Biology subjects

Farney, S. K.

Publications and source records attributed to Farney, S. K..

4 recordsLinked to original sources

bioRxiv2025

Discovery and performance of DNA methylation panels for cancer detection and classification in blood

Examining DNA in a liquid biopsy for non-invasive cancer detection relies on identifying dilute signal in a high background. This study aims to identify DNA methylation biomarkers for multi-cancer detection. Utilizing large tissue datasets, we apply novel search algorithms to discover confined biomarker panels capable of distinguishing tumor from normal and determining the tissue of origin. We explore the applicability to blood-based testing using targeted methylation sequencing followed by machine learning classification. We present an 8-marker panel, which successfully predicts tumors across 14 types with a 91% average sensitivity, maintaining a low false positive rate (< 0.04%). Additionally, a panel of 39 CpG sites exhibits accuracies ranging from 69% to 98% for identifying tissue of origin. When tested on 114 patient plasma samples (colon, liver, pancreatic, prostate, and stomach cancer), the 8-marker panel obtains an AUC of 0.78 with a 78% sensitivity among 32 early-stage patients (stage I-II), and 60% overall. Using the 39-marker panel in a multi-class classification model selecting only the best match, 54% of tumor samples were on average correctly assigned to the tissue of origin, and up to 80% when allowing more inclusive criteria. Using a limited set of biomarkers, our work contributes to advancing non-invasive cancer diagnostics.

cancer biology↗

bioRxiv2020

Impact of glycan positioning on HIV-1 Env glycan shield density, function, and antibody recognition

N-glycans, which represent >50% mass of the HIV-1 envelope (Env) trimer, play important roles for virus-cell entry and immune evasion. How each glycan unit interacts to shape the Env protein-sugar complex and affects Env function is not well understood. Here, high-resolution glycomics analysis of two Env variants from the same donor, with differing functional characteristics and N-glycosylation-site composition, revealed that changes to key N-glycosylation-site not only affected the Env structure at distant locations, but also had a ripple effect on Env-wide glycan processing, virus infectivity, and antibody recognition and virus neutralization. Specifically, the N262 glycan, although not located in the CD4-binding site, controlled Env binding to the CD4 receptor, affected the recognition of Env by several glycan-dependent broadly neutralizing antibodies, and altered heterogeneity of glycosylation at several sites, with N156, N160, and N448 displaying limited glycan processing. Molecular dynamic simulations visualized how specific oligosaccharide positions can move to compensate for loss of a glycan. This study demonstrates how changes in individual glycan units can alter molecular dynamics and processing of the Env-glycan shield and, consequently, Env function.

microbiology↗

bioRxiv2018

Accurate Prediction of Antibody Resistance in Clinical HIV-1 Isolates

Broadly neutralizing antibodies (bNAbs) targeting the HIV-1 envelope glycoprotein (Env) have promising utility in prevention and treatment of HIV-1 infection with several undergoing clinical trials. Due to high sequence diversity and mutation rate of HIV-1, viral isolates are often resistant to particular bNAbs. Resistant strains are commonly identified by time-consuming and expensive in vitro neutralization experiments. Here, we developed machine learning-based classifiers that accurately predict resistance of HIV-1 strains to 33 neutralizing antibodies. Notably, our classifiers achieved an overall prediction accuracy of 96% for 212 clinical isolates from patients enrolled in four different clinical trials. Moreover, use of the tree-based machine learning method gradient boosting machine enabled us to identify critical epitope features that distinguish between antibody resistance and sensitivity. The availability of an in silico antibody resistance predictor will facilitate informed decisions of antibody usage in clinical settings.

bioinformatics↗

bioRxiv2018

Epitope-based vaccine design yields fusion peptide-directed antibodies that neutralize diverse strains of HIV-1

A central goal of HIV-1-vaccine research is the elicitation of antibodies capable of neutralizing diverse primary isolates of HIV-1. Here we show that focusing the immune response to exposed N-terminal residues of the fusion peptide, a critical component of the viral entry machinery and the epitope of antibodies elicited by HIV-1 infection, through immunization with fusion peptide-coupled carriers and prefusion-stabilized envelope trimers, induces cross-clade neutralizing responses. In mice, these immunogens elicited monoclonal antibodies capable of neutralizing up to 31% of a cross-clade panel of 208 HIV-1 strains. Crystal and cryo-electron microscopy structures of these antibodies revealed fusion peptide-conformational diversity as a molecular explanation for the cross-clade neutralization. Immunization of guinea pigs and rhesus macaques induced similarly broad fusion peptide-directed neutralizing responses suggesting translatability. The N terminus of the HIV-1-fusion peptide is thus a promising target of vaccine efforts aimed at eliciting broadly neutralizing antibodies.

immunology↗