Biology subjects

Holloway, S. T.

Publications and source records attributed to Holloway, S. T..

3 recordsLinked to original sources

bioRxiv2025

Aggressive Neuroblastomas Start Growing after Infancy

In neuroblastoma, population screening during infancy failed to lower mortality because it primarily detected biologically indolent tumors rather than aggressive, life-threatening disease. The failure may reflect limited screening sensitivity, or disease onset after infancy among aggressive tumors. Here, we used an epigenetic mitotic clock based on fluctuating CpG DNA methylation to estimate patient-specific tumor mitotic ages and calendar ages in a cohort of unscreened children diagnosed with neuroblastoma. Aggressive cancers (stage 4) primarily started growing after the first year of life, making them undetectable by screening during infancy. In contrast, biologically more indolent tumors (stages 1, 2, 3 and 4S) often started growing in utero or during the first year of life, and affected children had better survival outcomes. Due to a short preclinical detectable phase of aggressive neuroblastomas, reducing mortality through screening is impractical as it would require frequent screening among older children. Patient-specific tumor-age estimation may help refine screening windows and improve early-detection strategies in other cancers where screening has so far failed to yield substantial mortality reductions.

cancer biology↗

bioRxiv2024

CNV-Profile Regression: A New Approach for Copy Number Variant Association Analysis in Whole Genome Sequencing Data

Copy number variants (CNVs) are DNA gains or losses involving >50 base pairs. Assessing CNV effects on disease risk requires consideration of several factors. First, there are no natural definitions for CNV loci. Second, CNV effects can depend on dosage and length. Third, CNV effects can be more accurately estimated when all CNV events in a genomic region are analyzed together to assess their joint effects. We propose a new framework for association analysis that directly models an individuals entire CNV profile within a genomic region. This framework represents an individuals CNVs using a CNV profile curve to capture variations in CNV length and dosage and to bypass the need to predefine CNV loci. CNV effects are estimated at each genome position, making the results comparable across different studies. To jointly estimate the effects of all CNVs, we use a Lasso penalty to select CNVs associated with the trait and integrate a weighted L2-fusion penalty to encourage similar effects of adjacent CNVs when supported by the data. Simulations show that the proposed model can more effectively identify causal CNVs while maintaining false positive rates comparable to baseline methods and yield more precise effect-size estimates across different settings. When applied to CNV derived from whole genome sequencing data of the Alzheimers Disease Sequencing Project, the proposed methods identify additional CNVs associated with Alzheimers Disease (AD). These identified CNVs overlap with several known AD-risk genes and are significantly enriched by biological processes related to neuron structures and functions crucial in AD development.

bioinformatics↗

bioRxiv2024

Mapping the Temporal Landscape of Breast Cancer Using Epigenetic Entropy

Although generally unknown, the age of a newly diagnosed tumor encodes valuable etiologic and prognostic information. Here, we estimate the age of breast cancers, defined as the time from the start of growth to detection, using a measure of epigenetic entropy derived from genome-wide methylation arrays. Based on an ensemble of neutrally fluctuating CpG (fCpG) sites, this stochastic epigenetic clock differs from conventional clocks that measure age-related increases in methylation. We show that younger tumors exhibit hallmarks of aggressiveness, such as increased proliferation and genomic instability, whereas older tumors are characterized by elevated immune infiltration, indicative of enhanced immune surveillance. These findings suggest that the clock captures a tumors effective growth rate resulting from the evolutionary-ecological competition between intrinsic growth potential and external systemic pressures. Because of the clocks ability to delineate old and stable from young and aggressive tumors, it has potential applications in risk stratification of early-stage breast cancers and guiding early detection efforts.

cancer biology↗