Diagnosis and prognostication of ductal adenocarcinomas of the pancreas based on genome-wide DNA methylation profiling by bacterial artificial chromosome array-based methylated CpG island amplification

Development and validation of a machine learning prognostic model based on an epigenomic signature in patients with pancreatic ductal adenocarcinoma

BACKGROUND

In Pancreatic Ductal Adenocarcinoma (PDAC), current prognostic scores are unable to fully capture the biological heterogeneity of the disease. While some approaches investigating the role of multi-omics in PDAC are emerging, the analysis of methylation data is under exploited.

MATERIALS AND METHODS

We analyzed CpG sites from two publicly available datasets, the TCGA-PAAD used as discovery set and the CPTAC-PDA as external test set. Single mutations and co-mutation of KRAS and TP53 genes were identified as targets, and differentially methylated CpG sites (DMC) were detected accordingly. We trained and validated Random Forest (RF) models to predict each target. Area Under the Receiver Operating Characteristic curve (AUROC) and Area Under the Precision-Recall curve (AUPRC) were used as performance metrics. Then, we performed consensus clustering from the DMCs to identify novel patients' profiles. Finally, we trained and validated a combination of eXtreme Gradient Boosting (XGB) and tree models to select an epigenomic prognostic determinant.

RESULTS

From 598 DMCs extracted, an RF model predicted KRAS and TP53 co-mutation on the external test set with AUROC of 0.77 and AUPRC of 0.87. The consensus clustering allowed us to identify 4 clusters (C1, C2, C3, and C4) of patients. The C4 cluster captured a subgroup of patients with favorable Overall Survival (OS) with respect to others. The XGB model perfectly predicted C4 vs other clusters on the discovery set. In both cohorts, patients were stratified into two risk groups according to methylation levels of cg16854533, individuated as the most important CpG site.

CONCLUSION

We analyzed methylation data to develop a classifier for the TP53 and KRAS mutational status. Four prognostic clusters were pointed out and a prognostic model using a CpG site was validated in an independent cohort. Our results evidence that the proposed use of methylation data facilitates risk stratification for PDAC.

Identifying drug candidates for pancreatic ductal adenocarcinoma based on integrative multiomics analysis

Background

Due to a lack of early diagnosis methods and effective drugs, pancreatic ductal adenocarcinoma (PDAC) has an extremely poor prognosis. DNA methylation, transcriptome expression and gene copy number variation (CNV) have critical relationships with development and progression of various diseases. The purpose of the study was to screen reliable early diagnostic biomarkers and potential drugs based on integrative multiomics analysis.

Methods

We used methylation, transcriptome and CNV profiles to build a diagnostic model for PDAC. The protein expression of three model-related genes were externally validated using PDAC samples. Then, potential therapeutic drugs for PDAC were identified by interaction information related to existing drugs and genes.

Results

Four significant differentially methylated regions (DMRs) were selected from 589 common DMRs to build a high-performance diagnostic model for PDAC. Then, four hub genes, PHF12, FXYD3, PRKCB and ZNF582, were obtained. The external validation results showed that PHF12, FXYD3 and PRKCB protein expression levels were all upregulated in tumor tissues compared with adjacent normal tissues (P<0.05). Promising candidate drugs with activity against PDAC were screened and repurposed through gene expression analysis of online datasets. The five drugs, including topotecan, PD-0325901, panobinostat, paclitaxel and 17-AAG, with the highest activity among 27 PDAC cell lines were filtered.

Conclusions

Overall, the diagnostic model built based on four significant DMRs could accurately distinguish tumor and normal tissues. The five drug candidates might be repurposed as promising therapeutics for particular PDAC patients.

An Overview of Epigenetic Methylation in Pancreatic Cancer Progression

Over the past decades, the aberrant epigenetic modification, apart from genetic alteration, has emerged as dispensable events mediating the transformation of pancreatic cancer (PC). However, the understanding of molecular mechanisms of methylation modifications, the most abundant epigenetic modifications, remains superficial. In this review, we focused on the mechanistic insights of DNA, histone, and RNA methylation that regulate the progression of PC. The methylation regulators including writer, eraser and reader participate in the modification of gene expression associated with cell proliferation, invasion and apoptosis. Some of recent clinical trials on methylation drug targeting were also discussed. Understanding the novel regulatory mechanisms in the methylation modification may offer alternative opportunities to improve therapeutic efficacy to fight against this dismal disease.

Clinicopathological impacts of DNA methylation alterations on pancreatic ductal adenocarcinoma: prediction of early recurrence based on genome-wide DNA methylation profiling

PURPOSE

The present study was conducted to clarify the clinicopathological impacts of DNA methylation alterations on pancreatic ductal adenocarcinoma (PDAC).

METHODS

Genome-wide DNA methylation screening was performed using the Infinium HumanMethylation450 BeadChip, and DNA methylation quantification was verified using pyrosequencing. We analyzed fresh-frozen tissues from an initial cohort (17 samples of normal control pancreatic tissue [C] from 17 patients without PDAC, and 34 samples of non-cancerous pancreatic tissue [N] and 82 samples of cancerous tissue [T] both obtained from 82 PDAC patients) and formalin-fixed paraffin-embedded T samples from 34 patients in a validation cohort.

RESULTS

The DNA methylation profiles of N samples tended to differ from those of C samples, and 91,907 probes showed significant differences in DNA methylation levels between C and T samples. Epigenetic clustering of T samples was significantly correlated with a larger tumor diameter and early recurrence (ER), defined as relapse within 6 months after surgery. Three marker CpG sites, applicable to formalin-fixed paraffin-embedded surgically resected materials regardless of their tumor cell content, were identified for prediction of ER. The sensitivity and specificity for detection of patients belonging to the ER group using a panel combining these three marker CpG sites, including a CpG site in the CDK14 gene, were 81.8% and 71.7% and 88.9% and 70.4% in the initial and validation cohorts, respectively.

CONCLUSION

These findings indicate that DNA methylation alterations may have a clinicopathological impact on PDAC. Application of our criteria will ultimately allow prediction of ER after surgery to improve the outcome of PDAC patients.

Genetic and pharmacological inhibition of TTK impairs pancreatic cancer cell line growth by inducing lethal chromosomal instability

Pancreatic ductal adenocarcinoma, which accounts for the majority of pancreatic cancers, is a lethal disease with few therapeutic options. Genomic profiling of pancreatic ductal adenocarcinoma has identified a complex and heterogeneous landscape. Understanding the molecular characteristics of pancreatic ductal adenocarcinoma will facilitate the identification of potential therapeutic strategies. We analyzed the gene expression profiles of primary tumors from patients compared to normal pancreas and identified high co-overexpression of core components of the spindle assembly checkpoint, including the protein kinase TTK (also known as MPS-1). We found overexpression of TTK protein in a subset of pancreatic ductal adenocarcinoma primary tumors and cell lines. siRNA-mediated depletion or catalytic inhibition of TTK resulted in an aberrant cell cycle profile, multi- and micro-nucleation, induction of apoptosis, and decreased cell proliferation and transformed growth. Selective catalytic inhibition of TTK caused override of the spindle assembly checkpoint-induced cell cycle arrest. Interestingly, we identified ubiquitin specific peptidase 16 (Usp16), an ubiquitin hydrolase, as a phosphorylation substrate of TTK. Usp16 regulates chromosomal condensation and G2/M progression by deubiquitinating histone H2A and polo-like kinase 1. Phosphomimetic mutants of Usp16 show enhanced proteosomal degradation and may prolong the G2/M transition allowing for correction of replication errors. Taken together, our results suggest a critical role for TTK in preventing aneuploidy-induced cell death in pancreatic cancer.

Multilayer-omics analyses of human cancers: exploration of biomarkers and drug targets based on the activities of the International Human Epigenome Consortium

Epigenetic alterations consisting mainly of DNA methylation alterations and histone modification alterations are frequently observed in cancers associated with chronic inflammation and/or persistent infection with viruses or other pathogenic microorganisms, or with cigarette smoking. Accumulating evidence suggests that alterations of DNA methylation are involved even in the early and precancerous stages. On the other hand, in patients with cancers, aberrant DNA methylation is frequently associated with tumor aggressiveness and poor patient outcome. Recently, epigenome alterations have been attracting a great deal of attention from researchers who are focusing on not only cancers but also neuronal, immune and metabolic disorders. In order to accurately identify disease-specific epigenome profiles that could be potentially applicable for disease prevention, diagnosis and therapy, strict comparison with standard epigenome profiles of normal tissues is indispensable. However, epigenome mechanisms show heterogeneity among tissues and cell lineages. Therefore, it is not easy to obtain a comprehensive picture of standard epigenome profiles of normal tissues. In 2010, the International Human Epigenome Consortium (IHEC) was established to coordinate the production of reference maps of human epigenomes for key cellular states. In order to gain substantial coverage of the human epigenome, the IHEC has set an ambitious goal to decipher at least 1000 epigenomes within the next 7–10 years. We consider that pathway analysis using genes showing multilayer-omics abnormalities, including genome, epigenome, transcriptome, proteome and metabolome abnormalities, may be useful for elucidating the molecular background of pathogenesis and for exploring possible therapeutic targets for each disease.

Single-CpG-resolution methylome analysis identifies clinicopathologically aggressive CpG island methylator phenotype clear cell renal cell carcinomas

To clarify the significance of DNA methylation alterations during renal carcinogenesis, methylome analysis using single-CpG-resolution Infinium array was performed on 29 normal renal cortex tissue (C) samples, 107 non-cancerous renal cortex tissue (N) samples obtained from patients with clear cell renal cell carcinomas (RCCs) and 109 tumorous tissue (T) samples. DNA methylation levels at 4830 CpG sites were already altered in N samples compared with C samples. Unsupervised hierarchical clustering analysis based on DNA methylation levels at the 801 CpG sites, where DNA methylation alterations had occurred in N samples and were inherited by and strengthened in T samples, clustered clear cell RCCs into Cluster A (n = 90) and Cluster B (n = 14). Clinicopathologically aggressive tumors were accumulated in Cluster B, and the cancer-free and overall survival rates of patients in this cluster were significantly lower than those of patients in Cluster A. Clear cell RCCs in Cluster B were characterized by accumulation of DNA hypermethylation on CpG islands and considered to be CpG island methylator phenotype (CIMP)-positive cancers. DNA hypermethylation of the CpG sites on the FAM150A, GRM6, ZNF540, ZFP42, ZNF154, RIMS4, PCDHAC1, KHDRBS2, ASCL2, KCNQ1, PRAC, WNT3A, TRH, FAM78A, ZNF671, SLC13A5 and NKX6-2 genes became hallmarks of CIMP in RCCs. On the other hand, Cluster A was characterized by genome-wide DNA hypomethylation. These data indicated that DNA methylation alterations at precancerous stages may determine tumor aggressiveness and patient outcome. Accumulation of DNA hypermethylation on CpG islands and genome-wide DNA hypomethylation may each underlie distinct pathways of renal carcinogenesis.

Abbreviations:

BAMCA

bacterial artificial chromosome array-based methylated CpG island amplification

C

normal renal cortex tissue obtained from patients without any primary renal tumor

CIMP

CpG island methylator phenotype

HCC

hepatocellular carcinoma

N

non-cancerous renal cortex tissue obtained from patients with clear cell renal cell carcinomas

NCBI

National Center for Biotechnology Information

RCC

renal cell carcinoma

T

tumorous tissue

TNM

Tumor-Node-Metastasis