Genome-scale methylation assessment did not identify prognostic biomarkers in oral tongue carcinomas

Genome-wide DNA methylation profiling in tongue squamous cell carcinoma

OBJECTIVES

To provide a comprehensive characterization of DNA methylome of oral tongue squamous cell carcinoma (OTSCC) and identify novel tumor-specific DNA methylation markers for early detection using saliva.

MATERIAL AND METHODS

Genome-wide DNA methylation analysis including six OTSCC matched adjacent non-tumoral tissue and saliva was performed using Infinium MethylationEPIC array. Differentially methylated levels of selected genes in our OTSCC cohort were further validated using OTSCC methylation data from The Cancer Genome Atlas database (TCGA). The methylation levels of a set of tumor-specific hypermethylated genes associated with a downregulated expression were evaluated in saliva. Receiver operating characteristic (ROC) curves were performed to assess the diagnostic value of DNA methylation markers.

RESULTS

A total of 25,890 CpGs (20,505 hypomethylated and 5385 hypermethylated) were differentially methylated (DMCpGs) between OTSCC and adjacent non-tumoral tissue. Hypermethylation of 11 tumor-specific genes was validated in OTSCC TCGA cohort. Of these 11 genes, A2BP1, ANK1, ALDH1A2, GFRA1, TTYH1, and PDE4B were also hypermethylated in saliva. These six salivary methylated genes showed high diagnostic accuracy (≥0.800) for discriminating patients from controls.

CONCLUSIONS

This is the first largest genome-wide DNA methylation study on OTSCC that identifies a group of novel tumor-specific DNA methylation markers with diagnostic potential in saliva.

Genomic and Evolutionary Characterization of Concurrent Intraductal Carcinoma and Adenocarcinoma of the Prostate

Intraductal carcinoma of the prostate (IDC-P) is a lethal prostate cancer subtype that generally coexists with invasive high-grade prostate acinar adenocarcinoma (PAC) but exhibits distinct biological features compared with concomitant adenocarcinoma. In this study, we performed whole-exome, RNA, and DNA-methylation sequencing of IDC-P, concurrent invasive high-grade PAC lesions, and adjacent normal prostate tissues isolated from 22 radical prostatectomy specimens. Three evolutionary patterns of concurrent IDC-P and PAC were identified: early divergent, late divergent, and clonally distant. In contrast to those with a late divergent evolutionary pattern, tumors with clonally distant and early divergent evolutionary patterns showed higher genomic, epigenomic, transcriptional, and pathologic heterogeneity between IDC-P and PAC. Compared with coexisting PAC, IDC-P displayed increased expression of adverse prognosis-associated genes. Survival analysis based on an independent cohort of 505 patients with metastatic prostate cancer revealed that IDC-P carriers with lower risk International Society of Urological Pathology (ISUP) grade 1-4 adenocarcinoma displayed a castration-resistant free survival as poor as those with the highest risk ISUP grade 5 tumors that lacked concurrent IDC-P. Furthermore, IDC-P exhibited robust cell-cycle progression and androgen receptor activities, characterized by an enrichment of cellular proliferation-associated master regulators and genes involved in intratumoral androgen biosynthesis. Overall, this study provides a molecular groundwork for the aggressive behavior of IDC-P and could help identify potential strategies to improve treatment of IDC-P.

SIGNIFICANCE

The genomic, transcriptomic, and epigenomic characterization of concurrent intraductal carcinoma and adenocarcinoma of the prostate deepens the biological understanding of this lethal disease and provides a genetic basis for developing targeted therapies.

Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: A systematic review of epigenome-wide studies

BACKGROUND

DNA methylation biomarkers have great potential in improving prognostic classification systems for patients with cancer. Machine learning (ML)-based analytic techniques might help overcome the challenges of analyzing high-dimensional data in relatively small sample sizes. This systematic review summarizes the current use of ML-based methods in epigenome-wide studies for the identification of DNA methylation signatures associated with cancer prognosis.

METHODS

We searched three electronic databases including PubMed, EMBASE, and Web of Science for articles published until 2 January 2023. ML-based methods and workflows used to identify DNA methylation signatures associated with cancer prognosis were extracted and summarized. Two authors independently assessed the methodological quality of included studies by a seven-item checklist adapted from 'A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies (PROBAST)' and from the 'Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK). Different ML methods and workflows used in included studies were summarized and visualized by a sunburst chart, a bubble chart, and Sankey diagrams, respectively.

RESULTS

Eighty-three studies were included in this review. Three major types of ML-based workflows were identified. 1) unsupervised clustering, 2) supervised feature selection, and 3) deep learning-based feature transformation. For the three workflows, the most frequently used ML techniques were consensus clustering, least absolute shrinkage and selection operator (LASSO), and autoencoder, respectively. The systematic review revealed that the performance of these approaches has not been adequately evaluated yet and that methodological and reporting flaws were common in the identified studies using ML techniques.

CONCLUSIONS

There is great heterogeneity in ML-based methodological strategies used by epigenome-wide studies to identify DNA methylation markers associated with cancer prognosis. In theory, most existing workflows could not handle the high multi-collinearity and potentially non-linearity interactions in epigenome-wide DNA methylation data. Benchmarking studies are needed to compare the relative performance of various approaches for specific cancer types. Adherence to relevant methodological and reporting guidelines are urgently needed.

Tumor molecular differences associated with outcome disparities of Black patients with head and neck cancer

BACKGROUND

Numerous studies of head and neck squamous cell carcinoma (HNSCC) have demonstrated disparate outcomes by race and ethnicity. Beyond known associations with socioeconomic variables, whether these are also associated with differences in tumor molecular composition has thus far been poorly explored.

METHODS

We downloaded clinical and multiplatform molecular data from The Cancer Genome Atlas and other published studies. These were compared between non-Hispanic Black (n = 43) and White (n = 354) patients with non-HPV-related tumors, using multivariable models. Publicly available validation cohorts were used.

RESULTS

Black patients had poorer progression-free survival than White patients. Tumors of Black patients had greater copy number aberrations, and increased SFRP1 methylation and miRNA-mediated PRG4 silencing associated with poor survival. PI3K/AkT/mTOR pathway proteins were differentially expressed.

CONCLUSIONS

There are molecular differences between tumors of Black and White patients that may partially account for differences in survival. These may inform targeted treatment decisions to achieve equitable outcomes.

Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases

Head and neck squamous cell carcinoma (HNSC) patients are at risk of suffering from both pulmonary metastases or a second squamous cell carcinoma of the lung (LUSC). Differentiating pulmonary metastases from primary lung cancers is of high clinical importance, but not possible in most cases with current diagnostics. To address this, we performed DNA methylation profiling of primary tumors and trained three different machine learning methods to distinguish metastatic HNSC from primary LUSC. We developed an artificial neural network that correctly classified 96.4% of the cases in a validation cohort of 279 patients with HNSC and LUSC as well as normal lung controls, outperforming support vector machines (95.7%) and random forests (87.8%). Prediction accuracies of more than 99% were achieved for 92.1% (neural network), 90% (support vector machine), and 43% (random forest) of these cases by applying thresholds to the resulting probability scores and excluding samples with low confidence. As independent clinical validation of the approach, we analyzed a series of 51 patients with a history of HNSC and a second lung tumor, demonstrating the correct classifications based on clinicopathological properties. In summary, our approach may facilitate the reliable diagnostic differentiation of pulmonary metastases of HNSC from primary LUSC to guide therapeutic decisions.

NDRG4 hypermethylation is a potential biomarker for diagnosis and prognosis of gastric cancer in Chinese population

In order to assess whether N-Myc downstream regulated gene 4 (NDRG4) methylation was associated with the diagnosis and prognosis of gastric cancer, we measured the methylation of NDRG4 promoter and gene body regions among 110 gastric cancer patients using quantitative methods (MethyLight and pyrosequencing). Both NDRG4 promoter and gene body methylation levels were increased in tumor tissues than paired adjacent normal tissues (P < 0.001). NDRG4 gene body methylation was found to be significantly associated with age and tumor differentiation. NDRG4 promoter hypermethylation was proved to be a predictor of poor overall survival. However, opposite result was observed among The Cancer Genome Atlas (TCGA) cohort. The findings from gastric cell lines and public databases have suggested that NDRG4 methylation level was inversely associated with NDRG4 transcription level. Subsequent luciferase reporter gene assay showed that promoter CpG island but not gene body CpG island was able to upregulate gene expression. Collectively, NDRG4 promoter hypermethylation contributed to the risk of gastric cancer and predicted a poor prognosis in Chinese gastric cancer patients. Moreover, the combined methylation levels of NDRG4 promoter and gene body served as diagnostic biomarkers in gastric cancer.