Establishment of Novel DNA Methylation-Based Prostate Cancer Subtypes and a Risk-Predicting Eight-Gene Signature

The Landscape of Prostate Tumour Methylation

Prostate cancer is characterized by profound clinical and molecular heterogeneity. While its genomic heterogeneity is well-characterized, its epigenomic heterogeneity remains less understood. We therefore created a compendium of 3,001 multi-ancestry prostate methylomes spanning normal tissue through localized disease of all grades to poly-metastatic disease. A subset of 884 samples had multi-omic DNA and/or RNA characterization. We identify four epigenomic subtypes that risk-stratify patients and reflect distinct evolutionary trajectories. We demonstrate extensive regulatory interplay between DNA ploidy and DNA methylation, with transcriptional consequences that vary across genes and disease stages. We define the epigenetic dysregulation signatures of the 15 most important clinico-molecular features, creating predictive models for each. For example, we identify specific epigenetic features that predict patient outcome and that are synergistic with clinico-genomic prognostic features. These results define a complex interplay between tumour genetics and epigenetics that converges to modify gene-expression programs and clinical presentation.

Circulating cell-free DNA methylation analysis of pancreatic cancer patients for early noninvasive diagnosis

Background

Aberrant hypermethylation of genomic DNA CpG islands (CGIs) is frequently observed in human pancreatic cancer (PAC). A plasma cell-free DNA (cfDNA) methylation analysis method can be utilized for the early and noninvasive detection of PAC. This study also aimed to differentiate PAC from other cancer types.

Methods

We employed the methylated CpG tandem amplification and sequencing (MCTA-Seq) method, which targets approximately one-third of CGIs, on plasma samples from PAC patients (n = 50) and healthy controls (n = 52), as well as from cancerous and adjacent noncancerous tissue samples (n = 66). The method's efficacy in detecting PAC and distinguishing it from hepatocellular carcinoma (HCC), colorectal cancer (CRC), and gastric cancer (GC) was evaluated. Additionally, a methylation score and typing system for PAC was also established.

Results

We identified a total of 120 cfDNA methylation biomarkers, including IRX4, KCNS2, and RIMS4, for the detection of PAC in blood. A panel comprising these biomarkers achieved a sensitivity of 97% and 86% for patients in the discovery and validation cohorts, respectively, with a specificity of 100% in both cohorts. The methylation scoring and typing systems were clinically applicable. Furthermore, we identified hundreds of differentially methylated cfDNA biomarkers between PAC and HCC, CRC, and GC. Certain combinations of these markers can be used in a highly specific (approximately 100%) algorithm to differentiate PAC from HCC, CRC, and GC in blood.

Conclusions

Our study identified cfDNA methylation markers for PAC, offering a novel approach for the early, noninvasive diagnosis of PAC.

meth-SemiCancer: a cancer subtype classification framework via semi-supervised learning utilizing DNA methylation profiles

BACKGROUND

Identification of the cancer subtype plays a crucial role to provide an accurate diagnosis and proper treatment to improve the clinical outcomes of patients. Recent studies have shown that DNA methylation is one of the key factors for tumorigenesis and tumor growth, where the DNA methylation signatures have the potential to be utilized as cancer subtype-specific markers. However, due to the high dimensionality and the low number of DNA methylome cancer samples with the subtype information, still, to date, a cancer subtype classification method utilizing DNA methylome datasets has not been proposed.

RESULTS

In this paper, we present meth-SemiCancer, a semi-supervised cancer subtype classification framework based on DNA methylation profiles. The proposed model was first pre-trained based on the methylation datasets with the cancer subtype labels. After that, meth-SemiCancer generated the pseudo-subtypes for the cancer datasets without subtype information based on the model's prediction. Finally, fine-tuning was performed utilizing both the labeled and unlabeled datasets.

CONCLUSIONS

From the performance comparison with the standard machine learning-based classifiers, meth-SemiCancer achieved the highest average F1-score and Matthews correlation coefficient, outperforming other methods. Fine-tuning the model with the unlabeled patient samples by providing the proper pseudo-subtypes, encouraged meth-SemiCancer to generalize better than the supervised neural network-based subtype classification method. meth-SemiCancer is publicly available at https://github.com/cbi-bioinfo/meth-SemiCancer .

A Combined CRISP3 and SPINK1 Prognostic Grade in EPS-Urine and Establishment of Models to Predict Prognosis of Patients With Prostate Cancer

Background

Prostate cancer (PCa) is characterized by significant heterogeneity. Thus, novel prognostic indicators are required to improve prognosis and treatment.

Methods

Cysteine rich secretory protein 3 (CRISP3) and serine peptidase inhibitor Kazal type 1 (SPINK1) levels in expressed prostatic secretion (EPS)-urine collected during digital rectal examination of 496 patients histologically diagnosed with PCa were detected via enzyme-linked immunosorbent assay. A combined CRISP3 and SPINK1 prognostic grade (CSPG) was defined using cut-off values from receiver operating characteristic curves. Log-rank Kaplan-Meier survival curves investigated differences in prognosis between groups. Univariate and multivariate Cox analyses investigated the CSPG relationship with biochemical recurrence (BCR), cancer-specific survival (CSS), and overall survival (OS). Three prognostic models were developed and validated.

Conclusions

CRISP3 and SPINK1 levels increased with Gleason score progression, pathological T stage, and metastasis status. CSPG in EPS-urine, which was an effective independent prognostic variable, accurately predicted the prognosis of patients with PCa. Three clinical prognostic models using the CSPG for BCR, CSS, and OS were developed and validated.

Establishment of Novel Prostate Cancer Risk Subtypes and A Twelve-Gene Prognostic Model

Prostate cancer (PCa) is the most common malignancy among men worldwide. However, its complex heterogeneity makes treatment challenging. In this study, we aimed to identify PCa subtypes and a gene signature associated with PCa prognosis. In particular, nine PCa-related pathways were evaluated in patients with PCa by a single-sample gene set enrichment analysis (ssGSEA) and an unsupervised clustering analysis (i.e., consensus clustering). We identified three subtypes with differences in prognosis (Risk_H, Risk_M, and Risk_L). Differences in the proliferation status, frequencies of known subtypes, tumor purity, immune cell composition, and genomic and transcriptomic profiles among the three subtypes were explored based on The Cancer Genome Atlas database. Our results clearly revealed that the Risk_H subtype was associated with the worst prognosis. By a weighted correlation network analysis of genes related to the Risk_H subtype and least absolute shrinkage and selection operator, we developed a 12-gene risk-predicting model. We further validated its accuracy using three public datasets. Effective drugs for high-risk patients identified using the model were predicted. The novel PCa subtypes and prognostic model developed in this study may improve clinical decision-making.