Young adults (YA) with non-small cell lung cancer (NSCLC): Snapshot of the oncogenic drivers and immune landscapes.

person Aaron Pruitt University of Kentucky, Lexington, KY info_outline Aaron Pruitt, Robert Hsu, Nishant Gandhi, Coral Olazapasti, Estelamari Rodriguez, Dwight Hall Owen, Jinesh Gheeya, Jun Zhang, Muhammad Furqan, Jaclyn LoPiccolo, Andrew Elliott, Ari VanderWalde, Stephanie Rock, Patrick C. Ma, Balazs Halmos, Zhonglin Hao

Authors person Aaron Pruitt University of Kentucky, Lexington, KY info_outline Aaron Pruitt, Robert Hsu, Nishant Gandhi, Coral Olazapasti, Estelamari Rodriguez, Dwight Hall Owen, Jinesh Gheeya, Jun Zhang, Muhammad Furqan, Jaclyn LoPiccolo, Andrew Elliott, Ari VanderWalde, Stephanie Rock, Patrick C. Ma, Balazs Halmos, Zhonglin Hao Organizations University of Kentucky, Lexington, KY, University of Southern California, Los Angeles, CA, Caris Life Sciences Research and Development, Phoenix, AZ, University of Miami, Miami, FL, University of Miami Sylvester Comprehensive Cancer Center, Miami, FL, The Ohio State University, Columbus, OH, The James, The Ohio State University Comprehensive Cancer Center, Columbus, OH, University of Kansas Medical Center (KUMC), Westwood, KS, University of Iowa, Carver College of Medicine, Iowa City, IA, Dana-Farber Cancer Institute, Boston, MA, Caris Life Sciences, Phoenix, AZ, Caris Life Sciences, Phenix, AZ, Penn State Milton S. Hershey Medical Center, Hershey, PA, Montefiore Einstein Comprehensive Cancer Center, Bronx, NY Abstract Disclosures Research Funding No funding sources reported Background: Approximately 5% of NSCLC occurs in patients 50 years or younger (median age:71) representing distinct clinicopathological features. Reports characterizing genomic alterations are scarce. Using a large real-world (RW) dataset, we characterize oncogenic drivers, and immune landscapes to better understand YA with NSCLC. Methods: 42,326 NSCLC specimens were analyzed using next-generation sequencing of DNA (DNA-592 panel or whole exome) or RNA (whole transcriptome) at Caris Life Sciences (Phoenix, AZ). YA (<=50 years) were categorized into three groups (years): 18-30 (A1, n=61), 31-40 (A2, n=277) & 41-50 (A3, n=1,549); vs >50 (A4, n=40,439). Composition of tumor microenvironment (TME) was estimated from bulk RNA sequencing using QuanTIseq method. RW survival on Osimertinib (Osi-OS) was obtained from insurance claims and calculated from initiation of Osi treatment to last contact and was compared between ages 18-50 and >50 years. Hazard ratio (HR) was calculated using the Cox proportional hazards model. Statistical significance was determined using Chi-square, Fisher’s Exact, Mann-Whitney U and log-rank tests and corrected for multiple comparisons where applicable (q<0.05). Results: Consistent with previous reports, NSCLC in YA was more prevalent in females, non-smokers and was associated with adenocarcinoma histology. Among driver alterations, ALK (IHC+), ROS1 , RET and NTRK1 fusion were enriched and KRAS mutations were reduced in YA. Interestingly, while the prevalence of KRAS G12D from transition (non-smoker related) and EGFR E746_A750 decreased with age, KRAS G12C from transversion increased with age in YA. YA were also associated with improved Osi-OS (HR: 0.79, median Osi-OS=37.4 months vs 32 months in >50 years old, p=0.019). High tumor mutation burden (>=10 mut/Mb), the prevalence of mutations in TP53 , KMT2D , NF1 , RBM10 , NFE2L2 and NOTCH1 increased while GNAS decreased with age. The expression of immune checkpoint genes such as PDCD1LG2 (A1/A4: 0.63, A3/A4: 0.89), LAG3 (A2/A4: 0.73, A3/A4: 0.89) and IFNG (A1/A4: 0.32, A3/A4: 0.75) were reduced, while M2 macrophage (A2/A4: 1.2) and neutrophil (A2/A4: 1.2) were increased in YA (all q<0.05). Conclusions: We report an increased prevalence of RET and NTRK1 fusions in YA and key differences in distributions of frequent KRAS and EGFR mutations in YA (vs A4) along with decreased expression of immune related genes in the tumor microenvironment. The implications are under active investigation. Driver A1 A2 A3 A4 ALK (F/IHC+) 25.6* 24.9* 10.0* 1.8 ROS1 (F) 4.7 7.3* 1.7* 0.5 RET (F) 0.0 4.0 2.3* 0.7 NTRK1 (F) 2.3* 0.0 0.3* 0.0 HER2 (M) 9.3* 4.0 1.2 1.5 EGFR L858R 0.0 4.4* 13.4* 24.8 EGFR E746_A750del 25.0 42.2* 32.1* 23.2 KRAS G12C 11.1* 14.3* 42.4 40.2 KRAS G12D 66.7* 25.7* 11.7 14.1 *q<0.05 comparing with A4. % prevalence EGFR / KRAS specific mutants is relative to all EGFR / KRAS mutants per age group. F: Fusion, M: Mutation.