Identifying tumor type and cell type-specific gene expression alterations in pediatric central nervous system tumors.

Central nervous system (CNS) tumors are the leading cause of pediatric cancer death, and these patients have an increased risk for developing secondary neoplasms. Due to the low prevalence of pediatric CNS tumors, major advances in targeted therapies have been lagging compared to other adult tumors. We collect single nuclei RNA-seq data from 84,700 nuclei of 35 pediatric CNS tumors and three non-tumoral pediatric brain tissues and characterize tumor heterogeneity and transcriptomic alterations. We distinguish cell subpopulations associated with specific tumor types including radial glial cells in ependymomas and oligodendrocyte precursor cells in astrocytomas. In tumors, we observe pathways important in neural stem cell-like populations, a cell type previously associated with therapy resistance. Lastly, we identify transcriptomic alterations among pediatric CNS tumor types compared to non-tumor tissues, while accounting for cell type effects on gene expression. Our results suggest potential tumor type and cell type-specific targets for pediatric CNS tumor treatment. Here we address current gaps in understanding single nuclei gene expression profiles of previously under-investigated tumor types and enhance current knowledge of gene expression profiles of single cells of various pediatric CNS tumors.

STREAK: A supervised cell surface receptor abundance estimation strategy for single cell RNA-sequencing data using feature selection and thresholded gene set scoring.

The accurate estimation of cell surface receptor abundance for single cell transcriptomics data is important for the tasks of cell type and phenotype categorization and cell-cell interaction quantification. We previously developed an unsupervised receptor abundance estimation technique named SPECK (Surface Protein abundance Estimation using CKmeans-based clustered thresholding) to address the challenges associated with accurate abundance estimation. In that paper, we concluded that SPECK results in improved concordance with Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data relative to comparative unsupervised abundance estimation techniques using only single-cell RNA-sequencing (scRNA-seq) data. In this paper, we outline a new supervised receptor abundance estimation method called STREAK (gene Set Testing-based Receptor abundance Estimation using Adjusted distances and cKmeans thresholding) that leverages associations learned from joint scRNA-seq/CITE-seq training data and a thresholded gene set scoring mechanism to estimate receptor abundance for scRNA-seq target data. We evaluate STREAK relative to both unsupervised and supervised receptor abundance estimation techniques using two evaluation approaches on six joint scRNA-seq/CITE-seq datasets that represent four human and mouse tissue types. We conclude that STREAK outperforms other abundance estimation strategies and provides a more biologically interpretable and transparent statistical model.

Tumor type and cell type-specific gene expression alterations in diverse pediatric central nervous system tumors identified using single nuclei RNA-seq.

Central nervous system (CNS) tumors are the leading cause of pediatric cancer death, and these patients have an increased risk for developing secondary neoplasms. Due to the low prevalence of pediatric CNS tumors, major advances in targeted therapies have been lagging compared to other adult tumors. We collected single nuclei RNA-seq data from 35 pediatric CNS tumors and three non-tumoral pediatric brain tissues (84,700 nuclei) and characterized tumor heterogeneity and transcriptomic alterations. We distinguished cell subpopulations associated with specific tumor types including radial glial cells in ependymomas and oligodendrocyte precursor cells in astrocytomas. In tumors, we observed pathways important in neural stem cell-like populations, a cell type previously associated with therapy resistance. Lastly, we identified transcriptomic alterations among pediatric CNS tumor types compared to non-tumor tissues, while accounting for cell type effects on gene expression. Our results suggest potential tumor type and cell type-specific targets for pediatric CNS tumor treatment. In this study, we address current gaps in understanding single nuclei gene expression profiles of previously uninvestigated tumor types and enhance current knowledge of gene expression profiles of single cells of various pediatric CNS tumors.

The tumor microenvironment of colorectal cancer metastases: opportunities in cancer immunotherapy.

About a fifth of individuals with colorectal cancer (CRC) present with disease metastasis at the time of diagnosis. While the role of the tumor microenvironment (TME) in governing CRC progression is undeniable, the role of the TME in either establishing or suppressing the formation of distant metastases of CRC is less well established. Despite advances in immunotherapy, many individuals with metastatic CRC do not respond to standard-of-care therapy. Therefore, understanding the role of the TME in establishing distant metastases is essential for developing new immunological agents. Here, we summarize our current understanding of the TME of CRC metastases, describe differences between the TME of primary tumors and their distant metastases, and discuss advances in the design and combinations of immunotherapeutic agents.

Variance-adjusted Mahalanobis (VAM): a fast and accurate method for cell-specific gene set scoring.

Statistical analysis of single cell RNA-sequencing (scRNA-seq) data is hindered by high levels of technical noise and inflated zero counts. One promising approach for addressing these challenges is gene set testing, or pathway analysis, which can mitigate sparsity and noise, and improve interpretation and power, by aggregating expression data to the pathway level. Unfortunately, methods optimized for bulk transcriptomics perform poorly on scRNA-seq data and progress on single cell-specific techniques has been limited. Importantly, no existing methods support cell-level gene set inference. To address this challenge, we developed a new gene set testing method, Variance-adjusted Mahalanobis (VAM), that integrates with the Seurat framework and can accommodate the technical noise, sparsity and large sample sizes characteristic of scRNA-seq data. The VAM method computes cell-specific pathway scores to transform a cell-by-gene matrix into a cell-by-pathway matrix that can be used for both data visualization and statistical enrichment analysis. Because the distribution of these scores under the null of uncorrelated technical noise has an accurate gamma approximation, both population and cell-level inference is supported. As demonstrated using simulated and real scRNA-seq data, the VAM method provides superior classification accuracy at a lower computation cost relative to existing single sample gene set testing approaches.