CEA, CYFRA21-1 and NSE are tumor markers used for monitoring the response to chemotherapy in advanced adenocarcinoma, squamous cell carcinoma and small-cell lung cancer, respectively. Their role in cancer immunotherapy needs to be elucidated. Patients with advanced non-small cell lung cancer (NSCLC) were treated with nivolumab 3 mg/kg every 2 weeks within the Italian Nivolumab Expanded Access Program. Blood samples were collected at baseline, at each cycle up to cycle 5 and then every two cycles until patient’s withdrawn from the study. All patients underwent a CT-scan after every 4 cycles of treatment and responses were classified according to RECIST 1.1. The biomarkers serum levels were measured with a chemiluminescent microparticle immunoassay for CEA and with an immuno radiometric assay for CYFRA21-1 and NSE. The markers values at baseline and after 4 cycles were used to analyze the relationship between their variation over baseline and the tumor response, evaluated as disease control rate (DCR: CR + PR + SD), and survival (PFS and OS). A total of 70 patients were evaluable for the analysis. Overall, a disease control was obtained in 24 patients (35.8%, 4 PR + 20 SD). After 4 cycles of nivolumab a CEA or CYFRA21-1 reduction ≥ 20% over the baseline was significantly associated with DCR (CEA, p = 0.021; CYFRA21-1, p < 0.001), PFS (CEA, p = 0.028; CYFRA21-1, p < 0.001) and OS (CEA, p = 0.026; CYFRA21-1, p = 0.019). Multivariate analysis confirmed the ability of CYFRA21-1 reduction ≥ 20% to predict DCR (p = 0.002) and PFS (p < 0.001). The reduction in serum level of CYFRA21-1 or CEA might be a reliable biomarker to predict immunotherapy efficacy in NSCLC patients. NSE was not significant for monitoring the efficacy of nivolumab.

Radiomics is defined as the use of automated or semi-automated post-processing and analysis of multiple features derived from imaging exams. Extracted features might generate models able to predict the molecular profile of solid tumors. The aim of this study was to develop a predictive algorithm to define the mutational status of EGFR in treatment-naïve patients with advanced non–small cell lung cancer (NSCLC). CT scans from 109 treatment-naïve patients with NSCLC (21 EGFR-mutant and 88 EGFR-wild type) underwent radiomics analysis to develop a machine learning model able to recognize EGFR-mutant from EGFR-WT patients via CT scans. A “test–retest” approach was used to identify stable radiomics features. The accuracy of the model was tested on an external validation set from another institution and on a dataset from the Cancer Imaging Archive (TCIA). The machine learning model that considered both radiomic and clinical features (gender and smoking status) reached a diagnostic accuracy of 88.1% in our dataset with an AUC at the ROC curve of 0.85, whereas the accuracy values in the datasets from TCIA and the external institution were 76.6% and 83.3%, respectively. Furthermore, 17 distinct radiomics features detected at baseline CT scan were associated with subsequent development of T790M during treatment with an EGFR inhibitor. In conclusion, our machine learning model was able to identify EGFR-mutant patients in multiple validation sets with globally good accuracy, especially after data optimization. More comprehensive training sets might result in further improvement of radiomics-based algorithms. Significance: These findings demonstrate that data normalization and “test–retest” methods might improve the performance of machine learning models on radiomics images and increase their reliability when used on external validation datasets.

Immune checkpoint blockade represents a major breakthrough in advanced non-small cell lung cancer (NSCLC) therapy. However, success is limited to a subset of patients and there is a critical need to identify robust biomarkers associated with clinical response. In this study, we assessed whether pre-existing immunological characteristics, as well as immune parameters measured during treatment, might provide such clinical guidance. We studied blood samples collected at baseline and during treatment in a cohort of advanced NSCLC patients (n = 74) treated with nivolumab. Several lymphocyte subsets and biomarkers were then correlated with overall survival (OS) as well as clinical response, assessed using RECIST criteria. We found that patients characterized by longer OS had higher levels of CD3⁺, CD4⁺, and CD8⁺ T cells but lower levels of NK cells at baseline. Moreover, that they displayed a statistically significant lower expression of PD-1 on both CD3⁺ and CD8⁺ T cells (p = 0.013 and p = 0.033, respectively). The pre-treatment level of exhausted T cells (CD8⁺PD1⁺Eomes⁺) was significantly lower in patients with controlled disease (CD), defined as partial response (PR), and stable disease (SD), compared to those with progressive disease (PD) (p = 0.046). In CD patients, the frequency of exhausted CD8⁺ T cells further decreased during treatment cycles (p = <0.0001, p = 0.0032, and p = 0.0239, respectively). In conclusion, our results suggest that the distribution of lymphocyte subsets and expression of PD-1 on T cells before treatment may help predict the outcome of anti-PD-1 treatment in NSCLC patients. In addition, assessing the initial levels of exhausted T cells as well as their decrease upon treatment may also predict response and clinical outcome.

Circulating tumor cells (CTCs) are cells spread from the primary tumor into the bloodstream that might represent an important biomarker in lung cancer. The prognosis of patients diagnosed with lung cancer is generally poor mainly due to late diagnosis. Recent evidences have reported that tumor aggressiveness is associated with the presence of CTCs in the blood stream; therefore several studies have focused their attention on CTC isolation, characterization and clinical significance. So far the CellSearch® system is the only approach approved by FDA for metastatic breast, prostate and colorectal cancer intended to detect CTCs of epithelial origin in whole blood and to assess prognosis. To date no specific biomarkers have been validated in lung cancer and the identification of novel tumor markers such as CTCs might highly contribute to lung cancer prognosis and management. In the present review the significance of CTC detection in lung cancer is examined through the analysis of the published studies in both non-small cell and small cell lung cancers; additionally the prognostic and the clinical role of CTC enumeration in treatment monitoring will be reported and discussed.