Citations

Immune-related Adverse Events (irAEs) are the most significant toxicities associated with the use of checkpoint inhibitors, and result from disinhibition of the host’s immune homeostasis. The adverse effects experienced from immunotherapy are significantly different from those of chemotherapy and, to a lesser extent, targeted therapy. Early recognition and diagnosis of these toxicities is often challenging, but is critically important because of the potentially life-threatening nature and associated morbidity. Gastrointestinal, dermatologic, endocrine, and liver toxicities are the most commonly observed. Less commonly, the eyes, pancreas, kidneys, lungs, bone marrow, or nervous system may be affected. Although most irAEs may resolve with supportive care or discontinuation of drug, in severe cases, they may require hospitalization and immune suppressants, such as steroids, and/or may even cause death. The management of immune-related side effects requires a multidisciplinary approach.

Abstract Purpose: The expression of programmed cell death ligand 1 (PD-L1) provides limited predictive value in identifying patients most likely to respond to immunotherapy. As the heterogeneity of PD-L1 expression may lead to sampling error and the misclassification of PD-L1 status, we assessed the distribution of PD-L1 expression in paired, resected multifocal lung cancers. Experimental Design: PD-L1 was assessed by IHC. Paired lesions were defined as independent primaries or related lesions using mate pair next-generation sequencing. Agreement statistics were used for analysis. Results: Sixty-seven multifocal lung cancers from 32 patients were sequenced and stained for PD-L1. There was agreement of PD-L1 expression by the tumor cells in paired lesions of 20 patients and disagreement of PD-L1 expression by the tumor cells in paired lesions of 12 patients (κ = 0.01). Sequencing identified that 23 patients had independent primary lung cancers and that 9 patients had related cancers. In paired lesions of patients with independent cancers, there was agreement of PD-L1 expression by the tumor cells in 12 patients and disagreement in 11 patients (κ = 0.31). In paired lesions of patients with related lung cancers, there was agreement of PD-L1 expression by the tumor cells in 8 patients and disagreement in 1 patient (κ = 0.73). Conclusions: The expression of PD-L1 is heterogeneous among paired independent lung cancers, but there are high levels of agreement in intrapulmonary metastasis. Clin Cancer Res; 22(9); 2177–82. ©2015 AACR.

Abstract Rationale Screening for lung cancer using low-dose computed tomography (CT) reduces lung cancer mortality. However, in addition to a high rate of benign nodules, lung cancer screening detects a large number of indolent cancers that generally belong to the adenocarcinoma spectrum. Individualized management of screen-detected adenocarcinomas would be facilitated by noninvasive risk stratification. Objectives To validate that Computer-Aided Nodule Assessment and Risk Yield (CANARY), a novel image analysis software, successfully risk stratifies screen-detected lung adenocarcinomas based on clinical disease outcomes. Methods We identified retrospective 294 eligible patients diagnosed with lung adenocarcinoma spectrum lesions in the low-dose CT arm of the National Lung Screening Trial. The last low-dose CT scan before the diagnosis of lung adenocarcinoma was analyzed using CANARY blinded to clinical data. Based on their parametric CANARY signatures, all the lung adenocarcinoma nodules were risk stratified into three groups. CANARY risk groups were compared using survival analysis for progression-free survival. Measurements and Main Results A total of 294 patients were included in the analysis. Kaplan-Meier analysis of all the 294 adenocarcinoma nodules stratified into the Good, Intermediate, and Poor CANARY risk groups yielded distinct progression-free survival curves (P < 0.0001). This observation was confirmed in the unadjusted and adjusted (age, sex, race, and smoking status) progression-free survival analysis of all stage I cases. Conclusions CANARY allows the noninvasive risk stratification of lung adenocarcinomas into three groups with distinct post-treatment progression-free survival. Our results suggest that CANARY could ultimately facilitate individualized management of incidentally or screen-detected lung adenocarcinomas.

Abstract Rationale Most immunocompetent patients diagnosed with latent tuberculosis infection (LTBI) will not progress to tuberculosis (TB) reactivation. However, current diagnostic tools cannot reliably distinguish nonprogressing from progressing patients a priori, and thus LTBI therapy must be prescribed with suboptimal patient specificity. We hypothesized that LTBI diagnostics could be improved by generating immunomarker profiles capable of categorizing distinct patient subsets by a combinatorial immunoassay approach. Objectives A combinatorial immunoassay analysis was applied to identify potential immunomarker combinations that distinguish among unexposed subjects, untreated patients with LTBI, and treated patients with LTBI and to differentiate risk of reactivation. Methods IFN-γ release assay (IGRA) was combined with a flow cytometric assay that detects induction of CD25+CD134+ coexpression on TB antigen–stimulated T cells from peripheral blood. The combinatorial immunoassay analysis was based on receiver operating characteristic curves, technical cut-offs, 95% bivariate normal density ellipse prediction, and statistical analysis. Risk of reactivation was estimated with a prediction formula. Measurements and Main Results Sixty-five out of 150 subjects were included. The combinatorial immunoassay approach identified at least four different T-cell subsets. The representation of these immune phenotypes was more heterogeneous in untreated patients with LTBI than in treated patients with LTBI or unexposed groups. Patients with IGRA(+) CD4+CD25+CD134+ T-cell phenotypes had the highest estimated reactivation risk (4.11 ± 2.11%). Conclusions These findings suggest that immune phenotypes defined by combinatorial assays may potentially have a role in identifying those at risk of developing TB; this potential role is supported by risk of reactivation modeling. Prospective studies will be needed to test this novel approach.