Biology subjects

West, J.

Publications and source records attributed to West, J..

At least 55 records · Page 3Linked to original sources

bioRxiv2021

Validated machine learning algorithm with sub-clonal sensitivity reveals widespread pan-cancer human leukocyte antigen loss of heterozygosity

Human leukocyte antigen loss of heterozygosity (HLA LOH) allows cancer cells to escape immune recognition by deleting HLA alleles, causing the suppressed presentation of tumor neoantigens that would otherwise bind to them. Despite its importance in immunotherapy response, few methods exist to detect HLA LOH, and their accuracy is not well understood. Here, we develop DASH (Deletion of Allele-Specific HLAs), a novel machine learning-based algorithm to detect HLA LOH from paired tumor-normal sequencing data. Through validation with cell line mixtures and patient-specific digital PCR, we demonstrate increased sensitivity compared to previously published tools and pave the way for clinical utility. Using DASH on 611 patients across 15 tumor types, we found that 18% of patients had HLA LOH. Moreover, we show inflated HLA LOH rates compared to genome-wide LOH and correlations between CD274 (PD-L1) expression and MSI status, suggesting the HLA LOH is a key immune resistance strategy.

bioinformatics↗

bioRxiv2021

Ultraspecific somatic SNV and indel detection in single neurons using primary template-directed amplification

Primary template-directed amplification (PTA) is an improved amplification technique for single-cell DNA sequencing. We generated whole-genome analysis of 76 single neurons and developed SCAN2, a computational method to accurately identify both clonal and non-clonal somatic (i.e., limited to a single neuron) single nucleotide variants (SNVs) and small insertions and deletions (indels) using PTA data. Our analysis confirms an increase in non-clonal somatic mutation in single neurons with age, but revises estimates for the rate of this accumulation to be 15 SNVs per year. We also identify artifacts in other amplification methods. Most importantly, we show that somatic indels also increase by at least 2 indels per year per neuron and that indels may have a larger impact on gene function than somatic SNVs in human neurons.

genomics↗

bioRxiv2021

Precision neoantigen discovery using large-scale immunopeptidomes and composite modeling of MHC peptide presentation

Major histocompatibility complex (MHC)-bound peptides that originate from tumor-specific genetic alterations, known as neoantigens, are an important class of anti-cancer therapeutic targets. Accurately predicting peptide presentation by MHC complexes is a key aspect of discovering therapeutically relevant neoantigens. Technological improvements in mass-spectrometry-based immunopeptidomics and advanced modeling techniques have vastly improved MHC presentation prediction over the past two decades. However, improvement in the sensitivity and specificity of prediction algorithms is needed for clinical applications such as the development of personalized cancer vaccines, the discovery of biomarkers for response to checkpoint blockade and the quantification of autoimmune risk in gene therapies. Toward this end, we generated allele-specific immunopeptidomics data using 25 mono-allelic cell lines and created Systematic HLA Epitope Ranking Pan Algorithm (SHERPA), a pan-allelic MHC-peptide algorithm for predicting MHC-peptide binding and presentation. In contrast to previously published large-scale mono-allelic data, we used an HLA-null K562 parental cell line and a stable transfection of HLA alleles to better emulate native presentation. Our dataset includes five previously unprofiled alleles that expand MHC binding pocket diversity in the training data and extend allelic coverage in underprofiled populations. To improve generalizability, SHERPA systematically integrates 128 mono-allelic and 384 multi-allelic samples with publicly available immunoproteomics data and binding assay data. Using this dataset, we developed two features that empirically estimate the propensities of genes and specific regions within gene bodies to engender immunopeptides to represent antigen processing. Using a composite model constructed with gradient boosting decision trees, multiallelic deconvolution and 2.15 million peptides encompassing 167 alleles, we achieved a 1.44 fold improvement of positive predictive value compared to existing tools when evaluated on independent mono-allelic datasets and a 1.15 fold improvement when evaluating on tumor samples. With a high degree of accuracy, SHERPA has the potential to enable precision neoantigen discovery for future clinical applications.

bioinformatics↗

bioRxiv2021

Characterization of changes in the hemagglutinin that accompanied the emergence of H3N2/1968 pandemic influenza viruses

The hemagglutinin (HA) of A/H3N2 pandemic influenza viruses (IAVs) of 1968 differed from its inferred avian precursor by eight amino acid substitutions. To determine their phenotypic effects, we studied recombinant variants of A/Hong Kong/1/1968 virus containing either human-type or avian-type amino acids in the corresponding positions of HA. The precursor HA displayed receptor binding profile and high conformational stability typical for duck IAVs. Substitutions Q226L and G228S, in addition to their known effects on receptor specificity and replication, marginally decreased HA stability. Substitutions R62I, D63N, D81N and N193S reduced HA binding avidity. Substitutions R62I, D63N, D81N and A144G promoted virus replication in human airway epithelial cultures. Analysis of HA sequences revealed that substitutions D63N and D81N accompanied by the addition of N-glycans represent common markers of avian H3 HA adaptation to mammals. Our results advance understanding of genotypic and phenotypic changes in IAV HA required for avian-to-human adaptation and pandemic emergence.

microbiology↗

bioRxiv2021

Dual Dean entrainment with volume ratio modulation for efficient co-encapsulation: Extreme single-cell indexing

The future of single cell diversity screens involves ever-larger sample sizes, dictating the need for higher throughput methods with low analytical noise to accurately describe the nature of the cellular system. Current approaches are limited by the Poisson statistic, requiring dilute cell suspensions and associated losses in throughput. In this contribution, we apply Dean entrainment to both cell and bead inputs, defining different volume packets to effect efficient co-encapsulation. Volume ratio scaling was explored to identify optimal conditions. This enabled the co-encapsulation of single cells with reporter beads at rates of ~1 million cells/hour, while increasing assay signal-to-noise with cell multiplet rates of ~2.5% and capturing ~70% of cells. The method, called Pirouette-seq, extends our capacity to investigate biological systems. TOC AbstractPirouette-seq involves cell and reporter bead inertial ordering for efficient co-encapsulation, achieving a throughput of 1 million cells/hour, a 2.5% multiplet rate and a 70% cell capture efficiency.

cell biology↗

bioRxiv2021

TNF signalling fine-tunes Langerhans cell transcriptional programmes mediating adaptive immunity.

Langerhans cells (LCs) reside in the epidermis as a dense network of immune system sentinels, coordinating both immunogenic and tolerogenic immune responses. To determine molecular switches directing induction of LC immune activation, we performed mathematical modelling of gene regulatory networks identified by single cell RNA sequencing of LCs exposed to TNF, a key pro-inflammatory signal produced by the skin. Our approach delineated three programmes of LC phenotypic activation (immunogenic, tolerogenic or ambivalent), and confirmed that TNF enhanced LC immunogenic programming. Through regulon analysis followed by mutual information modelling, we identified IRF1 as the key transcription factor for the regulation of immunogenicity in LCs. Application of a mathematical toggle switch model, coupling IRF1 with tolerance-inducing transcription factors, determined the key set of transcription factors regulating the switch between tolerance and immunogenicity, and correctly predicted LC behaviour in LCs derived from different body sites. Our findings provide a mechanistic explanation of how combinatorial interactions between different transcription factors can coordinate specific transcriptional programmes in human LCs, interpreting the microenvironmental context of the local tissue microenvironments.

systems biology↗

bioRxiv2020

Spatial structure impacts adaptive therapy by shaping intra-tumoral competition

(1) Background: Adaptive therapy aims to tackle cancer drug resistance by leveraging intra-tumoral competition between drug-sensitive and resistant cells. Motivated by promising results in prostate cancer there is growing interest in extending this approach to other cancers. Here we present a theoretical study of intra-tumoral competition during adaptive therapy, to identify under which circumstances it will be superior to aggressive treatment; (2) Methods: We use a 2-D, on-lattice, agent-based tumour model to examine the impact of different microenvironmental factors on the comparison between continuous drug administration and adaptive therapy. (3) Results: We show that the degree of crowding, the initial resistance fraction, the presence of resistance costs, and the rate of tumour cell turnover are key determinants of the benefit of adaptive therapy, and we study in detail how these factors alter competition between cells. We find that intra-specific competition between resistant cells plays an unexpectedly important role in the ability to control resistance. To conclude we show how differences in resistance cost and turnover change the tumours spatial organisation and may explain differences in cycling speed observed in a cohort of 67 prostate cancer patients undergoing intermittent androgen deprivation therapy; (4) Conclusion: Our work provides insights into how adaptive therapy leverages inter- and intra-specific competition to control resistance, and shows that the tumours spatial architecture will likely be an important factor in determining the quantitative benefit of adaptive therapy in patients.

cancer biology↗

bioRxiv2020

Antifragile therapy

Antifragility is a recently coined word used to describe the opposite of fragility. Systems or organisms can be described as antifragile if they derive a benefit from systemic variability, volatility, randomness, or disorder. Herein, we introduce a mathematical framework to quantify the fragility or antifragility of cancer cell lines in response to treatment variability. This framework enables straightforward prediction of the optimal dose treatment schedule for a range of treatment schedules with identical cumulative dose. We apply this framework to non-small-cell lung cancer cell lines with evolved resistance to ten anti-cancer drugs. We show the utility of this antifragile framework when applied to 1) treatment resistance, and 2) collateral sensitivity of sequential monotherapies.

cancer biology↗

bioRxiv2020

IsoMaTrix: a framework to visualize the isoclines of matrix games and quantify uncertainty in structured populations

SummaryEvolutionary game theory describes frequency-dependent selection for fixed, heritable strategies in a population of competing individuals using a payoff matrix, typically described using well-mixed assumptions (replicator dynamics). IsoMaTrix is an open-source package which computes the isoclines (lines of zero growth) of matrix games, and facilitates direct comparison of well-mixed dynamics to structured populations in two or three dimensions. IsoMaTrix is coupled with a Hybrid Automata Library module to simulate structured matrix games on-lattice. IsoMaTrix can also compute fixed points, phase flow, trajectories, velocities (and subvelocities), delineated "region plots" of positive/negative strategy velocity, and uncertainty quantification for stochastic effects in structured matrix games. We describe a result obtained via IsoMaTrixs spatial games functionality, which shows that the timing of competitive release in a cancer model (under continuous treatment) critically depends on the initial spatial configuration of the tumor. Availability and implementationThe code is available at: https://github.com/mathonco/isomatrix.

cancer biology↗

bioRxiv2020

Single cell RNA sequence analysis of human bone marrow samples reveals new targets for isolation of skeletal stem cells using DNA-coated gold nanoparticles

There is a wealth of data indicating human bone marrow derived stromal cells (HBMSCs) contain the skeletal stem cell (SSC) with the potential to differentiate along the stromal osteogenic, adipogenic and chondrogenic lineages. However, despite these advances, current methods to isolate skeletal stem cells (SSCs) from human tissues have proved challenging as no single specific marker has been identified limiting understanding of SSC fate, immunophenotype and the widespread clinical application of these cells. While a number of cell surface markers can enrich for SSCs, none of the proposed markers, alone, provide a platform to isolate single cells with the ability to form bone, cartilage, and adipose tissue in humans. The current study details the application of oligonucleotide-coated nanoparticles, spherical nucleic acids (SNAs), to rapidly isolate human cells using mRNAs signatures detected in SSCs in real time, to identify stem and progenitor skeletal populations using single cell RNA sequencing. Based on scRNA-seq of samples from 11 patients, this method was able to identify novel targets for SSC enrichment, which were assessed in a total of 80 patients. This methodology was able to isolate potential SSCs found at a frequency of <1 in 1,000,000 in human bone marrow, with a capacity for tri-lineage differentiation in vitro. The current approach provides new targets and a platform to advance SSC isolation, enrichment with significant therapeutic impact therein.

cell biology↗

bioRxiv2020

Spatially discrete signalling niches regulate fibroblast heterogeneity in human lung cancer

Fibroblasts are functionally heterogeneous cells, capable of promoting and suppressing tumour progression. Across cancer types, the extent and cause of this phenotypic diversity remains unknown. We used single-cell RNA sequencing and multiplexed immunohistochemistry to examine fibroblast heterogeneity in human lung and non-small cell lung cancer (NSCLC) samples. This identified seven fibroblast subpopulations: including inflammatory fibroblasts and myofibroblasts (representing terminal differentiation states), quiescent fibroblasts, proto-myofibroblasts (x2) and proto-inflammatory fibroblasts (x2). Fibroblast subpopulations were variably distributed throughout tissues but accumulated at discrete niches associated with differentiation status. Bioinformatics analyses suggested TGF-{beta}1 and IL-1 as key regulators of myofibroblastic and inflammatory differentiation respectively. However, in vitro analyses showed that whilst TGF-{beta}1 stimulation in combination with increased tissue tension could induce myofibroblast marker expression, it failed to fully re-capitulate ex-vivo phenotypes. Similarly, IL-1{beta} treatment only induced upregulation of a subset of inflammatory fibroblast marker genes. In silico modelling of ligand-receptor signalling identified additional pathways and cell interactions likely to be involved in fibroblast activation, which can be examined using publicly available R shiny applications (at the following links: myofibroblast activation and inflammatory fibroblast activation). This highlighted a potential role for IL-11 and IL-6 (among other ligands) in myofibroblast and inflammatory fibroblast activation respectively. This analysis provides valuable insight into fibroblast subtypes and differentiation mechanisms in NSCLC.

cancer biology↗

bioRxiv2020

SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search

The COVID-19 pandemic has sparked unprecedented mobilization of scientists, generating a deluge of papers that makes it hard for researchers to keep track and explore new directions. Search engines are designed for targeted queries, not for discovery of connections across a corpus. In this paper, we present SciSight, a system for exploratory search of COVID-19 research integrating two key capabilities: first, exploring associations between biomedical facets automatically extracted from papers (e.g., genes, drugs, diseases, patient outcomes); second, combining textual and network information to search and visualize groups of researchers and their ties. SciSight1 has so far served over 15K users with over 42K page views and 13% returns.

bioinformatics↗

bioRxiv2020

The Harsh Microenvironment in Early Breast Cancer Select for a Warburg Phenotype

The harsh microenvironment of ductal carcinoma in situ (DCIS) exerts strong evolutionary selection pressures on cancer cells. We hypothesize that the poor metabolic conditions near the ductal center foment the emergence of a Warburg Effect (WE) phenotype, wherein cells rapidly ferment glucose to lactic acid, even in normoxia. To test this hypothesis, we subjected pre-malignant breast cancer cells to different microenvironmental selection pressures using combinations of hypoxia, acidosis, low glucose, and starvation for many months, and isolated single clones for metabolic and transcriptomic profiling. The two harshest conditions selected for constitutively expressed WE phenotypes. RNA-seq analysis of WE clones identified the transcription factors NF[kcy]B and KLF4 as potential inducers of the WE phenotype. NF[kcy]B was highly phosphorylated in the glycolytic clones. In stained DCIS samples, KLF4 expression was enriched in the area with the harshest microenvironmental conditions. We simulated in vivo DCIS phenotypic evolution using a mathematical model calibrated from the in vitro results. The WE phenotype emerged in the poor metabolic conditions near the necrotic core. We propose that harsh microenvironments within DCIS select for a Warburg phenotype through constitutive transcriptional reprogramming, thus conferring a survival advantage and facilitating further growth and invasion.

cancer biology↗

bioRxiv2020

Turnover modulates the need for a cost of resistance in adaptive therapy

"Control and conquer" - this is the philosophy behind adaptive therapy, which seeks to exploit intra-tumoural competition to avoid, or at least, delay the emergence of therapy resistance in cancer. Motivated by promising results from theoretical, experimental and, most recently, a clinical study in prostate cancer, there is an increasing interest in extending this approach to other cancers. As such, it is urgent to understand the characteristics of a cancer which determine whether it will respond well to adaptive therapy, or not. A plausible candidate for such a selection criterion is the fitness cost of resistance. In this paper, we study a simple competition model between sensitive & resistant cell populations to investigate whether the presence of a cost is a necessary condition for adaptive therapy to extend the time to progression beyond that of a standard-of-care continuous therapy. We find that for tumours close to their environmental carrying capacity such a cost of resistance is not required. However, for tumours growing far from carrying capacity, a cost may be required to see meaningful gains. Notably, we show that in such cases it is important to consider the cell turnover in the tumour and we discuss its role in modulating the impact of a cost of resistance. Overall, our work helps to clarify under which circumstances adaptive therapy may be beneficial, and suggests that turnover may play an unexpectedly important role in the decision making process.

cancer biology↗

bioRxiv2020

Single platelet variability governs population sensitivity and initiates intrinsic heterotypic behaviours

Droplet microfluidics combined with flow cytometry was used for high throughput single platelet function analysis. A large-scale sensitivity continuum was shown to be a general feature of human platelets from individual donors, with hypersensitive platelets coordinating significant sensitivity gains in bulk platelet populations and shown to direct aggregation in droplet-confined minimal platelet systems. Sensitivity gains scaled with agonist potency (convulxin>TRAP-14>ADP) and reduced the collagen and thrombin activation threshold required for platelet population polarization into pro-aggregatory and pro-coagulant states. The heterotypic platelet response results from an intrinsic behavioural program. The method and findings invite future discoveries into the nature of hypersensitive platelets and how community effects produce population level behaviours in health and disease.

cell biology↗

bioRxiv2019

Single cell transcriptomic analysis identifies Langerhans cells immunocompetency is critical for IDO1- dependent ability to induce tolerogenic T cells.

Human epidermal Langerhans cells (LCs) can coordinate both immunogenic and tolerogenic immune responses, creating an attractive opportunity for immunomodulation strategies. To investigate transcriptional determinants of human primary LC tolerance we applied single cells RNA-sequencing combined with transcriptional network modelling and functional analysis. Unsupervised clustering of single cell transcriptomes revealed that steady-state LCs exist in immature and immunocompetent states, and become fully immunocompetent on migration. Interestingly, LC migration, which has been shown to result in upregulation of the transcription factor IRF4, led in parallel to increased expression of a tolerogenic gene module including IDO1, LGALS1, LAMTOR1 and IL10RA, which translated to efficient induction of regulatory T cells in co-culture assays by immunocompetent LCs. Using protein expression analysis and perturbation with inhibitors, we confirmed the role of IDO1 as a mediator of LC tolerogenic responses induced during LC migration. Computational analysis of regulons and Partial Information Decomposition analyses identified IRF4 as a key driver for LC tolerogenic programmes. The predicted IRF4-regulated genes were confirmed by analysis of CRISPR-Cas9 edited LCs. These findings suggest that efficient priming of tolerogenic responses by LCs requires upregulation of a migration-coupled maturation program which is superimposed with a tolerance-inducing genomic module.

systems biology↗

bioRxiv2019

Integrative analysis and machine learning based characterization of single circulating tumor cells

We collated publicly available single-cell expression profiles of circulating tumor cells (CTCs) and showed that CTCs across cancers lie on a near-perfect continuum of epithelial to mesenchymal (EMT) transition. Integrative analysis of CTC transcriptomes also highlighted the inverse gene expression pattern between PD-L1 and MHC, which is implicated in cancer immunotherapy. We used the CTCs expression profiles in tandem with publicly available peripheral blood mononuclear cell (PBMC) transcriptomes to train a classifier that accurately recognizes CTCs of diverse phenotype. Further, we used this classifier to validate circulating breast tumor cells captured using a newly developed microfluidic systems for label-free enrichment of CTCs.

genomics↗

bioRxiv2019

Revisiting Australian Ectocarpus subulatus (Phaeophyceae) from the Hopkins River: distribution, abiotic environment, and associated microbiota

Ectocarpus is a genus of common marine brown algae. In 1995 a strain of Ectocarpus was isolated from Hopkins River Falls, Victoria, Australia, constituting one of few available freshwater or nearly freshwater brown algae, and the only one belonging to Ectocarpus. It has since been used as a model to study acclimation and adaptation to low salinities and the role of its microbiota in these processes. However, little is known about the distribution of this strain or whether it represents a stable population. Furthermore, its microbiota may have been impacted by the long period of cultivation.\n\nTwenty-two years after the original finding we searched for Ectocarpus in the Hopkins River and surrounding areas. We found individuals with ITS and cox1 sequences identical to the original isolate at three sites upstream of Hopkins River Falls, but none at the original isolation site. The osmolarity of the water at these sites ranged from 74-170 mOsmol, and it was rich in sulfate. The diversity of bacteria associated with the algae in situ was approximately one order of magnitude higher than in previous studies of the original laboratory culture, and 95 alga-associated bacterial strains were isolated from E. subulatus filaments on site. In particular, Planctomycetes were abundant in situ but rare in the laboratory-cultured strain.\n\nOur results confirm that E. subulatus has stably colonized the Hopkins River, and the newly isolated algal and bacterial strains offer new possibilities to study the adaptation of Ectocarpus to low salinity and its interactions with its microbiome.

plant biology↗