Faculty Bibliography

INTRODUCTION/BACKGROUND:Programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) inhibitors show significant clinical activity in non-small-cell lung carcinoma (NSCLC). However, there is a relative lack of data on comparative efficacy of these drugs in the first-line setting versus chemotherapy-treated patients. We compared the efficacy and toxicity of these drugs in these 2 distinct groups of patients. MATERIALS AND METHODS/METHODS:statistic was used to assess heterogeneity. RESULTS:Seventeen distinct trials (8 with treatment-naive patients [n = 937]; 14 with chemotherapy-treated patients [n = 3620]; 5 with separate treatment-naive and previously treated arms) were included. Treatment-naive patients had a statistically significant higher ORR (30.2%; 95% confidence interval [CI], 22.70-38.2) than patients previously treated with chemotherapy (ORR, 20.1%; 95% CI, 17.5-22.9; P = .02). No significant differences in PFS were observed between the 2 groups. Treatment-naive patients had statistically significant higher rates of all grade pneumonitis compared with previously treated patients (4.9%; 95% CI, 3.4-6.7 vs. 3.0%; 95% CI, 2.0-4.1; P = .04); however, no significant differences in any other immune-related adverse events were observed. CONCLUSION/CONCLUSIONS:PD-1/PD-L1 inhibitor therapy for advanced NSCLC has a significantly higher ORR and a higher rate of immune-mediated pneumonitis when used in the first-line setting compared with chemotherapy treated patients.

Purpose: Determine the localized expression pattern and clinical significance of VISTA/PD-1H in human non-small cell lung cancer (NSCLC).Experimental Design: Using multiplex quantitative immunofluorescence (QIF), we performed localized measurements of VISTA, PD-1, and PD-L1 protein in 758 stage I-IV NSCLCs from 3 independent cohorts represented in tissue microarray format. The targets were selectively measured in cytokeratin⁺ tumor epithelial cells, CD3⁺ T cells, CD4⁺ T-helper cells, CD8⁺ cytotoxic T cells, CD20⁺ B lymphocytes and CD68⁺ tumor-associated macrophages. We determined the association between the targets, clinicopathological/molecular variables and survival. Genomic analyses of lung cancer cases from TCGA were also performed.Results: VISTA protein was detected in 99% of NSCLCs with a predominant membranous/cytoplasmic staining pattern. Expression in tumor and stromal cells was seen in 21% and 98% of cases, respectively. The levels of VISTA were positively associated with PD-L1, PD-1, CD8⁺ T cells and CD68⁺ macrophages. VISTA expression was higher in T-lymphocytes than in macrophages; and in cytotoxic T cells than in T-helper cells. Elevated VISTA was associated with absence of EGFR mutations and lower mutational burden in lung adenocarcinomas. Presence of VISTA in tumor compartment predicted longer 5-year survival.Conclusions: VISTA is frequently expressed in human NSCLC and shows association with increased tumor-infiltrating lymphocytes, PD-1 axis markers, specific genomic alterations and outcome. These results support the immunomodulatory role of VISTA in human NSCLC and suggests its potential as therapeutic target. Clin Cancer Res; 24(7); 1562-73. ©2017 AACR.

Differentiation between benign and malignant nodules is a problem encountered by radiologists when visualizing computed tomography (CT) scans. Adenocarcinomas and granulomas have a characteristic spiculated appearance and may be fluorodeoxyglucose avid, making them difficult to distinguish for human readers. In this retrospective study, we aimed to evaluate whether a combination of radiomic texture and shape features from noncontrast CT scans can enable discrimination between granulomas and adenocarcinomas. Our study is composed of CT scans of 195 patients from two institutions, one cohort for training ([Formula: see text]) and the other ([Formula: see text]) for independent validation. A set of 645 three-dimensional texture and 24 shape features were extracted from CT scans in the training cohort. Feature selection was employed to identify the most informative features using this set. The top ranked features were also assessed in terms of their stability and reproducibility across the training and testing cohorts and between scans of different slice thickness. Three different classifiers were constructed using the top ranked features identified from the training set. These classifiers were then validated on the test set and the best classifier (support vector machine) yielded an area under the receiver operating characteristic curve of 77.8%.

INTRODUCTION/BACKGROUND:Immune checkpoint pathways are key immune regulatory pathways that play a physiologic role in maintaining immune-homeostasis and are often co-opted by cancer cells to evade the host immune system. Recent developments in cancer immunotherapy, mainly drugs blocking the immune checkpoint pathways, have revolutionized the treatment paradigm for many solid tumors. A wide spectrum of immune-related adverse events (irAEs) have been described with the use of these agents which necessitate treatment with immunosuppression, lead to disruption of therapy and can on occasion be life-threatening. There are currently no clinically validated biomarkers to predict the risk of irAEs. Areas covered: In this review, the authors describe the current progress in identifying biomarkers for irAEs and potential future directions. Literature search was conducted using PubMed-MEDLINE, Embase and Scopus. In addition, abstracts from major conference proceedings were reviewed for relevant content. Expert commentary: The discovery of biomarkers for irAEs is currently in its infancy, however there are a lot of promising candidate biomarkers that are currently being investigated. Biomarkers that can identify patients at a higher risk of developing irAEs or lead to early detection of autoimmune toxicities are crucial to optimize patient selection for immune-oncology agents and to minimize toxicity with their use.

Mutations in the BRAF oncogene are found in 2-4% of all non-small cell lung cancer (NSCLC) patients. The most common activating mutation present within the BRAF oncogene is associated with valine substitution for glutamate at position 600 (V600E) within the BRAF kinase. BRAF-targeted therapies are effective in patients with melanoma and NSCLC harboring BRAF V600E mutation. In both melanoma and NSCLC, dual inhibition of both BRAF and the downstream mitogen-activated protein kinase (MEK) improves response rates compared with BRAF inhibition alone. BRAF-MEK combination therapy (dabrafenib plus trametinib) demonstrated tolerability and efficacy in a recent phase II clinical trial and was approved by the European Medicines Agency and United States Food and Drug Administration for patients with stage IV NSCLC harboring BRAF V600E mutation. Here, in this review, we outline the preclinical and clinical data for BRAF and MEK inhibitor combination treatment for NSCLC patients with BRAF V600E mutation.

Immune checkpoint inhibitors, mainly drugs targeting the programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) and cytotoxic T-lymphocyte antigen 4 (CTLA4) pathways, represent a remarkable advance in lung cancer treatment. Immune checkpoint inhibitors targeting PD-1 and PD-L1 are approved for the treatment of patients with non-small-cell lung cancer, with impressive clinical activity and durable responses in some patients. This review will summarize the mechanism of action of these drugs, the clinical development of these agents and the current role of these agents in the management of patients with lung cancer. In addition, the review will discuss the role of predictive biomarkers for optimal patient selection for immunotherapy and management of autoimmune side effects of these agents.

Lung cancer is responsible for a large proportion of cancer-related deaths across the globe, with delayed detection being perhaps the most significant factor for its high mortality rate. Though the National Lung Screening Trial argues for screening of certain at-risk populations, the practical implementation of these screening efforts has not yet been successful and remains in high demand. Radiomics refers to the computerized extraction of data from radiologic images, and provides unique potential for making lung cancer screening more rapid and accurate using machine learning algorithms. The quantitative features analyzed express subvisual characteristics of images which correlate with pathogenesis of diseases. These features are broadly classified into four categories: intensity, structure, texture/gradient, and wavelet, based on the types of image attributes they capture. Many studies have been done to show correlation between these features and the malignant potential of a nodule on a chest CT. In cancer patients, these nodules also have features that can be correlated with prognosis and mutation status. The major limitations of radiomics are the lack of standardization of acquisition parameters, inconsistent radiomic methods, and lack of reproducibility. Researchers are working on overcoming these limitations, which would make radiomics more acceptable in the medical community.