Multiplex digital profiling of DNA methylation heterogeneity for sensitive and cost-effective cancer detection in low-volume liquid biopsies.

Molecular alterations in cancerous tissues exhibit intercellular genetic and epigenetic heterogeneity, complicating the performance of diagnostic assays, particularly for early cancer detection. Conventional liquid biopsy methods have limited sensitivity and/or ability to assess epigenetic heterogeneity of rare epiallelic variants cost-effectively. We report an approach, named REM-DREAMing (Ratiometric-Encoded Multiplex Discrimination of Rare EpiAlleles by Melt), which leverages a digital microfluidic platform that incorporates a ratiometric fluorescence multiplex detection scheme and precise digital high-resolution melt analysis to enable low-cost, parallelized analysis of heterogeneous methylation patterns on a molecule-by-molecule basis for the detection of cancer in liquid biopsies.

Single-molecule epiallelic profiling of DNA derived from routinely collected Pap specimens for noninvasive detection 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.

Multiplex Digital Methylation-Specific PCR for Noninvasive Screening of Lung Cancer.

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.

Facile Coupling of Droplet Magnetofluidic-Enabled Automated Sample Preparation for Digital Nucleic Acid Amplification Testing and Analysis.

Digital nucleic acid amplification testing (dNAAT) and analysis techniques, such as digital polymerase chain reaction (PCR), have become useful clinical diagnostic tools. However, nucleic acid (NA) sample preparation preceding dNAAT is generally laborious and performed manually, thus creating the need for a simple sample preparation technique and a facile coupling strategy for dNAAT. Therefore, we demonstrate a simple workflow which automates magnetic bead-based extraction of NAs with a one-step transfer to dNAAT.

Nanoarray Digital Polymerase Chain Reaction with High-Resolution Melt for Enabling Broad Bacteria Identification and Pheno-Molecular Antimicrobial Susceptibility Test.

Toward combating infectious diseases caused by pathogenic bacteria, there remains an unmet need for diagnostic tools that can broadly identify the causative bacteria and determine their antimicrobial susceptibilities from complex and even polymicrobial samples in a timely manner. To address this need, a microfluidic and machine-learning-based platform that performs broad bacteria identification (ID) and rapid yet reliable antimicrobial susceptibility testing (AST) is developed. Specifically, this platform builds on "pheno-molecular AST", a strategy that transforms nucleic acid amplification tests (NAATs) into phenotypic AST through quantitative detection of bacterial genomic replication, and utilizes digital polymerase chain reaction (PCR) and digital high-resolution melt (HRM) to quantify and identify bacterial DNA molecules.

Highly Efficient Real-Time Droplet Analysis Platform for High-Throughput Interrogation of DNA Sequences by Melt.

Droplet microfluidic platforms have greatly enhanced the throughput and sensitivity of single-molecule and single-cell analyses. However, real-time analyses of individual droplets remain challenging. Most droplet microfluidic platforms have fundamental drawbacks that undermine their utility toward applications that rely on real-time monitoring to identify rare variants, such as bacterial persistence, drug discovery, antibody production, epigenetic biomarker analyses, etc.

Multilayer microfluidic array for highly efficient sample loading and digital melt analysis of DNA methylation.

Liquid biopsies contain a treasure of genetic and epigenetic biomarkers that contain information for the detection and monitoring of human disease. DNA methylation is an epigenetic modification that is critical to determining cellular phenotype and often becomes altered in many disease states. In cancer, aberrant DNA methylation contributes to carcinogenesis and can profoundly affect tumor evolution, metastatic potential, and resistance to therapeutic intervention.

Facile profiling of molecular heterogeneity by microfluidic digital melt.

This work presents a digital microfluidic platform called HYPER-Melt (high-density profiling and enumeration by melt) for highly parallelized copy-by-copy DNA molecular profiling. HYPER-Melt provides a facile means of detecting and assessing sequence variations of thousands of individual DNA molecules through digitization in a nanowell microchip array, allowing amplification and interrogation of individual template molecules by detecting HRM fluorescence changes due to sequence-dependent denaturation. As a model application, HYPER-Melt is used here for the detection and assessment of intermolecular heterogeneity of DNA methylation within the promoters of classical tumor suppressor genes.

A Position Statement on Population Data Science: The Science of Data about People.

Information is increasingly digital, creating opportunities to respond to pressing issues about human populations using linked datasets that are large, complex, and diverse. The potential social and individual benefits that can come from data-intensive science are large, but raise challenges of balancing individual privacy and the public good, building appropriate socio-technical systems to support data-intensive science, and determining whether defining a new field of inquiry might help move those collective interests and activities forward. A combination of expert engagement, literature review, and iterative conversations led to our conclusion that defining the field of Population Data Science (challenge 3) will help address the other two challenges as well.

Assessing privacy risks in population health publications using a checklist-based approach.

Objective: Recent growth in the number of population health researchers accessing detailed datasets, either on their own computers or through virtual data centers, has the potential to increase privacy risks. In response, a checklist for identifying and reducing privacy risks in population health analysis outputs has been proposed for use by researchers themselves. In this study we explore the usability and reliability of such an approach by investigating whether different users identify the same privacy risks on applying the checklist to a sample of publications.

Anonymization for outputs of population health and health services research conducted via an online data center.

Objective: Online data centers (ODCs) are becoming increasingly popular for making health-related data available for research. Such centers provide good privacy protection during analysis by trusted researchers, but privacy concerns may still remain if the system outputs are not sufficiently anonymized. In this article, we propose a method for anonymizing analysis outputs from ODCs for publication in academic literature.

Individual privacy versus public good: protecting confidentiality in health research.

Health and medical data are increasingly being generated, collected, and stored in electronic form in healthcare facilities and administrative agencies. Such data hold a wealth of information vital to effective health policy development and evaluation, as well as to enhanced clinical care through evidence-based practice and safety and quality monitoring. These initiatives are aimed at improving individuals' health and well-being.

Cost-effectiveness of an advance notification letter to increase colorectal cancer screening.

Objectives: The aim of this study is to evaluate the cost-effectiveness of a patient-direct mailed advance notification letter on participants of a National Bowel Cancer Screening Program (NBCSP) in Australia, which was launched in August 2006 and offers free fecal occult blood testing to all Australians turning 50, 55, or 65 years of age in any given year.

Methods: This study followed a hypothetical cohort of 50-year-old, 55-year-old, and 65-year-old patients undergoing fecal occult blood test (FOBT) screening through a decision analytic Markov model. The intervention compared two strategies: (i) advance letter, NBCSP, and FOBT compared with (ii) NBCSP and FOBT.

Data linkage infrastructure for cross-jurisdictional health-related research in Australia.

Background: The Centre for Data Linkage (CDL) has been established to enable national and cross-jurisdictional health-related research in Australia. It has been funded through the Population Health Research Network (PHRN), a national initiative established under the National Collaborative Research Infrastructure Strategy (NCRIS). This paper describes the development of the processes and methodology required to create cross-jurisdictional research infrastructure and enable aggregation of State and Territory linkages into a single linkage "map".

Application of logic models in a large scientific research program.

It is the purpose of this article to discuss the development and application of a logic model in the context of a large scientific research program within the Commonwealth Scientific and Industrial Research Organisation (CSIRO). CSIRO is Australia's national science agency and is a publicly funded part of Australia's innovation system. It conducts mission-driven scientific research focussed on delivering results with relevance and impact for Australia, where impact is defined and measured in economic, environmental and social terms at the national level.

Privacy and the use of health data for research.

Objective: We reviewed resources for researchers interested in privacy issues surrounding secondary use of health data for research. These included applicable privacy regulations and available information on privacy perception in Australia. The review is timely because the current Australian Population Health Research Network infrastructure investments are likely to attract new researchers to the field.

Mining unexpected temporal associations: applications in detecting adverse drug reactions.

In various real-world applications, it is very useful mining unanticipated episodes where certain event patterns unexpectedly lead to outcomes, e.g., taking two medicines together sometimes causing an adverse reaction.

Remote access methods for exploratory data analysis and statistical modelling: Privacy-Preserving Analytics.

This paper is concerned with the challenge of enabling the use of confidential or private data for research and policy analysis, while protecting confidentiality and privacy by reducing the risk of disclosure of sensitive information. Traditional solutions to the problem of reducing disclosure risk include releasing de-identified data and modifying data before release. In this paper we discuss the alternative approach of using a remote analysis server which does not enable any data release, but instead is designed to deliver useful results of user-specified statistical analyses with a low risk of disclosure.