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Computational immune profiling in lung adenocarcinoma reveals reproducible prognostic associations with implications for immunotherapy.

ABSTRACT: Non-small cell lung cancer is one of the leading causes of cancer-related death in the world. Lung adenocarcinoma, the most common type of non-small cell lung cancer, has been well characterized as having a dense lymphocytic infiltrate, suggesting that the immune system plays an active role in shaping this cancer's growth and development. Despite these findings, our understanding of how this infiltrate affects patient prognosis and its association with lung adenocarcinoma-specific clinical factors remains limited. To address these questions, we inferred the infiltration level of six distinct immune cell types from a series of four lung adenocarcinoma gene expression datasets. We found that naive B cell, CD8+ T cell, and myeloid cell-derived expression signals of immune infiltration were significantly predictive of patient survival in multiple independent datasets, with B cell and CD8+ T cell infiltration associated with prolonged prognosis and myeloid cell infiltration associated with shorter survival. These associations remained significant even after accounting for additional clinical variables. Patients stratified by smoking status exhibited decreased CD8+ T cell infiltration and altered prognostic associations, suggesting potential immunosuppressive mechanisms in smokers. Survival analyses accounting for immune checkpoint gene expression and cellular immune infiltrate indicated checkpoint protein-specific modulatory effects on CD8+ T cell and B cell function that may be associated with patient sensitivity to immunotherapy. Together, these analyses identified reproducible associations that can be used to better characterize the role of immune infiltration in lung adenocarcinoma and demonstrate the utility in using computational approaches to systematically characterize tissue-specific tumor-immune interactions.

SUBMITTER: Varn FS

PROVIDER: S-EPMC5980421 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Computational immune profiling in lung adenocarcinoma reveals reproducible prognostic associations with implications for immunotherapy.

Varn Frederick S FS Tafe Laura J LJ Amos Christopher I CI Cheng Chao C

Oncoimmunology 20180315 6

Non-small cell lung cancer is one of the leading causes of cancer-related death in the world. Lung adenocarcinoma, the most common type of non-small cell lung cancer, has been well characterized as having a dense lymphocytic infiltrate, suggesting that the immune system plays an active role in shaping this cancer's growth and development. Despite these findings, our understanding of how this infiltrate affects patient prognosis and its association with lung adenocarcinoma-specific clinical facto ...[more]

PMID: 29872556

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Project description:BackgroundLung cancer has been the focus of attention for many researchers in recent years due to its leading contribution to cancer-related death worldwide, with lung adenocarcinoma (LUAD) being the most common histological type. Ferroptosis, a novel iron-dependent form of regulated cell death, can be induced by sorafenib. Emerging evidence shows that triggering ferroptosis has potential as a cancer therapy. This work aimed to build a ferroptosis-related gene signature for predicting the outcome of LUAD.MethodsThe TCGA-LUAD dataset was set as the training cohort, and the GSE72094 and GSE68465 datasets were set as the validation cohorts. Sixty-two ferroptosis-related genes were retrieved from the literature. A univariate Cox regression model was constructed for the training cohort to preliminarily screen for potential prognostic ferroptosis-related genes. A gene signature was generated from a LASSO Cox regression model and assessed with the training and validation cohorts through Kaplan-Meier, Cox, and ROC analyses. In addition, the correlation between the risk score and autophagy-related genes was determined by the Pearson test. Finally, GSEA and immune infiltrating analyses were performed to better study the functional annotation of the signature and the role of each kind of immune cell.ResultsA ten-gene signature was constructed from the training cohort and validated in three cohorts by Kaplan-Meier and Cox regression analyses, revealing its independent prognostic value in LUAD. Moreover, a ROC analysis conducted with all cohort data confirmed the predictive ability of the ten-gene signature for LUAD prognosis. A total of 62.85% (308/490) of autophagy-related genes were found to be significantly correlated with risk scores. GSEA detailed the exact pathways related to the gene signature, and immune-infiltrating analyses identified crucial roles for resting mast cells and resting dendritic cells in the prognosis of LUAD.ConclusionsWe identified a novel ferroptosis-related ten-gene signature (PHKG2, PGD, PEBP1, NCOA4, GLS2, CISD1, ATP5G3, ALOX15, ALOX12B, and ACSL3) that can accurately predict LUAD prognosis and is closely linked to resting mast cells and resting dendritic cells.

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Dataset Information

Computational immune profiling in lung adenocarcinoma reveals reproducible prognostic associations with implications for immunotherapy.

Publications

Computational immune profiling in lung adenocarcinoma reveals reproducible prognostic associations with implications for immunotherapy.

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