P35.29 The Genomic Landscape of Lung Cancer Patients Highlights Age-Dependent Mutation Frequencies and Clinical Actionability in Young Patients

Background and Objectives: Identifying risk factors associated with psychiatrist-confirmed anxiety and depression among young lung cancer patients is very difficult because the incidence and prevalence rates are obviously lower than in middle-aged or elderly patients. Due to the nature of these rare events, logistic regression may not successfully identify risk factors. Therefore, this study aimed to propose a novel algorithm for solving this problem. Materials and Methods: A total of 1022 young lung cancer patients (aged 20–39 years) were selected from the National Health Insurance Research Database in Taiwan. A novel algorithm that incorporated a k-means clustering method with v-fold cross-validation into multiple correspondence analyses was proposed to optimally determine the risk factors associated with the depression and anxiety of young lung cancer patients. Results: Five clusters were optimally determined by the novel algorithm proposed in this study. Conclusions: The novel Multiple Correspondence Analysis–k-means (MCA–k-means) clustering algorithm in this study successfully identified risk factors associated with anxiety and depression, which are considered rare events in young patients with lung cancer. The clinical implications of this study suggest that psychiatrists need to be involved at the early stage of initial diagnose with lung cancer for young patients and provide adequate prescriptions of antipsychotic medications for young patients with lung cancer.

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The role of CD28 in the prognosis of young lung adenocarcinoma patients

BMC Cancer ◽

10.1186/s12885-020-07412-0 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Dantong Sun ◽

Lu Tian ◽

Tiantian Bian ◽

Han Zhao ◽

Junyan Tao ◽

...

Keyword(s):

Lung Cancer ◽

Lung Adenocarcinoma ◽

Cancer Patients ◽

Cox Regression ◽

Expression Patterns ◽

Young Patients ◽

Disease Free Survival ◽

Clinical Information ◽

Primary Tumors ◽

Lung Cancer Patients

Abstract Background The prognosis of lung cancer was found to be associated with a series of biomarkers related to the tumor immune microenvironment (TIME), which can modulate the biological behaviors and consequent outcomes of lung cancer. Therefore, establishing a prognostic model based on the TIME for lung cancer patients, especially young patients with lung adenocarcinoma (LUAD), is urgently needed. Methods In all, 809 lung cancer patients from the TCGA database and 71 young patients with LUAD in our center were involved in this study. Univariate and multivariate analysis based on clinical characteristics and TIME-related expression patterns (as evaluated by IHC) were performed to estimate prognosis and were verified by prognostic nomograms. Results Both LUAD and lung cancer patients with high CD28 expression had shorter disease-free survival (DFS) (P = 0.0011; P = 0.0001) but longer overall survival (OS) (P = 0.0001; P = 0.0282). TIME-related molecules combined with clinical information and genomic signatures could predict the prognosis of young patients with LUAD with robust efficiency and could be verified by the established nomogram based on the Cox regression model. In addition, CD28 expression was correlated with an abundance of lymphocytes and could modulate the TIME. Higher CD28 levels were observed in primary tumors than in metastatic tissues. Conclusion TIME-related molecules were identified as compelling biomarkers for predicting the prognosis of lung cancer, especially in a cohort of young patients. Furthermore, CD28, which is associated with poor DFS but long OS, might participate in the modulation of the TIME and has a different role in the prognosis of young patients with LUAD.

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