Identifying Biomarkers of Cisplatin Sensitivity in Non-Small Cell Lung Cancer via Comprehensive Integrative Analysis

Abstract

Background: Only 30-40% of non-small cell lung cancer (NSCLC) patients are clinically sensitive to cisplatin-based chemotherapy. Thus, it is necessary to identify biomarkers for personalized cisplatin chemotherapy in NSCLC. However, data heterogeneity and low-value density make it challenging to detect reliable cisplatin efficacy biomarkers using traditional analysis methods. Objective: This paper aims to find reliable cisplatin efficacy biomarkers for NSCLC patients using comprehensive integrative analysis. Method: We searched online resources and collected six NSCLC transcriptomics data sets with responses to cisplatin. The six data sets are divided into two groups: the learning group for biomarker identification and the test group for independent validation. We performed comprehensive integrative analysis under two kinds of frameworks, i.e., one-level and two-level, with three integrative models. Pathway analysis was performed to estimate the biological significance of the resulting biomarkers. For independent validation, logrank statistic was employed to test how significant the difference of Kaplan- Meier (KM) curves between two patient groups is, and the Cox proportional-hazards model was used to test how the expression of a gene is associated with patients’ survival time. Especially, a permutation test was performed to verify the predictive power of a biomarker panel on cisplatin efficacy. For comparison, we also analyzed each learning data set individually, in which three popular differential expression models, Limma, SAM, and RankSum, were used. Results: A total of 318 genes were identified as a core panel of cisplatin efficacy markers for NSCLC patients, exhibiting consistent differential expression between cisplatin-sensitive and –resistant groups across studies. A total of 129 of 344 KEGG pathways were found to be enriched in the core panel, reflecting a picture of the molecular mechanism of cisplatin resistance in NSCLC. By mapping onto the KEGG pathway tree, we found that a KEGG pathway-level I module, genetic information processing, is most active in the core panel with the highest activity ratio in response to cisplatin in NSCLC as expected. Related pathways include mismatch repair, nucleotide excision repair, aminoacyl-tRNA biosynthesis, and basal transcription factors, most of which respond to DNA double-strand damage in patients. Evaluation on two independent data sets demonstrated the predictive power of the core marker panel for cisplatin sensitivity in NSCLC. Also, some single markers, e.g., MST1R, were observed to be remarkably predictive of cisplatin resistance in NSCLC. Conclusion: Integrative analysis is more powerful in detecting biomarkers for cisplatin efficacy by overcoming data heterogeneity and low-value density in data sets, and the identified core panel (318 genes) can help develop personalized medicine of cisplatin chemotherapy for NSCLC patients.