Biology subjects

Coleman, I.

Publications and source records attributed to Coleman, I..

9 recordsLinked to original sources

bioRxiv2026

End-to-end evaluation of pipelines for metagenome-assembled genomes reveals hidden performance gaps

The generation of Metagenome Assembled Genomes (MAGs) has become a standard and basic step in the analysis of metagenomic data. This multi-step process, which includes assembly, binning, refinement, and quality control, has many alternative approaches, algorithms, and parameters. Determining the ideal approach for a given ecosystem and study, or highlighting algorithmic gaps in need of additional research and development, requires rigorous benchmarking. We present MAG-E (MAG pipeline Evaluator), a generalizable and expandable framework for end-to-end evaluation of entire MAG pipelines: from assembly, through binning, to quality control and filtering. MAG-E relies on simulations that are built to match an ecosystem of interest and provide a ground truth for accurate evaluation. To demonstrate the capabilities of MAG-E, we benchmark two assemblers, six binning algorithms, three binning modes, and three quality control and refinement methods in the context of the human gut microbiome. Our findings offer multiple insights into optimal MAG generation in this context. We find that metaSPAdes consistently outperforms MEGAHIT in terms of recall (completeness), and that COMEBin overall outperforms alternative binning algorithms, but has lower precision than SemiBin2. While multi-sample binning results in higher precision, as previously shown, single-sample binning has higher recall and leads to better overall performance with modern binners. Binning refinement, which combines bins from multiple different algorithms, leads to reduced performance. We further show that CheckM2 systematically overestimates completeness and underestimates contamination, and that this is partially ameliorated when using GUNC. Finally, we analyze performance at the contig level, and demonstrate that binning algorithms systematically underperform for prophages and fail to bin contigs that are shared between genomes. Overall, MAG-E offers deep insights into successes and gaps in this important analytic process.

bioinformatics↗

bioRxiv2025

Comparative metagenomics using pan-metagenomics graphs

Identifying microbial genomic factors underlying human phenotypes is a key goal of microbiome research. Sequence graphs are a highly effective tool for genome comparisons because they enable high-resolution de novo analyses that capture and contextualize complex genomic variation. However, applying sequence graphs to complex microbial communities remains challenging due to the scale and complexity of metagenomic data. Existing multi-sample sequence graphs used in these settings are highly complex, computationally expensive, less accurate than single-sample alternatives, and often involve arbitrary coarse-graining. Here, we present copangraph, a multi-sample sequence-graph-based analysis framework for comprehensive comparisons of genomic variation across metagenomes. Copangraph uses a novel homology-based graph, which provides both non-arbitrary, evolutionary-motivated grouping of sequences into the same node as well as flexibility in the scale of variation represented by the graph. Its construction relies on hybrid coassembly, a new coassembly approach in which single-sample graphs are first constructed separately and are then merged to create a multi-sample graph. We also present an algorithm that uses paired-end reads to improve detection of contiguous genomic regions, increasing accuracy. Our results demonstrate that copangraph captures sequence and variant information more accurately than alternative methods, provides graphs that are more suitable for comparative analysis than de Bruijn graphs, and is computationally tractable. We show that copangraph reflects meaningful metagenomic variation across diverse scenarios. Importantly, it enables significantly better performance than other metagenomic representations when predicting the gut colonization trajectories of Vancomycin-resistant Enterococcus. Our results underscore the value of our multi-sample, graph-based framework for comparative metagenomic analyses.

bioinformatics↗

bioRxiv2025

Genotoxic antibody-drug conjugates combined with Bcl-xL inhibitors enhance therapeutic efficacy in metastatic castration-resistant prostate cancer

Metastatic castration-resistant prostate cancer (mCRPC) is an aggressive subtype of prostate cancer (PC) without curative treatments. Antibody-drug conjugates (ADCs) emerged as promising cancer therapeutics that selectively deliver cytotoxic agents (payloads) to the tumors. Although ADCs have been successfully applied in the treatment of hematological and solid tumors, ADC monotherapy has not demonstrated durable responses in mCRPC and the mechanisms of PC resistance to ADCs have not been thoroughly investigated. Our study aimed to improve ADC efficacy using a new integrated approach for custom ADC design and multiplexing. To nominate rational combinations of ADC targets and ADC payloads, we (1) examined protein co-expression of three clinically relevant surface antigens-- B7 homolog 3 (B7-H3), prostate specific membrane antigen (PSMA), and six-transmembrane epithelial antigen of prostate-1 (STEAP1)--in a series of human mCRPC samples and (2) screened established ADC payloads and their combinations in mCRPC cell lines with different phenotypes. We identified synergistic interactions between DNA-damaging payloads and Bcl-xL inhibitor A-1331852 as well as their coordinated induction of the intrinsic apoptosis pathway. The functional relevance of isolated p53 loss and impaired PC responses to three genotoxic ADCs (B7-H3-seco-DUBA, PSMA-SG3249, and STEAP1-DXd) and their combinations with A-1331852 was established using genetic knockout models. Lastly, we found enhanced in vivo antitumor activity in mCRPC by combining the clinically relevant agents B7-H3-seco-DUBA (vobramitamab duocarmazine) and A-1331852. Collectively, our findings provide rationale for the development of ADC therapies combining genotoxic payloads with Bcl-xL inhibitors for mCRPC. SignificanceB7-H3, PSMA, and STEAP1 targeted ADC therapies combining genotoxic payloads with Bcl-xL inhibitors induce p53-dependant apoptotic cell death in mCRPC, providing a clinically viable strategy for the treatment of advanced prostate cancer.

cancer biology↗

bioRxiv2024

Metagenomic coverage bias at transcription start sites is correlated with gene expression

Metagenomic sequencing is presumed to provide unbiased sampling of all the genetic material in a sample. Downstream analysis methods, such as binning, gene copy number analysis, structural variations, or single nucleotide polymorphism analysis, commonly assume an even distribution across the genome after accounting for known artefacts such as GC content. We discovered coverage bias across gut microbiome species, manifesting as a difference in coverage before and after bacterial transcription start sites. Using matched metatranscriptomic and metagenomic sequencing data, we demonstrate that this bias correlates with gene expression. Potential artefacts such as the sequencing technology, reference genome used for alignment, and mappability bias were investigated across multiple datasets and shown to not be factors for association. While GC bias was found correlated with coverage bias, the association of coverage bias with gene expression remains significant after adjusting for GC bias. Paired-end read mapping demonstrated an enrichment in 5 read ends immediately downstream of the TSS which was partly a byproduct of unmapped reads upstream of the TSS. Our observations suggest the existence of strain-level variation where sequence variation in the promoter site region is preventing proper read alignment to the reference genome. The correlation of this phenomenon with gene expression may also reflect evolutionary footprints for fine-tuning the regulation of gene expression. Understanding the source of this sequence variation and the biological implications of this artefact will be useful not only to better characterise microbial functions but also to improve interpretations of strain level dynamics. ImportanceSequencing coverage calculated from metagenomic sequencing data is extensively used in the microbiome field, providing valuable information about microbial abundances, gene (functional) abundances, growth rates, and genomic variations. Understanding factors that impact the distribution of coverage along genomes is therefore important for multiple applications. In this study, we report on uneven read coverage across the transcription start sites of bacterial genomes that is correlated with gene expression levels. We determine that this bias is independent of multiple factors including GC bias, and arises due to higher strain divergence from reference genomes upstream of the transcript start site. We propose that evolutionary finetuning of gene expression in competitive microbial ecosystems can drive genetic mutations at the promoter site. Our findings suggest the potential to glean gene regulatory information from metagenomic data, and better understand how ecological factors shape genomes in the microbiome and their sequencing coverage.

bioinformatics↗

bioRxiv2022

Nucleosome patterns in circulating tumor DNA reveal transcriptional regulation of advanced prostate cancer phenotypes

Advanced prostate cancers comprise distinct phenotypes, but tumor classification remains clinically challenging. Here, we harnessed circulating tumor DNA (ctDNA) to study tumor phenotypes by ascertaining nucleosome positioning patterns associated with transcription regulation. We sequenced plasma ctDNA whole genomes from patient-derived xenografts representing a spectrum of androgen receptor active (ARPC) and neuroendocrine (NEPC) prostate cancers. Nucleosome patterns associated with transcriptional activity were reflected in ctDNA at regions of genes, promoters, histone modifications, transcription factor binding, and accessible chromatin. We identified the activity of key phenotype-defining transcriptional regulators from ctDNA, including AR, ASCL1, HOXB13, HNF4G, and NR3C1. Using these features, we designed a prediction model which distinguished NEPC from ARPC in patient plasma samples across three clinical cohorts with 97-100% sensitivity and 85-100% specificity. While phenotype classification is typically assessed by immunohistochemistry or transcriptome profiling, we demonstrate that ctDNA provides comparable results with numerous diagnostic advantages for precision oncology. STATEMENT OF SIGNIFICANCEThis study provides key insights into the dynamics of nucleosome positioning and gene regulation associated with cancer phenotypes that can be ascertained from ctDNA. The new methods established for phenotype classification extend the utility of ctDNA beyond assessments of DNA alterations with important implications for molecular diagnostics and precision oncology.

cancer biology↗

bioRxiv2022

A genotoxic antibody drug conjugate targeting CD276/B7H3 demonstrates efficacy across multiple biomarker defined classes of treatment refractory metastatic prostate cancer

Antibody-drug conjugates (ADCs) are promising targeted cancer therapy; however, patient selection based solely on target antigen expression without consideration for cytotoxic payload vulnerabilities has plateaued clinical benefits. Biomarkers to capture patients who might benefit from specific ADCs have not been systematically determined for any cancer. We present a comprehensive therapeutic and biomarker analysis of a B7H3-ADC with pyrrolobenzodiazepine(PBD) payload in 26 treatment-resistant, metastatic prostate cancer(mPC) models. B7H3 is a tumor-specific surface protein widely expressed in mPC, and PBD is a DNA cross-linking agent. B7H3 expression was necessary but not sufficient for B7H3-PBD-ADC responsiveness. RB1 deficiency and/or replication stress, characteristics of poor prognosis, conferred sensitivity and were associated with complete tumor regression in both neuroendocrine (NEPC) and androgen receptor positive(ARPC) prostate cancer models, even with low B7H3 levels. Non-ARPC models, which are currently lacking efficacious treatment, demonstrated the highest replication stress and were most sensitive to treatment. In RB1 wild-type ARPC tumors, SLFN11 expression or select DNA repair mutations in SLFN11 non-expressors governed response. Significantly, wild-type TP53 predicted non-responsiveness (7/8 models). Overall, biomarker-focused selection of models led to high efficacy of in vivo treatment. These data enable a paradigm shift to biomarker-driven trial designs for maximizing clinical benefit of ADC therapies.

cancer biology↗

bioRxiv2020

Subtype Heterogeneity and Epigenetic Convergence in Neuroendocrine Prostate Cancer

Neuroendocrine carcinomas (NEC) are tumors expressing markers of neuronal differentiation that can arise at different anatomic sites but have strong histological and clinical similarities. Here we report the chromatin landscapes of a range of human NECs and show convergence to the activation of a common epigenetic program. With a particular focus on treatment emergent neuroendocrine prostate cancer (NEPC), we analyzed cell lines, patient-derived xenograft (PDX) models and human clinical samples to show the existence of two distinct NEPC subtypes based on the expression of the neuronal transcription factors ASCL1 and NEUROD1. While in cell lines and PDX models these subtypes are mutually exclusive, single cell analysis of human clinical samples exhibit a more complex tumor structure with subtypes coexisting as separate sub-populations within the same tumor. These tumor sub-populations differ genetically and epigenetically contributing to intra- and inter-tumoral heterogeneity in human metastases. Overall our results provide a deeper understanding of the shared clinicopathological characteristics shown by NECs. Furthermore, the intratumoral heterogeneity of human NEPCs suggests the requirement of simultaneous targeting of coexisting tumor populations as a therapeutic strategy.

cancer biology↗

bioRxiv2019

Targeting RET Kinase in Neuroendocrine Prostate Cancer

Increased treatment of metastatic castration resistant prostate cancer (mCRPC) with second-generation anti-androgen therapies (ADT) has coincided with a greater incidence of lethal, aggressive variant prostate cancer (AVPC) tumors that have lost androgen receptor (AR) signaling. AVPC tumors may also express neuroendocrine markers, termed neuroendocrine prostate cancer (NEPC). Recent evidence suggests kinase signaling may be an important driver of NEPC. To identify targetable kinases in NEPC, we performed global phosphoproteomics comparing AR-negative to AR-positive prostate cancer cell lines and identified multiple altered signaling pathways, including enrichment of RET kinase activity in the AR-negative cell lines. Clinical NEPC and NEPC patient derived xenografts displayed upregulated RET transcript and RET pathway activity. Pharmacologically inhibiting RET kinase in NEPC models dramatically reduced tumor growth and cell viability in mouse and human NEPC models. Our results suggest that targeting RET in NEPC tumors with high RET expression and may be a novel treatment option.\n\nStatement of SignificanceThere are limited treatment options for patients with metastatic aggressive variant prostate cancer and none are curative. Here we identified aberrantly activated RET kinase signaling in multiple models of NEPC. Inhibiting RET restricted tumor growth, providing a novel approach for treating NEPC.

cancer biology↗

bioRxiv2018

EZH2 co-opts gain-of-function p53 mutants to promote cancer growth and metastasis

With the unfolding of more and more cancer-driven gain-of-function (GOF) mutants of p53, it is important to define a common mechanism to systematically target different mutants rather than develop strategies tailored to inhibit each mutant individually. Here, using RNA immunoprecipitation sequencing (RIP-seq) we identified EZH2 as a p53 mRNA-binding protein. EZH2 bound to the internal ribosome entry site (IRES) in the 5 untranslated region (5UTR) of p53 mRNA and enhanced p53 protein translation in a methyltransferase-independent manner. EZH2 augmented p53 GOF mutant-mediated cancer growth and metastasis by increasing p53 GOF mutant protein level. EZH2 overexpression associated with the worse outcome only in patients with p53-mutated cancer. Depletion of EZH2 by antisense oligonucleotides inhibited p53 GOF mutant-mediated cancer growth. Our findings reveal a non-methyltransferase function of EZH2 that controls protein translation of p53 GOF mutants, inhibition of which causes synthetic lethality in cancer cells expressing p53 GOF mutants.

cancer biology↗