LAUSR

Background: NCSLC is the leading cause of cancer deaths in the United States (USA). The 5-year survival rate is 22.9% (7% in those with metastases). Personalized therapies have shown promise but a better understanding of the genomic landscape of the tumor may help further improve survival. Here, we present the genomic landscape and key features of NCSLC patients using a real-world clinico-genomics dataset. Methods: The ConcertAI Genome360TM NCSLC dataset is a deeply curated real-world dataset of 11349 patients from the USA who have undergone a next generation sequencing test. This dataset covers >2000 genes. Here we present the demographic, clinical and genomic characteristics of this cohort. The top 20 genes with pathogenic mutations were identified and co-mutational analysis was performed to identify the most significant genes that were positively and negatively correlated to these genes using chi-squared test with Bonferroni correction. Other clinically relevant biomarkers such as tumor mutation burden (TMB), microsatellite instability (MSI) and Programmed death-ligand 1 (PD-L1) status were also characterised. Results: The median age at diagnosis was 70 years with an equal gender distribution. Adenocarcinoma (69%) and squamous cell carcinoma (24%) were the two most common histologies. Most patients were diagnosed at stage IV (57%) or III (19%). Brain, bone, and liver were the most prevalent metastatic sites. 22% patients had high expression of PD-L1, 28% had high TMB and only ~1% patients had high MSI. The 20 most frequently mutated genes along with their top 3 most significant (p-value < 3e-5) positively and negatively correlated genes are listed in Table 1. Conclusion: This analysis provides a deeper understanding of the genomic landscape of NSCLC. The co-mutational analysis provides insights into pathways that are perturbed together providing opportunities to develop treatments to overcome such co-mutations. Table 1. Top 20 mutated genes and their associated positively/negatively correlated genes. Gene Prevalence (%) Positively Correlated Genes Negatively Correlated Genes TP53 65 SOX2, NFE2L2, FGFR1 KRAS, MDM2, STK11 KRAS 28 STK11, RBM10, ATM EGFR, KMT2D, SOX2 CDKN2A 19 TP53, CCND1, SOX2 RB1 EGFR 13 MDM2, CDK4, CCNE1 STK11, KEAP1, KMT2D STK11 12 KEAP1, KRAS, MYC SOX2, PIK3CA, KMT2D MTAP 9 CDKN2A RB1 PIK3CA 7 SOX2, TP53, NFE2L2 KRAS, STK11 KEAP1 6 STK11, KRAS, CDKN2A EGFR SMARCA4 6 STK11, KEAP1, APC EGFR NF1 6 TP53, RASA1, PTPN11 KRAS RBM10 5 KRAS, IL7R, ATM TP53 KMT2D 7 TP53, NFE2L2, NOTCH1 KRAS, EGFR, STK11 DNMT3A 6 TET2, ASXL1, CHEK2 - RB1 7 TP53, PTEN, CCNE1 MTAP, KRAS, CDKN2A ARID1A 7 CDKN2A, TP53, FAT1 EGFR ATM 4 KRAS, NKX2-1, NFKBIA TP53 PTEN 7 RB1, TP53, KMT2D KRAS BRAF 11 SETD2, CDK6 KRAS MET 7 CDK6, MDM2, ROS1 KRAS TERT 4 MITF, PTCH1, EP300 - Citation Format: neeraj kumar singh, Kamal S. Saini, Sangavai Chakkrapani, Eric Severson, Nahush Nagaraj, Shakti Ramkissoon, Isagani Chico, Ariel Aguilo, Rohini George, Laura Vidal, Smita Agrawal. Genomic landscape of patients with non-small cell lung cancer (NSCLC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 923.