ORCID Profile
0000-0002-6398-9157
Current Organisation
University of Adelaide
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Publisher: MDPI AG
Date: 13-05-2022
Abstract: The development of therapies that target specific disease subtypes has dramatically improved outcomes for patients with breast cancer. However, survival gains have not been uniform across patients, even within a given molecular subtype. Large collections of publicly available drug screening data matched with transcriptomic measurements have facilitated the development of computational models that predict response to therapy. Here, we generated a series of predictive gene signatures to estimate the sensitivity of breast cancer s les to 90 drugs, comprising FDA-approved drugs or compounds in early development. To achieve this, we used a cell line-based drug screen with matched transcriptomic data to derive in silico models that we validated in large independent datasets obtained from cell lines and patient-derived xenograft (PDX) models. Robust computational signatures were obtained for 28 drugs and used to predict drug efficacy in a set of PDX models. We found that our signature for cisplatin can be used to identify tumors that are likely to respond to this drug, even in absence of the BRCA-1 mutation routinely used to select patients for platinum-based therapies. This clinically relevant observation was confirmed in multiple PDXs. Our study foreshadows an effective delivery approach for precision medicine.
Publisher: F1000 Research Ltd
Date: 03-06-2019
DOI: 10.12688/F1000RESEARCH.19236.1
Abstract: Advances in RNA sequencing (RNA-seq) technologies that measure the transcriptome of biological s les have revolutionised our ability to understand transcriptional regulatory programs that underpin diseases such as cancer. We recently published singscore - a single s le, rank-based gene set scoring method which quantifies how concordant the transcriptional profile of in idual s les are relative to specific gene sets of interest. Here we demonstrate the application of singscore to investigate transcriptional profiles associated with specific mutations or genetic lesions in acute myeloid leukemia. Using matched genomic and transcriptomic data available through the TCGA we show that scoring of appropriate signatures can distinguish s les with corresponding mutations, reflecting the ability of these mutations to drive aberrant transcriptional programs involved in leukemogenesis. We believe the singscore method is particularly useful for studying heterogeneity within a specific subsets of cancers, and as demonstrated, we show the ability of singscore to identify where alternative mutations appear to drive similar transcriptional programs.
Publisher: Cold Spring Harbor Laboratory
Date: 21-03-2022
DOI: 10.1101/2022.03.21.22269988
Abstract: Cancer cells invoke phenotypic plasticity programs to drive disease progression and evade chemotherapeutic insults, yet until now there have been no validated clinical therapies targeting this process. Here, we identify a phenotypic plasticity signature associated with poor survival in basal/triple-negative breast cancer, in which androgen signalling is prominent. We establish that anti-androgen therapies block cancer stem cell function and prevent chemotherapy-induced emergence of new cancer stem cells. In particular, the anti-androgen agent seviteronel synergizes with chemotherapy to improve chemotherapeutic inhibition of primary and metastatic tumour growth and prevent the emergence of chemotherapy-resistant disease. We validate cytoplasmic AR expression as a clinical phenotypic plasticity biomarker that predicts poor survival and poor response to chemotherapy, and positive response to seviteronel plus chemotherapy. This new targeted combination therapy validates modulating phenotypic plasticity as an effective strategy to prevent and treat chemotherapy-resistant cancers with transformative clinical potential. There are currently no curative therapies for patients with chemotherapy-resistant cancer. We demonstrate that modulating phenotypic plasticity prevents the emergence of chemotherapy-resistant disease in triple-negative breast cancer. This represents the first known validated clinical therapy leveraging phenotypic plasticity. Moreover, we identify a highly effective anti-androgen drug and a biomarker to select and treat patients best-suited to this new therapy. A clinical trial is underway ( NCT04947189 ). Blocking phenotypic plasticity is an effective targeted therapeutic strategy to treat cance
Publisher: F1000 Research Ltd
Date: 14-10-2019
DOI: 10.12688/F1000RESEARCH.19236.3
Abstract: Advances in RNA sequencing (RNA-seq) technologies that measure the transcriptome of biological s les have revolutionised our ability to understand transcriptional regulatory programs that underpin diseases such as cancer. We recently published singscore - a single s le, rank-based gene set scoring method which quantifies how concordant the transcriptional profile of in idual s les are relative to specific gene sets of interest. Here we demonstrate the application of singscore to investigate transcriptional profiles associated with specific mutations or genetic lesions in acute myeloid leukemia. Using matched genomic and transcriptomic data available through the TCGA we show that scoring of appropriate signatures can distinguish s les with corresponding mutations, reflecting the ability of these mutations to drive aberrant transcriptional programs involved in leukemogenesis. We believe the singscore method is particularly useful for studying heterogeneity within a specific subsets of cancers, and as demonstrated, we show the ability of singscore to identify where alternative mutations appear to drive similar transcriptional programs.
Publisher: Cold Spring Harbor Laboratory
Date: 05-05-2020
DOI: 10.1101/2020.05.04.077859
Abstract: Transcriptomic signatures are useful in defining the molecular phenotypes of cells, tissues, and patient s les. Their most successful and widespread clinical application is the stratification of breast cancer patients into molecular (PAM50) subtypes. In most cases, gene expression signatures are developed using transcriptome-wide measurements, thus methods that match signatures to s les typically require a similar degree of measurements. The cost and relatively large amounts of fresh starting material required for whole-transcriptome sequencing has limited clinical applications, and accordingly thousands of existing gene signatures are unexplored in a clinical context. Genes in a molecular signature can provide information about molecular phenotypes and their underlying transcriptional programs from tissue s les, however determining the transcriptional state of these genes typically requires the measurement of all genes across multiple s les to allow for comparison. An efficient assay and scoring method should quantify the relative abundance of signature genes with a minimal number of additional measurements. We identified genes with stable expression across a range of abundances, and with a preserved relative ordering across large numbers (thousands) of s les, allowing signature scoring, and supporting general data normalisation for transcriptomic data. Based on singscore, we have developed a new method, stingscore , which quantifies and summarises relative expression levels of signature genes from in idual s les through the inclusion of these “stably-expressed genes”. We show that our proposed list of stable genes has better stability across cancer and normal tissue data than previously proposed stable or housekeeping genes. Additionally, we show that signature scores computed from whole-transcriptome data are comparable to those calculated using only values for signature genes and our panel of stable genes. This new approach to gene expression signature analysis may facilitate the development of panel-type tests for gene expression signatures, thus supporting clinical translation of the powerful insights gained from cancer transcriptomic studies.
Publisher: F1000 Research Ltd
Date: 15-08-2019
DOI: 10.12688/F1000RESEARCH.19236.2
Abstract: Advances in RNA sequencing (RNA-seq) technologies that measure the transcriptome of biological s les have revolutionised our ability to understand transcriptional regulatory programs that underpin diseases such as cancer. We recently published singscore - a single s le, rank-based gene set scoring method which quantifies how concordant the transcriptional profile of in idual s les are relative to specific gene sets of interest. Here we demonstrate the application of singscore to investigate transcriptional profiles associated with specific mutations or genetic lesions in acute myeloid leukemia. Using matched genomic and transcriptomic data available through the TCGA we show that scoring of appropriate signatures can distinguish s les with corresponding mutations, reflecting the ability of these mutations to drive aberrant transcriptional programs involved in leukemogenesis. We believe the singscore method is particularly useful for studying heterogeneity within a specific subsets of cancers, and as demonstrated, we show the ability of singscore to identify where alternative mutations appear to drive similar transcriptional programs.
Publisher: Springer Science and Business Media LLC
Date: 21-06-2021
DOI: 10.1186/S13073-021-00920-Z
Abstract: Medulloblastoma (MB) is the most common malignant paediatric brain tumour and a leading cause of cancer-related mortality and morbidity. Existing treatment protocols are aggressive in nature resulting in significant neurological, intellectual and physical disabilities for the children undergoing treatment. Thus, there is an urgent need for improved, targeted therapies that minimize these harmful side effects. We identified candidate drugs for MB using a network-based systems-pharmacogenomics approach: based on results from a functional genomics screen, we identified a network of interactions implicated in human MB growth regulation. We then integrated drugs and their known mechanisms of action, along with gene expression data from a large collection of medulloblastoma patients to identify drugs with potential to treat MB. Our analyses identified drugs targeting CDK4, CDK6 and AURKA as strong candidates for MB all of these genes are well validated as drug targets in other tumour types. We also identified non-WNT MB as a novel indication for drugs targeting TUBB, CAD, SNRPA, SLC1A5, PTPRS, P4HB and CHEK2. Based upon these analyses, we subsequently demonstrated that one of these drugs, the new microtubule stabilizing agent, ixabepilone, blocked tumour growth in vivo in mice bearing patient-derived xenograft tumours of the Sonic Hedgehog and Group 3 subtype, providing the first demonstration of its efficacy in MB. Our findings confirm that this data-driven systems pharmacogenomics strategy is a powerful approach for the discovery and validation of novel therapeutic candidates relevant to MB treatment, and along with data validating ixabepilone in PDX models of the two most aggressive subtypes of medulloblastoma, we present the network analysis framework as a resource for the field.
Publisher: European Respiratory Society (ERS)
Date: 21-10-2022
DOI: 10.1183/13993003.01881-2021
Abstract: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which emerged in late 2019 has spread globally, causing a pandemic of respiratory illness designated coronavirus disease 2019 (COVID-19). A better definition of the pulmonary host response to SARS-CoV-2 infection is required to understand viral pathogenesis and to validate putative COVID-19 biomarkers that have been proposed in clinical studies. Here, we use targeted transcriptomics of formalin-fixed paraffin-embedded tissue using the NanoString GeoMX platform to generate an in-depth picture of the pulmonary transcriptional landscape of COVID-19, pandemic H1N1 influenza and uninfected control patients. Host transcriptomics showed a significant upregulation of genes associated with inflammation, type I interferon production, coagulation and angiogenesis in the lungs of COVID-19 patients compared to non-infected controls. SARS-CoV-2 was non-uniformly distributed in lungs (emphasising the advantages of spatial transcriptomics) with the areas of high viral load associated with an increased type I interferon response. Once the dominant cell type present in the s le, within patient correlations and patient–patient variation, had been controlled for, only a very limited number of genes were differentially expressed between the lungs of fatal influenza and COVID-19 patients. Strikingly, the interferon-associated gene IFI27 , previously identified as a useful blood biomarker to differentiate bacterial and viral lung infections, was significantly upregulated in the lungs of COVID-19 patients compared to patients with influenza. Collectively, these data demonstrate that spatial transcriptomics is a powerful tool to identify novel gene signatures within tissues, offering new insights into the pathogenesis of SARS-COV-2 to aid in patient triage and treatment.
Publisher: Springer Science and Business Media LLC
Date: 14-11-2019
DOI: 10.1186/S13059-019-1851-8
Abstract: Elucidation of regulatory networks, including identification of regulatory mechanisms specific to a given biological context, is a key aim in systems biology. This has motivated the move from co-expression to differential co-expression analysis and numerous methods have been developed subsequently to address this task however, evaluation of methods and interpretation of the resulting networks has been hindered by the lack of known context-specific regulatory interactions. In this study, we develop a simulator based on dynamical systems modelling capable of simulating differential co-expression patterns. With the simulator and an evaluation framework, we benchmark and characterise the performance of inference methods. Defining three different levels of “true” networks for each simulation, we show that accurate inference of causation is difficult for all methods, compared to inference of associations. We show that a z -score-based method has the best general performance. Further, analysis of simulation parameters reveals five network and simulation properties that explained the performance of methods. The evaluation framework and inference methods used in this study are available in the dcanr R/Bioconductor package. Our analysis of networks inferred from simulated data show that hub nodes are more likely to be differentially regulated targets than transcription factors. Based on this observation, we propose an interpretation of the inferred differential network that can reconstruct a putative causal network.
Publisher: Springer Science and Business Media LLC
Date: 05-2023
Publisher: Cold Spring Harbor Laboratory
Date: 06-11-2020
DOI: 10.1101/2020.11.04.20225557
Abstract: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that emerged in late 2019 has spread globally, causing a pandemic of respiratory illness designated coronavirus disease 2019 (COVID-19). Robust blood biomarkers that reflect tissue damage are urgently needed to better stratify and triage infected patients. Here, we use spatial transcriptomics to generate an in-depth picture of the pulmonary transcriptional landscape of COVID-19 (10 patients), pandemic H1N1 (pH1N1) influenza (5) and uninfected control patients (4). Host transcriptomics showed a significant upregulation of genes associated with inflammation, type I interferon production, coagulation and angiogenesis in the lungs of COVID-19 patients compared to non-infected controls. SARS-CoV-2 was non-uniformly distributed in lungs with few areas of high viral load and these were largely only associated with an increased type I interferon response. A very limited number of genes were differentially expressed between the lungs of influenza and COVID-19 patients. Specific interferon-associated genes (including IFI27 ) were identified as candidate novel biomarkers for COVID-19 differentiating this COVID-19 from influenza. Collectively, these data demonstrate that spatial transcriptomics is a powerful tool to identify novel gene signatures within tissues, offering new insights into the pathogenesis of SARS-COV-2 to aid in patient triage and treatment.
Publisher: Wiley
Date: 27-09-2023
DOI: 10.1111/IMM.13577
Abstract: The severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is known to present with pulmonary and extra‐pulmonary organ complications. In comparison with the 2009 pandemic (pH1N1), SARS‐CoV‐2 infection is likely to lead to more severe disease, with multi‐organ effects, including cardiovascular disease. SARS‐CoV‐2 has been associated with acute and long‐term cardiovascular disease, but the molecular changes that govern this remain unknown. In this study, we investigated the host transcriptome landscape of cardiac tissues collected at rapid autopsy from seven SARS‐CoV‐2, two pH1N1, and six control patients using targeted spatial transcriptomics approaches. Although SARS‐CoV‐2 was not detected in cardiac tissue, host transcriptomics showed upregulation of genes associated with DNA damage and repair, heat shock, and M1‐like macrophage infiltration in the cardiac tissues of COVID‐19 patients. The DNA damage present in the SARS‐CoV‐2 patient s les, were further confirmed by γ‐H2Ax immunohistochemistry. In comparison, pH1N1 showed upregulation of interferon‐stimulated genes, in particular interferon and complement pathways, when compared with COVID‐19 patients. These data demonstrate the emergence of distinct transcriptomic profiles in cardiac tissues of SARS‐CoV‐2 and pH1N1 influenza infection supporting the need for a greater understanding of the effects on extra‐pulmonary organs, including the cardiovascular system of COVID‐19 patients, to delineate the immunopathobiology of SARS‐CoV‐2 infection, and long term impact on health.
Publisher: Cold Spring Harbor Laboratory
Date: 12-09-2023
Publisher: Cold Spring Harbor Laboratory
Date: 16-02-2023
DOI: 10.1101/2023.02.15.528116
Abstract: Medulloblastoma (MB) is a malignant tumour of the cerebellum which can be classified into four major subgroups based on gene expression and genomic features. Single cell transcriptome studies have defined the cellular states underlying each MB subgroup, however the spatial organisation of these erse cell states and how this impacts response to therapy remains to be determined. Here, we used spatially resolved transcriptomics to define the cellular ersity within a sonic hedgehog (SHH) patient-derived model of MB and identify how cells specific to a transcriptional state or spatial location are pivotal in responses to treatment with the CDK4/6 inhibitor, Palbociclib. We integrated spatial gene expression with histological annotation and single cell gene expression data from MB, developing a analysis strategy to spatially map cell type responses within the hybrid system of human and mouse cells and their interface within an intact brain tumour section. We distinguish neoplastic and non-neoplastic cells within tumours and from the surrounding cerebellar tissue, further refining pathological annotation. We identify a regional response to Palbociclib, with reduced proliferation and induced neuronal differentiation in both treated tumours. Additionally, we resolve at a cellular resolution a distinct tumour interface where the tumour contacts neighbouring mouse brain tissue consisting of abundant astrocytes and microglia and continues to proliferate despite Palbociclib treatment. Our data highlight the power of using spatial transcriptomics to characterise the response of a tumour to a targeted therapy and provide further insights into the molecular and cellular basis underlying the response and resistance to CDK4/6 inhibitors in SHH MB.
Publisher: Springer Science and Business Media LLC
Date: 06-11-2018
Publisher: Cold Spring Harbor Laboratory
Date: 07-03-2022
DOI: 10.1101/2022.03.06.483195
Abstract: Functional analysis of high throughput experiments using pathway analysis is now ubiquitous. Though powerful, these methods often produce thousands of redundant results owing to knowledgebase redundancies upstream. This scale of results hinders extensive exploration by biologists and often leads to investigator biases due to previous knowledge and expectations. To address this issue, we present vissE, a flexible network-based analysis method that summarises redundancies into biological themes and provides various analytical modules to characterise and visualise them with respect to the underlying data, thus providing a comprehensive view of the biological system. We demonstrate vissE’s versatility by applying it to three different technologies: bulk, single-cell and spatial transcriptomics. Applying vissE to a factor analysis of a breast cancer spatial transcriptomic data, we identified stromal phenotypes that support tumour dissemination. Its adaptability allows vissE to enhance all existing gene-set enrichment and pathway analysis workflows, removing investigator bias from molecular discovery.
Publisher: Cold Spring Harbor Laboratory
Date: 15-03-2023
DOI: 10.1101/2023.03.15.532733
Abstract: Spatial molecular technologies have revolutionised the study of disease microenvironments by providing spatial context to tissue heterogeneity. Recent spatial technologies are increasing the throughput and spatial resolution of measurements, resulting in larger datasets. The added spatial dimension and volume of measurements poses an analytics challenge that has, in the short-term, been addressed by adopting methods designed for the analysis of single-cell RNA-seq data. Though these methods work well in some cases, not all necessarily translate appropriately to spatial technologies. A common assumption is that total sequencing depth, also known as library size, represents technical variation in single-cell RNA-seq technologies, and this is often normalised out during analysis. Through analysis of several different spatial datasets, we noted that this assumption does not necessarily hold in spatial molecular data. To formally assess this, we explore the relationship between library size and independently annotated spatial regions, across 23 s les from 4 different spatial technologies with varying throughput and spatial resolution. We found that library size confounded biology across all technologies, regardless of the tissue being investigated. Statistical modelling of binned total transcripts shows that tissue region is strongly associated with library size across all technologies, even after accounting for cell density of the bins. Through a benchmarking experiment, we show that normalising out library size leads to sub-optimal spatial domain identification using common graph-based clustering algorithms. On average, better clustering was achieved when library size effects were not normalised out explicitly, especially with data from the newer sub-cellular localised technologies. Taking these results into consideration, we recommend that spatial data should not be specifically corrected for library size prior to analysis unless strongly motivated. We also emphasise that spatial data are different to single-cell RNA-seq and care should be taken when adopting algorithms designed for single cell data.
Publisher: University of Queensland Library
Date: 2022
DOI: 10.14264/40C2222
Publisher: Elsevier BV
Date: 10-2023
Publisher: Oxford University Press (OUP)
Date: 30-09-2020
DOI: 10.1093/NAR/GKAA802
Abstract: Gene expression signatures have been critical in defining the molecular phenotypes of cells, tissues, and patient s les. Their most notable and widespread clinical application is stratification of breast cancer patients into molecular (PAM50) subtypes. The cost and relatively large amounts of fresh starting material required for whole-transcriptome sequencing has limited clinical application of thousands of existing gene signatures captured in repositories such as the Molecular Signature Database. We identified genes with stable expression across a range of abundances, and with a preserved relative ordering across thousands of s les, allowing signature scoring and supporting general data normalisation for transcriptomic data. Our new method, stingscore, quantifies and summarises relative expression levels of signature genes from in idual s les through the inclusion of these ‘stably-expressed genes’. We show that our list of stable genes has better stability across cancer and normal tissue data than previously proposed gene sets. Additionally, we show that signature scores computed from targeted transcript measurements using stingscore can predict docetaxel response in breast cancer patients. This new approach to gene expression signature analysis will facilitate the development of panel-type tests for gene expression signatures, thus supporting clinical translation of the powerful insights gained from cancer transcriptomic studies.
Publisher: Elsevier BV
Date: 10-2023
Publisher: Cold Spring Harbor Laboratory
Date: 24-04-2023
DOI: 10.1101/2023.04.23.538017
Abstract: To gain a better understanding of the complexity of gene expression in normal and diseased tissues it is important to account for the spatial context and identity of cell in situ . State-of-the-art spatial profiling technologies, such as the Nanostring GeoMx Digital Spatial Profiler (DSP), now allow quantitative spatially resolved measurement of the transcriptome in tissues. However, the bioinformatics pipelines currently used to analyse GeoMx data often fail to successfully account for the technical variability within the data and the complexity of experimental designs, thus limiting the accuracy and reliability of subsequent analysis. Carefully designed quality control workflows, that include in-depth experiment-specific investigations into technical variation and appropriate adjustment for such variation can address this issue. Here we present standR , a R/Bioconductor package that enables an end-to-end analysis of GeoMx DSP data. With four case studies from previously published experiments, we demonstrate how the standR workflow can enhance the statistical power of GeoMx DSP data analysis and how application of standR enables scientists to develop in-depth insights into the biology of interest.
Publisher: Cold Spring Harbor Laboratory
Date: 31-03-2022
DOI: 10.1101/2022.03.24.22272732
Abstract: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is known to present with pulmonary and extra-pulmonary organ complications. In comparison with the 2009 pandemic (pH1N1), SARS-CoV-2 infection is likely to lead to more severe disease, with multi-organ effects, including cardiovascular disease. SARS-CoV-2 has been associated with acute and long-term cardiovascular disease, but the molecular changes govern this remain unknown. In this study, we investigated the landscape of cardiac tissues collected at rapid autopsy from SARS-CoV-2, pH1N1, and control patients using targeted spatial transcriptomics approaches. Although SARS-CoV-2 was not detected in cardiac tissue, host transcriptomics showed upregulation of genes associated with DNA damage and repair, heat shock, and M1-like macrophage infiltration in the cardiac tissues of COVID-19 patients. The DNA damage present in the SARS-CoV-2 patient s les, were further confirmed by γ−H2Ax immunohistochemistry. In comparison, pH1N1 showed upregulation of Interferon-stimulated genes (ISGs), in particular interferon and complement pathways, when compared with COVID-19 patients. These data demonstrate the emergence of distinct transcriptomic profiles in cardiac tissues of SARS-CoV-2 and pH1N1 influenza infection supporting the need for a greater understanding of the effects on extra-pulmonary organs, including the cardiovascular system of COVID-19 patients, to delineate the immunopathobiology of SARS-CoV-2 infection, and long term impact on health.
Publisher: Oxford University Press (OUP)
Date: 09-05-2023
DOI: 10.1093/NAR/GKAD337
Abstract: Gene-set analysis (GSA) dominates the functional interpretation of omics data and downstream hypothesis generation. Despite its ability to summarise thousands of measurements into semantically interpretable components, GSA often results in hundreds of significantly enriched gene-sets. However, summarisation and effective visualisation of GSA results to facilitate hypothesis generation is still lacking. While some webservers provide gene-set visualization tools, there is still a need for tools that can effectively summarize and guide exploration of GSA results. To enable versatility, webservers accept gene lists as input, however, none provide end-to-end solutions for emerging data types such as single-cell and spatial omics. Here, we present vissE.Cloud, a webserver for end-to-end gene-set analysis, offering gene-set summarisation and highly interactive visualisation. vissE.Cloud uses algorithms from our earlier R package vissE to summarise GSA results by identifying biological themes. We maintain versatility by allowing analysis of gene lists, as well as, analysis of raw single-cell and spatial omics data, including CosMx and Xenium data, making vissE.Cloud the first webserver to provide end-to-end gene-set analysis of sub-cellular localised spatial data. Structuring the results hierarchically allows swift interactive investigations of results at the gene, gene-set, and clusters level. vissE.Cloud is freely available at www.vissE.Cloud.
Location: United Kingdom of Great Britain and Northern Ireland
Location: Australia
No related grants have been discovered for Dharmesh D. Bhuva.