Regulatory programs that control the function of stem cells are active in cancer and confer properties that promote progression and therapy resistance. However, the impact of a stem cell-like tumor ...phenotype (“stemness”) on the immunological properties of cancer has not been systematically explored. Using gene-expression–based metrics, we evaluated the association of stemness with immune cell infiltration and genomic, transcriptomic, and clinical parameters across 21 solid cancers. We found pervasive negative associations between cancer stemness and anticancer immunity. This occurred despite high stemness cancers exhibiting increased mutation load, cancer-testis antigen expression, and intratumoral heterogeneity. Stemness was also strongly associated with cell-intrinsic suppression of endogenous retroviruses and type I IFN signaling, and increased expression of multiple therapeutically accessible immunosuppressive pathways. Thus, stemness is not only a fundamental process in cancer progression but may provide a mechanistic link between antigenicity, intratumoral heterogeneity, and immune suppression across cancers.
JASPAR (http://jaspar.genereg.net) is an open-access database storing curated, non-redundant transcription factor (TF) binding profiles representing transcription factor binding preferences as ...position frequency matrices for multiple species in six taxonomic groups. For this 2016 release, we expanded the JASPAR CORE collection with 494 new TF binding profiles (315 in vertebrates, 11 in nematodes, 3 in insects, 1 in fungi and 164 in plants) and updated 59 profiles (58 in vertebrates and 1 in fungi). The introduced profiles represent an 83% expansion and 10% update when compared to the previous release. We updated the structural annotation of the TF DNA binding domains (DBDs) following a published hierarchical structural classification. In addition, we introduced 130 transcription factor flexible models trained on ChIP-seq data for vertebrates, which capture dinucleotide dependencies within TF binding sites. This new JASPAR release is accompanied by a new web tool to infer JASPAR TF binding profiles recognized by a given TF protein sequence. Moreover, we provide the users with a Ruby module complementing the JASPAR API to ease programmatic access and use of the JASPAR collection of profiles. Finally, we provide the JASPAR2016 R/Bioconductor data package with the data of this release.
JASPAR (http://jaspar.genereg.net) is the largest open-access database of matrix-based nucleotide profiles describing the binding preference of transcription factors from multiple species. The fifth ...major release greatly expands the heart of JASPAR-the JASPAR CORE subcollection, which contains curated, non-redundant profiles-with 135 new curated profiles (74 in vertebrates, 8 in Drosophila melanogaster, 10 in Caenorhabditis elegans and 43 in Arabidopsis thaliana; a 30% increase in total) and 43 older updated profiles (36 in vertebrates, 3 in D. melanogaster and 4 in A. thaliana; a 9% update in total). The new and updated profiles are mainly derived from published chromatin immunoprecipitation-seq experimental datasets. In addition, the web interface has been enhanced with advanced capabilities in browsing, searching and subsetting. Finally, the new JASPAR release is accompanied by a new BioPython package, a new R tool package and a new R/Bioconductor data package to facilitate access for both manual and automated methods.
Single-cell RNA sequencing has enabled the decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to cell types through unsupervised ...clustering followed by manual annotation or via 'mapping' to existing data. However, manual interpretation scales poorly to large datasets, mapping approaches require purified or pre-annotated data and both are prone to batch effects. To overcome these issues, we present CellAssign, a probabilistic model that leverages prior knowledge of cell-type marker genes to annotate single-cell RNA sequencing data into predefined or de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while controlling for batch and sample effects. We demonstrate the advantages of CellAssign through extensive simulations and analysis of tumor microenvironment composition in high-grade serous ovarian cancer and follicular lymphoma.
High-grade serous ovarian cancer (HGSC) exhibits extensive malignant clonal diversity with widespread but non-random patterns of disease dissemination. We investigated whether local immune ...microenvironment factors shape tumor progression properties at the interface of tumor-infiltrating lymphocytes (TILs) and cancer cells. Through multi-region study of 212 samples from 38 patients with whole-genome sequencing, immunohistochemistry, histologic image analysis, gene expression profiling, and T and B cell receptor sequencing, we identified three immunologic subtypes across samples and extensive within-patient diversity. Epithelial CD8+ TILs negatively associated with malignant diversity, reflecting immunological pruning of tumor clones inferred by neoantigen depletion, HLA I loss of heterozygosity, and spatial tracking between T cell and tumor clones. In addition, combinatorial prognostic effects of mutational processes and immune properties were observed, illuminating how specific genomic aberration types associate with immune response and impact survival. We conclude that within-patient spatial immune microenvironment variation shapes intraperitoneal malignant spread, provoking new evolutionary perspectives on HGSC clonal dispersion.
Display omitted
•Immune infiltrates vary across space within patients at the time of diagnosis•Immune infiltration shapes malignant cell evolutionary trajectories•T cell clones track with tumor clones across spatial sites within patients•Immune infiltrates and mutational processes show prognostic interactions
Integrated multi-region analysis of metastatic sites in patients with high-grade ovarian cancer highlights the connection between immune microenvironment variation and malignant spread, as well as the combinatorial prognostic value of immune and mutational features.
Single-cell RNA sequencing (scRNA-seq) is a powerful tool for studying complex biological systems, such as tumor heterogeneity and tissue microenvironments. However, the sources of technical and ...biological variation in primary solid tumor tissues and patient-derived mouse xenografts for scRNA-seq are not well understood.
We use low temperature (6 °C) protease and collagenase (37 °C) to identify the transcriptional signatures associated with tissue dissociation across a diverse scRNA-seq dataset comprising 155,165 cells from patient cancer tissues, patient-derived breast cancer xenografts, and cancer cell lines. We observe substantial variation in standard quality control metrics of cell viability across conditions and tissues. From the contrast between tissue protease dissociation at 37 °C or 6 °C, we observe that collagenase digestion results in a stress response. We derive a core gene set of 512 heat shock and stress response genes, including FOS and JUN, induced by collagenase (37 °C), which are minimized by dissociation with a cold active protease (6 °C). While induction of these genes was highly conserved across all cell types, cell type-specific responses to collagenase digestion were observed in patient tissues.
The method and conditions of tumor dissociation influence cell yield and transcriptome state and are both tissue- and cell-type dependent. Interpretation of stress pathway expression differences in cancer single-cell studies, including components of surface immune recognition such as MHC class I, may be especially confounded. We define a core set of 512 genes that can assist with the identification of such effects in dissociated scRNA-seq experiments.
Mutation signatures in cancer genomes reflect endogenous and exogenous mutational processes, offering insights into tumour etiology, features for prognostic and biologic stratification and ...vulnerabilities to be exploited therapeutically. We present a novel machine learning formalism for improved signature inference, based on multi-modal correlated topic models (MMCTM) which can at once infer signatures from both single nucleotide and structural variation counts derived from cancer genome sequencing data. We exemplify the utility of our approach on two hormone driven, DNA repair deficient cancers: breast and ovary (n = 755 samples total). We show how introducing correlated structure both within and between modes of mutation can increase accuracy of signature discovery, particularly in the context of sparse data. Our study emphasizes the importance of integrating multiple mutation modes for signature discovery and patient stratification, and provides a statistical modeling framework to incorporate additional features of interest for future studies.
We propose a multistate joint model to analyze interval‐censored event‐history data subject to within‐unit clustering and nonignorable missing data. The model is motivated by a study of the ...neurocysticercosis (NC) cyst evolution at the cyst‐level, taking into account the multiple cysts phases with intermittent missing data and loss to follow‐up, as well as the intra‐brain clustering of observations made on a predefined data collection schedule. Of particular interest in this study is the description of the process leading to cyst resolution, and whether this process varies by antiparasitic treatment. The model uses shared random effects to account for within‐brain correlation and to explain the hidden heterogeneity governing the missing data mechanism. We developed a likelihood‐based method using a Monte Carlo EM algorithm for the inference. The practical utility of the methods is illustrated using data from a randomized controlled trial on the effect of antiparasitic treatment with albendazole on NC cysts among patients from six hospitals in Ecuador. Simulation results demonstrate that the proposed methods perform well in the finite sample and misspecified models that ignore the data complexities could lead to substantial biases.
To synthesize the impact of improvement interventions related to care coordination, discharge support and care transitions on patient experience measures.
Systematic review. Searches were completed ...in six scientific databases, five specialty journals, and through snowballing. Eligibility included studies published in English (2015-2023) focused on improving care coordination, discharge support, or transitional care assessed by standardized patient experience measures as a primary outcome. Two independent reviewers made eligibility decisions and performed quality appraisals.
Of 1240 papers initially screened, 16 were included. Seven studies focused on care coordination activities, including three randomized controlled trials RCTs. These studies used enhanced supports such as improvement coaching or tailoring for vulnerable populations within Patient-Centered Medical Homes or other primary care sites. Intervention effectiveness was mixed or neutral relative to standard or models of care or simpler supports (e.g., improvement tool). Eight studies, including three RCTs, focused on enhanced discharge support, including patient education (e.g., teach back) and telephone follow-up; mixed or neutral results on the patient experience were also found and with more substantive risks of bias. One pragmatic trial on a transitional care intervention, using a navigator support, found significant changes only for the subset of uninsured patients and in one patient experience outcome, and had challenges with implementation fidelity.
Enhanced supports for improving care coordination, discharge education, and post-discharge follow-up had mixed or neutral effectiveness for improving the patient experience with care, compared to standard care or simpler improvement approaches. There is a need to advance the body of evidence on how to improve the patient experience with discharge support and transitional approaches.
PDF
