Faculty Bibliography

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, they are often associated with potentially fatal immune-mediated pneumonitis. Preliminary reports of trials suggest a difference in the rate of pneumonitis with PD-1 and PD-L1 inhibitors. We sought to determine the overall incidence of pneumonitis and differences according to type of inhibitors and prior chemotherapy use. METHODS:MEDLINE, Embase, and Scopus databases were searched up to November 2016. Rates of pneumonitis of any grade and grade ≥ 3 from all clinical trials investigating nivolumab, pembrolizumab, atezolizumab, durvalumab, and avelumab as single agents in NSCLC were collected. The incidence of pneumonitis across trials was calculated using DerSimonian-Laird random effects models. We compared incidences between PD-1 and PD-L1 inhibitors and between treatment naive and previously treated patients. RESULTS:Nineteen trials (12 with PD-1 inhibitors [n = 3,232] and 7 with PD-L1 inhibitors [n = 1,806]) were identified. PD-1 inhibitors were found to have statistically significant higher incidence of any grade pneumonitis compared with PD-L1 inhibitors (3.6%; 95% CI, 2.4%-4.9% vs 1.3%; 95% CI, 0.8%-1.9%, respectively; P = .001). PD-1 inhibitors were also associated with higher incidence of grade 3 or 4 pneumonitis (1.1%; 95% CI, 0.6%-1.7% vs 0.4%; 95% CI, 0%-0.8%; P = .02). Treatment naive patients had higher incidence of grade 1 through 4 pneumonitis compared with previously treated patients (4.3%; 95% CI, 2.4%-6.3% vs 2.8%; 95% CI, 1.7%- 4%; P = .03). CONCLUSIONS:There was a higher incidence of pneumonitis with use of PD-1 inhibitors compared with PD-L1 inhibitors. Higher rate of pneumonitis was more common in treatment naive patients.

We report a case of crizotinib-induced esophageal ulcers in a 45-year-old woman with metastatic anaplastic lymphoma kinase-positive non-small-cell lung cancer after 10 weeks of therapy. Endoscopic and pathologic findings were consistent with active inflammation with mid-esophageal ulceration and consistent with drug-induced esophagitis. Crizotinib was held and had a complete clinical and radiographic resolution of her symptoms. Patient was started on treatment with another anaplastic lymphoma kinase-targeted agent alectinib and has been tolerating it well without evidence of recurrence of esophagitis.

PURPOSE/OBJECTIVE:Distinguishing between benign granulmoas and adenocarcinomas is confounded by their similar visual appearance on routine CT scans. Unfortunately, owing to the inability to discriminate these lesions radigraphically, many patients with benign granulomas are subjected to unnecessary surgical wedge resections and biopsies for pathologic confirmation of cancer presence or absence. This suggests the need for improved computerized characterization of these nodules in order to distinguish between these two classes of lesions on CT scans. While there has been substantial interest in the use of textural analysis for radiomic characterization of lung nodules, relatively less work has been done in shape based characterization of lung nodules, particularly with respect to granulmoas and adenocarcinomas. The primary goal of this study is to evaluate the role of 3D shape features for discrimination of benign granulomas from malignant adenocarcinomas on lung CT images. Towards this end we present an integrated framework for segmentation, feature characterization and classification of these nodules on CT. METHODS:The nodule segmentation method starts with separation of lung regions from the surrounding lung anatomy. Next, the lung CT scans are projected into and represented in a three dimensional spectral embedding (SE) space, allowing for better determination of the boundaries of the nodule. This then enables the application of a gradient vector flow active contour (SEGvAC) model for nodule boundary extraction. A set of 24 shape features from both 2D slices and 3D surface of the segmented nodules are extracted, including features pertaining to the angularity, spiculation, elongation and nodule compactness. A feature selection scheme, PCA-VIP, is employed to identify the most discriminating set of features to distinguish granulmoas from adenocarcinomas within a learning set of 82 patients. The features thus identified were then combined with a support vector machine classifier and independently validated on a distinct test set comprising 67 patients. The performance of the classifier for both of the training and validation cohorts was evaluated by the area under receiver characteristic curve (ROC). RESULTS:We used 82 and 67 studies from two different institutions respectively for training and independent validation of the model and the shape features. The Dice coefficient between automatically segmented nodules by SEGvAC and the manual delineations by expert radiologists (readers) was 0.84± 0.04 whereas inter-reader segmentation agreement was 0.79± 0.12. We also identified a set of consistent features (Roughness, Convexity and Spherecity) that were found to be strongly correlated across both manual and automated nodule segmentations (R > 0.80, p < 0.0001) and capture the marginal smoothness and 3D compactness of the nodules. On the independent validation set of 67 studies our classifier yielded a ROC AUC of 0.72 and 0.64 for manually- and automatically segmented nodules respectively. On a subset of 20 studies, the AUCs for the two expert radiologists and 1 pulmonologist were found to be 0.82, 0.68 and 0.58 respectively. CONCLUSIONS:The major finding of this study was that certain shape features appear to differentially express between granulomas and adenocarcinomas and thus computer extracted shape cues could be used to distinguish these radiographically similar pathologies.

Purpose In addition to prospective trials for non-small-cell lung cancers (NSCLCs) that are driven by less common genomic alterations, registries provide complementary information on patient response to targeted therapies. Here, we present the results of an international registry of patients with RET-rearranged NSCLCs, providing the largest data set, to our knowledge, on outcomes of RET-directed therapy thus far. Methods A global, multicenter network of thoracic oncologists identified patients with pathologically confirmed NSCLC that harbored a RET rearrangement. Molecular profiling was performed locally by reverse transcriptase polymerase chain reaction, fluorescence in situ hybridization, or next-generation sequencing. Anonymized data-clinical, pathologic, and molecular features-were collected centrally and analyzed by an independent statistician. Best response to RET tyrosine kinase inhibition administered outside of a clinical trial was determined by RECIST v1.1. Results By April 2016, 165 patients with RET-rearranged NSCLC from 29 centers across Europe, Asia, and the United States were accrued. Median age was 61 years (range, 29 to 89 years). The majority of patients were never smokers (63%) with lung adenocarcinomas (98%) and advanced disease (91%). The most frequent rearrangement was KIF5B-RET (72%). Of those patients, 53 received one or more RET tyrosine kinase inhibitors in sequence: cabozantinib (21 patients), vandetanib (11 patients), sunitinib (10 patients), sorafenib (two patients), alectinib (two patients), lenvatinib (two patients), nintedanib (two patients), ponatinib (two patients), and regorafenib (one patient). The rate of any complete or partial response to cabozantinib, vandetanib, and sunitinib was 37%, 18%, and 22%, respectively. Further responses were observed with lenvantinib and nintedanib. Median progression-free survival was 2.3 months (95% CI, 1.6 to 5.0 months), and median overall survival was 6.8 months (95% CI, 3.9 to 14.3 months). Conclusion Available multikinase inhibitors had limited activity in patients with RET-rearranged NSCLC in this retrospective study. Further investigation of the biology of RET-rearranged lung cancers and identification of new targeted therapeutics will be required to improve outcomes for these patients.

INTRODUCTION:) have previously been identified in lung adenocarcinomas, but their frequency and clinical significance is unknown. METHODS:We prospectively analyzed 8551 consecutive lung adenocarcinomas using hybrid capture-based comprehensive genomic profiling (CGP) at the request of the individual treating physicians for the purpose of making therapy decisions. RESULTS:mutations. HER2 TMD mutations (V659 and G660) are found in other non-NSCLC malignancies, and analogous TMD mutations are also found in EGFR, HER3, and HER4. CONCLUSION:HER2 TMD mutations represent rare but distinct targetable driver mutations in lung adenocarcinoma. CGP capable of detecting diverse HER2 alterations, including HER2 TMD mutations, should be broadly adopted to identify all patients who may benefit from HER2-targeted therapies.