Prognostic Role of Soluble and Extracellular Vesicle-Associated PD-L1, B7-H3 and B7-H4 in Non-Small Cell Lung Cancer Patients Treated with Immune Checkpoint Inhibitors.

The treatment of non-small cell lung cancer (NSCLC) has changed dramatically with the advent of immune checkpoint inhibitors (ICIs). Despite encouraging results, their efficacy remains limited to a subgroup of patients. Circulating immune checkpoints in soluble (s) form and associated with extracellular vesicles (EVs) represent promising markers, especially in ICI-based therapeutic settings. We evaluated the prognostic role of PD-L1 and of two B7 family members (B7-H3, B7-H4), both soluble and EV-associated, in a cohort of advanced NSCLC patients treated with first- (n = 56) or second-line (n = 126) ICIs. In treatment-naïve patients, high baseline concentrations of sPD-L1 (>24.2 pg/mL) were linked to worse survival, whereas high levels of sB7-H3 (>0.5 ng/mL) and sB7-H4 (>63.9 pg/mL) were associated with better outcomes. EV characterization confirmed the presence of EVs positive for PD-L1 and B7-H3, while only a small portion of EVs expressed B7-H4. The comparison between biomarker levels at the baseline and in the first radiological assessment under ICI-based treatment showed a significant decrease in EV-PD-L1 and an increase in EV-B7H3 in patients in the disease response to ICIs. Our study shows that sPD-L1, sB7-H3 and sB7-H4 levels are emerging prognostic markers in patients with advanced NSCLC treated with ICIs and suggests potential EV involvement in the disease response to ICIs.

Exploring Response to Immunotherapy in Non-Small Cell Lung Cancer Using Delta-Radiomics.

Delta-radiomics is a branch of radiomics in which features are confronted after time or after introducing an external factor (such as treatment with chemotherapy or radiotherapy) to extrapolate prognostic data or to monitor a certain condition. Immune checkpoint inhibitors (ICIs) are currently revolutionizing the treatment of non-small cell lung cancer (NSCLC); however, there are still many issues in defining the response to therapy. Contrast-enhanced CT scans of 33 NSCLC patients treated with ICIs were analyzed; altogether, 43 lung lesions were considered. The radiomic features of the lung lesions were extracted from CT scans at baseline and at first reassessment, and their variation (delta, Δ) was calculated by means of the absolute difference and relative reduction. This variation was related to the final response of each lesion to evaluate the predictive ability of the variation itself. Twenty-seven delta features have been identified that are able to discriminate radiologic response to ICIs with statistically significant accuracy. Furthermore, the variation of nine features significantly correlates with pseudo-progression.

Radiomic Detection of EGFR Mutations in NSCLC.

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.