Pathway-based Approaches for Gene Signature Identification and Clinical Outcome Prediction
Date Issued
2015
Date
2015
Author(s)
Chang, Ya-Hsuan
Abstract
Different molecular levels including genome, epigenome, transcriptome, and proteome play important roles in development, differentiation, growth etc. as well as in medicine. Up to date, high throughput technologies such as microarrays and next generation sequencers (NGS) can be used to measure variations of different molecular levels and above data was named “omic data”. It provided powerful tools to discovery huge amount of molecular changes at once. Because of tumor heterogeneity, patients with the similar clinical pathology had the different treatment response and clinical outcome. Hence, in the studies, pathway-base analysis was used to reanalysis public datasets to explore signatures associated with treatment response or clinical outcome. These pathway-based signatures increased predict accuracy of treatment response or clinical outcome. In this dissertation, this methodology was used to find pathway-based signatures of lung adenocarcinoma and childhood high risk B-precursor acute lymphoblastic leukemia. Two studies were the 1st “Pathway-based gene signatures prediction clinical outcome of lung adenocarcinoma”, and 2nd “Apoptosis pathway signature for prediction of treatment response and clinical outcome in childhood high risk B-precursor acute lymphoblastic leukemia”. Study 1: Pathway-based gene signatures prediction clinical outcome of lung adenocarcinoma. Lung adenocarcinoma is often diagnosed at an advanced stage with poor prognosis. Patients with different clinical outcomes may have similar clinico-pathological characteristics. The results of previous studies for biomarkers for lung adenocarcinoma have generally been inconsistent and limited in clinical application. In this study, we used inverse-variance weighting to combine the hazard ratios for the four datasets and performed pathway analysis to identify prognosis-associated gene signatures. A total of 2,418 genes were found to be significantly associated with overall survival. Of these, a 21-gene signature in the HMGB1/RAGE signalling pathway, a 22-gene signature in the beta-adrenergic receptor regulation of ERK pathway, and a 31-gene signature in the clathrin-coated vesicle cycle pathway were significantly associated with prognosis of lung adenocarcinoma across all four datasets (all p-value< 0.05, log-rank test). We combined the scores for the three pathways to derive a combined pathway-based risk (CPBR) score. Then, three pathway-based signatures and the CPBR score were validated in two independent cohorts. Considering p-values and hazard ratios, three pathway-based signatures and CPBR score had more statistical significant than 2418 genes and showed the consistent results in four datasets. Pathway-based signatures portend the better prediction power for prognosis. Study 2: Apoptosis pathway signature for prediction of treatment response and clinical outcome in childhood high risk B-precursor acute lymphoblastic leukemia The most common cancer in children is acute lymphoblastic leukemia (ALL) and it had high cure rate, especially for B-precursor ALL. However, relapse due to drug resistance and overdose treatment reach the limitations in patient managements. In this study, integration of gene expression microarray data, logistic regression, analysis of microarray (SAM) method, and gene set analysis were performed to discover treatment response associated pathway-based signatures in the original cohort. Results showed that 3,72 probes were significantly associated with treatment response. After pathway analysis, only apoptosis pathway had significant association with treatment response. Apoptosis pathway signature (APS) derived from 15 significantly expressed genes had 88% accuracy for treatment response prediction. The APS was further validated in two independent cohorts. Results also showed that APS was significantly associated with induction failure time (adjusted hazard ratio [HR] =1.60, 95% confidence interval [CI] = [1.13, 2.27]) in the first cohort and significantly associated with event-free survival (adjusted HR=1.56, 95% CI= [1.13, 2.16]) or overall survival in the second cohort (adjusted HR=1.74, 95% CI= [1.24, 2.45]). APS not only can predict clinical outcome, but also provide molecular guidance of patient management. In conclusions, pathway-based analysis could be applied in gene expression profiling measured from high throughput technologies to identified signatures. Pathway-based signatures could not only provide prediction abilities for prognosis, treatment response, and adverse effect, and they may provide potential molecular guidance in clinical practice or targets of drug development after validation in large prospective cohorts.
Subjects
high-throughput technology
prognosis
pathway-based analysis
gene signature
treatment response
SDGs
Type
thesis
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