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Gene Co-Expression Analysis Predicts Genetic Variants Associated with Drug Responsiveness in Lung Cancer.

ABSTRACT: Responsiveness to drugs is an important concern in designing personalized treatment for cancer patients. Currently genetic markers are often used to guide targeted therapy. However, deeper understanding of the molecular basis for drug responses and discovery of new predictive biomarkers for drug sensitivity are much needed. In this paper, we present a workflow for identifying condition-specific gene co-expression networks associated with responses to the tyrosine kinase inhibitor, Erlotinib, in lung adenocarcinoma cell lines using data from the Cancer Cell Line Encyclopedia by combining network mining and statistical analysis. Particularly, we have identified multiple gene modules specifically co-expressed in the drug responsive cell lines but not in the unresponsive group. Interestingly, most of these modules are enriched on specific cytobands, suggesting potential copy number variation events on these loci. Our results therefore imply that there are multiple genetic loci with copy number variations associated with the Erlotinib responses. The existence of CNVs in these loci is also confirmed in lung cancer tissue samples using the TCGA data. Since these structural variations are inferred from functional genomics data, these CNVs are functional variations. These results suggest the condition specific gene co- expression network mining approach is an effective approach in predicting candidate biomarkers for drug responses.

SUBMITTER: Shroff S

PROVIDER: S-EPMC5001757 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Gene Co-Expression Analysis Predicts Genetic Variants Associated with Drug Responsiveness in Lung Cancer.

Shroff Sanaya S Zhang Jie J Huang Kun K

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science 20160720

Responsiveness to drugs is an important concern in designing personalized treatment for cancer patients. Currently genetic markers are often used to guide targeted therapy. However, deeper understanding of the molecular basis for drug responses and discovery of new predictive biomarkers for drug sensitivity are much needed. In this paper, we present a workflow for identifying condition-specific gene co-expression networks associated with responses to the tyrosine kinase inhibitor, Erlotinib, in ...[more]

PMID: 27570645

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Project description:Lung cancer is the most common cancer in men and women. This cancer is divided into two main types, namely non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Around 85 to 90 percent of lung cancers are NSCLC. Repositioning potent candidate drugs in NSCLC treatment is one of the important topics in cancer studies. Drug repositioning (DR) or drug repurposing is a method for identifying new therapeutic uses of existing drugs. The current study applies a computational drug repositioning method to identify candidate drugs to treat NSCLC patients. To this end, at first, the transcriptomics profile of NSCLC and healthy (control) samples was obtained from the GEO database with the accession number GSE21933. Then, the gene co-expression network was reconstructed for NSCLC samples using the WGCNA, and two significant purple and magenta gene modules were extracted. Next, a list of transcription factor genes that regulate purple and magenta modules' genes was extracted from the TRRUST V2.0 online database, and the TF-TG (transcription factors-target genes) network was drawn. Afterward, a list of drugs targeting TF-TG genes was obtained from the DGIdb V4.0 database, and two drug-gene interaction networks, including drug-TG and drug-TF, were drawn. After analyzing gene co-expression TF-TG, and drug-gene interaction networks, 16 drugs were selected as potent candidates for NSCLC treatment. Out of 16 selected drugs, nine drugs, namely Methotrexate, Olanzapine, Haloperidol, Fluorouracil, Nifedipine, Paclitaxel, Verapamil, Dexamethasone, and Docetaxel, were chosen from the drug-TG sub-network. In addition, nine drugs, including Cisplatin, Daunorubicin, Dexamethasone, Methotrexate, Hydrocortisone, Doxorubicin, Azacitidine, Vorinostat, and Doxorubicin Hydrochloride, were selected from the drug-TF sub-network. Methotrexate and Dexamethasone are common in drug-TG and drug-TF sub-networks. In conclusion, this study proposed 16 drugs as potent candidates for NSCLC treatment through analyzing gene co-expression, TF-TG, and drug-gene interaction networks.

Project description:BackgroundAnnual vaccination is the most effective prevention of influenza infection. Up to now, a series of studies have demonstrated the role of genetic variants in regulating the antibody response to influenza vaccine. However, among the Chinese population, the relationship between genetic factors and the responsiveness to influenza vaccination has not been clarified through genome-wide association study (GWAS).MethodA total of 1,968 healthy volunteers of Chinese descent were recruited and 1,582 of them were available for the subsequent two-stage analysis. In the discovery stage, according to our inclusion criteria, 123 of 1,582 subjects were selected as group 1 and received whole-genome sequencing to identify potential variants and genes. In the verification stage, 29 candidate variants identified by GWAS were selected for further validation in 481 subjects in group 2. Besides, we also analyzed nine variants from previously published reports in our study.ResultsMultivariate logistic regression analysis showed that compared with the TT genotype of ZBTB46 rs2281929, the TC + CC genotype was associated with a lower risk of low responsiveness to influenza vaccination adjusted for gender and age (Group 2: P = 7.75E-05, OR = 0.466, 95%CI = 0.319-0.680; Combined group: P = 1.18E-06, OR = 0.423, 95%CI = 0.299-0.599). In the combined group, IQGAP2 rs2455230 GC + CC genotype was correlated with a lower risk of low responsiveness to influenza vaccination compared with the GG genotype (P = 8.90E-04, OR = 0.535, 95%CI = 0.370-0.774), but the difference was not statistically significant in group 2 (P = 0.008). The antibody fold rises of subjects with ZBTB46 rs2281929 TT genotype against H1N1, H3N2,and B were all significantly lower than that of subjects with TC + CC genotype (P < 0.001). Compared with IQGAP2 rs2455230 GC + CC carriers, GG carriers had lower antibody fold rises to H1N1 (P = 0.001) and B (P = 0.032). The GG genotype of rs2455230 tended to be correlated with lower antibody fold rises (P = 0.096) against H3N2, but the difference was not statistically significant. No correlation was found between nine SNPs from previously published reports and the serological response to influenza vaccine in our study.ConclusionOur study identified two novel candidate missense variants, ZBTB46 rs2281929 and IQGAP2 rs2455230, were associated with the immune response to influenza vaccination among the Chinese population. Identifying these variants will provide more evidence for future research and improve the individualized influenza vaccination program.

Project description:BACKGROUND: Toll like receptors (TLRs) signaling pathways, including the adaptor protein Mal encoded by the TIRAP gene, play a central role in the development of acute lung injury (ALI). Recently, the TIRAP variants have been described association with susceptibility to inflammatory diseases. The aim of this study was to investigate whether genetic variants in TIRAP are associated with the development of ALI. METHODS: A case-control collection from Han Chinese of 298 healthy subjects, 278 sepsis-associated ALI and 288 sepsis alone patients were included. Three tag single nucleotide polymorphisms (SNPs) of the TIRAP gene and two additional SNPs that have previously showed association with susceptibility to other inflammatory diseases were genotyped by direct sequencing. The differences of allele, genotype and haplotype frequencies were evaluated between three groups. RESULTS: The minor allele frequencies of both rs595209 and rs8177375 were significantly increased in ALI patients compared with both healthy subjects (odds ratio (OR) = 1.47, 95% confidence interval (CI):1.15-1.88, P = 0.0027 and OR = 1.97, 95% CI: (1.38-2.80), P = 0.0001, respectively) and sepsis alone patients (OR = 1.44, 95% CI: 1.12-1.85, P = 0.0041 and OR = 1.82, 95% CI: 1.28-2.57, P = 0.00079, respectively). Haplotype consisting of these two associated SNPs strengthened the association with ALI susceptibility. The frequency of haplotype AG (rs595209A, rs8177375G) in the ALI samples was significantly higher than that in the healthy control group (OR = 2.13, 95% CI: 1.46-3.09, P = 0.00006) and the sepsis alone group (OR = 2.24, 95% CI: 1.52-3.29, P = 0.00003). Carriers of the haplotype CA (rs595209C, rs8177375A) had a lower risk for ALI compared with healthy control group (OR = 0.69, 95% CI: 0.54-0.88, P = 0.0003) and sepsis alone group (OR = 0.71, 95% CI: 0.55-0.91, P = 0.0006). These associations remained significant after adjustment for covariates in multiple logistic regression analysis and for multiple comparisons. CONCLUSIONS: These results indicated that genetic variants in the TIRAP gene might be associated with susceptibility to sepsis-associated ALI in Han Chinese population. However, the association needs to be replicated in independent studies.

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Gene Co-Expression Analysis Predicts Genetic Variants Associated with Drug Responsiveness in Lung Cancer.

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Gene Co-Expression Analysis Predicts Genetic Variants Associated with Drug Responsiveness in Lung Cancer.

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