Dataset Information

Impact of Pulmonary Tuberculosis on the EGFR Mutational Status and Clinical Outcome in Patients with Lung Adenocarcinoma.

ABSTRACT:

Purpose

Although it has been suggested that pulmonary tuberculosis (TB) is associated with increased risk of lung cancer, the exact mechanism is not clearly identified. We investigated the effect of pulmonary TB on the epidermal growth factor receptor (EGFR) mutational status and clinical outcome in patients with pulmonary adenocarcinoma.

Materials and methods

We reviewed data of patients diagnosed with pulmonary adenocarcinoma harboring EGFR mutations and treated at our institution from 2008 to 2015. We divided our population into two groups: patients with pre-existing TB lesions on chest computed tomography scan (TB group) and those without the lesions (non-TB group). We compared the differences in EGFR mutational status, response to tyrosine kinase inhibitors (TKIs) and survival between the two groups.

Results

A total of 477 patients with pulmonary adenocarcinoma were analyzed. One hundred eighty-three patients (39%) had EGFR-mutated tumors and 100 (21%) patients had pre-existing TB lesions. The frequency of EGFR mutation was significantly higher in the TB group compared with the non-TB group (56% vs. 34%, p=0.038). Pre-existing TB lesions were independently associated with more frequent EGFR mutations in multivariate analysis (odds ratio, 1.43). In addition, both the progression-free survival (9.1 months vs. 11.6 months, p=0.020) and the overall survival (19.4 months vs. 24.5 months, p=0.014) after first-line EGFR-TKIs were significantly shorter in the TB group than in the non-TB group.

Conclusion

Previous pulmonary TB may be associated with more frequent EGFR mutations and poorer treatment response to EGFR-TKIs in patients with pulmonary adenocarcinoma.

SUBMITTER: Hwang IK

PROVIDER: S-EPMC6333978 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundRecently, extensive cancer genomic studies have revealed mutational and clinical data of large cohorts of cancer patients. For example, the Pan-Lung Cancer 2016 dataset (part of The Cancer Genome Atlas project), summarises the mutational and clinical profiles of different subtypes of Lung Cancer (LC). Mutational and clinical signatures have been used independently for tumour typification and prediction of metastasis in LC patients. Is it then possible to achieve better typifications and predictions when combining both data streams?MethodsIn a cohort of 1144 Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LSCC) patients, we studied the number of missense mutations (hereafter, the Total Mutational Load TML) and distribution of clinical variables, for different classes of patients. Using the TML and different sets of clinical variables (tumour stage, age, sex, smoking status, and packs of cigarettes smoked per year), we built Random Forest classification models that calculate the likelihood of developing metastasis.ResultsWe found that LC patients different in age, smoking status, and tumour type had significantly different mean TMLs. Although TML was an informative feature, its effect was secondary to the "tumour stage" feature. However, its contribution to the classification is not redundant with the latter; models trained using both TML and tumour stage performed better than models trained using only one of these variables. We found that models trained in the entire dataset (i.e., without using dimensionality reduction techniques) and without resampling achieved the highest performance, with an F1 score of 0.64 (95%CrI [0.62, 0.66]).ConclusionsClinical variables and TML should be considered together when assessing the likelihood of LC patients progressing to metastatic states, as the information these encode is not redundant. Altogether, we provide new evidence of the need for comprehensive diagnostic tools for metastasis.