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Spatial profiling of non-small cell lung cancer provides insights into tumorigenesis and immunotherapy response.

ABSTRACT: Lung cancer is the second most common cancer worldwide and a leading cause of cancer-related deaths. Despite advances in targeted therapy and immunotherapy, the prognosis remains unfavorable, especially in metastatic cases. This study aims to identify molecular changes in non-small cell lung cancer (NSCLC) patients based on their response to treatment. Using tumor and matched immune cell rich peritumoral tissues, we perform a retrospective, comprehensive spatial transcriptomic analysis of a proven malignant NSCLC sample treated with immune checkpoint inhibitor (ICI). In addition to T cells, other immune cell types, such as B cells and macrophages, were also activated in responders to ICI treatment. In particular, B cells and B cell-mediated immunity pathways are consistently found to be activated. Analysis of the histologic subgroup (lung squamous cell carcinoma, LUSC; lung adenocarcinoma, LUAD) of NSCLC also confirms activation of B cell mediated immunity. Analysis of B cell subtypes shows that B cell subtypes were more activated in immune cell-rich tissues near tumor tissue. Furthermore, increased expression of B cell immunity-related genes is associated with better prognosis. These findings provide insight into predicting ICI treatment responses and identifying appropriate candidates for immunotherapy in NSCLC patients.

SUBMITTER: Kim J

PROVIDER: S-EPMC11297140 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Spatial profiling of non-small cell lung cancer provides insights into tumorigenesis and immunotherapy response.

Kim Joon J Yong Seung Hyun SH Jang Gyuho G Kim Yumin Y Park Raekil R Koh Hyun-Hee HH Kim Sehui S Oh Chang-Myung CM Lee Sang Hoon SH

Communications biology 20240802 1

Lung cancer is the second most common cancer worldwide and a leading cause of cancer-related deaths. Despite advances in targeted therapy and immunotherapy, the prognosis remains unfavorable, especially in metastatic cases. This study aims to identify molecular changes in non-small cell lung cancer (NSCLC) patients based on their response to treatment. Using tumor and matched immune cell rich peritumoral tissues, we perform a retrospective, comprehensive spatial transcriptomic analysis of a prov ...[more]

PMID: 39095464

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Project description:BackgroundAdvanced non-small cell lung cancer (NSCLC) presents significant treatment challenges, with chemo-immunotherapy emerging as a promising approach. This study explores the potential of lipidomic biomarkers to predict responses to chemo-immunotherapy in advanced non-small cell lung cancer (NSCLC) patients.MethodsA prospective analysis was conducted on 68 NSCLC patients undergoing chemo-immunotherapy, divided into disease control (DC) and progressive disease (PD) groups based on treatment response. Pre-treatment serum samples were subjected to lipidomic profiling using liquid chromatography-mass spectrometry (LC-MS). Key predictive lipids (biomarkers) were identified through projection to latent structures discriminant analysis. A biomarker combined model and a clinical combined model were developed to enhance the prediction accuracy. The predictive performances of the clinical combined model in different histological subtypes were also performed.ResultsSix lipids were identified as the key lipids. The expression levels of PC(16:0/18:2), PC(16:0/18:1), PC(16:0/18:0), CE(20:1), and PC(14:0/18:1) were significantly up-regulated. While the expression level of TAG56:7-FA18:2 was significantly down-regulated. The biomarker combined model demonstrated a receiver operating characteristic (ROC) curve of 0.85 (95% CI: 0.75-0.95) in differentiating the PD from the DC. The clinical combined model exhibited an AUC of 0.87 (95% CI: 0.79-0.96) in differentiating the PD from the DC. The clinical combined model demonstrated good discriminability in DC and PD patients in different histological subtypes with the AUC of 0.78 (95% CI: 0.62-0.96), 0.79 (95% CI: 0.64-0.94), and 0.86 (95% CI: 0.52-1.00) in squamous cell carcinoma, large cell carcinoma, and adenocarcinoma subtype, respectively. Pathway analysis revealed the metabolisms of linoleic acid, alpha-linolenic acid, glycerolipid, arachidonic acid, glycerophospholipid, and steroid were implicated in the chemo-immunotherapy response in advanced NSCLC.ConclusionLipidomic profiling presents a highly accurate method for predicting responses to chemo-immunotherapy in patients with advanced NSCLC, offering a potential avenue for personalized treatment strategies.

Project description:Lung Cancer remains the leading cause of cancer deaths in the USA and worldwide. Non-small cell lung cancer (NSCLC) harbors high transcriptomic intratumor heterogeneity (RNA-ITH) that limits the reproducibility of expression-based prognostic models. In this study, we used multiregional RNA-seq data (880 tumor samples from 350 individuals) from both public (TRACERx) and internal (MDAMPLC) cohorts to investigate the effect of RNA-ITH on prognosis in localized NSCLC at the gene, signature, and tumor microenvironment levels. At the gene level, the maximal expression of hazardous genes (expression negatively associated with survival) but the minimal expression of protective genes (expression positively associated with survival) across different regions within a tumor were more prognostic than the average expression. Following that, we examined whether multiregional expression profiling can improve the performance of prognostic signatures. We investigated 11 gene signatures collected from previous publications and one signature developed in this study. For all of them, the prognostic prediction accuracy can be significantly improved by converting the regional expression of signature genes into sample-specific expression with a simple function-taking the maximal expression of hazardous genes and the minimal expression of protective genes. In the tumor microenvironment, we found a similar rule also seems applicable to immune ITH. We calculated the infiltration levels of major immune cell types in each region of a sample based on expression deconvolution. Prognostic analysis indicated that the region with the lowest infiltration level of protective or highest infiltration level of hazardous immune cells determined the prognosis of NSCLC patients. Our study highlighted the impact of RNA-ITH on the prognostication of NSCLC, which should be taken into consideration to optimize the design and application of expression-based prognostic biomarkers and models. Multiregional assays have the great potential to significantly improve their applications to prognostic stratification.

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Spatial profiling of non-small cell lung cancer provides insights into tumorigenesis and immunotherapy response.

Publications

Spatial profiling of non-small cell lung cancer provides insights into tumorigenesis and immunotherapy response.

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