ORCID Profile
0000-0002-7239-5828
Current Organisations
Victorian Life Sciences Computation Initiative
,
Monash University
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Software engineering not elsewhere classified | Developmental genetics (incl. sex determination) | Genetics | Regenerative medicine (incl. stem cells)
ANZSRC Socio-Economic Objective (SEO)
Related Links
Publications
Invasion in breast lesions: the role of the epithelial–stroma barrier
Publisher: Wiley
Date: 13-02-2018
DOI: 10.1111/HIS.13446
Abstract: Despite the significant biological, behavioural and management differences between ductal carcinoma in situ (DCIS) and invasive carcinoma of the breast, they share many morphological and molecular similarities. Differentiation of these two different lesions in breast pathological diagnosis is based typically on the presence of an intact barrier between the malignant epithelial cells and stroma namely, the myoepithelial cell (MEC) layer and surrounding basement membrane (BM). Despite being robust diagnostic criteria, the identification of MECs and BM to differentiate in-situ from invasive carcinoma is not always straightforward. The MEC layer around DCIS may be interrupted and/or show an altered immunoprofile. MECs may be absent in some benign locally infiltrative lesions such as microglandular adenosis and infiltrating epitheliosis, and occasionally in non-infiltrative conditions such as apocrine lesions, and in these contexts this does not denote malignancy or invasive disease with metastatic potential. MECs may also be absent around some malignant lesions such as some forms of papillary carcinoma, yet these behave in an indolent fashion akin to some DCIS. In Paget's disease, malignant mammary epithelial cells extend anteriorly from the ducts to infiltrate the epidermis of the nipple but do not typically infiltrate through the BM into the dermis. Conversely, BM-like material can be seen around invasive carcinoma cells and around metastatic tumour cell deposits. Here, we review the role of MECs and BM in breast pathology and highlight potential clinical implications. We advise caution in interpretation of MEC features in breast pathology and mindfulness of the substantive evidence base in the literature associated with behaviour and clinical outcome of lesions classified as benign on conventional morphological examination before changing classification to an invasive lesion on the sole basis of MEC characteristics.
Ductal carcinoma in situ – update on risk assessment and management
Publisher: Wiley
Date: 04-12-2015
DOI: 10.1111/HIS.12796
Abstract: Ductal carcinoma in situ (DCIS) accounts for ~20-25% of breast cancers. While DCIS is not life-threatening, it may progress to invasive carcinoma over time, and treatment intended to prevent invasive progression may itself cause significant morbidity. Accurate risk assessment is therefore necessary to avoid over- or undertreatment of an in idual patient. In this review we will outline the evidence for current management of DCIS, discuss approaches to DCIS risk assessment and challenges facing identification of novel DCIS biomarkers.
Phenotypic characterisation of breast cancer: the role of CDC42
Publisher: Springer Science and Business Media LLC
Date: 27-04-2017
Betamax: towards optimal sampling strategies for high-throughput screens.
Publisher: Mary Ann Liebert Inc
Date: 06-2012
Abstract: S le size is a critical component in the design of any high-throughput genetic screening approach. S le size determination from assumptions or limited data at the planning stages, though standard practice, may at times be unreliable because of the difficulty of a priori modeling of effect sizes and variance. Methods to update the s le size estimate during the course of the study could improve statistical power. In this article, we introduce an approach to estimate the power and update it continuously during the screen. We use this estimate to decide where to s le next to achieve maximum overall statistical power. Finally, in simulations, we demonstrate significant gains in study recall over the naive strategy of equal s le sizes while maintaining the same total number of s les.
Thioredoxin-interacting protein is an independent risk stratifier for breast ductal carcinoma in situ.
Publisher: Elsevier BV
Date: 12-2018
DOI: 10.1038/S41379-018-0086-7
Abstract: Current clinicopathological parameters are useful predictors of breast ductal carcinoma in situ behavior, but they are insufficient to define high-risk patients for disease progression precisely. Thioredoxin-interacting protein (TXNIP) is a key player of oxidative stress. This study aims to evaluate the role of TXNIP as a predictor of ductal carcinoma in situ progression. Tissue microarrays from 776 pure ductal carcinoma in situ and 239 mixed ductal carcinoma in situ and invasive tumors were constructed. All patients were treated at a single institution with a long-term follow-up and TXNIP expression was assessed using immunohistochemistry. TXNIP expression was investigated in terms of associations with clinicopathological and molecular features and patient outcome. Loss/reduced cytoplasmic expression of TXNIP was associated with features of aggressiveness including high nuclear grade (p = 1.6 × 10
Chemokine (C‐C motif) receptor 7 (CCR7) associates with the tumour immune microenvironment but not progression in invasive breast carcinoma
Publisher: Wiley
Date: 30-03-2017
DOI: 10.1002/CJP2.65
Pattern mining across many massive biological networks
Publisher: Springer New York
Date: 24-08-2012
An integrative network approach to map the transcriptome to the phenome
Publisher: Springer Berlin Heidelberg
Date: 2008
An integrative approach to characterize disease-specific pathways and their coordination: a case study in cancer.
Publisher: Springer Science and Business Media LLC
Date: 03-2008
DOI: 10.1186/1471-2164-9-S1-S12
Abstract: The most common application of microarray technology in disease research is to identify genes differentially expressed in disease versus normal tissues. However, it is known that, in complex diseases, phenotypes are determined not only by genes, but also by the underlying structure of genetic networks. Often, it is the interaction of many genes that causes phenotypic variations. In this work, using cancer as an ex le, we develop graph-based methods to integrate multiple microarray datasets to discover disease-related co-expression network modules. We propose an unsupervised method that take into account both co-expression dynamics and network topological information to simultaneously infer network modules and phenotype conditions in which they are activated or de-activated. Using our method, we have discovered network modules specific to cancer or subtypes of cancers. Many of these modules are consistent with or supported by their functional annotations or their previously known involvement in cancer. In particular, we identified a module that is predominately activated in breast cancer and is involved in tumor suppression. While in idual components of this module have been suggested to be associated with tumor suppression, their coordinated function has never been elucidated. Here by adopting a network perspective, we have identified their interrelationships and, particularly, a hub gene PDGFRL that may play an important role in this tumor suppressor network. Using a network-based approach, our method provides new insights into the complex cellular mechanisms that characterize cancer and cancer subtypes. By incorporating co-expression dynamics information, our approach can not only extract more functionally homogeneous modules than those based solely on network topology, but also reveal pathway coordination beyond co-expression.
Functional and genomic characterization of a xenograft model system for the study of metastasis in triple-negative breast cancer
Publisher: The Company of Biologists
Date: 2018
DOI: 10.1242/DMM.032250
Abstract: Triple-negative breast cancer represents 10-20% of all human ductal adenocarcinomas and has a poor prognosis relative to other subtypes. Hence, new molecular targets for therapeutic intervention are necessary. Analyses of panels of human or mouse cancer lines derived from the same in idual that differ in their cellular phenotypes but not in genetic background have been instrumental in defining the molecular players that drive the various hallmarks of cancer. To determine the molecular regulators of metastasis in triple-negative breast cancer, we completed a rigorous in vitro and in vivo characterization of four populations of the MDA-MB-231 human breast cancer line ranging in aggressiveness from non-metastatic to spontaneously metastatic to lung, liver, spleen and lymph node. Single nucleotide polymorphism (SNP) array analyses and genome-wide mRNA expression profiles of tumour cells isolated from orthotopic mammary xenografts were compared among the four lines to define both cell autonomous pathways and genes associated with metastatic proclivity. Gene set enrichment analysis demonstrated an unexpected association between both ribosome biogenesis and mRNA metabolism and metastatic capacity. Differentially expressed genes or families of related genes were allocated to one of four categories, associated with either metastatic initiation (for ex le CTSC, ENG, BMP2), metastatic virulence (e.g. ADAMTS1, TIE1) metastatic suppression (e.g. CST1, CST2, CST4, CST6, SCNNA1, BMP4) or metastatic avirulence (e.g. CD74). Collectively, this model system based on MDA-MB-231 cells should be useful for the assessment of gene function in the metastatic cascade and also for the testing of novel experimental therapeutics for the treatment of triple-negative breast cancer.
Interactive design of GPU-accelerated Image Data Flow Graphs and cross-platform deployment using multi-lingual code generation
Publisher: Cold Spring Harbor Laboratory
Date: 20-11-2020
DOI: 10.1101/2020.11.19.386565
Abstract: Modern life science relies heavily on fluorescent microscopy and subsequent quantitative bio-image analysis. The current rise of graphics processing units (GPUs) in the context of image processing enables batch processing large amounts of image data at unprecedented speed. In order to facilitate adoption of this technology in daily practice, we present an expert system based on the GPU-accelerated image processing library CLIJ: The CLIJ-assistant keeps track of which operations formed an image and suggests subsequent operations. It enables new ways of interaction with image data and image processing operations because its underlying GPU-accelerated image data flow graphs (IDFGs) allow changes to parameters of early processing steps and instantaneous visualization of their final results. Operations, their parameters and connections in the IDFG are stored at any point in time enabling the CLIJ-assistant to offer an undo-function for virtually unlimited rewinding parameter changes. Furthermore, to improve reproducibility of image data analysis workflows and interoperability with established image analysis platforms, the CLIJ-assistant can generate code from IDFGs in programming languages such as ImageJ Macro, Java, Jython, JavaScipt, Groovy, Python and C++ for later use in ImageJ, Fiji, Icy, Matlab, QuPath, Jupyter Notebooks and Napari. We demonstrate the CLIJ-assistant for processing image data in multiple scenarios to highlight its general applicability. The CLIJ-assistant is open source and available online: clij.github.io/assistant/
Aurora Kinase B, a novel regulator of TERF1 binding and telomeric integrity
Publisher: Oxford University Press (OUP)
Date: 10-10-2017
DOI: 10.1093/NAR/GKX904
An integrative network approach to map the transcriptome to the phenome.
Publisher: Mary Ann Liebert Inc
Date: 08-2009
scikit-image: image processing in Python
Publisher: PeerJ
Date: 19-06-2014
DOI: 10.7717/PEERJ.453
Graph-based Active Learning of Agglomeration (GALA)
Publisher: Frontiers Media SA
Date: 04-04-2014
Evaluating the breast cancer predisposition role of rare variants in genes associated with low-penetrance breast cancer risk SNPs.
Publisher: Springer Science and Business Media LLC
Date: 09-01-2018
An integrative modular approach to systematically predict gene-phenotype associations.
Publisher: Springer Science and Business Media LLC
Date: 2010
DOI: 10.1186/1471-2105-11-S1-S62
Abstract: Complex human diseases are often caused by multiple mutations, each of which contributes only a minor effect to the disease phenotype. To study the basis for these complex phenotypes, we developed a network-based approach to identify coexpression modules specifically activated in particular phenotypes. We integrated these modules, protein-protein interaction data, Gene Ontology annotations, and our database of gene-phenotype associations derived from literature to predict novel human gene-phenotype associations. Our systematic predictions provide us with the opportunity to perform a global analysis of human gene pleiotropy and its underlying regulatory mechanisms. We applied this method to 338 microarray datasets, covering 178 phenotype classes, and identified 193,145 phenotype-specific coexpression modules. We trained random forest classifiers for each phenotype and predicted a total of 6,558 gene-phenotype associations. We showed that 40.9% genes are pleiotropic, highlighting that pleiotropy is more prevalent than previously expected. We collected 77 ChIP-chip datasets studying 69 transcription factors binding over 16,000 targets under various phenotypic conditions. Utilizing this unique data source, we confirmed that dynamic transcriptional regulation is an important force driving the formation of phenotype specific gene modules. We created a genome-wide gene to phenotype mapping that has many potential implications, including providing potential new drug targets and uncovering the basis for human disease phenotypes. Our analysis of these phenotype-specific coexpression modules reveals a high prevalence of gene pleiotropy, and suggests that phenotype-specific transcription factor binding may contribute to phenotypic ersity. All resources from our study are made freely available on our online Phenotype Prediction Database [1].
Finding and Following: A deep learning-based pipeline for tracking platelets during thrombus formationin vivoandex vivo
Publisher: Cold Spring Harbor Laboratory
Date: 04-10-2023
Metabolic sensing in AgRP neurons integrates homeostatic state with dopamine signalling in the striatum
Publisher: eLife Sciences Publications, Ltd
Date: 12-01-2022
DOI: 10.7554/ELIFE.72668
Abstract: Agouti-related peptide (AgRP) neurons increase motivation for food, however, whether metabolic sensing of homeostatic state in AgRP neurons potentiates motivation by interacting with dopamine reward systems is unexplored. As a model of impaired metabolic-sensing, we used the AgRP-specific deletion of carnitine acetyltransferase ( Crat ) in mice. We hypothesised that metabolic sensing in AgRP neurons is required to increase motivation for food reward by modulating accumbal or striatal dopamine release. Studies confirmed that Crat deletion in AgRP neurons (KO) impaired ex vivo glucose-sensing, as well as in vivo responses to peripheral glucose injection or repeated palatable food presentation and consumption. Impaired metabolic-sensing in AgRP neurons reduced acute dopamine release (seconds) to palatable food consumption and during operant responding, as assessed by GRAB-DA photometry in the nucleus accumbens, but not the dorsal striatum. Impaired metabolic-sensing in AgRP neurons suppressed radiolabelled 18F-fDOPA accumulation after ~30 min in the dorsal striatum but not the nucleus accumbens. Impaired metabolic sensing in AgRP neurons suppressed motivated operant responding for sucrose rewards during fasting. Thus, metabolic-sensing in AgRP neurons is required for the appropriate temporal integration and transmission of homeostatic hunger-sensing to dopamine signalling in the striatum.
Functional annotation and network reconstruction through cross-platform integration of microarray data
Publisher: Springer Science and Business Media LLC
Date: 16-01-2005
DOI: 10.1038/NBT1058
Abstract: The rapid accumulation of microarray data translates into a need for methods to effectively integrate data generated with different platforms. Here we introduce an approach, 2(nd)-order expression analysis, that addresses this challenge by first extracting expression patterns as meta-information from each data set (1(st)-order expression analysis) and then analyzing them across multiple data sets. Using yeast as a model system, we demonstrate two distinct advantages of our approach: we can identify genes of the same function yet without coexpression patterns and we can elucidate the cooperativities between transcription factors for regulatory network reconstruction by overcoming a key obstacle, namely the quantification of activities of transcription factors. Experiments reported in the literature and performed in our lab support a significant number of our predictions.
Glucose-sensing in AgRP neurons integrates homeostatic energy state with dopamine signalling in the striatum
Publisher: Cold Spring Harbor Laboratory
Date: 23-03-2021
DOI: 10.1101/2021.03.22.436393
Abstract: Hunger increases the motivation of an organism to seek out and consume highly palatable energy dense foods. While hunger-sensing Agouti-related peptide (AgRP) neurons influence this process, whether metabolic detection of homeostatic state via metabolic sensing in AgRP neurons potentiates motivation through the midbrain dopamine system is unexplored. Here, we used the AgRP-specific deletion of carnitine acetyltransferase (Crat), a metabolic enzyme regulating glucose and fatty acid oxidation, as a model of impaired metabolic-sensing in AgRP neurons. We then tested the hypothesis that appropriate metabolic-sensing in AgRP neurons is required to increase food reward motivation by modulating accumbal or striatal dopamine release. Electrophysiological studies confirm that Crat deletion in AgRP neurons (KO) impairs normal ex vivo glucose-sensing, and in vivo photometry experiments show that AgRP neurons in KO mice do not exhibit normal responses to repeated palatable food presentation and consumption, highlighting that this model is appropriate to test the hypothesis. Fiber photometry experiments, using the dopamine sensor GRAB-DA, revealed that impaired metabolic-sensing reduces acute dopamine release (seconds) in the nucleus accumbens, but not the dorsal striatum, to palatable food consumption and during operant responding. Positron electron tomography (PET) methods indicated that impaired metabolic-sensing in AgRP neurons suppressed radiolabelled 18F-fDOPA accumulation after ∼30 minutes in the dorsal striatum but not the ventral striatum, suggesting a role for AgRP neurons to restrict a long term post-ingestive dopamine response in the dorsal striatum. Finally, impaired metabolic-sensing in AgRP neurons suppresses motivated operant responding for sucrose rewards. Notably, these behavioural effects are potentiated in the hungry state and therefore highlight that metabolic-sensing in AgRP neurons is required for the appropriate temporal integration and transmission of homeostatic hunger-sensing to dopamine signalling in the striatum.
Ductal Carcinoma In Situ Biology, Biomarkers, and Diagnosis
Publisher: Frontiers Media SA
Date: 23-10-2017
Enhanced GAB2 Expression Is Associated with Improved Survival in High-Grade Serous Ovarian Cancer and Sensitivity to PI3K Inhibition.
Publisher: American Association for Cancer Research (AACR)
Date: 06-2015
DOI: 10.1158/1535-7163.MCT-15-0039
Abstract: Identification of genomic alterations defining ovarian carcinoma subtypes may aid the stratification of patients to receive targeted therapies. We characterized high-grade serous ovarian carcinoma (HGSC) for the association of lified and overexpressed genes with clinical outcome using gene expression data from 499 HGSC patients in the Ovarian Tumor Tissue Analysis cohort for 11 copy number lified genes: ATP13A4, BMP8B, CACNA1C, CCNE1, DYRK1B, GAB2, PAK4, RAD21, TPX2, ZFP36, and URI. The Australian Ovarian Cancer Study and The Cancer Genome Atlas datasets were also used to assess the correlation between gene expression, patient survival, and tumor classification. In a multivariate analysis, high GAB2 expression was associated with improved overall and progression-free survival (P = 0.03 and 0.02), whereas high BMP8B and ATP13A4 were associated with improved progression-free survival (P = 0.004 and P = 0.02). GAB2 overexpression and copy number gain were enriched in the AOCS C4 subgroup. High GAB2 expression correlated with enhanced sensitivity in vitro to the dual PI3K/mTOR inhibitor PF-04691502 and could be used as a genomic marker for identifying patients who will respond to treatments inhibiting PI3K signaling. Mol Cancer Ther 14(6) 1495–503. ©2015 AACR.
SciPy 1.0: fundamental algorithms for scientific computing in Python
Publisher: Springer Science and Business Media LLC
Date: 03-02-2020
DOI: 10.1038/S41592-019-0686-2
Abstract: SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
Atypical ductal hyperplasia: update on diagnosis, management, and molecular landscape
Publisher: Springer Science and Business Media LLC
Date: 02-05-2018
Legumain is an independent predictor for invasive recurrence in breast ductal carcinoma in situ.
Publisher: Elsevier BV
Date: 05-2019
DOI: 10.1038/S41379-018-0180-X
Abstract: Legumain is a proteolytic enzyme that plays a role in the regulation of cell proliferation in invasive breast cancer. Studies evaluating its role in ductal carcinoma in situ (DCIS) are lacking. Here, we aimed to characterize legumain protein expression in DCIS and evaluate its prognostic significance. Legumain was assessed immunohistochemically in a tissue microarray of a well-characterized cohort of DCIS (n = 776 pure DCIS and n = 239 DCIS associated with invasive breast cancer (DCIS-mixed)). Legumain immunoreactivity was scored in tumor cells and surrounding stroma and related to clinicopathological parameters and patient outcome. High legumain expression was observed in 23% of pure DCIS and was associated with features of high-risk DCIS including higher nuclear grade, comedo necrosis, hormone receptor negativity, HER2 positivity, and higher proliferation index. Legumain expression was higher in DCIS associated with invasive breast cancer than in pure DCIS (p < 0.0001). In the DCIS-mixed cohort, the invasive component showed higher legumain expression than the DCIS component (p < 0.0001). Legumain was an independent predictor of shorter local recurrencefree interval for all recurrences (p = 0.0003) and for invasive recurrences (p = 0.002). When incorporated with other risk factors, legumain provided better patient risk stratification. High legumain expression is associated with poor prognosis in DCIS and could be a potential marker to predict DCIS progression to invasive disease.
Relationship of the Breast Ductal Carcinoma In Situ Immune Microenvironment with Clinicopathological and Genetic Features.
Publisher: American Association for Cancer Research (AACR)
Date: 31-08-2017
DOI: 10.1158/1078-0432.CCR-17-0743
Abstract: Purpose: The immune microenvironment of breast ductal carcinoma in situ (DCIS) has yet to be fully explored, and the relationship of immune cells to genetic features of DCIS is unknown. Experimental Design: We quantified tumor associated lymphocytes (TIL) and evaluated PD-L1 protein levels by immunohistochemistry in a cohort of pure DCIS (138 and 79 cases, respectively), some of which had copy number (n = 55) and mutation data (n = 20). Results: TILs were identified in the stroma surrounding DCIS (119/138, 86%) and present at a median TIL score of 5% (range, 0%–90%). Most DCIS were negative for tumor cell PD-L1 staining (89%), but 25% of cases were positive for immune cell staining. We observed that, as in invasive breast cancer, TILs and PD-L1 positivity were significantly greater in high-grade (P = 0.002/0.035), ER-negative (P = 0.02/0.02), and ERBB2- lified tumors (P & 0.001/0.048). Comedo necrosis was significantly positively associated with TILs (P & 0.0001) but not with PD-L1. The TILs score was significantly higher in cases with TP53 mutation (P = 0.03) but not with PIK3CA or GATA3 mutation. In the cases with copy number data, both the fraction of the genome altered and the number of telomeric imbalances were significantly positively correlated with TILs (both P & 0.001). This result strongly contrasted with invasive breast cancer data, where aneuploidy was not correlated to TIL levels. Conclusions: Although a small cohort, our data suggest a preliminary model by which the progression of DCIS to invasive carcinoma may involve an altered relationship of tumor copy number with the immune microenvironment, possibly by the immunoediting of the tumor. Clin Cancer Res 23(17) 5210–7. ©2017 AACR.
Synaptic circuits and their variations within different columns in the visual system of Drosophila.
Publisher: Proceedings of the National Academy of Sciences
Date: 19-10-2015
Abstract: Circuit diagrams of brains are generally reported only as absolute or consensus networks these diagrams fail to identify the accuracy of connections, however, for which multiple circuits of the same neurons must be documented. For this reason, the modular composition of the Drosophila visual system, with many identified neuron classes, is ideal. Using EM, we identified synaptic connections in the fly’s second visual relay neuropil, or medulla, in the 20 neuron classes in a so-called “core connectome,” those neurons present in seven neighboring columns. These connections identify circuits for motion. Their error rates for wiring reveal that % of contacts overall are not part of a consensus circuit but incorporate errors of either omission or commission. Autapses are occasionally seen.
Synaptic Cleft Segmentation in Non-isotropic Volume Electron Microscopy of the Complete Drosophila Brain
Publisher: Springer International Publishing
Date: 2018
The knob protein KAHRP assembles into a ring-shaped structure that underpins virulence complex assembly.
Publisher: Public Library of Science (PLoS)
Date: 09-05-2019
Appraisal of the technologies and review of the genomic landscape of ductal carcinoma in situ of the breast
Publisher: Springer Science and Business Media LLC
Date: 16-06-2015
Clinical and biological roles of Kelch-like family member 7 in breast cancer: a marker of poor prognosis
Publisher: Springer Science and Business Media LLC
Date: 09-04-2018
DOI: 10.1007/S10549-018-4777-Z
Abstract: The functions of many proteins are tightly regulated with a complex array of cellular functions including ubiquitination. In cancer cells, aberrant ubiquitination may promote the activity of oncogenic pathways with subsequent tumour progression. Kelch-like family member 7 (KLHL7) is involved in the regulation of ubiquitination and may play a role in breast cancer (BC). Present study aims to evaluate the biological and clinical usefulness of KLHL7 in BC utilising large well-characterised cohorts with long-term follow-up. The relationships between KLHL7 gene copy number alteration (CNA) and mRNA expression and clinicopathological variables and clinical outcomes were evaluated in 1980 patients from the METABRIC BC cohort. Prognostic significance of KLHL7 mRNA was validated using the Breast Cancer Gene-Expression Miner v4.0 datasets (n = 5206). KLHL7 protein expression was assessed using immunohistochemistry in a large annotated series of early-stage BC (n = 917) with long-term follow-up. KLHL7 CNA was significantly correlated with its mRNA expression. KLHL7 mRNA expression was higher in luminal B and basal-like molecular subtypes and in higher grade tumours. Increased KLHL7 protein expression was significantly correlated with features of aggressive phenotype including lymphovascular invasion, high histological grade, hormonal receptor negativity, high PIK3CA and p53 expression. Outcome analysis showed that high KLHL7 expression is an independent predictor of shorter survival (p = 0.0011). KLHL7 appears to play an important role in BC progression. High KLHL7 protein expression identified a subgroup of BC with aggressive behaviour and provided independent prognostic information.
Geometric characteristics of collagen have independent prognostic significance in breast ductal carcinoma in situ: an image analysis study.
Publisher: Elsevier BV
Date: 10-2019
DOI: 10.1038/S41379-019-0296-7
Abstract: Collagen plays a key role in normal and malignant tissue homeostasis. While the prognostic significance of collagen fiber remodeling in invasive breast cancer has been studied, its role in ductal carcinoma in situ (DCIS) remains poorly defined. Using image analysis, we aimed to evaluate the prognostic significance of the geometric characteristics of collagen surrounding DCIS. A large well-characterized cohort of DCIS comprising pure DCIS (n = 610) and DCIS coexisting with invasive carcinoma (n = 180) were histochemically stained for collagen using picrosirius red. ImageJ software was used to assess collagen density, degree of collagen fiber dispersion and directionality in relation to DCIS ducts' boundary. We developed a collagen prognostic index and evaluated its prognostic significance. A poor index was observed in 24% of the pure DCIS and was associated with determinants of high-risk DCIS including higher nuclear grade, comedo type necrosis, hormonal receptor negativity, HER2 positivity and high proliferation index. High collagen prognostic index was associated with the collagen remodeling protein prolyl-4-hydroxlase alpha subunit 2 and the hypoxia-related protein hypoxia inducible factor 1α. DCIS coexisting with invasive breast cancer had a higher collagen prognostic index than pure DCIS ( p < 0.0001). High index was an independent poor prognostic factor for DCIS recurrence for all recurrences (HR = 2.3, p = 0.005) and just invasive recurrences (HR = 3.4, p = 0.003). Interaction between collagen prognostic index and radiotherapy showed that the index was associated with poor outcome even with adjuvant radiotherapy ( p = 0.0001). Collagen reorganization around DCIS is associated with poor outcome and provides a potential predictor for disease progression and resistance to radiotherapy. Mechanistic studies are warranted to decipher the underlying mechanisms.
Recruitment of the adaptor protein 2 complex by the human immunodeficiency virus type 2 envelope protein is necessary for high levels of virus release.
Publisher: American Society for Microbiology
Date: 15-03-2006
DOI: 10.1128/JVI.80.6.2924-2932.2006
Abstract: The envelope (Env) protein of human immunodeficiency virus type 2 (HIV-2) and the HIV-1 Vpu protein stimulate the release of retroviral particles from human cells that restrict virus production, an activity that we call the enhancement of virus release (EVR). We have previously shown that two separate domains in the HIV-2 envelope protein are required for this activity: a glycine-tyrosine-x-x-hydrophobic (GYxxθ) motif in the cytoplasmic tail and an unmapped region in the ectodomain of the protein. We here report that the cellular partner of the GYxxθ motif is the adaptor protein complex AP-2. The mutation of this motif or the depletion of AP-2 by RNA interference abrogated EVR activity and changed the cellular distribution of the Env from a predominantly punctate pattern to a more diffuse distribution. Since the L domain of equine infectious anemia virus (EIAV) contains a Yxxθ motif that interacts with AP-2, we used both wild-type and L domain-defective particles of HIV-1 and EIAV to examine whether the HIV-2 Env EVR function was analogous to L domain activity. We observed that the production of all particles was stimulated by HIV-2 Env or Vpu, suggesting that the L domain and EVR activities play independent roles in the release of retroviruses. Interestingly, we found that the cytoplasmic tail of the murine leukemia virus (MLV) Env could functionally substitute for the HIV-2 Env tail, but it did so in a manner that did not require a Yxxθ motif or AP-2. The cellular distribution of the chimeric HIV-2/MLV Env was significantly less punctate than the wild-type Env, although confocal analysis revealed an overlap in the steady-state locations of the two proteins. Taken together, these data suggest that the essential GYxxθ motif in the HIV-2 Env tail recruits AP-2 in order to direct Env to a cellular pathway or location that is necessary for its ability to enhance virus release but that an alternate mechanism provided by the MLV Env tail can functionally substitute.
Integrative Array Analyzer: a software package for analysis of cross-platform and cross-species microarray data.
Publisher: Oxford University Press (OUP)
Date: 03-05-2006
DOI: 10.1093/BIOINFORMATICS/BTL163
Abstract: Summary: The rapid accumulation of microarray data translates into an urgent need for tools to perform integrative microarray analysis. Integrative Array Analyzer is a comprehensive analysis and visualization software toolkit, which aims to facilitate the reuse of the large amount of cross-platform and cross-species microarray data. It is composed of the data preprocess module, the co-expression analysis module, the differential expression analysis module, the functional and transcriptional annotation module and the graph visualization module. Availability: Contact: xjzhou@usc.edu
Loss of Heterozygosity
Publisher: Wiley
Date: 15-09-2016
Frequent Pattern Discovery in Multiple Biological Networks: Patterns and Algorithms
Publisher: Springer Science and Business Media LLC
Date: 29-12-2012
Machine learning of hierarchical clustering to segment 2D and 3D images.
Publisher: Public Library of Science (PLoS)
Date: 20-08-2013
Functional analysis of genes in regions commonly amplified in high-grade serous and endometrioid ovarian cancer.
Publisher: American Association for Cancer Research (AACR)
Date: 14-03-2013
DOI: 10.1158/1078-0432.CCR-12-3433
Abstract: Purpose: Ovarian cancer has the highest mortality rate of all the gynecologic malignancies and is responsible for approximately 140,000 deaths annually worldwide. Copy number lification is frequently associated with the activation of oncogenic drivers in this tumor type, but their cytogenetic complexity and heterogeneity has made it difficult to determine which gene(s) within an licon represent(s) the genuine oncogenic driver. We sought to identify licon targets by conducting a comprehensive functional analysis of genes located in the regions of lification in high-grade serous and endometrioid ovarian tumors. Experimental Design: High-throughput siRNA screening technology was used to systematically assess all genes within regions commonly lified in high-grade serous and endometrioid cancer. We describe the results from a boutique siRNA screen of 272 genes in a panel of 18 ovarian cell lines. Hits identified by the functional viability screen were further interrogated in primary tumor cohorts to determine the clinical outcomes associated with lification and gene overexpression. Results: We identified a number of genes as critical for cellular viability when lified, including URI1, PAK4, GAB2, and DYRK1B. Integration of primary tumor gene expression and outcome data provided further evidence for the therapeutic use of such genes, particularly URI1 and GAB2, which were significantly associated with survival in 2 independent tumor cohorts. Conclusion: By taking this integrative approach to target discovery, we have streamlined the translation of high-resolution genomic data into preclinical in vitro studies, resulting in the identification of a number of genes that may be specifically targeted for the treatment of advanced ovarian tumors. Clin Cancer Res 19(6) 1411–21. ©2013 AACR.
High resolution segmentation of neuronal tissues from low depth-resolution em imagery
Publisher: Springer Berlin Heidelberg
Date: 2011
Breast ductal carcinoma in situ carry mutational driver events representative of invasive breast cancer.
Publisher: Elsevier BV
Date: 07-2017
DOI: 10.1038/MODPATHOL.2017.21
Abstract: The spectrum of genomic alterations in ductal carcinoma in situ (DCIS) is relatively unexplored, but is likely to provide useful insights into its biology, its progression to invasive carcinoma and the risk of recurrence. DCIS (n=20) with a range of phenotypes was assessed by massively parallel sequencing for mutations and copy number alterations and variants validated by Sanger sequencing. PIK3CA mutations were identified in 11/20 (55%), TP53 mutations in 6/20 (30%), and GATA3 mutations in 9/20 (45%). Screening an additional 91 cases for GATA3 mutations identified a final frequency of 27% (30/111), with a high proportion of missense variants (8/30). TP53 mutations were exclusive to high grade DCIS and more frequent in PR-negative tumors compared with PR-positive tumors (P=0.037). TP53 mutant tumors also had a significantly higher fraction of the genome altered by copy number than wild-type tumors (P=0.005), including a significant positive association with lification or gain of ERBB2 (P<0.05). The association between TP53 mutation and ERBB2 lification was confirmed in a wider DCIS cohort using p53 immunohistochemistry as a surrogate marker for TP53 mutations (P=0.03). RUNX1 mutations and MAP2K4 copy number loss were novel findings in DCIS. Frequent copy number alterations included gains on 1q, 8q, 17q, and 20q and losses on 8p, 11q, 16q, and 17p. Patterns of genomic alterations observed in DCIS were similar to those previously reported for invasive breast cancers, with all DCIS having at least one bona fide breast cancer driver event. However, an increase in GATA3 mutations and fewer copy number changes were noted in DCIS compared with invasive carcinomas. The role of such alterations as prognostic and predictive biomarkers in DCIS is an avenue for further investigation.
Electron Microscopy Reconstruction of Brain Structure Using Sparse Representations over Learned Dictionaries.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2013
Identification of a stress-sensitive anorexigenic neurocircuit from medial prefrontal cortex to lateral hypothalamus in humans and mice
Publisher: Cold Spring Harbor Laboratory
Date: 09-09-2021
DOI: 10.1101/2021.09.07.459350
Abstract: By modeling neural network dynamics related to homeostatic state and BMI, we identified a novel pathway projecting from the medial prefrontal cortex (mPFC) to the lateral hypothalamus (LH) in humans. We then assessed the physiological role and dissected the function of this mPFC-LH circuit in mice. In vivo recordings of population calcium activity revealed that this glutamatergic mPFC-LH pathway is activated in response to acute stressors and inhibited during food consumption, suggesting a role in stress-related control over food intake. Consistent with this role, inhibition of this circuit increased feeding and sucrose seeking during mild stressors, but not under non-stressful conditions. Finally, chemogenetic or optogenetic activation of the mPFC-LH pathway is sufficient to suppress food intake and sucrose-seeking in mice. These studies identify a glutamatergic mPFC-LH as a novel stress-sensitive anorexigenic neural pathway involved in the cortical control of food intake.
Collagen (XI) alpha-1 chain is an independent prognostic factor in breast ductal carcinoma in situ.
Publisher: Elsevier BV
Date: 10-2019
DOI: 10.1038/S41379-019-0286-9
Abstract: Collagen11A1 (COL11A1) is a fibrillary type collagen constituting a minor component of the extracellular matrix and plays role in tissue tensile strength. Overexpression of COL11A1 expression is associated with aggressive behavior and poor outcome in several human malignancies. In this study, we evaluated the association between COL11A1 expression and clinicopathological parameters of the breast ductal carcinoma in situ (DCIS) and its prognostic value. COL11A1 protein expression was assessed immunohistochemically in a large well-characterized cohort of DCIS including pure (n = 776) and DCIS associated with invasive carcinoma (DCIS-mixed, n = 239). COL11A1 expression was assessed in tumor cells and surrounding stromal cells, and correlated with clinicopathological parameters, immunoprofile and disease outcome. In pure DCIS, high COL11A1 expression was observed in tumor cells and surrounding stromal cells in 25 and 13% of cases, respectively. Higher COL11A1 expression within the stromal cells was associated with hormone receptor negative, HER2 enriched and triple negative molecular subtypes and showed a positive linear correlation with proliferation index, dense tumor infiltrating lymphocytes and hypoxia-inducible factor 1 alpha. COL11A1 expression in tumor and stromal cells was significantly higher in DCIS associated with invasive carcinoma than in pure DCIS, and within the DCIS-mixed cohort, the invasive component showed higher COL11A1 expression than the DCIS component (all, p < 0.0001). Overexpression of stromal COL11A1 was an independent predictor of shorter local recurrence-free interval for all recurrences (HR = 13.2, 95% CI = 6.9-25.4, p < 0.0001) and for invasive recurrences (HR = 11.2, 95% CI = 4.9-25.8, p < 0.0001). When incorporated with other risk factors, stromal COL11A1 provided better patient risk stratification. DCIS with higher stromal COL11A1 expression showed poor outcome even with adjuvant radiotherapy management. In conclusion, overexpression of stromal COL11A1 is associated with invasive recurrence in DCIS and is a potential marker to predict the response to radiotherapy.
A novel method for radial hydride analysis in zirconium alloys: HAPPy
Publisher: Elsevier BV
Date: 02-2022
Multimodal imaging reveals membrane skeleton reorganisation during reticulocyte maturation and differences in dimple and rim regions of mature erythrocytes
Publisher: Elsevier BV
Date: 2022
Reply to the Baader–Meinhof phenomenon in ductal carcinoma in situ of the breast
Publisher: Wiley
Date: 05-05-2016
DOI: 10.1111/HIS.12968
Genomic Aberrations of BRCA1-Mutated Fallopian Tube Carcinomas
Publisher: Elsevier BV
Date: 06-2014
DOI: 10.1016/J.AJPATH.2014.02.013
Abstract: Intraepithelial carcinomas of the fallopian tube are putative precursors to high-grade serous carcinomas of the ovary and peritoneum. Molecular characterization of these early precursors is limited but could be the key to identifying tumor biomarkers for early detection. This study presents a genome-wide copy number analysis of occult fallopian tube carcinomas identified through risk-reducing prophylactic oophorectomy from three women with germline BRCA1 mutations, demonstrating that extensive genomic aberrations are already established at this early stage. We found no indication of a difference in the level of genomic aberration observed in fallopian tube carcinomas compared with high-grade serous ovarian carcinomas. These findings suggest that spread to the peritoneal cavity may require no or very little further tumor evolution, which raises the question of what is the real window of opportunity to detect high-grade serous peritoneal carcinoma arising from the fallopian tube before it spreads. Nonetheless, the similarity of the genomic aberrations to those observed in high-grade serous ovarian carcinomas suggests that genetic biomarkers identified in late-stage disease may be relevant for early detection.
Joint genome-wide profiling of miRNA and mRNA expression in Alzheimer's disease cortex reveals altered miRNA regulation.
Publisher: Public Library of Science (PLoS)
Date: 02-2010
Analyzing microtomography data with Python and the scikit-image library
Publisher: Springer Science and Business Media LLC
Date: 12-2016
DOI: 10.1186/S40679-016-0031-0
Abstract: The exploration and processing of images is a vital aspect of the scientific workflows of many X-ray imaging modalities. Users require tools that combine interactivity, versatility, and performance. is an open-source image processing toolkit for the Python language that supports a large variety of file formats and is compatible with 2D and 3D images. The toolkit exposes a simple programming interface, with thematic modules grouping functions according to their purpose, such as image restoration, segmentation, and measurements. users benefit from a rich scientific Python ecosystem that contains many powerful libraries for tasks such as visualization or machine learning. combines a gentle learning curve, versatile image processing capabilities, and the scalable performance required for the high-throughput analysis of X-ray imaging data.
Gene Aging Nexus: a web database and data mining platform for microarray data on aging.
Publisher: Oxford University Press (OUP)
Date: 03-01-2007
DOI: 10.1093/NAR/GKL798
Usefulness and limitations of dK random graph models to predict interactions and functional homogeneity in biological networks under a pseudo-likelihood parameter estimation approach
Publisher: Springer Science and Business Media LLC
Date: 03-09-2009
Abstract: Many aspects of biological functions can be modeled by biological networks, such as protein interaction networks, metabolic networks, and gene coexpression networks. Studying the statistical properties of these networks in turn allows us to infer biological function. Complex statistical network models can potentially more accurately describe the networks, but it is not clear whether such complex models are better suited to find biologically meaningful subnetworks. Recent studies have shown that the degree distribution of the nodes is not an adequate statistic in many molecular networks. We sought to extend this statistic with 2nd and 3rd order degree correlations and developed a pseudo-likelihood approach to estimate the parameters. The approach was used to analyze the MIPS and BIOGRID yeast protein interaction networks, and two yeast coexpression networks. We showed that 2nd order degree correlation information gave better predictions of gene interactions in both protein interaction and gene coexpression networks. However, in the biologically important task of predicting functionally homogeneous modules, degree correlation information performs marginally better in the case of the MIPS and BIOGRID protein interaction networks, but worse in the case of gene coexpression networks. Our use of dK models showed that incorporation of degree correlations could increase predictive power in some contexts, albeit sometimes marginally, but, in all contexts, the use of third-order degree correlations decreased accuracy. However, it is possible that other parameter estimation methods, such as maximum likelihood, will show the usefulness of incorporating 2nd and 3rd degree correlations in predicting functionally homogeneous modules.
A new Python library to analyse skeleton images confirms malaria parasite remodelling of the red blood cell membrane skeleton
Publisher: PeerJ
Date: 11-01-2018
DOI: 10.7287/PEERJ.PREPRINTS.3521V1
Abstract: We present Skan (Skeleton analysis), a Python library for the analysis of the skeleton structures of objects. It was inspired by the “analyse skeletons” plugin for the Fiji image analysis software, but its extensive Application Programming Interface (API) allows users to examine and manipulate any intermediate data structures produced during the analysis. Further, its use of common Python data structures such as SciPy sparse matrices and pandas data frames opens the results to analysis within the extensive ecosystem of scientific libraries available in Python. We demonstrate the validity of Skan’s measurements by comparing its output to the established Analyze Skeletons Fiji plugin, and, with a new scanning electron microscopy (SEM)-based method, we confirm that the malaria parasite Plasmodium falciparum remodels the host red blood cell cytoskeleton, increasing the average distance between spectrin-actin junctions.
A new Python library to analyse skeleton images confirms malaria parasite remodelling of the red blood cell membrane skeleton
Publisher: PeerJ
Date: 15-02-2018
DOI: 10.7717/PEERJ.4312
Abstract: We present Skan (Skeleton analysis), a Python library for the analysis of the skeleton structures of objects. It was inspired by the “analyse skeletons” plugin for the Fiji image analysis software, but its extensive Application Programming Interface (API) allows users to examine and manipulate any intermediate data structures produced during the analysis. Further, its use of common Python data structures such as SciPy sparse matrices and pandas data frames opens the results to analysis within the extensive ecosystem of scientific libraries available in Python. We demonstrate the validity of Skan’s measurements by comparing its output to the established Analyze Skeletons Fiji plugin, and, with a new scanning electron microscopy (SEM)-based method, we confirm that the malaria parasite Plasmodium falciparum remodels the host red blood cell cytoskeleton, increasing the average distance between spectrin-actin junctions.
Related Organisations
Howard Hughes Medical Institute Janelia Farm Research Campus
Location: United States of America
Related Funding Activities
Discovery Projects - Grant ID: DP230103097
Start Date: 2023
End Date: 12-2025
Amount: $674,300.00
Funder: Australian Research Council
View Funded Activity