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Comparative Study
, 140 (4), 930-937

KRAS Driven Expression Signature Has Prognostic Power Superior to Mutation Status in Non-Small Cell Lung Cancer

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Comparative Study

KRAS Driven Expression Signature Has Prognostic Power Superior to Mutation Status in Non-Small Cell Lung Cancer

Ádám Nagy et al. Int J Cancer.

Abstract

KRAS is the most frequently mutated oncogene in non-small cell lung cancer (NSCLC). However, the prognostic role of KRAS mutation status in NSCLC still remains controversial. We hypothesize that the expression changes of genes affected by KRAS mutation status will have the most prominent effect and could be used as a prognostic signature in lung cancer. We divided NSCLC patients with mutation and RNA-seq data into KRAS mutated and wild type groups. Mann-Whitney test was used to identify genes showing altered expression between these cohorts. Mean expression of the top five genes was designated as a "transcriptomic fingerprint" of the mutation. We evaluated the effect of this signature on clinical outcome in 2,437 NSCLC patients using univariate and multivariate Cox regression analysis. Mutation of KRAS was most common in adenocarcinoma. Mutation status and KRAS expression were not correlated to prognosis. The transcriptomic fingerprint of KRAS include FOXRED2, KRAS, TOP1, PEX3 and ABL2. The KRAS signature had a high prognostic power. Similar results were achieved when using the second and third set of strongest genes. Moreover, all cutoff values delivered significant prognostic power (p < 0.01). The KRAS signature also remained significant (p < 0.01) in a multivariate analysis including age, gender, smoking history and tumor stage. We generated a "surrogate signature" of KRAS mutation status in NSCLC patients by computationally linking genotype and gene expression. We show that secondary effects of a mutation can have a higher prognostic relevance than the primary genetic alteration itself.

Keywords: Cox regression; Mann-Whitney analysis; SNP; TCGA; biomarker; lung cancer; microarrays; mutation; survival.

Figures

Figure 1
Figure 1
Analysis workflow for the literature survey (a) and for the database setup (b).
Figure 2
Figure 2
KRAS gene per se has no correlation to survival in NSCLC. Analysis of the effect of KRAS mutation (a) and expression (b) on survival in NSCLC AC patients. When investigating different cutoff values across all patients (c), none of the threshold values between the lower and upper quartile of expression reached statistical significance. The strongest achieved p values is marked by a red circle in (c). [Color figure can be viewed at wileyonlinelibrary.com]
Figure 3
Figure 3
The surrogate signature of KRAS mutation status has a high prognostic power. Signature comprising the mean expression of the top five genes (a). When investigating different cutoff values between the lower and upper quartiles of expression for the surrogate signature, every cutoff value achieved high significance (b). Similar results were achieved when using the second (c) and the third (d) set of five strongest genes. [Color figure can be viewed at wileyonlinelibrary.com]

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References

    1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA Cancer J Clin 2015;65:5–29. - PubMed
    1. Ettinger DS, Akerley W, Borghaei H, et al. Non‐small cell lung cancer. J Natl Compr Canc Netw 2012;10:1236–71. - PubMed
    1. Alamgeer M, Ganju V, Watkins DN. Novel therapeutic targets in non‐small cell lung cancer. Curr Opin Pharmacol 2013;13:394–401. - PubMed
    1. Paez JG, Janne PA, Lee JC, et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 2004;304:1497–500. - PubMed
    1. Cappuzzo F, Hirsch FR, Rossi E, et al. Epidermal growth factor receptor gene and protein and gefitinib sensitivity in non‐small‐cell lung cancer. J Natl Cancer Inst 2005;97:643–55. - PubMed

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