Biology subjects

Willie, E.

Publications and source records attributed to Willie, E..

6 recordsLinked to original sources

bioRxiv2024

Spatial mapping reveals unique cellular interactions and enhanced tertiary lymphoid structures in responders to anti-PD-1 therapy in mucosal head and neck cancers.

Survival in recurrent/metastatic head and neck mucosal squamous cell carcinoma (HNmSCC) remains poor. Anti-programmed death (PD)-1 therapies have demonstrated improved survival with lower toxicity when compared to standard chemotherapy. However, response to anti-PD-1 therapy remains modest, at 13-17%. We evaluated the tumor microenvironment (TME) using Imaging Mass Cytometry (IMC) on 27 tumor specimens from 24 advanced HNmSCC patients prior to receiving anti-PD-1 based treatment. We show significantly increased central memory T cells and B cells in responders (n=8) when compared to non-responders (n=16). Spatial mapping identified interactions between phenotypically distinct malignant squamous cells with CD8+ T cells, CD4+ Tregs and endothelial cells in responders, and avoidance of these cells in non-responders. Importantly, regional analysis shows responders have more abundant tertiary lymphoid structures (TLS), with TLS proportion >20% also associated with longer progression free survival. Together these findings define the immune landscape associated with response to anti-PD-1 treatment in HNmSCCs.

cancer biology↗

bioRxiv2023

Exploring archaeogenetic studies of dental calculus to shed light on past human migrations in Oceania

The Pacific islands have experienced multiple waves of human migrations, providing a case study for exploring the potential of using the microbiome to study human migration. We performed a metagenomic study of archaeological dental calculus from 103 ancient individuals, originating from 12 Pacific islands and spanning a time range of [~]3000 years. Oral microbiome DNA preservation in calculus is far higher than that of human DNA in archaeological bone from the Pacific, and comparable to that seen in calculus from temperate regions. Variation in the microbial community composition was minimally driven by time period and geography within the Pacific, while comparison with samples from Europe, Africa, and Asia reveal the microbial communities of Pacific calculus samples to be distinctive. Phylogenies of individual bacterial species in Pacific calculus reflect geography. Archaeological dental calculus shows potential to yield information about past human migrations, complementing studies of the human genome.

microbiology↗

bioRxiv2023

Ensemble of similarity metrics with a multiview self-organizing map improves cell clustering in highly multiplexed imaging cytometry data.

Highly multiplexed in situ imaging cytometry assays have enabled researchers to scru-tinize cellular systems at an unprecedented level. With the capability of these assays to simultaneously profile the spatial distribution and molecular features of many cells, unsuper-vised machine learning, and in particular clustering algorithms, have become indispensable for identifying cell types and subsets based on these molecular features. However, the most widely used clustering approaches applied to these novel technologies were developed for cell suspension technologies and may not be optimal for in situ imaging assays. In this work, we systematically evaluated the performance of various similarity metrics used to quan-tify the similarity between cells when clustering. Our results demonstrate that performance in cell clustering varies significantly when different similarity metrics were used. Lastly, we propose FuseSOM, an ensemble clustering algorithm employing hierarchical multi-view learning of similarity metrics and self-organizing maps (SOM). Using a stratified subsam-pling analysis framework, FuseSOM exhibits superior clustering performance compared to the current best-practice clustering approaches for in situ imaging cytometry data analysis.

bioinformatics↗

bioRxiv2022

Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2

The recent emergence of multi-sample multi-condition single-cell multi-cohort studies allow researchers to investigate different cell states. The effective integration of multiple large-cohort studies promises biological insights into cells under different conditions that individual studies cannot provide. Here, we present scMerge2, a scalable algorithm that allows data integration of atlas-scale multi-sample multi-condition single-cell studies. We have generalised scMerge2 to enable the merging of millions of cells from single-cell studies generated by various single-cell technologies. Using a large COVID-19 data collection with over five million cells from 1000+ individuals, we demonstrate that scMerge2 enables multi-sample multi-condition scRNA-seq data integration from multiple cohorts and reveals signatures derived from cell-type expression that are more accurate in discriminating disease progression. Further, we demonstrate that scMerge2 can remove dataset variability in CyTOF, imaging mass cytometry and CITE-seq experiments, demonstrating its applicability to a broad spectrum of single-cell profiling technologies.

bioinformatics↗

bioRxiv2022

Loss of FBXO11 function establishes a stem cell program in acute myeloid leukemia through dysregulation of the mitochondrial protease LONP1

Acute myeloid leukemia (AML) is an aggressive cancer with very poor outcomes. To identify additional drivers of leukemogenesis, we analyzed sequence data from 1,727 unique individual AML patients, which revealed mutations in ubiquitin ligase family genes in 11.2% of adult AML samples with mutual exclusivity. The Skp1/Cul1/Fbox (SCF) E3 ubiquitin ligase complex gene FBXO11 was the most significantly downregulated gene of the SCF complex in AML. FBXO11 catalyzes K63-linked ubiquitination of a novel target, LONP1, which promotes entry into mitochondria, thereby enhancing mitochondrial respiration. Reduced mitochondrial respiration secondary to FBXO11 depletion imparts myeloid-biased stem cell properties in primary CD34+ hematopoietic stem progenitor cells (HSPC). In a human xenograft model, depletion of FBXO11 cooperated with AML1-ETO and mutant KRASG12D to generate serially transplantable AML enriched for primitive cells. Our findings suggest that reduced FBXO11 primes HSPC for myeloid-biased self-renewal through attenuation of LONP1-mediated regulation of mitochondrial respiration.

cancer biology↗

bioRxiv2020

Geographic heterogeneity impacts drug resistance predictions in Mycobacterium tuberculosis

The efficacy of antibiotic drug treatments in tuberculosis (TB) is significantly threatened by the development of drug resistance. There is a need for a robust diagnostic system that can accurately predict drug resistance in patients. In recent years, researchers have been taking advantage of whole-genome sequencing (WGS) data to infer antibiotic resistance. In this work we investigate the power of machine learning tools in inferring drug resistance from WGS data on three distinct datasets differing in their geographical diversity. We analyzed data from the Relational Sequencing TB Data Platform, which comprises global isolates from 32 different countries, the PATRIC database, containing isolates contributed by researchers around the world, and isolates collected by the British Columbia Centre for Disease Control in Canada. We predicted drug resistance to the first-line drugs: isoniazid, rifampicin, ethambutol, pyrazinamide, and streptomycin. We focused on the genes which previous evidence suggests are involved in drug resistance in TB. We called single-nucleotide polymorphisms using the Snippy pipeline, then applied different machine learning models. Following best practices, we chose the best parameters for each model via cross-validation on the training set and evaluated the performance via the sensitivity-specificity tradeoffs on the testing set. To the best of our knowledge, our study is the first to predict antibiotic resistance in TB across multiple datasets. We obtained a performance comparable to that seen in previous studies, but observed that performance may be negatively affected when training on one dataset and testing on another, suggesting the importance of geographical heterogeneity in drug resistance predictions. In addition, we investigated the importance of each gene within each model, and recapitulated some previously known biology of drug resistance. This study paves the way for further investigations, with the ultimate goal of creating an accurate, interpretable and globally generalizable model for predicting drug resistance in TB. Author summaryDrug resistance in pathogenic bacteria such as Mycobacterium tuberculosis can be predicted by an application of machine learning models to next-generation sequencing data. The received wisdom is that following standard protocols for training commonly used machine learning models should produce accurate drug resistance predictions. In this paper, we propose an important caveat to this idea. Specifically, we show that considering geographical diversity is critical for making accurate predictions, and that different geographic regions may have disparate drug resistance mechanisms that are predominant. By comparing the results within and across a regional dataset and two international datasets, we show that model performance may vary dramatically between settings. In addition, we propose a new method for extracting the most important variants responsible for predicting resistance to each first-line drug, and show that it is to recapitulate a large amount of what is known about the biology of drug resistance in Mycobacterium tuberculosis.

bioinformatics↗