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8555 Background: Recent changes to the standard of care for unresectable stage III NSCLC include chemoradiation followed by consolidative immunotherapy (IO). Pneumonitis is a well-known complication of radiotherapy (RT) and has been increasingly reported in association with IO. Although rare, pneumonitis can cause severe morbidity and possibly death in extreme cases. Differentiating RT and IO-induced pneumonitis (RTP vs IOP) is crucial for acute management and future considerations of individualized treatment. However, the clinical and radiological features of RTP and IOP may be similar and often indistinguishable on computed tomography (CT). Texture-based CT radiomics has previously been used to distinguish benign and malignant nodules on lung CT. In this study, we explore if radiomic features extracted from lung CT can distinguish between RTP and IOP. Methods: From 236 patients with stage III NSCLC who underwent chemoradiation followed by consolidative durvalumab, we identified 110 cases of treatment-related pneumonitis. IOP cases were identified through a retrospective review of electronic medical records and independently verified by a thoracic oncologist using features such as bilateral lung involvement, inflammatory changes outside the field of RT, temporal relationship to IO, and response to treatment. Inflammatory lesions were manually annotated using Slicer 3D. After excluding cases without discernible cause and non-identifiable lung lesions (n = 61), we included 49 cases in the study (RTP n = 20; IOP n = 29). A total of 555 features from Gabor, Laws, Laplace, and Haralick feature families were extracted on a pixel level from post-treatment CT images. A support vector machine (SVM) classifier was trained with the most discriminating features identified by Wilcoxon rank-sum test feature selection method. The classifier performance for distinguishing RTP vs. IOP was assessed by averaging the area under the receiver operating characteristic curve (AUC) values computed over 100 iterations of threefold cross-validation. Results: We identified the top 5 radiomic texture features distinguishing RTP from IOP including Haralick entropy, Haralick info, Laws median, and high- and low-frequency Gabor. Using 3-fold cross-validation, the SVM classifier model built on the radiomic features achieved an AUC of 0.83 (95% confidence interval, 0.78 - 0.86). Conclusions: Pneumonitis is a severe complication of both RT and IO that must be taken into consideration when evaluating future risks of IO-based therapies. The distinction between RTP and IOP remains challenging based on CT findings alone. Radiomic texture features analysis of post-treatment CT images can potentially differentiate RTP from IOP in stage III NSCLC patients who received RT followed by consolidative durvalumab. Additional multi-site independent validation of these quantitative image-based biomarkers is warranted.

245 Background: Despite low-dose computed tomography (LDCT) screening for lung cancer recommended by the United States Preventive Services Task Force (USPSTF) demonstrating a relative reduction in mortality, there remains low rates of testing nationwide. Yet studies are limited regarding specific differences in screening rates amongst various outpatient care settings. Methods: We performed retrospective chart reviews of patients followed by resident providers within an academic internal medicine residency program who met USPSTF guidelines for lung cancer screening between 2015-2020. This was conducted at three separate outpatient clinic sites including a state-funded academic institution, inner city community health center, and veteran affairs medical center. Data collection included patient demographic and smoking histories as well as rates of ordered and completed LDCT screening. Results: A total of 832 patients were identified as current or former smokers between the ages of 55 and 80 years: 320 from Hartford Hospital Community Health Center (HHCHC), 262 from University of Connecticut Health Center (UCHC), and 250 from the Veteran Affairs (VA) Medical Center. 85 (27%) of these patients from HHCHC, 84 (32%) from UCHC, and 56 (22%) from the VA met USPSTF eligibility criteria for LDCT screening. Overall compliance rates of screening were found to be 44% at HHCHC, 59.5% at UCHC, and 51.8% at the VA. Results are outlined in Table. Conclusions: Screening rates for lung cancer with LDCT remain low but have been steadily improving throughout the United States following new recommendations and increased awareness provided by multiple medical organizations. We sought to compare differences in compliance rates amongst various outpatient clinics within the same internal medicine residency program at University of Connecticut. Our findings demonstrate significant differences in LDCT screening for lung cancer between the program’s community health center versus its state and federally funded outpatient clinics. Automatic reminders to providers can potentially improve rates of lung cancer screening. Patients should also be educated about the importance of screening to improve adherence with imaging. [Table: see text]

9006 Background: MET exon 14 alterations comprise a novel class of lung cancer drivers. MET tyrosine kinase inhibitors (TKIs) are active in patients with these cancers, but objective response rates (ORRs) are modest (~30%-40%). A subset of these cancers may harbor intrinsic resistance. Moreover, patients with initial benefit invariably develop acquired resistance. We set out to identify potential resistance mechanisms. Methods: We studied patients with stage IV MET exon 14-altered lung cancers who received a MET TKI. When feasible, tumor and/or plasma samples were collected, prioritizing paired pre- and post-TKI collection. Tumor samples underwent targeted mass spectrometry analysis (Nantomics) and DNA- (including MSK-IMPACT)/RNA-based (MSK-Fusion) next-generation sequencing (NGS). Plasma cfDNA underwent targeted NGS. ORR and progression-free survival (PFS) were assessed (RECIST v1.1). Results: 74 patients received a MET TKI (1 TKI n = 55; ≥2 TKIs n = 19). 91% received crizotinib as their 1st TKI. Pre-TKI MET levels in tumor tissue (range 0-2120 amol/µg) were associated with outcomes: ORR 63% (n = 7/11) and median PFS 6.9 mos with detectable MET vs ORR 0% (n = 0/5) and median PFS 4.6 mos with undetectable MET (HR for PFS 0.3). Pre-TKI RAS pathway activation was associated with response: ORR 0% (n = 0/6) with KRAS/NF1/RASA1 mutation vs ORR 29% (n = 25/87) in others. Similar outcomes were observed with pre-TKI KRAS expression (n = 16, all with detectable KRAS levels): ORR 0% (n = 0/2) in KRAS ≥700 amol/µg vs ORR 50% (n = 7/14) < 700 amol/µg. Acquired resistance (Jackman criteria) was seen in 29 patients, 9 with paired pre-/post-treatment samples. On-target acquired resistance was found in 2/9 patients (22%): MET D1228N (n = 1), HGF amplification (n = 1). Potential off-target acquired resistance mechanisms were found in 5/9 pts (44%): KRAS G13V (n = 1), RASA1 S742* (n = 1), MDM2 amplification (n = 2), EGFR amplification (n = 1). Conclusions: Lack of MET expression or RAS pathway activation is associated with poor MET TKI outcomes in MET exon14-altered lung cancers. On-target acquired resistance is found in < 25% of patients; HGF amplification is a novel mechanism. Off-target intrinsic/acquired resistance may be mediated by RAS/MDM2/EGFR pathway activation.

11583 Background: Lung cancers driven by oncogenic RET fusions have lower response rates to targeted monotherapy such as cabozantinib (28%) relative to response rates typically observed in ALK- or ROS1- rearranged lung adenocarcinomas (60-80%). Methods: To identify targetable co-dependencies or cooperating pathways for RET fusion-positive lung cancers, we performed high-throughput chemical and genetic screens to find FDA-approved drugs or genes that when inhibited, would synergize with cabozantinib in RET fusion-positive lung cancer cell lines. In addition we performed NGS of a pair of pre-treatment and post-cabozantinib progression samples. Results: We identified EGFR siRNAs and anti-EGFR drugs as synergistic with cabozantinib. Combinations of drugs that target EGFR (cetuximab, afatinib, erlotinib, gefitinib, neratinib) and RET (cabozantinib, CEP-32496, lenvatinib, vandetanib) were more effective at reducing growth of RET cell lines than any single agent in vitro and in xenograft models. Cabozantinib treatment of RET fusion-positive cell lines inhibited EGFR and RET phosphorylation, an observation not seen in RET wild-type cell lines. Co-immunoprecipitation studies reveal that RET and EGFR interact. Ectopic expression of CCDC6-RET in NIH-3T3 or human bronchial epithelial cells resulted in upregulation of multiple ERBB receptors and ligands (not seen in a ROS1 fusion-positive cell line) and a concomitant increase in EGFR stability. Treatment with ERBB pathway ligands or overexpression of EGFR decreased sensitivity to cabozantinib in two RET fusion-positive cell lines. Finally, sequencing of a pair of pre-treatment and post-progression samples from a lung cancer patient treated with cabozantinib revealed acquired amplification of EGFR in the latter sample. Conclusions: Taken together, these results suggest that the tumorigenic potential of RET fusion oncogenes is dependent on deregulation of ERBB-activated pathways and that a combination of RET and EGFR drugs could be more effective in treating RET fusion-positive tumors. Moreover, amplification of EGFR is a potential driver of resistance to cabozantinib in RET-rearranged lung cancers.