Correction: Refining colorectal cancer classification and clinical stratification through a single-cell atlas.
Ontology highlight
ABSTRACT:
SUBMITTER: Khaliq AM
PROVIDER: S-EPMC9277898 | biostudies-literature | 2022 Jul
REPOSITORIES: biostudies-literature
Publications
Correction: Refining colorectal cancer classification and clinical stratification through a single-cell atlas.
Khaliq Ateeq M AM Erdogan Cihat C Kurt Zeyneb Z Turgut Sultan Sevgi SS Grunvald Miles W MW Rand Tim T Khare Sonal S Borgia Jefrey A JA Hayden Dana M DM Pappas Sam G SG Govekar Henry R HR Kam Audrey E AE Reiser Jochen J Turaga Kiran K Radovich Milan M Zang Yong Y Qiu Yingjie Y Liu Yunlong Y Fishel Melissa L ML Turk Anita A Gupta Vineet V Al-Sabti Ram R Subramanian Janakiraman J Kuzel Timothy M TM Sadanandam Anguraj A Waldron Levi L Hussain Arif A Saleem Mohammad M El-Rayes Bassel B Salahudeen Ameen A AA Masood Ashiq A
Genome biology 20220713 1
Similar Datasets
Project description:BackgroundColorectal cancer (CRC) consensus molecular subtypes (CMS) have different immunological, stromal cell, and clinicopathological characteristics. Single-cell characterization of CMS subtype tumor microenvironments is required to elucidate mechanisms of tumor and stroma cell contributions to pathogenesis which may advance subtype-specific therapeutic development. We interrogate racially diverse human CRC samples and analyze multiple independent external cohorts for a total of 487,829 single cells enabling high-resolution depiction of the cellular diversity and heterogeneity within the tumor and microenvironmental cells.ResultsTumor cells recapitulate individual CMS subgroups yet exhibit significant intratumoral CMS heterogeneity. Both CMS1 microsatellite instability (MSI-H) CRCs and microsatellite stable (MSS) CRC demonstrate similar pathway activations at the tumor epithelial level. However, CD8+ cytotoxic T cell phenotype infiltration in MSI-H CRCs may explain why these tumors respond to immune checkpoint inhibitors. Cellular transcriptomic profiles in CRC exist in a tumor immune stromal continuum in contrast to discrete subtypes proposed by studies utilizing bulk transcriptomics. We note a dichotomy in tumor microenvironments across CMS subgroups exists by which patients with high cancer-associated fibroblasts (CAFs) and C1Q+TAM content exhibit poor outcomes, providing a higher level of personalization and precision than would distinct subtypes. Additionally, we discover CAF subtypes known to be associated with immunotherapy resistance.ConclusionsDistinct CAFs and C1Q+ TAMs are sufficient to explain CMS predictive ability and a simpler signature based on these cellular phenotypes could stratify CRC patient prognosis with greater precision. Therapeutically targeting specific CAF subtypes and C1Q + TAMs may promote immunotherapy responses in CRC patients.
| S-EPMC9092724 | biostudies-literature
Project description:To determine and dissect the extent of tumor, immunological, and stromal heterogeneity in CRC patients, we performed droplet-based scRNA-seq on 16 racially diverse, treatment naïve CRC patient tissue samples and seven adjacent normal colonic tissue samples (totaling 23 samples), yielded 49,589 single cells enabling high-resolution depiction of the cellular diversity and heterogeneity within the tumor and microenvironmental cells.
2022-04-19 | GSE200997 | GEO
Project description:Refining Colorectal Cancer Classification and Clinical Stratification Through a Single-Cell Atlas
| PRJNA827831 | ENA
Project description:While spatial proteomics by fluorescence imaging has quickly become an essential discovery tool for researchers, fast and scalable methods to classify and embed single-cell protein distributions in such images are lacking. Here, we present the design and analysis of the results from the competition Human Protein Atlas - Single-Cell Classification hosted on the Kaggle platform. This represents a crowd-sourced competition to develop machine learning models trained on limited annotations to label single-cell protein patterns in fluorescent images. The particular challenges of this competition include class imbalance, weak labels and multi-label classification, prompting competitors to apply a wide range of approaches in their solutions. The winning models serve as the first subcellular omics tools that can annotate single-cell locations, extract single-cell features and capture cellular dynamics.
| S-EPMC9550622 | biostudies-literature
Project description:Malaria parasites have a complex life cycle featuring diverse developmental strategies, each uniquely adapted to navigate specific host environments. Here we use single-cell transcriptomics to illuminate gene usage across the transmission cycle of the most virulent agent of human malaria - Plasmodium falciparum. We reveal developmental trajectories associated with the colonization of the mosquito midgut and salivary glands and elucidate the transcriptional signatures of each transmissible stage. Additionally, we identify both conserved and non-conserved gene usage between human and rodent parasites, which point to both essential mechanisms in malaria transmission and species-specific adaptations potentially linked to host tropism. Together, the data presented here, which are made freely available via an interactive website, provide a fine-grained atlas that enables intensive investigation of the P. falciparum transcriptional journey. As well as providing insights into gene function across the transmission cycle, the atlas opens the door for identification of drug and vaccine targets to stop malaria transmission and thereby prevent disease.
| S-EPMC8159942 | biostudies-literature
Project description:BackgroundCancer-associated fibroblasts (CAFs), integral to the tumour microenvironment, are pivotal in cancer progression, exhibiting either pro-tumourigenic or anti-tumourigenic functions. Their inherent phenotypic and functional diversity allows for the subdivision of CAFs into various subpopulations. While several classification systems have been suggested for different cancer types, a unified molecular classification of CAFs on a single-cell pan-cancer scale has yet to be established.MethodsWe employed a comprehensive single-cell transcriptomic atlas encompassing 12 solid tumour types. Our objective was to establish a novel molecular classification and to elucidate the evolutionary trajectories of CAFs. We investigated the functional profiles of each CAF subtype using Single-Cell Regulatory Network Inference and Clustering and single-cell gene set enrichment analysis. The clinical relevance of these subtypes was assessed through survival curve analysis. Concurrently, we employed multiplex immunofluorescence staining on tumour tissues to determine the dynamic changes of CAF subtypes across different tumour stages. Additionally, we identified the small molecule procyanidin C1 (PCC1) as a target for matrix-producing CAF (matCAF) using molecular docking techniques and further validated these findings through in vitro and in vivo experiments.ResultsIn our investigation of solid tumours, we identified four molecular clusters of CAFs: progenitor CAF (proCAF), inflammatory CAF (iCAF), myofibroblastic CAF (myCAF) and matCAF, each characterised by distinct molecular traits. This classification was consistently applicable across all nine studied solid tumour types. These CAF subtypes displayed unique evolutionary pathways, functional roles and clinical relevance in various solid tumours. Notably, the matCAF subtype was associated with poorer prognoses in several cancer types. The targeting of matCAF using the identified small molecule, PCC1, demonstrated promising antitumour activity.ConclusionsCollectively, the various subtypes of CAFs, particularly matCAF, are crucial in the initiation and progression of cancer. Focusing therapeutic strategies on targeting matCAF in solid tumours holds significant potential for cancer treatment.
| S-EPMC10751516 | biostudies-literature
Project description:BACKGROUND:The current proposed model of colorectal tumorigenesis is based primarily on CpG island methylator phenotype (CIMP), microsatellite instability (MSI), KRAS, BRAF, and methylation status of 0-6-Methylguanine DNA Methyltransferase (MGMT) and classifies tumors into five subgroups. The aim of this study is to validate this molecular classification and test its prognostic relevance. METHODS:Three hundred two patients were included in this study. Molecular analysis was performed for five CIMP-related promoters (CRABP1, MLH1, p16INK4a, CACNA1G, NEUROG1), MGMT, MSI, KRAS, and BRAF. Methylation in at least 4 promoters or in one to three promoters was considered CIMP-high and CIMP-low (CIMP-H/L), respectively. RESULTS:CIMP-H, CIMP-L, and CIMP-negative were found in 7.1, 43, and 49.9% cases, respectively. One hundred twenty-three tumors (41%) could not be classified into any one of the proposed molecular subgroups, including 107 CIMP-L, 14 CIMP-H, and two CIMP-negative cases. The 10?year survival rate for CIMP-high patients [22.6% (95%CI: 7-43)] was significantly lower than for CIMP-L or CIMP-negative (p?=?0.0295). Only the combined analysis of BRAF and CIMP (negative versus L/H) led to distinct prognostic subgroups. CONCLUSION:Although CIMP status has an effect on outcome, our results underline the need for standardized definitions of low- and high-level CIMP, which clearly hinders an effective prognostic and molecular classification of colorectal cancer.
| S-EPMC3356027 | biostudies-literature
Project description:Immunotherapy is a mainstay of non-small cell lung cancer (NSCLC) management. While tumor mutational burden (TMB) correlates with response to immunotherapy, little is known about the relationship between the baseline immune response and tumor genotype. Using single-cell RNA sequencing, we profiled 361,929 cells from 35 early-stage NSCLC lesions. We identified a cellular module consisting of PDCD1+CXCL13+ activated T cells, IgG+ plasma cells, and SPP1+ macrophages, referred to as the lung cancer activation module (LCAMhi). We confirmed LCAMhi enrichment in multiple NSCLC cohorts, and paired CITE-seq established an antibody panel to identify LCAMhi lesions. LCAM presence was found to be independent of overall immune cell content and correlated with TMB, cancer testis antigens, and TP53 mutations. High baseline LCAM scores correlated with enhanced NSCLC response to immunotherapy even in patients with above median TMB, suggesting that immune cell composition, while correlated with TMB, may be a nonredundant biomarker of response to immunotherapy.
| S-EPMC8728963 | biostudies-literature
Project description:Colorectal cancer (CRC) ranks as the second leading cause of cancer deaths globally. In recent years, short-read single-cell RNA sequencing (scRNA-seq) has been instrumental in deciphering tumor heterogeneities. However, these studies only enable gene-level quantification but neglect alterations in transcript structures arising from alternative end processing or splicing. In this study, we integrated short- and long-read scRNA-seq of CRC samples to build an isoform-resolution CRC transcriptomic atlas. We identified 394 dysregulated transcript structures in tumor epithelial cells, including 299 resulting from various combinations of splicing events. Second, we characterized genes and isoforms associated with epithelial lineages and subpopulations exhibiting distinct prognoses. Among 31,935 isoforms with novel junctions, 330 were supported by The Cancer Genome Atlas RNA-seq and mass spectrometry data. Finally, we built an algorithm that integrated novel peptides derived from open reading frames of recurrent tumor-specific transcripts with mass spectrometry data and identified recurring neoepitopes that may aid the development of cancer vaccines.
| S-EPMC11480860 | biostudies-literature
Project description:The brain consists of organized ensembles of cells that exhibit distinct morphologies, cellular connectivity, and dynamic biochemistries that control the executive functions of an organism. However, the relationships between chemical heterogeneity, cell function, and phenotype are not always understood. Recent advancements in matrix-assisted laser desorption/ionization mass spectrometry have enabled the high-throughput, multiplexed chemical analysis of single cells, capable of resolving hundreds of molecules in each mass spectrum. We developed a machine learning workflow to classify single cells according to their mass spectra based on cell groups of interest (GOI), e.g., neurons vs astrocytes. Three data sets from various cell groups were acquired on three different mass spectrometer platforms representing thousands of individual cell spectra that were collected and used to validate the single cell classification workflow. The trained models achieved >80% classification accuracy and were subjected to the recently developed instance-based model interpretation framework, SHapley Additive exPlanations (SHAP), which locally assigns feature importance for each single-cell spectrum. SHAP values were used for both local and global interpretations of our data sets, preserving the chemical heterogeneity uncovered by the single-cell analysis while offering the ability to perform supervised analysis. The top contributing mass features to each of the GOI were ranked and selected using mean absolute SHAP values, highlighting the features that are specific to the defined GOI. Our approach provides insight into discriminating the chemical profiles of the single cells through interpretable machine learning, facilitating downstream analysis and validation.
| S-EPMC7374983 | biostudies-literature