Background: Lung cancer (LCa) is the most frequently diagnosed and lethal cancer worldwide. Histopathological subtyping, which has important therapeutic and prognostic implications, requires material collection through invasive procedures, which might be insufficient to enable definitive diagnosis. Aberrant DNA methylation is an early event in carcinogenesis, detectable in circulating cell-free DNA (ccfDNA). Herein, we aimed to assess methylation of selected genes in ccfDNA from LCa patients and determine its accuracy for tumor subtyping. Methods: Methylation levels of APC, HOXA9, RARβ2, and RASSF1A were assessed in three independent study groups (study group #1: 152 tissue samples; study group #2: 129 plasma samples; study group #3: 28 benign lesions of lung) using quantitative methylation-specific PCR. Associations between gene promoter methylation levels and LCa subtypes were evaluated using non-parametric tests. Receiver operating characteristic (ROC) curve analysis was performed. Results: In study group #2, HOXA9 and RASSF1A displayed higher methylation levels in small-cell lung cancer (SCLC) than in non-small-cell lung cancer (NSCLC). HOXA9 displayed high sensitivity (63.8%), whereas RASSF1A disclosed high specificity (96.2%) for SCLC detection in ccfDNA. Furthermore, HOXA9 methylation levels showed to be higher in squamous cell carcinoma in comparison with adenocarcinoma in study group #1. Conclusions: Methylation level assessments in ccfDNA may provide a minimally invasive procedure for LCa subtyping, complementing standard diagnostic procedures.

Accurate classification of lung cancer, as well as the differentiation between primary and metastatic carcinoma to the lung, mostly performed on biopsy or fine needle aspiration specimens, is critical for decisions on therapy and for determining prognosis. The limited amount of biopsy material available for morphological assessment has stimulated attempts to improve diagnostic accuracy through the use of immunohistochemistry (IHC), but an optimal IHC diagnostic algorithm has not been firmly established. We evaluated, on a retrospective series of biopsy specimens, the performance of a four-antibody IHC panel for accurate subclassification of non-small cell lung carcinoma (NSCLC) and for identification of metastatic carcinoma. Tumor morphology was assessed and IHC for CK7, CK20, TTF-1, and p63 was performed according to a two-step algorithm. Matched resection specimens served as gold standard and were compared with the corresponding biopsy. Of 443 biopsy specimens studied, 325 were diagnosed as primary carcinoma of the lung, 198 (44.7 %) as adenocarcinoma, 9 (2 %) as possibly adenosquamous carcinoma, 127 (28.7 %) as squamous cell carcinoma, and 40 (9 %) as NSCLC not further classifiable. Ten cases (2.3 %) were classified as adenocarcinoma of unknown origin and 58 (13 %) as metastasis. Importantly, of the primary lung adenocarcinomas, 35 (17.7 %) had been considered on clinical grounds as a metastasis from a previously diagnosed primary tumor. Of the 55 cases submitted to surgical resection in 47 (85.5 %) the biopsy diagnosis was confirmed, revealing substantial agreement (κ value = 0.757). Our two-step approach allows for accurate subclassification of NSCLC and also to distinguish between primary lung adenocarcinoma and metastasis, notably of colorectal adenocarcinoma, with crucial implications for appropriate patient management.

Lung cancer (LC) cells frequently express high levels of programmed death-ligand 1 (PD-L1). Although these levels grossly correlate with the likelihood of response to specific checkpoint inhibitors, the response prediction is rather imperfect, and more accurate predictive biomarkers are mandatory. We examined the methylation profile of RAD51B (RAD51Bme) as a candidate predictive biomarker for anti-PD-1 therapy efficacy in non-small cell lung cancer (NSCLC), correlating with patients’ outcome. PD-L1 immunoexpression and RAD51Bme levels were analysed in NSCLC samples obtained from patients not treated with anti-PD-1 (Untreated Cohort (#1)) and patients treated with PD-1 blockade (Treated Cohort (#2)). Of a total of 127 patients assessed, 58.3% depicted PD-L1 positivity (PD-L1+). RAD51Bme levels were significantly associated with PD-L1 immunoexpression. Patients with PD-1 blockade clinical benefit disclosed higher RAD51Bme levels (p = 0.0390) and significantly lower risk of disease progression (HR 0.37; 95% CI: 0.15–0.88; p = 0.025). Combining RAD51Bme+ with PD-L1+ improved the sensitivity of the test to predict immunotherapy response. PD-L1+ was also associated with lower risk of death (HR 0.35; 95% CI: 0.15–0.81; p = 0.014). Thus, RAD51Bme levels might be combined with validated predictive biomarker PD-L1 immunostaining to select patients who will most likely experience clinical benefit from PD-1 blockade. The predictive value of RAD51Bme should be confirmed in prospective studies.

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