Biology subjects

Stover, D. G.

Publications and source records attributed to Stover, D. G..

7 recordsLinked to original sources

bioRxiv2026

CNSigs: An R Package for the Identification of Copy Number Mutational Signatures

Copy number aberrations (CNAs) are gains and losses of large genomic segments present across most cancer types and are a hallmark of cancer genomic alterations. However, the processes underlying CNAs and characteristic patterns of CNAs are poorly understood. Bioinformatic advances have identified underlying single nucleotide variant mutational signatures resulting from distinct mutational processes, yet development of algorithms able to uncover similar signatures for CNAs remains less advanced. Using segmented data files from DNA sequencing, six copy number features are extracted for signature determination: segment size, breakpoints, copy number oscillation, changepoint size, copy number, and breakpoints per chromosome arm, along with ploidy. Mixed model approaches and non-negative matrix factorization are utilized to derive CNA signatures across cancer types. The full methodology was packaged in a publicly available, robust R package, CNSigs. To verify reproducibility, we derived five signatures from two independent breast cancer datasets (total n>3000), demonstrating high accuracy (average cosine similarity = 0.89). Pan-cancer application of CNSigs in TCGA resulted in derivation of 13 pan-cancer signatures which were significantly associated with disease-specific survival. Benchmarking CNSigs to two other CNA signature approaches within TCGA demonstrated non-overlapping signatures and favorable compute speed for CNSigs. We evaluated n=24 pairs of tumor and circulating tumor DNA (ctDNA) that demonstrated that CNSigs are detectable and reproducible via ctDNA, with significant association of CNSig11 with metastatic triple-negative breast cancer progression-free survival specifically for taxane chemotherapy. CNSigs association with immunophenotype was evaluated in low-grade glioma and CNSig3 was found to be highly prognostic yet complementary to immune features. The CNSigs allows researchers to easily analyze their own samples to derive copy number signatures and evaluate clinical associations. We demonstrate its potential application in ctDNA and association with treatment response. The development of this package allows further investigation of underlying processes that may be responsible for CNA fingerprints.

bioinformatics↗

bioRxiv2026

Multidimensional Cellular Micro-Compartments to Model Invasive Lobular Carcinoma Dormancy

Invasive lobular carcinoma (ILC) accounts for 10-15% of breast cancers. Despite favorable responses to anti-estrogen therapy, the dissemination of cancer cells and resistance to therapies are significant risks for patients with ILC. Late recurrences are prevalent in ILC, suggesting that disseminated tumor cell dormancy may be a mechanism preceding their late overt growth into metastatic lesions. Herein, we investigated the relationship between anti-estrogen resistance and dormancy through multidimensional in vitro models. The bioengineered platforms recapitulated the morphological characteristics of ILC and highlighted its distinction from invasive ductal carcinoma. Inducing a dormant phenotype revealed epigenetic changes and enhanced chemical and mechanical sensing of anti-estrogen-resistant ILC cells to the substrate surface, with p27Kip1 signaling playing a central role. We propose this platform as a high-throughput method to investigate the propensity of dormancy and its manifestation via a simplified and expedited approach.

bioengineering↗

bioRxiv2025

Estrogen Signaling During Abrupt Involution Leads to Long-Term Metabolic Dysfunction Similar to Estrogen Receptor Negative Breast Cancer

Epidemiological data links lack of breastfeeding with increased risk of breast cancer. Breast tissue undergoes remodeling to pre-pregnancy state after birth through involution. Long-term breastfeeding leads to gradual involution (GI). Lack of breastfeeding leads to abrupt involution (AI). While estrogen impacts repopulation of adipocytes, AI causes several precancerous changes in the mouse mammary gland. The impact of AI on adipocyte repopulation and metabolism is yet to be elucidated. ObjectivesTo investigate effects of AI on mammary gland metabolism and its potential link to breast cancer. MethodsAt partum (day 0), FVB/n dams were randomized to AI or GI and standardized to 6 pups. AI mice had pups removed on day 7 postpartum to mimic short-term breastfeeding. GI mice had 3 pups each were removed on day 28 and 31 postpartum to mimic gradual weaning. Mammary glands were harvested on day 28, 56, and 120 postpartum. Subset of AI mice had long-term sustained release tamoxifen placed subscapular on day 8 postpartum. Metabolic changes were assessed using: 1) transcriptional; 2) functional; 3) oxidative stress; and 4) metabolites analysis. ResultsDay 28 GI glands sustains/continues milk synthesis pathways impacting metabolic comparison with day28 AI glands. Day 28 AI when compared to day 56 GI showed upregulation of estrogen signaling, neutrophil degranulation, glucose metabolism, RNA synthesis, and down regulation of adipogenesis and glycolysis. At day 120, AI glands had downregulation of oxidative phosphorylation and upregulation of mitochondria dysfunction similar to pregnancy associated estrogen receptor negative breast cancer. Tamoxifen treatment of AI dams showed metabolic pathways and estrogen signaling similar to that of GI glands on day 28. ConclusionEarly metabolic phenotypes in AI and GI glands may be caused by differences in adipocyte repopulation related to estrogen signaling. Long-term metabolic effects of AI lead to similar metabolic effects found in breast cancer.

cancer biology↗

bioRxiv2024

FinaleMe: Predicting DNA methylation by the fragmentation patterns of plasma cell-free DNA

Analysis of DNA methylation in cell-free DNA (cfDNA) reveals clinically relevant biomarkers but requires specialized protocols and sufficient input material that limits its applicability. Millions of cfDNA samples have been profiled by genomic sequencing. To maximize the gene regulation information from the existing dataset, we developed FinaleMe, a non-homogeneous Hidden Markov Model (HMM), to predict DNA methylation of cfDNA and, therefore, tissues-of-origin directly from plasma whole-genome sequencing (WGS). We validated the performance with 80 pairs of deep and shallow-coverage WGS and whole-genome bisulfite sequencing (WGBS) data.

bioinformatics↗

bioRxiv2022

Replicative Instability Drives Cancer Progression

In the past decade, defective DNA repair has been increasingly linked with cancer progression. Human tumors with markers of defective DNA repair and increased replication stress have been shown to exhibit genomic instability and poor survival rates across tumor types. Here we utilize-omics data from two independent consortia to identify the genetic underpinnings of replication stress, therapy resistance, and primary carcinoma to brain metastasis in BRCA wildtype tumors. In doing so, we have defined a new pan-cancer class of tumors characterized by replicative instability (RIN). RIN is defined by genomic evolution secondary to replicative challenge. Our data supports a model whereby defective single-strand break repair, translesion synthesis, and non-homologous end joining effectors drive RIN. Collectively, we find that RIN accelerates cancer progression by driving copy number alterations and transcriptional program rewiring that promote tumor evolution. Statement of SignificanceDefining the genetic basis of genomic instability with wildtype BRCA repair effectors is a significant unmet need in cancer research. Here we identify and characterize a pan-cancer cohort of tumors driven by replicative instability (RIN). We find that RIN drives therapy resistance and distant metastases across multiple tumor types.

cancer biology↗

bioRxiv2021

MAESTRO affords 'breadth and depth' for mutation testing

The ability to assay large numbers of low-abundance mutations is crucial in biomedicine. Yet, the technical hurdles of sequencing multiple mutations at extremely high depth and accuracy remain daunting. For sequencing low-level mutations, its either depth or breadth but not both. Here, we report a simple and powerful approach to accurately track thousands of distinct mutations with minimal reads. Our technique called MAESTRO (minor allele enriched sequencing through recognition oligonucleotides) employs massively-parallel mutation enrichment to empower duplex sequencing--one of the most accurate methods--to track up to 10,000 low-frequency mutations with up to 100-fold less sequencing. In example use cases, we show that MAESTRO could enable mutation validation from cancer genome sequencing studies. We also show that it could track thousands of mutations from a patients tumor in cell-free DNA, which may improve detection of minimal residual disease from liquid biopsies. In all, MAESTRO improves the breadth, depth, accuracy, and efficiency of mutation testing.

molecular biology↗

bioRxiv2019

Machine learning predicts rapid relapse of triple negative breast cancer

PurposeMetastatic relapse of triple-negative breast cancer (TNBC) within 2 years of diagnosis is associated with particularly aggressive disease and a distinct clinical course relative to TNBCs that relapse beyond 2 years. We hypothesized that rapid relapse TNBCs (rrTNBC; metastatic relapse or death <2 years) reflect unique genomic features relative to late relapse (lrTNBC; >2 years).\n\nPatients and MethodsWe identified 453 primary TNBCs from three publicly-available datasets and characterized each as rrTNBc, lrTNBC, or no relapse (nrTNBC: no relapse/death with at least 5 years follow-up). We compiled primary tumor clinical and multi-omic data, including transcriptome (n=453), copy number alterations (CNAs; n=317), and mutations in 171 cancer-related genes (n=317), then calculated published gene expression and immune signatures.\n\nResultsPatients with rrTNBC were higher stage at diagnosis (Chi-square p<0.0001) while lrTNBC were more likely to be non-basal PAM50 subtype (Chi-square p=0.03). Among 125 expression signatures, five immune signatures were significantly higher in nrTNBCs while lrTNBC were enriched for eight estrogen/luminal signatures (all FDR p<0.05). There was no significant difference in tumor mutation burden or percent genome altered across the groups. Among mutations, only TP53 mutations were significantly more frequent in rrTNBC compared to lrTNBC (Fisher exact FDR p=0.009). To develop an optimal classifier, we used 77 significant clinical and omic features to evaluate six modeling approaches encompassing simple, machine learning, and artificial neural network (ANN). Support vector machine outperformed other models with average receiver-operator characteristic area under curve >0.75.\n\nConclusionsWe provide a new approach to define TNBCs based on timing of relapse. We identify distinct clinical and genomic features that can be incorporated into machine learning models to predict rapid relapse of TNBC.

cancer biology↗