Peikert, T.

Purpose Mycobacterium avium complex lung disease (MAC-LD) represents the most common form of nontuberculous mycobacterial lung infection, particularly affecting women and older adults. Clinical management remains challenging, including prediction and differentiation between progressive and non-progressive disease. Overexpression of PD-1 pathways have been reported in progressive disease, and we hypothesize that PD-1 inhibition can restore antigen-specific immune responses detectable by FluoroSpot assay. Methods Peripheral blood mononuclear cells from 22 participants, including progressive and non-progressive MAC-LD patients, healthy controls, and individuals with latent tuberculosis infection (LTBI) were stimulated with nontuberculous mycobacteria and Mycobacterium tuberculosis antigens. Cytokine secretion of interferon-gamma (IFN-γ) and interleukin-2 (IL-2) were quantified using dual FluoroSpot assay, with and without PD-1 blockade. Results Progressive MAC-LD cases demonstrated significant increases in glycopeptidolipid (GPL)- and PPD-stimulated IFN-γ responses following PD-1 blockade, which was not seen in healthy controls, LTBI, or non-progressive MAC-LD patients. After GPL stimulation and in the presence of anti-PD-1, the number of IFN-γ responses increased from 25% to 75% in samples from progressive MAC-LD patients. This supports an association between PD-1–mediated T-cell dysfunction and disease progression. Mycobacterial MTB300 peptide pool antigen failed to discriminate MAC-LD from healthy controls, underscoring antigen specificity. Conclusions Our pilot study demonstrates that IFN-γ FluoroSpot assays, particularly when combining GPL or PPD stimulation and PD-1 inhibition, may help identify progressive MAC-LD. These findings also support other studies reporting that immune checkpoint dysregulation can be present in some patients with progressive and severe MAC-LD patients. Additional studies are warranted to determine its pathogenic role.

Abstract Rationale Stereotactic body radiation therapy (SBRT) represents an effective therapeutic strategy for early-stage non–small cell lung cancer (NSCLC); however, local and systemic recurrences represent ongoing challenges. Computed tomography (CT) radiomics-based risk models can potentially be used to predict the risk of local recurrence on pretreatment CT scans. Objective Development of a radiomics model to predict local recurrence after SBRT in patients with NSCLC. Methods This single-institution study includes a retrospective case-control training set (20 patients with local recurrence and 40 control subjects) and an independent validation set (198 consecutive cases) of patients with early-stage NSCLC treated with SBRT. Tumors were semiautomatically segmented, and 102 quantitative radiomic features, including texture, landscape, spatial, nodule shape, and nodule surface features, were extracted. These features were included in three separate multivariable models to predict the risk of recurrence on the basis of pre-SBRT, post-SBRT, and the difference between the pre-SBRT and post-SBRT scans (Delta model). The pre-SBRT model was subsequently validated in an independent validation set. Results Thirteen independent variables were selected for the models using the Boruta algorithm. The sensitivity, specificity, and area under the curve of the pre-SBRT, post-SBRT, and Delta models were 85%, 90%, and 0.91; 85%, 92.5%, and 0.92; and 85%, 92.5%, and 0.94, respectively. The pre-SBRT model was validated in the independent validation set (area under the curve, 0.89; confidence interval, 0.83–0.92), because this model was believed to be the most useful to assist in individualized treatment planning. Conclusions Radiomic analysis facilitated the development of three high-performing models predicting local recurrence using either pre-SBRT CT, post-SBRT CT, or the change between these two. We successfully validated the most clinically relevant model, the pre-SBRT model. Although this model needs further validation, it may facilitate individualized surveillance, treatment planning, and selection of adjuvant therapy.