Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Jan 31;14(1):e1002223.
doi: 10.1371/journal.pmed.1002223. eCollection 2017 Jan.

Master Regulators of Oncogenic KRAS Response in Pancreatic Cancer: An Integrative Network Biology Analysis

Affiliations
Free PMC article

Master Regulators of Oncogenic KRAS Response in Pancreatic Cancer: An Integrative Network Biology Analysis

Shivan Sivakumar et al. PLoS Med. .
Free PMC article

Abstract

Background: KRAS is the most frequently mutated gene in pancreatic ductal adenocarcinoma (PDAC), but the mechanisms underlying the transcriptional response to oncogenic KRAS are still not fully understood. We aimed to uncover transcription factors that regulate the transcriptional response of oncogenic KRAS in pancreatic cancer and to understand their clinical relevance.

Methods and findings: We applied a well-established network biology approach (master regulator analysis) to combine a transcriptional signature for oncogenic KRAS derived from a murine isogenic cell line with a coexpression network derived by integrating 560 human pancreatic cancer cases across seven studies. The datasets included the ICGC cohort (n = 242), the TCGA cohort (n = 178), and five smaller studies (n = 17, 25, 26, 36, and 36). 55 transcription factors were coexpressed with a significant number of genes in the transcriptional signature (gene set enrichment analysis [GSEA] p < 0.01). Community detection in the coexpression network identified 27 of the 55 transcription factors contributing to three major biological processes: Notch pathway, down-regulated Hedgehog/Wnt pathway, and cell cycle. The activities of these processes define three distinct subtypes of PDAC, which demonstrate differences in survival and mutational load as well as stromal and immune cell composition. The Hedgehog subgroup showed worst survival (hazard ratio 1.73, 95% CI 1.1 to 2.72, coxPH test p = 0.018) and the Notch subgroup the best (hazard ratio 0.62, 95% CI 0.42 to 0.93, coxPH test p = 0.019). The cell cycle subtype showed highest mutational burden (ANOVA p < 0.01) and the smallest amount of stromal admixture (ANOVA p < 2.2e-16). This study is limited by the information provided in published datasets, not all of which provide mutational profiles, survival data, or the specifics of treatment history.

Conclusions: Our results characterize the regulatory mechanisms underlying the transcriptional response to oncogenic KRAS and provide a framework to develop strategies for specific subtypes of this disease using current therapeutics and by identifying targets for new groups.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Oncogenic KRAS is regulated by three groups of master regulators.
A. Volcano plot showing the magnitude of the differential gene expression between murine mock ductal cells and murine cre ductal cells (with activated oncogenic Kras). Each dot represents one probe with detectable expression in both conditions. The coloured dots mark the threshold (p < 0.05 and log2 fold-change > 1) for defining a gene as differentially expressed. B. Ras GTPase assay shows increased GTPase activity in cre cells blotted with pan-Ras antibody (M, mock; c, cre). C. Visual representation of master regulators (MRs) identified with msVIPER analysis (p < 0.01). The nodes in the networks represent the 55 master regulators (large dots) and the corresponding inferred targets (smaller dots). The edges in the network represent the regulatory relationship between regulators and the inferred targets. The colours highlight the community structure of the network identified via greedy optimization of modularity. The three groups of nodes correspond to a total of 27 master regulators and represent three distinct disease processes enriched for cell cycle (pink), Hedgehog/Wnt signalling (blue), and Notch signalling (green) pathways. D. For the 27 MRs in the three core processes, the heat map shows their activity (first column) and differential expression in the KRAS signature (second column) as obtained by Virtual Inference of Protein-activity by Enriched Regulon (VIPER) analysis. “Expression” refers to the differential expression value after KRAS induction (cell line experiment). The colour code of in the heat map corresponds to the t-statistic value obtained after limma differential expression analysis, with blue representing down-regulated genes and red representing up-regulated genes after KRAS activation. “Activity” refers to the differential protein activity value after KRAS induction with red or blue representing activation or inactivation, respectively. The protein activity score is quantitatively inferred by the aREA algorithm in VIPER by systematically analysing expression of genes coexpressed with the transcription factor (TF).
Fig 2
Fig 2. Clustering of master regulators into different functional groups.
Heat maps showing the similarity between the samples in the A. ICGC and B. TCGA cohorts as measured by “signature distance” between the MRs activity profiles [27]. Unsupervised analysis identified three classes of tumours with differential activities of the three identified disease processes: cell cycle (pink), Hedgehog/Wnt (blue), and Notch (green).
Fig 3
Fig 3. Differences in survival and mutational burden between subtypes.
A. Kaplan–Meier survival curves of the different tumour subgroups using the ICGC cohort. Numbers of subjects at risk at the start of each time interval are shown above the x-axis. The groups overall showed significant survival differences (logrank p-value = 1.8e–4). More specifically, Hedeghog/Wnt group HR = 1.73, 95% CI 1.1 to 2.72, coxPH test p-value = 0.018; Notch group HR = 0.62, 95% CI 0.42 to 0.93, coxPH test p-value = 0.019; when compared to the cell cycle group and after correcting for gender, age, and tumour stage. B. Kaplan–Meier survival curves of the different tumour subgroups using the TCGA cohort for subsets of individuals that did or did not receive adjuvant targeted therapy treatment. Numbers of subjects at risk at the start of each time interval are shown above the x-axis. Hedeghog/Wnt group HR = 4.12, 95% CI 1.2 to 13.8, coxPH test p-value = 0.02; when compared to the cell cycle group and after correcting for gender, age, tumour stage, and radiation therapy indicator. C. Mutations in key genes and pathways in pancreatic cancer. The upper and middle panels show the frequency of altered samples by copy number changes (gains refer to amplifications of >5 copies); the bottom panel shows the frequency of altered samples by nonsilent single nucleotide variants, small insertions, or deletions with moderate-to-high biological effect.
Fig 4
Fig 4. Subtypes show different immune activity.
A. Bar plot showing the Pearson partial correlation t-statistic difference between Hedgehog and Notch for ssGSEA pathway enrichment scores significantly associated with one subtype or the other. B. Boxplots from the ESTIMATE analysis showing the variation in the stromal and immune content between the Hedgehog, Notch, and cell cycle subgroups for both the ICGC and TCGA cohorts. C. Bar plot showing the difference in Pearson partial correlation coefficient difference between Hedgehog and Notch for estimated CIBERSORT leukocyte cell fractions significantly associated with one subtype or the other. A negative difference highlights strong association with Hedgehog, whereas a positive value indicates a strong association with Notch.

Similar articles

See all similar articles

Cited by 7 articles

See all "Cited by" articles

References

    1. CRUK. Pancreatic Cancer Risks and Causes 2014. http://www.cancerresearchuk.org/about-cancer/type/pancreatic-cancer/about/pancreatic-cancer-risks-and-causes.
    1. Campbell PJ, Yachida S, Mudie LJ, Stephens PJ, Pleasance ED, Stebbings LA, et al. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature. 2010;467(7319):1109–13. 10.1038/nature09460 - DOI - PMC - PubMed
    1. Smit VT, Boot AJ, Smits AM, Fleuren GJ, Cornelisse CJ, Bos JL. KRAS codon 12 mutations occur very frequently in pancreatic adenocarcinomas. Nucleic Acids Res. 1988;16(16):7773–82. - PMC - PubMed
    1. Jones S, Zhang X, Parsons DW, Lin JC, Leary RJ, Angenendt P, et al. Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science. 2008;321(5897):1801–6. 10.1126/science.1164368 - DOI - PMC - PubMed
    1. Waddell N, Pajic M, Patch AM, Chang DK, Kassahn KS, Bailey P, et al. Whole genomes redefine the mutational landscape of pancreatic cancer. Nature. 2015;518(7540):495–501. 10.1038/nature14169 - DOI - PMC - PubMed
Feedback