Evaluating Stacked Methylation Markers for Blood-Based Multicancer Detection.

The ability to detect several types of cancer using a non-invasive, blood-based test holds the potential to revolutionize oncology screening. We mined tumor methylation array data from the Cancer Genome Atlas (TCGA) covering 14 cancer types and identified two novel, broadly-occurring methylation markers at TLX1 and GALR1. To evaluate their performance as a generalized blood-based screening approach, along with our previously reported methylation biomarker, ZNF154, we rigorously assessed each marker individually or combined. Utilizing TCGA methylation data and applying logistic regression models within each individual cancer type, we found that the three-marker combination significantly increased the average area under the ROC curve (AUC) across the 14 tumor types compared to single markers (p = 1.158 × 10-10; Friedman test). Furthermore, we simulated dilutions of tumor DNA into healthy blood cell DNA and demonstrated increased AUC of combined markers across all dilution levels. Finally, we evaluated assay performance in bisulfite sequenced DNA from patient tumors and plasma, including early-stage samples. When combining all three markers, the assay correctly identified nine out of nine lung cancer plasma samples. In patient plasma from hepatocellular carcinoma, ZNF154 alone yielded the highest combined sensitivity and specificity values averaging 68% and 72%, whereas multiple markers could achieve higher sensitivity or specificity, but not both. Altogether, this study presents a comprehensive pipeline for the identification, testing, and validation of multi-cancer methylation biomarkers with a considerable potential for detecting a broad range of cancer types in patient blood samples.

Leveraging locus-specific epigenetic heterogeneity to improve the performance of blood-based DNA methylation biomarkers.

BACKGROUND: Variation in intercellular methylation patterns can complicate the use of methylation biomarkers for clinical diagnostic applications such as blood-based cancer testing. Here, we describe development and validation of a methylation density binary classification method called EpiClass (available for download at https://github.com/Elnitskilab/EpiClass ) that can be used to predict and optimize the performance of methylation biomarkers, particularly in challenging, heterogeneous samples such as liquid biopsies. This approach is based upon leveraging statistical differences in single-molecule sample methylation density distributions to identify ideal thresholds for sample classification. RESULTS: We developed and tested the classifier using reduced representation bisulfite sequencing (RRBS) data derived from ovarian carcinoma tissue DNA and controls. We used these data to perform in silico simulations using methylation density profiles from individual epiallelic copies of ZNF154, a genomic locus known to be recurrently methylated in numerous cancer types. From these profiles, we predicted the performance of the classifier in liquid biopsies for the detection of epithelial ovarian carcinomas (EOC). In silico analysis indicated that EpiClass could be leveraged to better identify cancer-positive liquid biopsy samples by implementing precise thresholds with respect to methylation density profiles derived from circulating cell-free DNA (cfDNA) analysis. These predictions were confirmed experimentally using DREAMing to perform digital methylation density analysis on a cohort of low volume (1-ml) plasma samples obtained from 26 EOC-positive and 41 cancer-free women. EpiClass performance was then validated in an independent cohort of 24 plasma specimens, derived from a longitudinal study of 8 EOC-positive women, and 12 plasma specimens derived from 12 healthy women, respectively, attaining a sensitivity/specificity of 91.7%/100.0%. Direct comparison of CA-125 measurements with EpiClass demonstrated that EpiClass was able to better identify EOC-positive women than standard CA-125 assessment. Finally, we used independent whole genome bisulfite sequencing (WGBS) datasets to demonstrate that EpiClass can also identify other cancer types as well or better than alternative methylation-based classifiers. CONCLUSIONS: Our results indicate that assessment of intramolecular methylation density distributions calculated from cfDNA facilitates the use of methylation biomarkers for diagnostic applications. Furthermore, we demonstrated that EpiClass analysis of ZNF154 methylation was able to outperform CA-125 in the detection of etiologically diverse ovarian carcinomas, indicating broad utility of ZNF154 for use as a biomarker of ovarian cancer.

Significant associations between driver gene mutations and DNA methylation alterations across many cancer types.

Recent evidence shows that mutations in several driver genes can cause aberrant methylation patterns, a hallmark of cancer. In light of these findings, we hypothesized that the landscapes of tumor genomes and epigenomes are tightly interconnected. We measured this relationship using principal component analyses and methylation-mutation associations applied at the nucleotide level and with respect to genome-wide trends. We found that a few mutated driver genes were associated with genome-wide patterns of aberrant hypomethylation or CpG island hypermethylation in specific cancer types. In addition, we identified associations between 737 mutated driver genes and site-specific methylation changes. Moreover, using these mutation-methylation associations, we were able to distinguish between two uterine and two thyroid cancer subtypes. The driver gene mutation-associated methylation differences between the thyroid cancer subtypes were linked to differential gene expression in JAK-STAT signaling, NADPH oxidation, and other cancer-related pathways. These results establish that driver gene mutations are associated with methylation alterations capable of shaping regulatory network functions. In addition, the methodology presented here can be used to subdivide tumors into more homogeneous subsets corresponding to underlying molecular characteristics, which could improve treatment efficacy.

Robust Detection of DNA Hypermethylation of ZNF154 as a Pan-Cancer Locus with in Silico Modeling for Blood-Based Diagnostic Development.

Sites that display recurrent, aberrant DNA methylation in cancer represent potential biomarkers for screening and diagnostics. Previously, we identified hypermethylation at the ZNF154 CpG island in 15 solid epithelial tumor types from 13 different organs. In this study, we measure the magnitude and pattern of differential methylation of this region across colon, lung, breast, stomach, and endometrial tumor samples using next-generation bisulfite amplicon sequencing. We found that all tumor types and subtypes are hypermethylated at this locus compared with normal tissue. To evaluate this site as a possible pan-cancer marker, we compare the ability of several sequence analysis methods to distinguish the five tumor types (184 tumor samples) from normal tissue samples (n = 34). The classification performance for the strongest method, measured by the area under (the receiver operating characteristic) curve (AUC), is 0.96, close to a perfect value of 1. Furthermore, in a computational simulation of circulating tumor DNA, we were able to detect limited amounts of tumor DNA diluted with normal DNA: 1% tumor DNA in 99% normal DNA yields AUCs of up to 0.79. Our findings suggest that hypermethylation of the ZNF154 CpG island is a relevant biomarker for identifying solid tumor DNA and may have utility as a generalizable biomarker for circulating tumor DNA.

Pan-cancer stratification of solid human epithelial tumors and cancer cell lines reveals commonalities and tissue-specific features of the CpG island methylator phenotype.

BACKGROUND: The term CpG island methylator phenotype (CIMP) has been used to describe widespread DNA hypermethylation at CpG-rich genomic regions affecting clinically distinct subsets of cancer patients. Even though there have been numerous studies of CIMP in individual cancer types, a uniform analysis across tissues is still lacking. RESULTS: We analyze genome-wide patterns of CpG island hypermethylation in 5,253 solid epithelial tumors from 15 cancer types from TCGA and 23 cancer cell lines from ENCODE. We identify differentially methylated loci that define CIMP+ and CIMP- samples, and we use unsupervised clustering to provide a robust molecular stratification of tumor methylomes for 12 cancer types and all cancer cell lines. With a minimal set of 89 discriminative loci, we demonstrate accurate pan-cancer separation of the 12 CIMP+/- subpopulations, based on their average levels of methylation. Tumor samples in different CIMP subclasses show distinctive correlations with gene expression profiles and recurrence of somatic mutations, copy number variations, and epigenetic silencing. Enrichment analyses indicate shared canonical pathways and upstream regulators for CIMP-targeted regions across cancer types. Furthermore, genomic alterations showing consistent associations with CIMP+/- status include genes involved in DNA repair, chromatin remodeling genes, and several histone methyltransferases. Associations of CIMP status with specific clinical features, including overall survival in several cancer types, highlight the importance of the CIMP+/- designation for individual tumor evaluation and personalized medicine. CONCLUSIONS: We present a comprehensive computational study of CIMP that reveals pan-cancer commonalities and tissue-specific differences underlying concurrent hypermethylation of CpG islands across tumors. Our stratification of solid tumors and cancer cell lines based on CIMP status is data-driven and agnostic to tumor type by design, which protects against known biases that have hindered classic methods previously used to define CIMP. The results that we provide can be used to refine existing molecular subtypes of cancer into more homogeneously behaving subgroups, potentially leading to more uniform responses in clinical trials.

Recurrent patterns of DNA methylation in the ZNF154, CASP8, and VHL promoters across a wide spectrum of human solid epithelial tumors and cancer cell lines.

The study of aberrant DNA methylation in cancer holds the key to the discovery of novel biological markers for diagnostics and can help to delineate important mechanisms of disease. We have identified 12 loci that are differentially methylated in serous ovarian cancers and endometrioid ovarian and endometrial cancers with respect to normal control samples. The strongest signal showed hypermethylation in tumors at a CpG island within the ZNF154 promoter. We show that hypermethylation of this locus is recurrent across solid human epithelial tumor samples for 15 of 16 distinct cancer types from TCGA. Furthermore, ZNF154 hypermethylation is strikingly present across a diverse panel of ENCODE cell lines, but only in those derived from tumor cells. By extending our analysis from the Illumina 27K Infinium platform to the 450K platform, to sequencing of PCR amplicons from bisulfite treated DNA, we demonstrate that hypermethylation extends across the breadth of the ZNF154 CpG island. We have also identified recurrent hypomethylation in two genomic regions associated with CASP8 and VHL. These three genes exhibit significant negative correlation between methylation and gene expression across many cancer types, as well as patterns of DNaseI hypersensitivity and histone marks that reflect different chromatin accessibility in cancer vs. normal cell lines. Our findings emphasize hypermethylation of ZNF154 as a biological marker of relevance for tumor identification. Epigenetic modifications affecting the promoters of ZNF154, CASP8, and VHL are shared across a vast array of tumor types and may therefore be important for understanding the genomic landscape of cancer.