using known gene markers. Using millions of TME single cells, MetaTiME learns meta-components that encode independent components of gene expression observed across cancer types. The meta-components are biologically interpretable as cell types, cell states, and signaling activities. By projecting onto the MetaTiME space, we provide a tool to annotate cell states and signature continuums for TME scRNA-seq data. Leveraging epigenetics data, MetaTiME reveals critical transcriptional regulators for the cell states. Overall, MetaTiME learns data-driven meta-components that depict cellular states and gene regulators for tumor immunity and cancer immunotherapy.

Targeted protein degradation (TPD) has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell’s endogenous protein degradation machinery. However, the susceptibility of proteins for targeting by TPD approaches, termed “degradability”, is largely unknown. Here, we developed a machine learning model, model-free analysis of protein degradability (MAPD), to predict degradability from features intrinsic to protein targets. MAPD shows accurate performance in predicting kinases that are degradable by TPD compounds [with an area under the precision–recall curve (AUPRC) of 0.759 and an area under the receiver operating characteristic curve (AUROC) of 0.775] and is likely generalizable to independent non-kinase proteins. We found five features with statistical significance to achieve optimal prediction, with ubiquitination potential being the most predictive. By structural modeling, we found that E2-accessible ubiquitination sites, but not lysine residues in general, are particularly associated with kinase degradability. Finally, we extended MAPD predictions to the entire proteome to find 964 disease-causing proteins (including proteins encoded by 278 cancer genes) that may be tractable to TPD drug development.

The role of MYC in transcriptional reprogramming in prostate cancer remains poorly characterized. Here, MYC overexpression antagonizes the canonical AR transcriptional program leading to prostate tumor initiation and progression by disrupting transcriptional pause release at AR-regulated genes.

As studies continue sequencing with deeper coverage, computational processing of these profiles has become increasingly resource consuming. Here the authors designed an efficient computational method called Chromap to align and preprocess high throughput sequencing data from chromatin profiling techniques, including ChIP-seq, Hi-C, or scATAC-seq, with a major decrease in runtime.

The impact of genetic fusions on degrons, which are motifs for ubiquitin-mediated protein degradation, has not been fully explored. Here, the authors analyse fusion genes affecting degrons in pan-cancer genomics data, validate their functional impact and find enrichment for both internal and C-terminal degron losses.

Neuroendocrine carcinomas (NECs) arise from different anatomic sites, but have similar histological and clinical features. Here, the authors show that the epigenetic landscape of a range of NECs converges towards a common epigenetic state, while distinct subtypes occur within neuroendocrine prostate cancer contributing to intratumor heterogeneity in clinical samples.

Abstract 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.

Abstract We present Model-based AnalysEs of Transcriptome and RegulOme (MAESTRO), a comprehensive open-source computational workflow ( http://github.com/liulab-dfci/MAESTRO ) for the integrative analyses of single-cell RNA-seq (scRNA-seq) and ATAC-seq (scATAC-seq) data from multiple platforms. MAESTRO provides functions for pre-processing, alignment, quality control, expression and chromatin accessibility quantification, clustering, differential analysis, and annotation. By modeling gene regulatory potential from chromatin accessibilities at the single-cell level, MAESTRO outperforms the existing methods for integrating the cell clusters between scRNA-seq and scATAC-seq. Furthermore, MAESTRO supports automatic cell-type annotation using predefined cell type marker genes and identifies driver regulators from differential scRNA-seq genes and scATAC-seq peaks.

ENCODE is a resource comprising thousands of functional genomic datasets. Here, the authors present custom annotation within ENCODE for cancer, highlighting a workflow that can help prioritise key elements in oncogenesis.

Characterization of the distance over which TF binding influences gene expression is important for inferring target genes. Here the authors study this relationship using thousands of genomic data sets, finding two classes of TFs with distinct ranges of regulatory influence modulated by chromatin states of topologically associated domains.

Abstract We developed Lisa ( http://lisa.cistrome.org/ ) to predict the transcriptional regulators (TRs) of differentially expressed or co-expressed gene sets. Based on the input gene sets, Lisa first uses histone mark ChIP-seq and chromatin accessibility profiles to construct a chromatin model related to the regulation of these genes. Using TR ChIP-seq peaks or imputed TR binding sites, Lisa probes the chromatin models using in silico deletion to find the most relevant TRs. Applied to gene sets derived from targeted TF perturbation experiments, Lisa boosted the performance of imputed TR cistromes and outperformed alternative methods in identifying the perturbed TRs.

Abstract Despite growing numbers of immune checkpoint blockade (ICB) trials with available omics data, it remains challenging to evaluate the robustness of ICB response and immune evasion mechanisms comprehensively. To address these challenges, we integrated large-scale omics data and biomarkers on published ICB trials, non-immunotherapy tumor profiles, and CRISPR screens on a web platform TIDE ( http://tide.dfci.harvard.edu ). We processed the omics data for over 33K samples in 188 tumor cohorts from public databases, 998 tumors from 12 ICB clinical studies, and eight CRISPR screens that identified gene modulators of the anticancer immune response. Integrating these data on the TIDE web platform with three interactive analysis modules, we demonstrate the utility of public data reuse in hypothesis generation, biomarker optimization, and patient stratification.

BRAF is a serine/threonine kinase that harbors activating mutations in ∼7% of human malignancies and ∼60% of melanomas. Despite initial clinical responses to BRAF inhibitors, patients frequently develop drug resistance. To identify candidate therapeutic targets for BRAF inhibitor resistant melanoma, we conduct CRISPR screens in melanoma cells harboring an activating BRAF mutation that had also acquired resistance to BRAF inhibitors. To investigate the mechanisms and pathways enabling resistance to BRAF inhibitors in melanomas, we integrate expression, ATAC-seq, and CRISPR screen data. We identify the JUN family transcription factors and the ETS family transcription factor ETV5 as key regulators of CDK6, which together enable resistance to BRAF inhibitors in melanoma cells. Our findings reveal genes contributing to resistance to a selective BRAF inhibitor PLX4720, providing new insights into gene regulation in BRAF inhibitor resistant melanoma cells.

Abstract Background Acute myeloid leukemia (AML), caused by the abnormal proliferation of immature myeloid cells in the blood or bone marrow, is one of the most common hematologic malignancies. Currently, the interactions between malignant myeloid cells and the immune microenvironment, especially T cells and B cells, remain poorly characterized. Methods In this study, we systematically analyzed the T cell receptor and B cell receptor (TCR and BCR) repertoires from the RNA-seq data of 145 pediatric and 151 adult AML samples as well as 73 non-tumor peripheral blood samples. Results We inferred over 225,000 complementarity-determining region 3 (CDR3) sequences in TCR α, β, γ, and δ chains and 1,210,000 CDR3 sequences in B cell immunoglobulin (Ig) heavy and light chains. We found higher clonal expansion of both T cells and B cells in the AML microenvironment and observed many differences between pediatric and adult AML. Most notably, adult AML samples have significantly higher level of B cell activation and more secondary Ig class switch events than pediatric AML or non-tumor samples. Furthermore, adult AML with highly expanded IgA2 B cells, which might represent an immunosuppressive microenvironment, are associated with regulatory T cells and worse overall survival. Conclusions Our comprehensive characterization of the AML immune receptor repertoires improved our understanding of T cell and B cell immunity in AML, which may provide insights into immunotherapies in hematological malignancies.

Summary: Repair of DNA double-stranded breaks (DSBs) during lymphocyte development is essential for V(D)J recombination and forms the basis of immunoglobulin variable region diversity. Understanding of this process in lymphogenesis has historically been centered on the study of RAG1/2 recombinases and a set of classical non-homologous end-joining factors. Much less has been reported regarding the role of chromatin modifications on this process. Here, we show a role for the non-redundant histone H3 lysine methyltransferase, Setd2, and its modification of lysine-36 trimethylation (H3K36me3), in the processing and joining of DNA ends during V(D)J recombination. Loss leads to mis-repair of Rag-induced DNA DSBs, especially when combined with loss of Atm kinase activity. Furthermore, loss reduces immune repertoire and a severe block in lymphogenesis as well as causes post-mitotic neuronal apoptosis. Together, these studies are suggestive of an important role of Setd2/H3K36me3 in these two mammalian developmental processes that are influenced by double-stranded break repair. : Biological Sciences; Molecular Biology; Immunology; Cell Biology Subject Areas: Biological Sciences, Molecular Biology, Immunology, Cell Biology

Abstract Background RNA sequencing has become a ubiquitous technology used throughout life sciences as an effective method of measuring RNA abundance quantitatively in tissues and cells. The increase in use of RNA-seq technology has led to the continuous development of new tools for every step of analysis from alignment to downstream pathway analysis. However, effectively using these analysis tools in a scalable and reproducible way can be challenging, especially for non-experts. Results Using the workflow management system Snakemake we have developed a user friendly, fast, efficient, and comprehensive pipeline for RNA-seq analysis. VIPER (Visualization Pipeline for RNA-seq analysis) is an analysis workflow that combines some of the most popular tools to take RNA-seq analysis from raw sequencing data, through alignment and quality control, into downstream differential expression and pathway analysis. VIPER has been created in a modular fashion to allow for the rapid incorporation of new tools to expand the capabilities. This capacity has already been exploited to include very recently developed tools that explore immune infiltrate and T-cell CDR (Complementarity-Determining Regions) reconstruction abilities. The pipeline has been conveniently packaged such that minimal computational skills are required to download and install the dozens of software packages that VIPER uses. Conclusions VIPER is a comprehensive solution that performs most standard RNA-seq analyses quickly and effectively with a built-in capacity for customization and expansion.

Analyses of data from high-throughput genomic technologies are challenging given large data dimensionality. Here, Liu and colleagues describe a method called MANCIE (Matrix Analysis and Normalization by Concordant Information Enhancement) that can conduct genomic data normalization and bias correction to detect biologically relevant information.

Long non-coding RNAs (lncRNA; >200 base pair nucleic acids with little protein-coding capacity) are emerging as potentially important regulators of oncogenesis. Here the authors show tumour suppressive lncRNA sponge function for the protein products of prostate cancer driver genes.

Summary: Lysine-Specific Demethylase 1 (LSD1, KDM1A) functions as a transcriptional corepressor through demethylation of histone 3 lysine 4 (H3K4) but has a coactivator function on some genes through mechanisms that are unclear. We show that LSD1, interacting with CoREST, associates with and coactivates androgen receptor (AR) on a large fraction of androgen-stimulated genes. A subset of these AR/LSD1-associated enhancer sites have histone 3 threonine 6 phosphorylation (H3T6ph), and these sites are further enriched for androgen-stimulated genes. Significantly, despite its coactivator activity, LSD1 still mediates H3K4me2 demethylation at these androgen-stimulated enhancers. FOXA1 is also associated with LSD1 at AR-regulated enhancer sites, and a FOXA1 interaction with LSD1 enhances binding of both proteins at these sites. These findings show that LSD1 functions broadly as a regulator of AR function, that it maintains a transcriptional repression function at AR-regulated enhancers through H3K4 demethylation, and that it has a distinct AR-linked coactivator function mediated by demethylation of other substrates. : Cai et al. show that lysine-specific demethylase 1 (LSD1), although generally a transcriptional corepressor through H3K4 demethylation, functions broadly as a coactivator for androgen receptor and interacts with FOXA1 on androgen-stimulated genes. LSD1-mediated H3K4 demethylation persists at these sites, indicating a distinct coactivator function mediated by demethylation of other substrates.

Motivation: The chromatin profile measured by ATAC-seq, ChIP-seq, or DNase-seq experiments can identify genomic regions critical in regulating gene expression and provide insights on biological processes such as diseases and development. However, quality control and processing chromatin profiling data involves many steps, and different bioinformatics tools are used at each step. It can be challenging to manage the analysis. Results: We developed a Snakemake pipeline called CHIPS (CHromatin enrIchment ProcesSor) to streamline the processing of ChIP-seq, ATAC-seq, and DNase-seq data. The pipeline supports single- and paired-end data and is flexible to start with FASTQ or BAM files. It includes basic steps such as read trimming, mapping, and peak calling. In addition, it calculates quality control metrics such as contamination profiles, polymerase chain reaction bottleneck coefficient, the fraction of reads in peaks, percentage of peaks overlapping with the union of public DNaseI hypersensitivity sites, and conservation profile of the peaks. For downstream analysis, it carries out peak annotations, motif finding, and regulatory potential calculation for all genes. The pipeline ensures that the processing is robust and reproducible. Availability: CHIPS is available at https://github.com/liulab-dfci/CHIPS.