Dataset Information

Risk stratification for lung adenocarcinoma on EGFR and TP53 mutation status, chemotherapy, and PD-L1 immunotherapy.

ABSTRACT: The overall survival rates for lung cancer remain unsatisfactorily low, even for patients with biomarkers for which target therapies or immunotherapies are recommended. Better identification of at-risk patients is needed to achieve more effective personalized treatment. Here, we derived a risk-stratifying gene signature consisting of five genes that had the greatest differential expression by stage from lung adenocarcinoma (LUAD) transcriptomes. The new gene signature enabled survival prognosis for multiple LUAD datasets from different platforms of transcriptomics and risk stratification for patients with and without a mutation in TP53 or EGFR, with high and low levels of PD-L1, and with and without adjuvant chemotherapy treatment. Using these evaluations, it was also shown to be more robust compared to several other gene signatures. Functional analysis of the five genes and their protein-protein interaction partners indicated that they are functionally enriched in cell cycle, endocytosis, and EGFR regulation, which are biological processes associated with lung cancer and drug resistance. Extensive discussions on related experimental studies suggest that the five genes are novel and sensible targets for developing new drugs and/or tackling drug resistance problems for LUAD.

SUBMITTER: Wu CH

PROVIDER: S-EPMC6792489 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Risk stratification for lung adenocarcinoma on EGFR and TP53 mutation status, chemotherapy, and PD-L1 immunotherapy.

Wu Chih-Hsun CH Hwang Ming-Jing MJ

Cancer medicine 20190813 13

The overall survival rates for lung cancer remain unsatisfactorily low, even for patients with biomarkers for which target therapies or immunotherapies are recommended. Better identification of at-risk patients is needed to achieve more effective personalized treatment. Here, we derived a risk-stratifying gene signature consisting of five genes that had the greatest differential expression by stage from lung adenocarcinoma (LUAD) transcriptomes. The new gene signature enabled survival prognosis ...[more]

PMID: 31407494

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Project description:BackgroundDespite disappointing outcomes from immuno-monotherapy, studies reported that NSCLC patients with EGFR mutation may possibly benefit from combined immunotherapy. Whether the response to prior EGFR-TKI has association with the outcomes of subsequent immunotherapy remains unclear.Patients and methodsAdvanced NSCLC patients with resistance to EGFR-TKIs and received ICI treatment from January 2016 to June 2019 were retrospectively analyzed. Single cell sequencing and flow cytometry were conducted to explore the difference of cell components in tumor microenvironments (TME). A 1:3 matched case-control study was conducted to compare the clinical effects of combined immunotherapy with standard chemotherapy as second-line treatment.ResultsFifty-eight patients treated with anti-PD-1/PD-L1 based immunotherapy behind EGFR-TKI treatment were enrolled. Correlation analysis showed TKI-PFS had a significantly negative association with corresponding IO-PFS (r = -0.35, p = 0.006). TKI-PFS cutoff 10 months had the most significant predictive function for posterior immunotherapy and was validated to be an independent predictor by uni- and multivariate analyses. Kaplan-Meier analysis showed that patients with TKI-PFS less than 10 months had significantly prolonged IO-PFS and higher ORR than those with long (median PFS, 15.1 vs 3.8 months; HR, 0.26, p = 0.0002; ORR, 31.8 versus 10%, p = 0.04). Single cell RNA-seq revealed that the cell components were varied among patients after treatment with EGFR-TKI. Patients with short TKI-PFS demonstrated a relatively higher proportion of CD8 effector cells and lower ratio of M2 like macrophage to M1 like macrophages, which was validated by flow cytometry. Case-control study demonstrated that combined immunotherapy achieved significantly longer PFS (HR, 0.51, 95% CI: 0.31-0.85, p = 0.02), longer OS (HR, 0.48, 95% CI: 0.26-0.89, p = 0.05) and higher ORR (33.3 vs 10.0%, p = 0.02) than traditional chemotherapy for patients with short TKI-PFS.ConclusionPatients with short TKI-PFS conferred better response to immunotherapy than those with long. The status of TME were different among those two populations. Combined ICI treatment could promisingly be a better choice than classical chemotherapy in second-line setting for patients with short TKI-PFS and no T790M mutation. Underlying mechanisms need to be further explored.

Project description:ObjectiveThe detection of epidermal growth factor receptor (EGFR) mutation and programmed death ligand-1 (PD-L1) expression status is crucial to determine the treatment strategies for patients with non-small-cell lung cancer (NSCLC). Recently, the rapid development of radiomics including but not limited to deep learning techniques has indicated the potential role of medical images in the diagnosis and treatment of diseases.MethodsEligible patients diagnosed/treated at the West China Hospital of Sichuan University from January 2013 to April 2019 were identified retrospectively. The preoperative CT images were obtained, as well as the gene status regarding EGFR mutation and PD-L1 expression. Tumor region of interest (ROI) was delineated manually by experienced respiratory specialists. We used 3D convolutional neural network (CNN) with ROI information as input to construct a classification model and established a prognostic model combining deep learning features and clinical features to stratify survival risk of lung cancer patients.ResultsThe whole cohort (N = 1262) was divided into a training set (N = 882, 70%), validation set (N = 125, 10%), and test set (N = 255, 20%). We used a 3D convolutional neural network (CNN) to construct a prediction model, with AUCs of 0.96 (95% CI: 0.94-0.98), 0.80 (95% CI: 0.72-0.88), and 0.73 (95% CI: 0.63-0.83) in the training, validation, and test cohorts, respectively. The combined prognostic model showed a good performance on survival prediction in NSCLC patients (C-index: 0.71).ConclusionIn this study, a noninvasive and effective model was proposed to predict EGFR mutation and PD-L1 expression status as a clinical decision support tool. Additionally, the combination of deep learning features with clinical features demonstrated great stratification capabilities in the prognostic model. Our team would continue to explore the application of imaging markers for treatment selection of lung cancer patients.

Dataset Information

Risk stratification for lung adenocarcinoma on EGFR and TP53 mutation status, chemotherapy, and PD-L1 immunotherapy.

Publications

Risk stratification for lung adenocarcinoma on EGFR and TP53 mutation status, chemotherapy, and PD-L1 immunotherapy.

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