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With a 5-year survival rate of less than 50%, ovarian high-grade serous carcinoma (HGSC) is one of the most highly aggressive gynecological malignancies affecting women today. The high mortality rate of HGSC is largely attributable to delays in diagnosis, as most patients remain undiagnosed until the late stages of -disease. There are currently no recommended screening tests for ovarian cancer and there thus remains an urgent need for new diagnostic methods, particularly those that can detect the disease at early stages when clinical intervention remains effective. While diagnostics for ovarian cancer share many of the same technical hurdles as for other cancer types, the low prevalence of the disease in the general population, coupled with a notable lack of sensitive and specific biomarkers, have made the development of a clinically useful screening strategy particularly challenging. Here, we present a detailed review of the overall landscape of ovarian cancer diagnostics, with emphasis on emerging methods that employ novel protein, genetic, epigenetic and imaging-based biomarkers and/or advanced diagnostic technologies for the noninvasive detection of HGSC, particularly in women at high risk due to germline mutations such as BRCA1/2. Lastly, we discuss the translational potential of these approaches for achieving a clinically implementable solution for screening and diagnostics of early-stage ovarian cancer as a means of ultimately improving patient outcomes in both the general and high-risk populations.

The ability to accurately measure aberrant DNA methylation levels is integral to the understanding of DNA methylation biology. It is well-established that in cancer, the largest, and thus, most biologically important absolute gains of DNA methylation levels occur at CpG sites with low native levels while the largest losses occur at CpG sites with high native levels. Conventional wisdom assumes that the observed association between the degree of the alterations and the native levels are largely due to the limitations of change within the DNA methylation scale. Here, we present evidence that this association is largely caused by alterations occurring as a global rate of change relative to the native level. We show that DNA methylation alterations can be accurately compared by calculating the rate of change relative to the native level. Most importantly, this approach enables the identification of more biologically significant DNA methylation alterations. The authors show that, when looking at differential methylation, assessing relative changes rather than absolute changes finds more biologically relevant targets.

Background Despite improvements in cancer management, most pancreatic cancers are still diagnosed at an advanced stage. We have recently identified promoter DNA methylation of the genes ADAMTS1 and BNC1 as potential blood biomarkers of pancreas cancer. In this study, we validate this biomarker panel in peripheral cell-free tumor DNA of patients with pancreatic cancer. Results Sensitivity and specificity for each gene are as follows: ADAMTS1 87.2% and 95.8% (AUC = 0.91; 95% CI 0.71–0.86) and BNC1 64.1% and 93.7% (AUC = 0.79; 95% CI 0.63–0.78). When using methylation of either gene as a combination panel, sensitivity increases to 97.3% and specificity to 91.6% (AUC = 0.95; 95% CI 0.77–0.90). Adding pre-operative CA 19-9 values to the combined two-gene methylation panel did not improve sensitivity. Methylation of ADAMTS1 was found to be positive in 87.5% (7/8) of stage I, 77.8% (7/9) of stage IIA, and 90% (18/20) of stage IIB disease. Similarly, BNC1 was positive in 62.5% (5/8) of stage I patients, 55.6% (5/9) of stage IIA, and 65% (13/20) of patients with stage IIB disease. The two-gene panel ( ADAMTS1 and/or BNC1 ) was positive in 100% (8/8) of stage I, 88.9% (8/9) of stage IIA, and 100% (20/20) of stage IIB disease. The sensitivity and specificity of the two-gene panel for localized pancreatic cancer (stages I and II), where the cancer is eligible for surgical resection with curative potential, was 94.8% and 91.6% respectively. Additionally, the two-gene panel exhibited an AUC of 0.95 (95% CI 0.90–0.98) compared to 57.1% for CA 19-9 alone. Conclusion The methylation status of ADAMTS1 and BNC1 in cfDNA shows promise for detecting pancreatic cancer during the early stages when curative resection of the tumor is still possible. This minimally invasive blood-based biomarker panel could be used as a promising tool for diagnosis and screening in a select subset of high-risk populations.

Background Extracellular vesicles (EVs) have emerged as an exciting tool for targeted delivery of therapeutics for a wide range of diseases. As nano-scale membrane-bound particles derived from living cells, EVs possess inherent capabilities as carriers of biomolecules. However, the translation of EVs into viable therapeutic delivery vehicles is challenged by lengthy and inefficient processes for cargo loading and pre- and post-loading purification of EVs, resulting in limited quantity and consistency of engineered EVs. Results In this work, we develop a fast and streamlined method to load surface protein-specific subpopulations of EVs with miRNA by electroporating EVs, while they are bound to antibody-coated beads. We demonstrate the selection of CD81 + EV subpopulation using magnetic microbeads, facilitating rapid EV manipulations, loading, and subsequent purification processes. Our approach shortens the time per post-electroporation EV wash by 20-fold as compared to the gold standard EV washing method, ultracentrifugation, resulting in about 2.5-h less time required to remove unloaded miRNA. In addition, we addressed the challenge of nonspecific binding of cargo molecules due to affinity-based EV selection, lowering the purity of engineered EVs, by implementing innovative strategies, including poly A carrier RNA-mediated blocking and dissociation of residual miRNA and EV-like miRNA aggregates following electroporation. Conclusions Our streamlined method integrates magnetic bead-based selection with electroporation, enabling rapid and efficient loading of miRNA into CD81 + EVs. This approach not only achieves comparable miRNA loading efficiency to conventional bulk electroporation methods but also concentrates CD81 + EVs and allows for simple electroporation parameter adjustment, promising advancements in therapeutic RNA delivery systems with enhanced specificity and reduced toxicity.

There remains tremendous interest in developing liquid biopsy assays for detection of cancer‐specific alterations, such as mutations and DNA methylation, in cell‐free DNA (cfDNA) obtained through noninvasive blood draws. However, liquid biopsy analysis is often challenging due to exceedingly low fractions of circulating tumor DNA (ctDNA), necessitating the use of extended tumor biomarker panels. While multiplexed PCR strategies provide advantages such as higher throughput, their implementation is often hindered by challenges such as primer‐dimers and PCR competition. Alternatively, digital PCR (dPCR) approaches generally offer superior performance, but with constrained multiplexing capability. This paper describes development and validation of the first multiplex digital methylation‐specific PCR (mdMSP) platform for simultaneous analysis of four methylation biomarkers for liquid‐biopsy‐based detection of non‐small cell lung cancer (NSCLC). mdMSP employs a microfluidic device containing four independent, but identical modules, housing a total of 40 160 nanowells. Analytical validation of the mdMSP platform demonstrates multiplex detection at analytical specificities as low as 0.0005%. The clinical utility of mdMSP is also demonstrated in a cohort of 72 clinical samples of low‐volume liquid biopsy specimens from patients with computed tomography (CT)‐scan indeterminant pulmonary nodules, exhibiting superior clinical performance when compared to traditional MSP assays for noninvasive detection of early‐stage NSCLC. This paper describes the development and analytical/clinical validation of the first mdMSP platform for the simultaneous analysis of four DNA methylation biomarkers for liquid‐biopsy‐based detection of early‐stage NSCLC. The demonstrated analytical sensitivity/specificity and clinical utility indicate the potential of mdMSP platform to be a complementary method for improved CT screening of lung cancer.

We previously reported CHFR methylation in a subset of colorectal cancer (CRC; ∼30%) with high concordance with microsatellite instability (MSI). We also showed that CHFR methylation predicted for sensitivity to docetaxel, whereas the MSI‐high phenotypes were sensitive to gemcitabine. We hypothesized that this subset of patients with CRC would be selectively sensitive to gemcitabine and docetaxel. We enrolled a Phase 2 trial of gemcitabine and docetaxel in patients with MSI‐high and/or CHFR methylated CRC. The primary objective was Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 response rate. Enrolled patients were treated with gemcitabine 800 mg/m2 on days 1 and 8 and docetaxel 70 mg/m2 on day 8 of each 21‐day cycle. A total of 6 patients with CHFR‐methylated, MSI‐high CRC were enrolled from September 2012 to August 2016. The study was closed in September of 2017 due to poor accrual prior to reaching the first interim assessment of response rate, which would have occurred at 10 patients. No RECIST criteria tumor responses were observed, with 3 patients (50%) having stable disease as best response, 1 lasting more than 9 months. Median progression‐free survival (PFS) was 1.79 months (95% confidence interval [CI] = 1.28, not available [NA]) and median overall survival (OS) was 15.67 months (95% CI = 4.24, NA). Common grade 3 toxicities were lymphopenia (67%), leukopenia (33%), and anemia (33%). Although negative, this study establishes a proof‐of‐concept for the implementation of epigenetic biomarkers (CHFR methylation/MSI) as inclusion criteria in a prospective clinical trial to optimize combinatorial strategies in the era of personalized medicine. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? CHFR silencing via DNA methylation has been suggested to be predictive of taxane sensitivity in diverse tumors. The frequent association of CHFR methylation with microsatellite instability (MSI) suggested a possible combination therapy with gemcitabine, because the MSI phenotype may result in sensitivity to nucleoside analogues. WHAT QUESTION DID THIS STUDY ADDRESS? We hypothesized that metastatic colorectal cancer (mCRC), which have CHFR methylation and MSI phenotype were sensitive to gemcitabine and docetaxel, and have designed this Phase 2 trial in biomarker‐selected mCRC to test this prediction. WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE? The study enrolled a molecularly defined subgroup of patients with colorectal cancer (CRC) and showed that the combination is safe in this population. Nevertheless, due to poor enrollment and early termination, no conclusions on the primary and secondary end points could be made. HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE? This study supports the feasibility of implementing DNA methylation markers in a prospective clinical trial and further efforts toward their application as predictive biomarkers for therapeutic agents in defined subsets of patients are warranted.

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.

Recent advances in molecular analyses of ovarian cancer have revealed a wealth of promising tumour‐specific biomarkers, including protein, DNA mutations and methylation; however, reliably detecting such alterations at satisfactorily high sensitivity and specificity through low‐cost methods remains challenging, especially in early‐stage diseases. Here we present PapDREAM, a new approach that enables detection of rare, ovarian‐cancer‐specific aberrations of DNA methylation from routinely‐collected cervical Pap specimens. The PapDREAM approach employs a microfluidic platform that performs highly parallelized digital high‐resolution melt to analyze locus‐specific DNA methylation patterns on a molecule‐by‐molecule basis at or near single CpG‐site resolution at a fraction (< 1/10th) of the cost of next‐generation sequencing techniques. We demonstrate the feasibility of the platform by assessing intermolecular heterogeneity of DNA methylation in a panel of methylation biomarker loci using DNA derived from Pap specimens obtained from a cohort of 43 women, including 18 cases with ovarian cancer and 25 cancer‐free controls. PapDREAM leverages systematic multidimensional bioinformatic analyses of locus‐specific methylation heterogeneity to improve upon Pap‐specimen‐based detection of ovarian cancer, demonstrating a clinical sensitivity of 50% at 99% specificity in detecting ovarian cancer cases with an area under the receiver operator curve of 0.90. We then establish a logistic regression model that could be used to identify high‐risk patients for subsequent clinical follow‐up and monitoring. The results of this study support the utility of PapDREAM as a simple, low‐cost screening method with the potential to integrate with existing clinical workflows for early detection of ovarian cancer. Key points We present a microfluidic platform for detection and analysis of rare, heterogeneously methylated DNA within Pap specimens towards detection of ovarian cancer. The platform achieves high sensitivity (fractions <0.00005%) at a suitably low cost (∼$25) for routine screening applications. Furthermore, it provides molecule‐by‐molecule quantitative analysis to facilitate further study on the effect of heterogeneous methylation on cancer development. Ovarian cancer cells exhibit heterogeneous methylation patterns that could be leveraged as biomarkers for early detection. Cellular fragments and debris from ovarian tumors migrate to the cervix, where they are accessible via Pap specimen collection. The microfluidic HYPER‐melt platform digitizes bisulfite‐converted DNA into thousands of nanochambers to isolate heterogeneous epialleles, and uses digital high resolution melt to profile methylation patterns. A bioinformatic pipeline combines patterns from a multi‐gene panel into a methylation density score that gives the probability that a given sample contains ovarian cancer DNA.

Background Colon cancer (CC) is treatable if detected in its early stages. Improved CC detection assays that are highly sensitive, specific, and available at point of care are needed. In this study, we systematically selected and tested methylated markers that demonstrate high sensitivity and specificity for detection of CC in tissue and circulating cell-free DNA. Methods Hierarchical analysis of 22 candidate CpG loci was conducted using The Cancer Genome Atlas (TCGA) COAD 450K HumanMethylation database. Methylation of 13 loci was analyzed using quantitative multiplex methylation-specific PCR (QM-MSP) in a training set of fresh frozen colon tissues ( N  = 53). Hypermethylated markers were identified that were highest in cancer and lowest in normal colon tissue using the 75th percentile in Mann–Whitney analyses and the receiver operating characteristic (ROC) statistic. The cumulative methylation status of the marker panel was assayed in an independent test set of fresh frozen colon tissues ( N  = 52) using conditions defined and locked in the training set. A minimal marker panel of 6 genes was defined based on ROC area under the curve (AUC). Plasma samples ( N  = 20 colorectal cancers, stage IV and N  = 20 normal) were tested by cMethDNA assay to evaluate marker performance in liquid biopsy. Results In the test set of samples, compared to normal tissue, a 6-gene panel showed 100% sensitivity and 90% specificity for detection of CC, and an AUC of 1.00 (95% CI 1.00, 1.00). In stage IV colorectal cancer plasma versus normal, an 8-gene panel showed 95% sensitivity, 100% specificity, and an AUC of 0.996 (95% CI 0.986, 1.00) while a 5-gene subset showed 100% sensitivity, 100% specificity, and an AUC of 1.00 (95% CI 1.00, 1.00), highly concordant with our observations in tissue. Conclusions We identified high performance methylated DNA marker panels for detection of CC. This knowledge has set the stage for development and implementation of novel, automated, self-contained CC detection assays in tissue and blood which can expeditiously and accurately detect colon cancer in both developed and underdeveloped regions of the world, enabling optimal use of limited resources in low- and middle-income countries.

Despite numerous technical hurdles, the realization of true personalized medicine is becoming a progressive reality for the future of patient care. With the development of new techniques and tools to measure the genetic signature of tumors, biomarkers are increasingly being used to detect occult tumors, determine the choice of treatment and predict outcomes. Methylation of CpG islands at the promoter region of genes is a particularly exciting biomarker as it is cancer-specific. Older methods to detect methylation were cumbersome, operator-dependent and required large amounts of DNA. However, a newer technique called methylation on beads has resulted in a more uniform, streamlined and efficient assay. Furthermore, methylation on beads permits the extraction and processing of miniscule amounts of methylated tumor DNA in the peripheral blood. Such a technique may aid in the clinical detection and treatment of cancers in the future.