Pornpat Athamanolap

DREAMing: a simple and ultrasensitive method for assessing intratumor epigenetic heterogeneity directly from liquid biopsies

Many cancers comprise heterogeneous populations of cells at primary and metastatic sites throughout the body. The presence or emergence of distinct subclones with drug-resistant genetic and epigenetic phenotypes within these populations can greatly complicate therapeutic intervention. Liquid biopsies of peripheral blood from cancer patients have been suggested as an ideal means of sampling intratumor genetic and epigenetic heterogeneity for diagnostics, monitoring and therapeutic guidance. However, current molecular diagnostic and sequencing methods are not well suited to the routine assessment of epigenetic heterogeneity in difficult samples such as liquid biopsies that contain intrinsically low fractional concentrations of circulating tumor DNA (ctDNA) and rare epigenetic subclonal populations. Here we report an alternative approach, deemed DREAMing (Discrimination of Rare EpiAlleles by Melt), which uses semi-limiting dilution and precise melt curve analysis to distinguish and enumerate individual copies of epiallelic species at single-CpG-site resolution in fractions as low as 0.005%, providing facile and inexpensive ultrasensitive assessment of locus-specific epigenetic heterogeneity directly from liquid biopsies. The technique is demonstrated here for the evaluation of epigenetic heterogeneity at p14ARF and BRCA1 gene-promoter loci in liquid biopsies obtained from patients in association with non-small cell lung cancer (NSCLC) and myelodysplastic/myeloproliferative neoplasms (MDS/MPN), respectively.

Trainable high resolution melt curve machine learning classifier for large-scale reliable genotyping of sequence variants

High resolution melt (HRM) is gaining considerable popularity as a simple and robust method for genotyping sequence variants. However, accurate genotyping of an unknown sample for which a large number of possible variants may exist will require an automated HRM curve identification method capable of comparing unknowns against a large cohort of known sequence variants. Herein, we describe a new method for automated HRM curve classification based on machine learning methods and learned tolerance for reaction condition deviations. We tested this method in silico through multiple cross-validations using curves generated from 9 different simulated experimental conditions to classify 92 known serotypes of Streptococcus pneumoniae and demonstrated over 99% accuracy with 8 training curves per serotype. In vitro verification of the algorithm was tested using sequence variants of a cancer-related gene and demonstrated 100% accuracy with 3 training curves per sequence variant. The machine learning algorithm enabled reliable, scalable, and automated HRM genotyping analysis with broad potential clinical and epidemiological applications.

Defining, distinguishing and detecting the contribution of heterogeneous methylation to cancer heterogeneity

DNA methylation is a fundamental means of epigenetic gene regulation that occurs in virtually all cell types. In many higher organisms, including humans, it plays vital roles in cell differentiation and homeostatic maintenance of cell phenotype. The control of DNA methylation has traditionally been attributed to a highly coordinated, linear process, whose dysregulation has been associated with numerous pathologies including cancer, where it occurs early in, and even prior to, the development of neoplastic tissues. Recent experimental evidence has demonstrated that, contrary to prevailing paradigms, methylation patterns are actually maintained through inexact, dynamic processes. These processes normally result in minor stochastic differences between cells that accumulate with age. However, various factors, including cancer itself, can lead to substantial differences in intercellular methylation patterns, viz. methylation heterogeneity. Advancements in molecular biology techniques are just now beginning to allow insight into how this heterogeneity contributes to clonal evolution and overall cancer heterogeneity. In the current review, we begin by presenting a didactic overview of how the basal bimodal methylome is established and maintained. We then provide a synopsis of some of the factors that lead to the accrual of heterogeneous methylation and how this heterogeneity may lead to gene silencing and impact the development of cancerous phenotypes. Lastly, we highlight currently available methylation assessment techniques and discuss their suitability to the study of heterogeneous methylation.

Nested Machine Learning Facilitates Increased Sequence Content for Large-Scale Automated High Resolution Melt Genotyping

High Resolution Melt (HRM) is a versatile and rapid post-PCR DNA analysis technique primarily used to differentiate sequence variants among only a few short amplicons. We recently developed a one-vs-one support vector machine algorithm (OVO SVM) that enables the use of HRM for identifying numerous short amplicon sequences automatically and reliably. Herein, we set out to maximize the discriminating power of HRM + SVM for a single genetic locus by testing longer amplicons harboring significantly more sequence information. Using universal primers that amplify the hypervariable bacterial 16 S rRNA gene as a model system, we found that long amplicons yield more complex HRM curve shapes. We developed a novel nested OVO SVM approach to take advantage of this feature and achieved 100% accuracy in the identification of 37 clinically relevant bacteria in Leave-One-Out-Cross-Validation. A subset of organisms were independently tested. Those from pure culture were identified with high accuracy, while those tested directly from clinical blood bottles displayed more technical variability and reduced accuracy. Our findings demonstrate that long sequences can be accurately and automatically profiled by HRM with a novel nested SVM approach and suggest that clinical sample testing is feasible with further optimization.

Microbial Typing by Machine Learned DNA Melt Signatures

There is still an ongoing demand for a simple broad-spectrum molecular diagnostic assay for pathogenic bacteria. For this purpose, we developed a single-plex High Resolution Melt (HRM) assay that generates complex melt curves for bacterial identification. Using internal transcribed spacer (ITS) region as the phylogenetic marker for HRM, we observed complex melt curve signatures as compared to 16S rDNA amplicons with enhanced interspecies discrimination. We also developed a novel Naïve Bayes curve classification algorithm with statistical interpretation and achieved 95% accuracy in differentiating 89 bacterial species in our library using leave-one-out cross-validation. Pilot clinical validation of our method correctly identified the etiologic organisms at the species-level in 59 culture-positive mono-bacterial blood culture samples with 90% accuracy. Our findings suggest that broad bacterial sequences may be simply, reliably and automatically profiled by ITS HRM assay for clinical adoption.

Droplet Array Platform for High-Resolution Melt Analysis of DNA Methylation Density

High-resolution melting (HRM) has garnered significant interest as an analytical technique for a number of applications, including DNA methylation detection, due to its inherent sensitivity and robustness. In this study, we describe a miniaturized assay platform for quantitative methylation density analysis using a microfluidic droplet array cartridge. We demonstrate that the DNA methylation level of the RASSF1A promoter can be directly analyzed using HRM. PCR products were generated by amplifying bisulfite-treated DNA with varying CpG densities using CpG island-flanking primer sets. Subsequent HRM analysis on the miniaturized droplet platform shows distinct melting curve profiles associated with methylation levels, which was verified using a conventional benchtop PCR-HRM system. The characteristic melting temperature (Tm) of the PCR products was used to directly quantify the respective levels of DNA methylation density. Our approach provides a key advantage over current gold standard methods such as methylation-specific PCR (MSP), which are incapable of providing specific information regarding the overall methylation density of the target genes. The miniaturized platform establishes a practical approach to methylation density profiling from multiple DNA samples with a potential application in point-of-care diagnostics.

Mobile nucleic acid amplification testing (mobiNAAT) for Chlamydia trachomatis screening in hospital emergency department settings.

Management of curable sexually-transmitted infections (STI) such as Chlamydia can be revolutionized by highly sensitive nucleic acid testing that is deployable at the point-of-care (POC). Here we report the development of a mobile nucleic acid amplification testing (mobiNAAT) platform utilizing a mobile phone and droplet magnetofluidics to deliver NAAT in a portable and accessible format. By using magnetic particles as a mobile substrate for nucleic acid capture and transport, fluid handling is reduced to particle translocation on a simple magnetofluidic cartridge assembled with reagents for nucleic acid purification and amplification. A mobile phone user interface operating in tandem with a portable Bluetooth-enabled cartridge-processing unit facilitates process integration. We tested 30 potentially Chlamydia trachomatis (CT)-infected patients in a hospital emergency department and confirmed that mobiNAAT showed 100% concordance with laboratory-based NAAT. Concurrent evaluation by a nontechnical study coordinator who received brief training via an embedded mobile app module demonstrated ease of use and reproducibility of the platform. This work demonstrates the potential of mobile nucleic acid testing in bridging the diagnostic gap between centralized laboratories and hospital emergency departments.

Integrated Bacterial Identification and Antimicrobial Susceptibility Testing Using PCR and High-Resolution Melt

Accurate and timely diagnostics are critical for managing bacterial infections. The current gold standard, culture-based diagnostics, can provide clinicians with comprehensive diagnostic information including bacterial identity and antimicrobial susceptibility, but they often require several days of turnaround time, which leads to compromised clinical outcome and promotes the spread of antibiotic resistance. Nucleic acid amplification tests such as PCR have significantly accelerated the detection of specific bacteria but generally lack the capacities for broad-based bacterial identification or antimicrobial susceptibility testing. Here, we report an integrated assay based on PCR and high-resolution melt (HRM) for rapid diagnosis for bacterial infections. In our assay, we measure bacterial growth in the presence or absence of certain antibiotics with real-time quantitative PCR or digital PCR to determine antimicrobial susceptibility. In addition, we use HRM and a machine learning algorithm to identify bacterial species based on melt-curve profiles of the 16S rRNA gene in an automated fashion. As a demonstration, we correctly identified the bacterial species and their antimicrobial susceptibility profiles for multiple unknown samples in blinded tests within ∼6.5 h.

A mobile phone-operated droplet magnetofluidic assay platform for nucleic acid amplification testing

Nucleic acid amplification is a powerful technique for rapid pathogen detection due to its high analytical sensitivity, specificity and speed, but the complexity of multistep assay preparation confines their use to laboratories maintained by medical scientists and specialized equipment. Using a droplet magnetofluidic approach to miniaturization for nucleic acid testing at the point of care, we report the development of a mobile phone-enabled sample-to-answer nucleic acid testing platform. Fluidic processing is drastically simplified by a novel cartridge design that facilitates sequential magnetic particle extraction and reconstitution with a single rotary actuator. The frontend design employs a mobile app designed to facilitate process automation through Bluetooth communication and data acquisition via integrated CMOS sensor. To validate the performance and clinical relevance of the assay platform, we tested 13 potentially Chlamydia trachomatis (CT)-infected patients in a hospital emergency department and obtained results which fully correlated with laboratory-based NAAT. This work illustrates a novel and broadly expandable approach to molecular diagnostic assay automation by utilizing the strengths of magnetofluidics and smartphone technology.

Abstract 4666: DREAMing as a simple and low cost alternative for the assessment of methylation in ultra rare DNA

Background: Current approaches for the assessment of methylation, such as methylation-specific PCR (MSP) and next-generation bisulfite sequencing (BS-Seq) are fundamentally limited in their ability to detect and assess heterogeneous methylation patterns (epialleles) in ultra-rare ( Methods: We expand upon the underlying theory of DREAMing and provide guidelines for the development of single-copy sensitive DREAMing assays. We further elucidate methods for tailoring DREAMing assays to samples of interest and compare the performance of these assays to commonly employed techniques including quantitative MSP (qMSP) and BS-Seq. Results: Development of single-copy sensitive DREAMing assays for a number of loci associated with classic tumor-specific methylation such as CHFR and RASSF1A as well as a candidate pan-cancer locus are reported. These assays are then used to analyze methylation in cfDNA derived from the plasma of cancer-positive and healthy patients. DREAM analysis reveals that DREAMing can readily detect over an order of magnitude more epialleles when directly compared to qMSP and BS-Seq assays of the same locus. Some of the challenges associated with distinguishing potential tumor-specific aberrant methylation from background methylation are then discussed and proposed solutions are demonstrated. Lastly, methods for optimizing DREAMing assays for specific sample types are discussed. Conclusions: DREAMing is a recently introduced method for the assessment of locus-specific methylation in samples containing ultra rare target DNA. Its low cost and simplicity coupled with the ability to provide enhanced, single-copy detection of heterogeneous methylation make DREAMing an attractive option over traditional techniques for demanding specimens such as cfDNA and rare cell populations. DREAMing has potential utility in the evaluation of DNA methylation dynamics in cell populations, prenatal testing, as well as clear use in early cancer diagnostic, companion diagnostic and predictive applications. Citation Format: Thomas R. Pisanic, Pornpat Athamanolap, Brendan F. Miller, Vincent Wu, Laura Elnitski, Tza-Huei Wang. DREAMing as a simple and low cost alternative for the assessment of methylation in ultra rare DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4666. doi:10.1158/1538-7445.AM2017-4666