Immune microenvironment composition in non-small cell lung cancer and its association with survival.

OBJECTIVES: In non-small cell lung cancer (NSCLC), the immune system and possibly its composition affect survival. In this in silico study, the immune infiltrate composition in NSCLC patients was evaluated. METHODS: Gene expression data of tumors from early NSCLC patients were obtained from Gene Expression Omnibus (GEO). With CIBERSORT, 22 immune cell fractions were estimated. RESULTS: The immune infiltrate of 1430 pretreatment NSCLC patients contained mostly plasma cells, macrophages and CD8 T cells. Higher fractions of resting mast and CD4 T-helper cells were associated with longer overall survival (OS) (HR = 0.95, P < 0.01; HR = 0.98, = 0.04, respectively) and higher fractions of M2 macrophages and active dendritic cells with shorter survival (HR = 1.02, P = 0.03; HR = 1.03, P = 0.05, respectively). Adenocarcinoma patients with survival data (n = 587) showed higher fractions of resting mast and resting CD4 T cells, and lower M0 macrophages than squamous cell carcinoma (n = 254), which were associated with OS (HR = 0.95, P = 0.04; HR = 0.97, P = 0.01; HR = 1.03, P = 0.01, respectively). Fractions of memory B cells, naïve CD4 T cells and neutrophils had different associations with survival depending on the subtype. Smokers had had higher fractions of regulatory T cell, follicular helper T cell, neutrophil and M2 macrophage, which were associated with shorter survival (HR = 1.3, P < 0.01; HR = 1.13, P = 0.02; HR = 1.09, P = 0.03; HR = 1.04, P = 0.02, respectively). CONCLUSION: Pretreatment differences in immune cell composition in NSCLC are associated with survival and depend on smoking status and histological subtype. Smokers' immune composition is associated with lower survival.

First-line tyrosine kinase inhibitors in EGFR mutation-positive non-small-cell lung cancer: a network meta-analysis.

BACKGROUND: EGFR-tyrosine kinase inhibitors (EGFR-TKIs) including afatinib, dacomitinib, erlotinib, gefitinib, and osimertinib have proven efficacy in terms of progression-free survival (PFS) in patients with non-small-cell lung cancer (NSCLC) harboring EGFR mutations. However, an overall view for comparing efficacy and toxicity on a meta-level is lacking. This study compared efficacy and toxicity of first-line treatment with five different EGFR-TKIs by conducting a network meta-analysis (NMA). METHODS: A systematic review was performed, aiming to find eligible literature. Data of PFS, overall survival (OS), objective response rate (ORR), and adverse events were extracted. An NMA based on Bayesian statistics was established to synthesize the efficacy and toxicity of all treatments. RESULTS: Thirteen randomized controlled trials, including data from 3,539 patients with EGFR-mutated NSCLC, were analyzed. Rank probabilities showed that osimertinib had a potentially better efficacy in terms of PFS and OS compared to all other TKIs. For ORR, afatinib and osimertinib showed a trend of superiority compared to the other four TKIs. Furthermore, there was a high risk of diarrhea and rash for patients treated with afatinib or dacomitinib as well as a moderate risk for treatment with erlotinib, gefitinib, and osimertinib. CONCLUSION: Our study showed a favorable efficacy of osimertinib in terms of PFS and OS compared to all other EGFR-TKIs in patients with NSCLC harboring activating EGFR mutations. Furthermore, gefitinib, erlotinib, and osimertinib were associated with fewer toxicities compared to the other TKIs. Therefore, osimertinib is indicated as a preferable first-line TKI in patients with activating EGFR-mutated NSCLC.

Immunoglobulin-like transcript 3 is expressed by myeloid-derived suppressor cells and correlates with survival in patients with non-small cell lung cancer.

Myeloid-derived suppressor cells (MDSCs) play an important role in immune suppression and accumulate under pathologic conditions such as cancer and chronic inflammation. They comprise a heterogeneous population of immature myeloid cells that exert their immunosuppressive function via a variety of mechanisms. Immunoglobulin-like transcript 3 (ILT3) is a receptor containing immunoreceptor tyrosine-based inhibition motifs (ITIMs) that can be expressed on antigen-presenting cells and is an important regulator of dendritic cell tolerance. ILT3 exists in a membrane-bound and a soluble form and can interact with a yet unidentified ligand on T cells and thereby induce T-cell anergy, regulatory T cells, or T suppressor cells. In this study, we analyzed freshly isolated peripheral blood mononuclear cells (PBMCs) of 105 patients with non-small cell lung cancer and 20 healthy controls and demonstrated for the first time that ILT3 is expressed on MDSCs. We show that increased levels of circulating MDSCs correlate with reduced survival. On the basis of ILT3 cell surface expression, an ILT3low and ILT3high population of polymorphonuclear (PMN)-MDSCs could be distinguished. Interestingly, in line with the immunosuppressive function of ILT3 on dendritic cells, patients with an increased proportion of PMN-MDSCs and an increased fraction of the ILT3high subset had a shorter median survival than patients with elevated PMN-MDSC and a smaller ILT3high fraction. No correlation between the ILT3high subset and other immune variables was found. ILT3 expressed on MDSCs might reflect a previously unknown mechanism by which this cell population induces immune suppression and could therefore be an attractive target for immune intervention.

Evaluation of elastix-based propagated align algorithm for VOI- and voxel-based analysis of longitudinal (18)F-FDG PET/CT data from patients with non-small cell lung cancer (NSCLC).

BACKGROUND: Deformable image registration allows volume of interest (VOI)- and voxel-based analysis of longitudinal changes in fluorodeoxyglucose (FDG) tumor uptake in patients with non-small cell lung cancer (NSCLC). This study evaluates the performance of the elastix toolbox deformable image registration algorithm for VOI and voxel-wise assessment of longitudinal variations in FDG tumor uptake in NSCLC patients. METHODS: Evaluation of the elastix toolbox was performed using (18)F-FDG PET/CT at baseline and after 2 cycles of therapy (follow-up) data in advanced NSCLC patients. The elastix toolbox, an integrated part of the IMALYTICS workstation, was used to apply a CT-based non-linear image registration of follow-up PET/CT data using the baseline PET/CT data as reference. Lesion statistics were compared to assess the impact on therapy response assessment. Next, CT-based deformable image registration was performed anew on the deformed follow-up PET/CT data using the original follow-up PET/CT data as reference, yielding a realigned follow-up PET dataset. Performance was evaluated by determining the correlation coefficient between original and realigned follow-up PET datasets. The intra- and extra-thoracic tumors were automatically delineated on the original PET using a 41% of maximum standardized uptake value (SUVmax) adaptive threshold. Equivalence between reference and realigned images was tested (determining 95% range of the difference) and estimating the percentage of voxel values that fell within that range. RESULTS: Thirty-nine patients with 191 tumor lesions were included. In 37/39 and 12/39 patients, respectively, thoracic and non-thoracic lesions were evaluable for response assessment. Using the EORTC/SUVmax-based criteria, 5/37 patients had a discordant response of thoracic, and 2/12 a discordant response of non-thoracic lesions between the reference and the realigned image. FDG uptake values of corresponding tumor voxels in the original and realigned reference PET correlated well (R (2)=0.98). Using equivalence testing, 94% of all the voxel values fell within the 95% range of the difference between original and realigned reference PET. CONCLUSIONS: The elastix toolbox impacts lesion statistics and therefore therapy response assessment in a clinically significant way. The elastix toolbox is therefore not applicable in its current form and/or standard settings for PET response evaluation. Further optimization and validation of this technique is necessary prior to clinical implementation.

Prognostic classification with laboratory parameters or imaging techniques in small-cell lung cancer.

PURPOSE: Our aim in this study was to compare prognostic models based on laboratory tests with a model including imaging information in small-cell lung cancer. PATIENTS AND METHODS: A retrospective analysis was performed on 156 consecutive patients. Three existing models based on laboratory tests and performance status (PS) and a model based on disease stage assessed by imaging techniques and PS were tested with Cox regression analysis. RESULTS: The 3 laboratory-based models and the imaging-based model were significant in predicting prognosis in our patient group, with hazard ratios of 1.6-3 for medium prognosis groups and 2.6-6.1 for poor prognosis groups compared with good prognosis groups. Models based on laboratory tests appear to predict survival probabilities at least as well as a model with information from imaging techniques. CONCLUSION: Prognostic models using PS and laboratory tests provide a similar estimation of survival of patients with small-cell lung cancer as the combination of PS and disease stage assessed by imaging tests.