DNA methylation profiling to assess pathogenicity of BRCA1 unclassified variants in breast cancer

Identification of Wnt/β-Catenin- and Autophagy-Related lncRNA Signature for Predicting Immune Efficacy in Pancreatic Adenocarcinoma

Pancreatic cancer is one of the tumors with a poor prognosis. Therefore, it is significant and urgent to explore effective biomarkers for risk stratification and prognosis prediction to promote individualized treatment and prolong the survival of patients with PAAD. In this study, we identified Wnt/β-catenin- and autophagy-related long non-coding RNAs (lncRNAs) and demonstrated their role in predicting immune efficacy for PAAD patients. The univariate and multivariate Cox proportional hazards analyses were used to construct a prognostic risk model based on six autophagy- and Wnt/β-catenin-related lncRNAs (warlncRNAs): LINC01347, CASC8, C8orf31, LINC00612, UCA1, and GUSBP11. The high-risk patients were significantly associated with poor overall survival (OS). The receiver operating characteristic (ROC) curve analysis was used to assess the predictive accuracy of the prognostic risk model. The prediction efficiency was supported by the results of an independent validation cohort. Subsequently, a prognostic nomogram combining warlncRNAs with clinical indicators was constructed and showed a good predictive efficiency for survival risk stratification. Furthermore, functional enrichment analysis demonstrated that the signature according to warlncRNAs is closely linked to malignancy-associated immunoregulatory pathways. Correlation analysis uncovered that warlncRNAs' signature was considerably associated with immunocyte infiltration, immune efficacy, tumor microenvironment score, and drug resistance.

Peripheral blood BRCA1 methylation profiling to predict familial ovarian cancer

Objective

Familial cancer appears at a young age and its incidence is increasing. About 12% of familial ovarian cancer cases are associated with BRCA1/2 mutations (BRCAm). In this study, we investigated BRCA1 methylation may predict ovarian cancer in those with a family history of cancer (FHC) but without BRCA1/2 mutations (BRCAwt).

Methods

Using peripheral blood DNA from 55 subjects without a history of cancer [cancer(−)] and 52 ovarian cancer patients, we examined BRCA1 promoter methylation through bisulfite sequencing of the promoter and expressed the results as the cumulative methylation index. Then, we evaluated the BRCA1 promoter methylation according to BRCA1/2 germline mutations.

Results

BRCA1 methylation was more prevalent in the BRCAm cancer(−) group than in the BRCAwt cancer(−) group and ovarian cancer patients (p=0.031 and p=0.019, respectively). In the BRCAwt cancer(−) group, BRCA1 methylation was more prevalent in those with an FHC than in those without one and in the BRCAm cancer(−) group with an FHC (p=0.001 and p<0.001, respectively).

Conclusion

Our data suggest a predictive role of BRCA1 methylation profile for ovarian cancer in those without a history of cancer but with an FHC. BRCA1 methylation has important implications for diagnostic and predictive testing of those with BRCAwt cancer(−) status with FHC.

Minimalist approaches to cancer tissue-of-origin classification by DNA methylation

Classification of cancers by tissue-of-origin is fundamental to diagnostic pathology. While the combination of clinical data, tissue histology, and immunohistochemistry is usually sufficient, there remains a small but not insignificant proportion of difficult-to-classify cases. These challenging cases provide justification for ancillary molecular testing, including high-throughput DNA methylation array profiling, which promises cell-of-origin information and compatibility with formalin-fixed specimens. While diagnostically powerful, methylation profiling platforms are costly and technically challenging to implement, particularly for less well-resourced laboratories. To address this, we simulated the performance of "minimalist" methylation-based tests for cancer classification using publicly-available and internal institutional profiling data. These analyses showed that small and focused sets of the most informative CpG biomarkers from the arrays are sufficient for accurate diagnoses. As an illustrative example, one classifier, using information from just 53 out of about 450,000 available CpG probes, achieved an accuracy of 94.5% on 2575 fresh primary validation cases across 28 cancer types from The Cancer Genome Atlas Network. By training minimalist classifiers on formalin-fixed primary and metastatic cases, generally high accuracies were also achieved on additional datasets. These results support the potential of minimalist methylation testing, possibly via quantitative PCR and targeted next-generation sequencing platforms, in cancer classification.

Genetic and epigenetic profiling of BRCA1/2 in ovarian tumors reveals additive diagnostic yield and evidence of a genomic BRCA1/2 DNA methylation signature

Poly-ADP-ribose-polymerase inhibitor (PARPi) treatment is indicated for advanced-stage ovarian tumors with BRCA1/2 deficiency. The "BRCAness" status is thought to be attributed to a tumor phenotype associated with a specific epigenomic DNA methylation profile. Here, we examined the diagnostic impact of combined BRCA1/2 sequence, copy number, and promoter DNA methylation analysis, and evaluated whether genomic DNA methylation patterns can predict the BRCAness in ovarian tumors. DNA sequencing of 172 human tissue samples of advanced-stage ovarian adenocarcinoma identified 36 samples with a clinically significant tier 1/2 sequence variants (point mutations and in/dels) and 9 samples with a CNV causing a loss of function in BRCA1/2. DNA methylation analysis of the promoter of BRCA1/2 identified promoter hypermethylation of BRCA1 in two mutation-negative samples. Computational modeling of genome-wide methylation markers, measured using Infinium EPIC arrays, resulted in a total accuracy of 0.75, sensitivity: 0.83, specificity: 0.64, positive predictive value: 0.76, negative predictive value: 0.74, and area under the receiver's operating curve (AUC): 0.77, in classifying tumors harboring a BRCA1/2 defect from the rest. These findings indicate that the assessment of CNV and promoter DNA methylation in BRCA1/2 increases the cumulative diagnostic yield by 10%, compared with the 20% yield achieved by sequence variant analysis alone. Genomic DNA methylation data can partially predict BRCAness in ovarian tumors; however, further investigation in expanded BRCA1/2 cohorts is needed, and the effect of other double strand DNA repair gene defects in these tumors warrants further investigations.

Machine learning on genome-wide association studies to predict the risk of radiation-associated contralateral breast cancer in the WECARE Study

The purpose of this study was to identify germline single nucleotide polymorphisms (SNPs) that optimally predict radiation-associated contralateral breast cancer (RCBC) and to provide new biological insights into the carcinogenic process. Fifty-two women with contralateral breast cancer and 153 women with unilateral breast cancer were identified within the Women’s Environmental Cancer and Radiation Epidemiology (WECARE) Study who were at increased risk of RCBC because they were ≤ 40 years of age at first diagnosis of breast cancer and received a scatter radiation dose > 1 Gy to the contralateral breast. A previously reported algorithm, preconditioned random forest regression, was applied to predict the risk of developing RCBC. The resulting model produced an area under the curve (AUC) of 0.62 (p = 0.04) on hold-out validation data. The biological analysis identified the cyclic AMP-mediated signaling and Ephrin-A as significant biological correlates, which were previously shown to influence cell survival after radiation in an ATM-dependent manner. The key connected genes and proteins that are identified in this analysis were previously identified as relevant to breast cancer, radiation response, or both. In summary, machine learning/bioinformatics methods applied to genome-wide genotyping data have great potential to reveal plausible biological correlates associated with the risk of RCBC.

Acceleration in the DNA methylation age in breast cancer tumours from very young women

Breast cancer in very young women (≤35 years; BCVY) presents more aggressive and complex biological features than their older counterparts (BCO). Our aim was to evaluate methylation differences between BCVY and BCO and their DNA epigenetic age. EPIC and 450k Illumina methylation arrays were used in 67 breast cancer tumours, including 32 from BCVY, for methylation study and additionally we analysed their epigenetic age. We identified 2 219 CpG sites differently-methylated in BCVY vs. BCO (FDR < 0.05; β-value difference ± 0.1). The signature showed a general hypomethylation profile with a selective small hypermethylation profile located in open-sea regions in BCVY against BCO and normal tissue. Strikingly, BCVY presented a significant increased epigenetic age-acceleration compared with older women. The affected genes were enriched for pathways in neuronal-system pathways, cell communication, and matrix organisation. Validation in an independent sample highlighted consistent higher expression of HOXD9, and PCDH10 genes in BCVY. Regions implicated in the hypermethylation profile were involved in Notch signalling pathways, the immune system or DNA repair. We further validated HDAC5 expression in BCVY. We have identified a DNA methylation signature that is specific to BCVY and have shown that epigenetic age-acceleration is increased in BCVY.

Analysis of polymorphisms in genes associated with the FA/BRCA pathway in three patients with multiple primary malignant neoplasms

Cases of more than three primary cancers are very rare. This study analyzed the genetic susceptibility of gene polymorphisms in three patients with multiple primary malignant neoplasms and examined the possible pathogenesis. The clinical data and whole genome sequence of three patients (1 with 5 primary cancers, 1 with 4 primary cancers, and 1 with 3 primary cancers) were aligned with a series of databases. We found the three patients contained a total of seven types of malignant tumours (endometrial cancer, ovarian cancer, breast cancer, colon cancer, ureter cancer, bladder cancer and kidney cancer). It was found that the varied genes in Patient 1 (5 primary cancers) were BRIP1, FANCG, NBN, AXIN2, SRD5A2, and CEBPA. Patient 2 (4 primary cancers) had variations in the following genes: BMPR1A, FANCD2, MLH3, BRCA2, and FANCM. Patient 3 (3 primary cancers) had variations in the following genes: MEN1, ATM, MSH3, BRCA1, FANCL, CEBPA, and FANCA. String software was used to analyze the KEGG pathway of the variations in these three samples, which revealed that the genes are involved in the Fanconi anaemia pathway. Defects in DNA damage repair may be one of the causes of multiple primary cancers.

Methylation deregulation of miRNA promoters identifies miR124-2 as a survival biomarker in Breast Cancer in very young women

MiRNAs are part of the epigenetic machinery, and are also epigenetically modified by DNA methylation. MiRNAs regulate expression of different genes, so any alteration in their methylation status may affect their expression. We aimed to identify methylation differences in miRNA encoding genes in breast cancer affecting women under 35 years old (BCVY), in order to identify potential biomarkers in these patients. In Illumina Infinium MethylationEPIC BeadChip samples (metEPICVal), we analysed the methylation of 9,961 CpG site regulators of miRNA-encoding genes present in the array. We identified 193 differentially methylated CpG sites in BCVY (p-value < 0.05 and methylation differences ±0.1) that regulated 83 unique miRNA encoding genes. We validated 10 CpG sites using two independent datasets based on Infinium Human Methylation 450k array. We tested gene expression of miRNAs with differential methylation in BCVY in a meta-analysis using The Cancer Genome Atlas (TCGA), Clariom D and Affymetrix datasets. Five miRNAs (miR-9, miR-124-2, miR-184, miR-551b and miR-196a-1) were differently expressed (FDR p-value < 0.01). Finally, only miR-124-2 shows a significantly different gene expression by quantitative real-time PCR. MiR-124-hypomethylation presents significantly better survival rates for older patients as opposed to the worse prognosis observed in BCVY, identifying it as a potential specific survival biomarker in BCVY.

Genome-wide methylomic and transcriptomic analyses identify subtype-specific epigenetic signatures commonly dysregulated in glioma stem cells and glioblastoma

Glioma stem cells (GSCs), a subpopulation of tumor cells, contribute to tumor heterogeneity and therapy resistance. Gene expression profiling classified glioblastoma (GBM) and GSCs into four transcriptomically-defined subtypes. Here, we determined the DNA methylation signatures in transcriptomically pre-classified GSC and GBM bulk tumors subtypes. We hypothesized that these DNA methylation signatures correlate with gene expression and are uniquely associated either with only GSCs or only GBM bulk tumors. Additional methylation signatures may be commonly associated with both GSCs and GBM bulk tumors, i.e., common to non-stem-like and stem-like tumor cell populations and correlating with the clinical prognosis of glioma patients. We analyzed Illumina 450K methylation array and expression data from a panel of 23 patient-derived GSCs. We referenced these results with The Cancer Genome Atlas (TCGA) GBM datasets to generate methylomic and transcriptomic signatures for GSCs and GBM bulk tumors of each transcriptomically pre-defined tumor subtype. Survival analyses were carried out for these signature genes using publicly available datasets, including from TCGA. We report that DNA methylation signatures in proneural and mesenchymal tumor subtypes are either unique to GSCs, unique to GBM bulk tumors, or common to both. Further, dysregulated DNA methylation correlates with gene expression and clinical prognoses. Additionally, many previously identified transcriptionally-regulated markers are also dysregulated due to DNA methylation. The subtype-specific DNA methylation signatures described in this study could be useful for refining GBM sub-classification, improving prognostic accuracy, and making therapeutic decisions.