Single-cell analysis of human non-small cell lung cancer lesions refines tumor classification and patient stratification

Cancer Cell ◽  
2021 ◽  
Author(s):  
Andrew M. Leader ◽  
John A. Grout ◽  
Barbara B. Maier ◽  
Barzin Y. Nabet ◽  
Matthew D. Park ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Single Cell ◽  
Cell Lung Cancer ◽  
Single Cell Analysis ◽  
Small Cell ◽  
Tumor Classification ◽  
Patient Stratification ◽  
Cell Analysis ◽  
Small Cell Lung

scholarly journals Fragmentation of Small-cell Lung Cancer Regulatory States in Heterotypic Microenvironments

2020 ◽  
Author(s):  
Dylan L. Schaff ◽  
Shambhavi Singh ◽  
Kee-Beom Kim ◽  
Matthew D. Sutcliffe ◽  
Kwon-Sik Park ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Single Cell ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Neuroendocrine Cells ◽  
Fluctuation Analysis ◽  
Alveolar Type ◽  
Small Cell Lung ◽  
Primary Tumors

AbstractSmall-cell lung cancers derive from pulmonary neuroendocrine cells, which have stemlike properties to reprogram into other cell types upon lung injury. It is difficult to uncouple the plasticity of these transformed cells from heritable changes that evolve in primary tumors or select in metastases to distant organs. Approaches to single-cell profiling are also problematic if the required sample dissociation activates injury-like signaling and reprogramming. Here, we defined cell-state heterogeneities in situ through laser capture microdissection-based 10-cell transcriptomics coupled with stochastic-profiling fluctuation analysis. Using labeled cells from a small-cell lung cancer mouse model initiated by neuroendocrine deletion of p53 and Rb, we profiled cell-to-cell transcriptional-regulatory heterogeneity in spheroid cultures and liver colonies seeded intravenously. Fluctuating transcripts in vitro were partly shared with other epithelial-spheroid models, and candidate heterogeneities increased considerably when cells were delivered to the liver. Colonization of immunocompromised animals drove the fractional appearance of alveolar type II-like markers and poised cells for paracrine stimulation from immune cells and hepatocytes. Immunocompetency further exaggerated the fragmentation of tumor states in the liver, yielding mixed stromal signatures evident in bulk sequencing from autochthonous tumors and metastases. We identified dozens of transcript heterogeneities that recur irrespective of biological context; their mapped orthologs brought together observations of murine and human small-cell lung cancer. Candidate heterogeneities recurrent in the liver also stratified primary human tumors into discrete groups not readily explained by molecular subtype. We conclude that heterotypic interactions in the liver and lung are an accelerant for intratumor heterogeneity in small-cell lung cancer.Statement of significanceThe single-cell regulatory heterogeneity of small-cell lung cancer becomes increasingly elaborate in the liver, a common metastatic site for the disease.

scholarly journals Morphological Subtypes of Tumor Spread Through Air Spaces in Non-Small Cell Lung Cancer: Prognostic Heterogeneity and Its Underlying Mechanism

2021 ◽  
Vol 11 ◽  
Author(s):  
Huikang Xie ◽  
Hang Su ◽  
Erjia Zhu ◽  
Chang Gu ◽  
Shengnan Zhao ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Prospective Study ◽  
Small Cell Lung Cancer ◽  
Single Cell ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Tumor Spread ◽  
Specimen Handling ◽  
Spread Through Air Spaces

BackgroundTumor spread through air spaces (STAS) has three morphologic subtypes: single cells, micropapillary clusters, and solid nests. However, whether their respective clinical significance is similar remains unclear.MethodsWe retrospectively reviewed 803 patients with resected non-small cell lung cancer (NSCLC) from January to December 2009. Recurrence-free survival (RFS) and overall survival (OS) were compared among patients stratified by STAS subtypes. We also performed a prospective study of NSCLC resection specimens to evaluate the influence of a prosecting knife on the presence of STAS subtypes during specimen handling (83 cases).ResultsSTAS was found in 370 NSCLCs (46%), including 47 single cell STAS (13%), 187 micropapillary cluster STAS (50%), and 136 solid nest STAS (37%). STAS-negative patients had significantly better survival than patients with micropapillary cluster STAS (RFS: P < 0.001; OS: P < 0.001) and solid nest STAS (RFS: P < 0.001; OS: P < 0.001), but similar survival compared with those with single cell STAS (RFS: P = 0.995; OS: P = 0.71). Multivariate analysis revealed micropapillary cluster (RFS: P < 0.001; OS: P < 0.001) and solid nest STAS (RFS: P = 0.001; OS: P = 0.003) to be an independent prognostic indicator, but not for single cell STAS (RFS: P = 0.989; OS: P = 0.68). Similar results were obtained in subgroup analysis of patients with adenocarcinoma. The prospective study of NSCLC specimens suggested that 18 cases were considered as STAS false-positive, and most were singe cell pattern (13/18, 72%).ConclusionsSingle cell STAS was the common morphologic type of artifacts produced by a prosecting knife. A precise protocol of surgical specimen handling is required to minimize artifacts as much as possible.

scholarly journals Gene Expression Profiling as a Potential Tool for Precision Oncology in Non-Small Cell Lung Cancer

Cancers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 4734
Author(s):  
Sara Hijazo-Pechero ◽  
Ania Alay ◽  
Raúl Marín ◽  
Noelia Vilariño ◽  
Cristina Muñoz-Pinedo ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Transcriptional Profiling ◽  
Acquired Resistance ◽  
Molecular Classification ◽  
Small Cell ◽  
Precision Oncology ◽  
Patient Stratification ◽  
Small Cell Lung

Recent technological advances and the application of high-throughput mutation and transcriptome analyses have improved our understanding of cancer diseases, including non-small cell lung cancer. For instance, genomic profiling has allowed the identification of mutational events which can be treated with specific agents. However, detection of DNA alterations does not fully recapitulate the complexity of the disease and it does not allow selection of patients that benefit from chemo- or immunotherapy. In this context, transcriptional profiling has emerged as a promising tool for patient stratification and treatment guidance. For instance, transcriptional profiling has proven to be especially useful in the context of acquired resistance to targeted therapies and patients lacking targetable genomic alterations. Moreover, the comprehensive characterization of the expression level of the different pathways and genes involved in tumor progression is likely to better predict clinical benefit from different treatments than single biomarkers such as PD-L1 or tumor mutational burden in the case of immunotherapy. However, intrinsic technical and analytical limitations have hindered the use of these expression signatures in the clinical setting. In this review, we will focus on the data reported on molecular classification of non-small cell lung cancer and discuss the potential of transcriptional profiling as a predictor of survival and as a patient stratification tool to further personalize treatments.

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