RESUMO
OBJECTIVES: 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.
Assuntos
Adenocarcinoma/genética , Carcinoma Pulmonar de Células não Pequenas/genética , Neoplasias Pulmonares/genética , Mutação/genética , Proteínas Serina-Treonina Quinases/genética , Proteína Supressora de Tumor p53/genética , Quinases Proteína-Quinases Ativadas por AMP , Adenocarcinoma/diagnóstico , Adenocarcinoma/mortalidade , Idoso , Carcinoma Pulmonar de Células não Pequenas/diagnóstico , Carcinoma Pulmonar de Células não Pequenas/mortalidade , Estudos de Coortes , Éxons/genética , Feminino , Humanos , Neoplasias Pulmonares/diagnóstico , Neoplasias Pulmonares/mortalidade , Masculino , Proteínas de Membrana/genética , Grupos Populacionais , Prognóstico , Proteínas Proto-Oncogênicas p21(ras)/genética , Proteínas Proto-Oncogênicas p21(ras)/metabolismo , Análise de Sobrevida , SuéciaRESUMO
Numerous protein biomarkers have been analyzed to improve prognostication in non-small cell lung cancer, but have not yet demonstrated sufficient value to be introduced into clinical practice. Here, we aimed to develop and validate a prognostic model for surgically resected non-small cell lung cancer. A biomarker panel was selected based on (1) prognostic association in published literature, (2) prognostic association in gene expression data sets, (3) availability of reliable antibodies, and (4) representation of diverse biological processes. The five selected proteins (MKI67, EZH2, SLC2A1, CADM1, and NKX2-1 alias TTF1) were analyzed by immunohistochemistry on tissue microarrays including tissue from 326 non-small cell lung cancer patients. One score was obtained for each tumor and each protein. The scores were combined, with or without the inclusion of clinical parameters, and the best prognostic model was defined according to the corresponding concordance index (C-index). The best-performing model was subsequently validated in an independent cohort consisting of tissue from 345 non-small cell lung cancer patients. The model based only on protein expression did not perform better compared to clinicopathological parameters, whereas combining protein expression with clinicopathological data resulted in a slightly better prognostic performance (C-index: all non-small cell lung cancer 0.63 vs 0.64; adenocarcinoma: 0.66 vs 0.70, squamous cell carcinoma: 0.57 vs 0.56). However, this modest effect did not translate into a significantly improved accuracy of survival prediction. The combination of a prognostic biomarker panel with clinicopathological parameters did not improve survival prediction in non-small cell lung cancer, questioning the potential of immunohistochemistry-based assessment of protein biomarkers for prognostication in clinical practice.