Biology subjects

Haider, S.

Publications and source records attributed to Haider, S..

6 recordsLinked to original sources

bioRxiv2018

Integrin αVβ6-EGFR crosstalk regulates bidirectional force transmission and controls breast cancer invasion

The mechanical properties of the extracellular matrix within tumours control multiple cellular functions that drive cancer invasion and metastasis. However, the mechanisms controlling microenvironmental force sensation and transmission, and how these regulate transcriptional reprogramming and invasion, are unclear. Our aim was to understand how mechanical inputs are transmitted bidirectionally and translated into biochemical and transcriptional outputs to drive breast cancer progression. We reveal that adhesion receptor and growth factor receptor crosstalk regulates a bidirectional feedback mechanism co-ordinating force-dependent transcriptional regulation and invasion.\n\nIntegrin V{beta}6 drives invasion in a range of carcinomas and is a potential therapeutic target. V{beta}6 exhibits unique biophysical properties that promote force-generation and increase matrix rigidity. We employed an inter-disciplinary approach incorporating proteomics, biophysical techniques and multi-modal live-cell imaging to dissect the role of V{beta}6-EGFR crosstalk on transmission of mechanical signals bidirectionally between the extracellular matrix and nucleus.\n\nWe show that V{beta}6 expression correlates with poor prognosis in triple-negative breast cancer (TNBC) and drives invasion of TNBC cells. Moreover, our data show that a complex regulatory mechanism exists involving crosstalk between V{beta}6 integrin and EGFR that impacts matrix stiffness, force transmission to the nucleus, transcriptional reprogramming and microenvironment rigidity. V{beta}6 engagement triggers EGFR & MAPK signalling and V{beta}6-EGFR crosstalk regulates mutual receptor trafficking mechanisms. Consequently, EGF stimulation suppresses V{beta}6-mediated force-application on the matrix and nuclear shuttling of force-dependent transcriptional co-activators YAP/TAZ. Finally, we show that crosstalk between V{beta}6 & EGFR regulates TNBC invasion.\n\nWe propose a model whereby V{beta}6-EGFR crosstalk regulates matrix stiffening, but also the transmission of extracellular forces into the cell in order to co-ordinate transcriptional reprogramming and invasion. To exploit adhesion receptors and receptor tyrosine kinases therapeutically, it will be essential to understand the integration of their signalling functions and how crosstalk mechanisms influence invasion and the response of tumours to molecular therapeutics.

cancer biology↗

bioRxiv2018

Hypoxia-induced switch in SNAT2/SLC38A2 regulation generates endocrine-resistance in breast cancer

Tumor hypoxia is associated with poor patient outcomes in estrogen receptor- (ER) positive breast cancer. Hypoxia is known to affect tumor growth by reprogramming metabolism and regulating amino acid (AA) uptake. Here we show that the glutamine transporter, SNAT2, is the AA transporter most frequently induced by hypoxia in breast cancer and it is regulated by HIF1 both in-vitro and in-vivo in xenografts. SNAT2 induction in MCF7 cells was also regulated by ER but it became predominantly a HIF-1-dependent gene under hypoxia. Relevant to this, binding sites for both HIF-1 and ER overlap in SNAT2s cis-regulatory elements. In addition, the downregulation of SNAT2 by the ER antagonist fulvestrant was reverted in hypoxia.\n\nOverexpression of SNAT2 in-vitro to recapitulate the levels induced by hypoxia caused enhanced growth, particularly after ER inhibition, in hypoxia, or when glutamine levels were low. SNAT2 upregulation in-vivo caused complete resistance to anti-estrogen and, partially, anti-VEGF therapies. Finally, high SNAT2 expression levels correlate with HIF-1 and worse outcome in patients given anti-estrogen therapy. Our findings show a switch in regulation of SNAT2 between ER and HIF-1, leading to endocrine resistance in hypoxia. Development of drugs targeting SNAT2 may be of value for a subset of hormone-resistant breast cancer.

cancer biology↗

bioRxiv2018

Subnetwork-based prognostic biomarkers exhibit performance and robustness superior to gene-based biomarkers in breast cancer

BackgroundEffective classification of cancer patients into groups with differential survival remains an important and unsolved challenge. Biomarkers have been developed based on mRNA abundance data, but their replicability and clinical utility is modest. Integrating functional information, such as pathway data, has been suggested to improve biomarker performance. To date, however, the advantages of subnetwork-based biomarkers have not been quantified.\n\nResultsWe deeply sampled the population of prognostic gene-based and subnetwork-based biomarkers in a breast cancer meta-dataset of 4,960 patients. Analysing the performance and robustness of 22,000,000 gene biomarkers and 6,250,000 subnetwork biomarkers across twenty different training:testing cohort partitions of the meta-dataset revealed that subnetwork biomarkers exhibit superior overall performance and higher concordance across partitions. We find evidence of an upper bound for optimal biomarker size of [~]200 genes or [~]100 subnetworks. Additionally, with both biomarker feature types, larger biomarkers tend to show less consistency in performance across partitions, suggestive of over-fitting. Finally, an evaluation of varying training cohort sizes quantifies the effects of training cohort size.\n\nConclusionsMany groups are developing techniques for exploiting network-based representations of biological pathways to characterize cancer and other diseases. By considering the distribution of gene- and subnetwork-based biomarkers, we show that pathway data improves performance and replicability, and that smaller biomarkers are more robust across patient cohorts. These insights may facilitate development of clinically useful biomarkers.

bioinformatics↗

bioRxiv2018

Network-Based Biomarkers Enable Cross-Disease Biomarker Discovery

Biomarkers lie at the heart of precision medicine, biodiversity monitoring, agricultural pathogen detection, amongst others. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers almost always involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. We therefore created SIMMS: an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We applied SIMMS to multiple data-types across four diseases, and in each it reproducibly identified subtypes, made superior predictions to the best bespoke approaches, and identified known and novel signaling nodes. To demonstrate its ability on a new dataset, we measured 33 genes/nodes of the PIK3CA pathway in 1,734 FFPE breast tumours and created a four-subnetwork prediction model. This model significantly out-performed existing clinically-used molecular tests in an independent 1,742-patient validation cohort. SIMMS is generic and can work with any molecular data or biological network, and is freely available at: https://cran.r-project.org/web/packages/SIMMS.

bioinformatics↗

bioRxiv2018

Non-Canonical Activation of CREB Mediates Neuroprotection in a C. elegans Model of Excitotoxic Necrosis

Excitotoxicity, caused by exaggerated neuronal stimulation by Glutamate (Glu), is a major cause of neurodegeneration in brain ischemia. While we know that neurodegeneration is triggered by overstimulation of Glu-Receptors (GluRs), the subsequent mechanisms that lead to cellular demise remain controversial. Surprisingly, signaling downstream of GluRs can also activate neuroprotective pathways. The strongest evidence involves activation of the transcription factor cAMP Response Element Binding-protein (CREB), widely recognized for its importance in synaptic plasticity. Canonical views describe CREB as a phosphorylation-triggered transcription factor, where transcriptional activation involves CREB phosphorylation and association with CREB Binding Protein (CBP). However, given CREBs ubiquitous cross-tissue expression, the multitude of cascades leading to CREB phosphorylation, and its ability to regulate thousands of genes, it remains unclear how CREB exerts closely-tailored, differential neuroprotective responses in excitotoxicity. A non-canonical, alternative cascade for activation of CREB-mediated transcription involves the CREB co-factor cAMP-regulated transcriptional co-activator (CRTC), and may be independent of CREB phosphorylation. To identify cascades that activate CREB in excitotoxicity we use a C. elegans model of neurodegeneration by excitotoxic necrosis. We demonstrate that CREBs neuroprotective effect is conserved, and seems most effective in neurons with moderate Glu exposure. We find that factors mediating canonical CREB activation are not involved. Instead, phosphorylation-independent CREB activation in nematode excitotoxic necrosis hinges on CRTC. CREB-mediated transcription that depends on CRTC, but not on CREB phosphorylation, might lead to expression of a specific subset of neuroprotective genes. Elucidating conserved mechanisms of excitotoxicity-specific CREB activation can help us focus on core neuroprotective programs in excitotoxicity.

neuroscience↗

bioRxiv2017

BPG: Seamless, Automated and Interactive Visualization of Scientific Data

We introduce BPG, an easy-to-use framework for generating publication-quality, highly-customizable plots in the R statistical environment. This open-source package includes novel methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it ideal for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for seamless integration with computational pipelines. BPG is available at http://labs.oicr.on.ca/boutros-lab/software/bpg

bioinformatics↗