Mutation patterns in a population-based non-small cell lung cancer cohort and prognostic impact of concomitant mutations in KRAS and TP53 or STK11

Abstract

Non-small cell lung cancer (NSCLC) is a heterogeneous disease with unique combinations of somatic molecular alterations in individual patients, as well as significant differences in populations across the world with regard to mutation spectra and mutation frequencies. Here we aim to describe mutational patterns and linked clinical parameters in a population-based NSCLC cohort. Materials and methods: Using targeted resequencing the mutational status of 82 genes was evaluated in a consecutive Swedish surgical NSCLC cohort, consisting of 352 patient samples from either fresh frozen or formalin fixed paraffin embedded (FFPE) tissues. The panel covers all exons of the 82 genes and utilizes reduced target fragment length and two-strand capture making it compatible with degraded FFPE samples. Results: We obtained a uniform sequencing coverage and mutation load across the fresh frozen and FFPE samples by adaption of sequencing depth and bioinformatic pipeline, thereby avoiding a technical bias between these two sample types. At large, the mutation frequencies resembled the frequencies seen in other western populations, except for a high frequency of KRAS hotspot mutations (43%) in adenocarcinoma patients. Worse overall survival was observed for adenocarcinoma patients with a mutation in either TP53, STK11 or SMARCA4. In the adenocarcinoma KRAS-mutated group poor survival appeared to be linked to concomitant TP53 or STK11 mutations, and not to KRAS mutation as a single aberration. Similar results were seen in the analysis of publicly available data from the cBioPortal. In squamous cell carcinoma a worse prognosis could be observed for patients with MLL2 mutations, while CSMD3 mutations were linked to a better prognosis. Conclusion: Here we have evaluated the mutational status of a NSCLC cohort. We could not confirm any survival impact of isolated driver mutations. Instead, concurrent mutations in TP53 and STK11 were shown to confer poor survival in the KRAS-positive adenocarcinoma subgroup.

STK11 (LKB1) mutations in metastatic NSCLC: Prognostic value in the real world

“…The current data, in a large observational genomic study among patients receiving routine clinical care, support the association of STK11m with poor treatment outcomes previously observed in smaller clinical trial cohorts [18][19][20][21][22][23][24]. Shorter OS and reduced response rates have been observed in patients with STK11m versus STK11wt non-squamous metastatic NSCLC treated with durvalumab (with or without tremelimumab) across multiple phase 1/2 trials [18].…”

“…PD-L1 expression on tumor cells has been used to guide treatment selection, and more recently tumor mutational burden (TMB) has shown potential as a predictive biomarker for IO benefit [12][13][14][15]. Mutations in individual genes and co-mutation patterns have also been linked to patient response to standard chemotherapy and/or IO in advanced NSCLC [16][17][18][19][20][21][22][23][24].…”

Concomitant TP53 Mutation Confers Worse Prognosis in EGFR-Mutated Non-Small Cell Lung Cancer Patients Treated with TKIs

“…Numerous studies demonstrated the role of TP53 mutations in predicting poor prognosis of advanced NSCLC patients [9,[11][12][13][14][15], and this was confirmed also in the subgroup of NSCLC patients carrying EGFR mutations [8,9,16]. In particular, different recent studies showed that the concurrent presence of TP53 mutations negatively affects response to TKIs in EGFR-mutated NSCLC patients, suggesting a role for these gene mutations in determining primary resistance to these drugs [6,7,[17][18][19][20].…”

Development of a novel prognostic score combining clinicopathologic variables, gene expression, and mutation profiles for lung adenocarcinoma

“…The high-throughput sequencing technology has made it possible a comprehensive interrogation of whole transcriptome and genome of tumor tissues at an increasingly reasonable cost [4,5]. Previous studies focused on finding prognostic signatures based on gene expressio n [6][7][8][9] or mutation [10][11][12] for LUAD patients. For example, Li et al [7] reported gene expression-based models with an average C-index of 0.604 on testing datasets from TCGA-LUAD in predicting overall survival (OS).…”