Publications by authors named "Mary L Stackpole"
Motivation: Cell-free DNA (cfDNA) released by dying cells from damaged or diseased tissues can lead to elevated tissue-specific DNA, which is traceable and quantifiable through unique DNA methylation patterns. Therefore, tracing cfDNA origins by analyzing its methylation profiles holds great potential for detecting and monitoring a range of diseases, including cancers. However, deconvolving tissue-specific cfDNA remains challenging for broader applications and research due to the scarcity of specialized, user-friendly bioinformatics tools.
View Article and Find Full Text PDFBackground: The current noninvasive prognostic evaluation methods for hepatocellular carcinoma (HCC), which are largely reliant on radiographic imaging features and serum biomarkers such as alpha-fetoprotein (AFP), have limited effectiveness in discriminating patient outcomes. Identification of new prognostic biomarkers is a critical unmet need to improve treatment decision-making. Epigenetic changes in cell-free DNA (cfDNA) have shown promise in early cancer diagnosis and prognosis.
View Article and Find Full Text PDFComprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring.
Proc Natl Acad Sci U S A
July 2023
Article Synopsis
- Plasma cell-free DNA (cfDNA) serves as a noninvasive biomarker for identifying cell death across different organs, which can help in detecting and monitoring diseases.
- The study presents a comprehensive tissue methylation atlas created from 521 noncancer samples, identifying specific methylation patterns that aid in accurately determining the tissue origin of cfDNA.
- A new deep-learning model called supervised tissue deconvolution is developed, which shows improved sensitivity and accuracy for analyzing cfDNA, with applications in disease diagnosis and tracking treatment effects.
Article Synopsis
- * The researchers developed a method called cfMethyl-Seq, which allows for more efficient sequencing of methylation patterns in cell-free DNA, providing cost-effective analysis.
- * When tested on a large cohort of cancer patients, the method demonstrated high specificity and decent sensitivity for detecting various cancer stages, providing accurate tissue origin identification as well.
Purpose: Cell-free DNA (cfDNA) offers a noninvasive approach to monitor cancer. Here we develop a method using whole-exome sequencing (WES) of cfDNA for simultaneously monitoring the full spectrum of cancer treatment outcomes, including minimal residual disease (MRD), recurrence, evolution, and second primary cancers.
Experimental Design: Three simulation datasets were generated from 26 patients with cancer to benchmark the detection performance of MRD/recurrence and second primary cancers.
Cell-free DNA (cfDNA) is attractive for many applications, including detecting cancer, identifying the tissue of origin, and monitoring. A fundamental task underlying these applications is SNV calling from cfDNA, which is hindered by the very low tumor content. Thus sensitive and accurate detection of low-frequency mutations (<5%) remains challenging for existing SNV callers.
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