scispace - formally typeset
Open AccessJournal ArticleDOI

Single-cell profiling of tumor heterogeneity and the microenvironment in advanced non-small cell lung cancer

TLDR
In this paper, the authors analyzed 42 tissue biopsy samples from stage III/IV NSCLC patients by single cell RNA sequencing and presented the large scale, single cell resolution profiles of advanced NSCLCs.
Abstract
Lung cancer is a highly heterogeneous disease. Cancer cells and cells within the tumor microenvironment together determine disease progression, as well as response to or escape from treatment. To map the cell type-specific transcriptome landscape of cancer cells and their tumor microenvironment in advanced non-small cell lung cancer (NSCLC), we analyze 42 tissue biopsy samples from stage III/IV NSCLC patients by single cell RNA sequencing and present the large scale, single cell resolution profiles of advanced NSCLCs. In addition to cell types described in previous single cell studies of early stage lung cancer, we are able to identify rare cell types in tumors such as follicular dendritic cells and T helper 17 cells. Tumors from different patients display large heterogeneity in cellular composition, chromosomal structure, developmental trajectory, intercellular signaling network and phenotype dominance. Our study also reveals a correlation of tumor heterogeneity with tumor associated neutrophils, which might help to shed light on their function in NSCLC. Comprehensive profiles of tumour and microenvironment are critical to understand heterogeneity in non-small cell lung cancer (NSCLC). Here, the authors profile 42 late-stage NSCLC patients with single-cell RNA-seq, revealing immune landscapes that are associated with cancer subtype or heterogeneity.

read more

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI

Adaptive immune resistance at the tumour site: mechanisms and therapeutic opportunities

TL;DR: Why defining AIR mechanisms at the tumour site should be a key focus to direct future drug development as well as practical approaches to improve current cancer therapy are discussed.
Journal ArticleDOI

Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer

TL;DR: This article used GeoMx spatial profiling and single-cell RNA sequencing to identify cell-associated bacteria and the host cells with which they interact, as well as uncovering alterations in transcriptional pathways that are involved in inflammation, metastasis, cell dormancy and DNA repair.
Journal ArticleDOI

An Epigenetic Role of Mitochondria in Cancer

TL;DR: The roles of mitochondria in key metabolites required for epigenetics modification and in cell fate regulation are summarized and the current strategy in cancer therapies via targeting epigenetic modifiers and related enzymes in metabolic regulation is discussed.
Journal ArticleDOI

High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer

TL;DR: In this paper , the authors dissected the non-small cell lung cancer (NSCLC) tumor microenvironment at high resolution by integrating 1.283,972 single cells from 556 samples and 318 patients across 29 datasets, including cells with low mRNA content.
References
More filters
Journal ArticleDOI

Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks

TL;DR: Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
Journal ArticleDOI

GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses.

TL;DR: GEPIA (Gene Expression Profiling Interactive Analysis) fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources.
Journal ArticleDOI

The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells

TL;DR: Monocle is described, an unsupervised algorithm that increases the temporal resolution of transcriptome dynamics using single-cell RNA-Seq data collected at multiple time points that revealed switch-like changes in expression of key regulatory factors, sequential waves of gene regulation, and expression of regulators that were not known to act in differentiation.
Related Papers (5)