Identification of a prognostic immune‐related signature for small cell lung cancer

Abstract

Purpose As a subgroup of lung cancer, small cell lung cancer (SCLC) is characterized by a short tumor doubling time, high rates of early occurred distant cancer spread, and poor outcomes. Despite its exquisite sensitivity to chemotherapy and radiotherapy, acquired drug resistance and tumor progression are typical. This study aimed to develop a robust signature based on immune‐related genes to predict the outcome of patients with SCLC. Methods The expression data of 77 SCLC patients from George's cohort were divided into training set and testing set, and 1534 immune‐related genes from ImmPort database were used to generate and validate the signature. Cox proportional hazards and the Kaplan–Meier analysis were used for developing and testing the prognostic signature. Single‐sample gene set enrichment analysis was used to determine immune cell infiltration phenotypes. Results A 10‐gene model comprising NR3C1, NR1D2, TANK, ARAF, HDGF, INHBE, LRSAM1, PLXNA1, PML, and SP1 with the highest frequency after 1000 interactions, was chosen to construct immune‐related signature. This signature showed robust predictive value for SCLC patients’ survival in both training and testing sets. This signature was weakly associated with the clinic pathological values like TNM stage. Furthermore, patients with low risk presented with activation of immune signal pathways, and specific immune cell infiltration with high levels of CD56bright NK cells but low levels of CD8+ T cells, mast cells, and helper T cells. Conclusion The present study developed immune‐related signature that may help predict the prognosis of SCLC patients, which reflects an unappreciated level of heterogeneity of immunophenotype associated with diverse prognosis for specific subsets in this highly lethal cancer type.

Chemo-Immunotherapy in First Line Extensive Stage Small Cell Lung Cancer (ES-SCLC): A Systematic Review and Meta-Analysis

“…In Checkmate032, a study assessing nivolumab alone or nivolumab and ipilimumab in recurrent ES-SCLC, both PD-L1 status and TMB were assessed as predictive biomarkers with no evidence of an association between PD-L1 expression and ORR [ 12 ]. In addition to biomarkers, there have been early investigations into identifying SCLC subtypes based on transcription factor expression [ 38 ], as well as varying immune signatures [ 39 ]. However, these models are not yet validated [ 40 ].…”

Comprehensive Exploration of M2 Macrophages and Its Related Genes for Predicting Clinical Outcomes and Drug Sensitivity in Lung Squamous Cell Carcinoma

“…Lung cancer, the leading cause of cancer death throughout the world, claims the lives of more than 350 people every day [ 19 ]. During the last decade, although there was a steep decline in lung cancer incidence for advanced cases based on the changes in cancer screening and treatment, only 15% of patients with NSCLC can live beyond five years [ 20 ]. LUSC, the most common pathological subtype of NSCLC, known for its high tumor heterogeneity, showed a significant therapeutic difference in immunotherapy response rate [ 21 ].…”

Diagnostic criteria and evolving molecular characterisation of pulmonary neuroendocrine carcinomas

“…95,98 A study of SCLC human tissue samples showed considerable overlap between the SCLC-I phenotype and tumours with YAP1 transcriptional predominance, and indicated that an inflamed transcriptional signature correlated inversely with expression of neuroendocrine markers. 102 Current evidence also suggests that SCLC-I are associated with longer survival than noninflamed SCLC, [101][102][103] possibly due to immune interactions. Interestingly, retrospective analysis of a small number of tissue samples from patients who responded to immune checkpoint blockade during the IMPower133 trial showed a trend toward better response in tumours with the "SCLC-I" phenotype, but included too few patients to draw definitive conclusions.…”