Biology subjects

Weber, J. S.

Publications and source records attributed to Weber, J. S..

4 recordsLinked to original sources

bioRxiv2024

Gut microbiome is associated with recurrence-free survival in patients with resected Stage IIIB-D or Stage IV melanoma treated with immune checkpoint inhibitors

The gut microbiome (GMB) has been associated with outcomes of immune checkpoint blockade therapy in melanoma, but there is limited consensus on the specific taxa involved, particularly across different geographic regions. We analyzed pre-treatment stool samples from 674 melanoma patients participating in a phase-III trial of adjuvant nivolumab plus ipilimumab versus nivolumab, across three continents and five regions. Longitudinal analysis revealed that GMB was largely unchanged following treatment, offering promise for lasting GMB-based interventions. In region-specific and cross-region meta-analyses, we identified pre-treatment taxonomic markers associated with recurrence, including Eubacterium, Ruminococcus, Firmicutes, and Clostridium. Recurrence prediction by these markers was best achieved across regions by matching participants on GMB compositional similarity between the intra-regional discovery and external validation sets. AUCs for prediction ranged from 0.83-0.94 (depending on the initial discovery region) for patients closely matched on GMB composition (e.g., JSD [≤]0.11). This evidence indicates that taxonomic markers for prediction of recurrence are generalizable across regions, for individuals of similar GMB composition. HighlightsO_LIOverall gut microbiome (GMB) composition is largely unchanged during ICB treatment. C_LIO_LIGMB composition varies by geographic region C_LIO_LIWe identified gut bacterial markers associated with recurrence in region-specific analyses. C_LIO_LIRegion-identified markers are generalizable if GMB composition is taken into account by matching. C_LI

cancer biology↗

bioRxiv2021

The transmission of African Animal Trypanosomiasis (nagana) in two districts in the forest zone of Ghana

The African Animal Trypanosomiasis (nagana) is caused by several species of trypanosome species which results in significant clinical diseases. This consequently leads to huge economic losses. We carried out a cross-sectional survey to investigate the composition of vectors, parasite diversity and intensities in two districts in the Eastern region of Ghana. Cytochrome c oxidase subunit 1 (COI) and internal transcribed spacer (ITS1) dependent PCR assays identified tsetse fly species and trypanosome parasites circulating in the area. A total of 229 tsetse flies, 65 pigs and 20 were sampled. Female and male flies 155 (51.8%), 74 (32.3%) respectively were collected with biconical traps. The overall vector density of 4.3 flies/trap/day was observed. Typanosome prevalence of 58.9% (95% CI: 52.5-65.1), 46.2% (95% CI: 34.6-58.1) and 0.0% (95% CI: 0.0-16.1) in tsetse flies, pigs and cattle respectively were detected. Trypanosoma congolense was the predominant species with a prevalence of 80.7% (95% CI: 73.3-86.5) in flies and 60.0% (95% CI: 42.3-75.4). There was evidence of multiple trypanosome infection with T. congolense/T. simiae occurring highest with prevalence of 38.0% (95% CI: 30.7-46.9). The parasite prevalence in pigs across the communities was high with significant differences associated between locations (Chi2 = 28.06, 95% CI: 0.05-0.81, P=0.0009). Blood meal with flies revealed feeding on both domestic Sus scrofa domesticus (pig) and Phacochoerus africanus (warthog). Tsetse flies in this area remain highly infected with trypanosomes and continue to transmit the parasites to livestock and human populations in the communities.

evolutionary biology↗

bioRxiv2020

Nivolumab and Ipilimumab in Metastatic Melanoma Are Associated with Distinct Immune Landscape Changes and Response-Associated Immunophenotypes

The reshaping of the immune landscape by nivolumab (NIVO) and ipilimumab (IPI) and its relation to patient outcomes is not well-described. We used high-parameter flow cytometry and a novel computational platform, CytoBrute, to define immunophenotypes of up to 15 markers to assess peripheral blood samples from metastatic melanoma patients receiving sequential NIVO>IPI or IPI>NIVO. The two treatments were associated with distinct immunophenotypic changes and had differing profiles associated with response. Only two immunophenotypes were shared but had opposing relationships to response/survival. To understand the impact of sequential treatment on response/survival, phenotypes that changed after the initial treatment and differentiated response in the other cohort were identified. Immunophenotypic changes occurring post-NIVO were predominately associated with response to IPI>NIVO, but changes occurring post-IPI were predominately associated with progression with NIVO>IPI. Among these changes, CD4+CD38+CD39+CD127-GARP- T-cell subsets were increased after IPI treatment and were negatively associated with response/survival for the NIVO>IPI cohort.

immunology↗

bioRxiv2019

A Deep Learning Approach for Rapid Mutational Screening in Melanoma

Image-based analysis as a rapid method for mutation detection can be advantageous in research or clinical settings when tumor tissue is limited or unavailable for direct testing. Here, we applied a deep convolutional neural network (CNN) to whole slide images of melanomas from 256 patients and developed a fully automated model that first selects for tumor-rich areas (Area Under the Curve AUC=0.96) then predicts for the presence of mutated BRAF in our test set (AUC=0.72) Model performance was cross-validated on melanoma images from The Cancer Genome Atlas (AUC=0.75). We confirm that the mutated BRAF genotype is linked to phenotypic alterations at the level of the nucleus through saliency mapping and pathomics analysis, which reveal that cells with mutated BRAF exhibit larger and rounder nuclei. Not only do these findings provide additional insights on how BRAF mutations affects tumor structural characteristics, deep learning-based analysis of histopathology images have the potential to be integrated into higher order models for understanding tumor biology, developing biomarkers, and predicting clinical outcomes.

pathology↗