Meet the 2021 Scholars

Genetic Interaction Networks in Breast Cancer

Breast cancer is the most common cancer in women worldwide and the second leading cause of death by cancer. In 2020, 27,400 women were diagnosed with breast cancer, representing 25% of all new cancer diagnosis. Triple-negative (TN) breast cancer (lacking expression of the markers for the estrogen, progesterone, and human epidermal growth factor receptor 2 (HER2) receptors) represent ~15% of these cases. TNBC is the most aggressive in terms of tumour growth and metastasis and is associated with earlier age of onset and the worse overall outcome. This is in part due to the complexity of alterations that occur in TNBC and still there is poor understanding of those which are causal. This project develops an approach to examine complex mutational events at scale

Professor Morag Park
 

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Analysis of 2D and 3D models of neural electrical activity in Parkinson’s disease models

Parkinson’s disease (PD), a central nervous system disorder that leads to dopaminergic (DA) neuronal cell death, has no disease-modifying treatments. The pathways leading to DA neuronal cell death and PD patient phenotypes are not understood. Some genes leading to familial forms of PD are known. Mouse models with deletions or mutations of genes causing familial PD do not model the disease phenotypes seen in humans. The ideal way to study the cellular mechanisms leading to human disease is to use human cells. Taking advantage of several technological breakthroughs, the Fon lab uses human blood cells, reprogrammed into induced pluripotent stem cells (hiPSCs). Real brain tissue contains a mixture of cell types, and to better model brain tissue we use 3D models termed midbrain organoids (hMOs), which contain DA neurons and a mixture of other brain cells. We have recorded functional activity present in hMOs using Multi-electrode-array (MEA) recordings from familial models of PD and controls. The midbrain DA neurons in the human substania nigra, the part of the brain affected by PD, fire in a consistent bursting pattern and we detect a similar pattern in the hMOs. We aim to distinguish phenotypic differences between neurons and hMOs derived from monogenic models of PD and the matching isogenic controls. To do this we must develop scripts and algorithms to measure features from the raw MEA data. Additionally, very little is known about the functional properties of iPSC-derived DA neurons or hMOs. We aim to develop analysis systems to quantify signal and network dynamics in these systems.
 

Professor Edward Fon

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EV biomarkers in saliva and blood from head & neck cancer patients by ChIP-seq & RNA-seq

Background: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide in 2018. HNSCC can be distinguished into HPV-negative and HPV-positive HNSCC. Limited biomarkers in clinical settings and careful physical examination remains the primary approach for detection. Therefore, new biomarkers are needed for early diagnosis and rapid treatment. Exosomes are emerging as biomarkers and many studies have shown the miRNA-based biomarkers in the plasma/serum of head and neck cancer (HNC). Objective: To identify exosome-associated biomarkers from head and neck squamous cell carcinoma patients‚Äô biofluids using chromatin immunoprecipitation sequencing (ChIP-seq) and RNA sequencing (RNA-seq) assays. Methods: In our laboratory, we have identified HPV-positive and HPV-negative using droplet digital Polymerase Chain Reaction (ddPCR) on cell free DNA obtained from head and neck cancer plasma and saliva (n=52 patients). We will further explore the exosomes isolated from these two subtypes (HPV +ve and HPV -ve) and include healthy controls to determine the epigenome and transcriptomic profile. In this retrospective study, we will integrate ChIP-seq and RNA-seq assays of exosomes, which will be more accurate than gene expression alone. The first goal of this program is to perform ChIP-seq and RNA-seq assays on exosomes isolated from HNC patients -- Äô biofluids. Next, we will validate our findings using ddPCR and qPCR in the tumors and bodily fluids. The third step is to set up a DNA methylation and transcriptome panel and perform a longitudinal study. Exosome-liquid biopsy is an exciting new field, and EVs could serve as biomarkers in early cancer detection, response to therapies and detection of recurrent HNCSS as well as other types of cancers.

Professor Julia Burnier

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Development of a Severity Scoring System for Intermediate/Mild Zellweger Spectrum Disorders

Zellweger Spectrum Disorders (ZSD) are a group of rare, autosomal recessive, multisystemic disorders characterized by a defect in peroxisome biogenesis. ZSD is a phenotypic continuum ranging from severe to mild, however, severity of symptoms cannot be entirely predicted by genotype or biochemical abnormalities. The lack of a robust tool to assess disease severity is a major limitation in the management and treatment this disorder. Our objective is to develop and validate a robust and quantitative severity scoring system based on the body systems that can be affected in individuals with intermediate/mild ZSD. A validated ZSD severity scoring system would allow clinicians to identify prognostic features in ZSD patients and reliably quantify disease burden. Moreover, it could be used for classification and stratification of patients in clinical trials that seek to evaluate potential treatments for ZSD, to distinguish clinical endpoints, and for monitoring disease progression and treatment responses.

Professor Nancy Braverman
 

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>200k exomes to resolve the genomic overlap of social isolation with AD risk factors

Alzheimer's disease and related dementias (ADRD) is a major public health burden‚ Äì compounding over upcoming years due to longevity. Recently, epidemiological evidence hinted at the experience of social isolation in accelerating ADRD onset. In ~500,000 UK Biobank participants, we revisited traditional risk factors for developing ADRD in the context of loneliness and lacking social support. Across these measures of subjective and objective social deprivation, we have identified strong links between individual's social capital and various indicators of ADRD risk, which replicated across both population cohorts. The quality and quantity of daily social encounters may have deep connections with key aetiopathological factors, including stress management, cardiovascular effects on the heart-brain axis, sleep quality, substance use, and cognitive resource. Our population-scale findings call for urgent research efforts on the role of social lifestyle markers in ADRD neurodegeneration. As a next step, we are leveraging the open-access exomes of >200,000 UK Biobank participants to further identify genetic variation in the coding sequences of known ADRD genes as well as other psychiatric disease loci, and whether these genetic links can be replicated.

Professor Danilo Bzdok

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Evaluating the role of nuclear genes encoding mitochondrial and tRNA proteins in neurodegeneration

Systemic mitochondrial dysfunction has been described in neurodegenerative diseases and may arise as a consequence of abnormal mitochondrial DNA or due to abnormal interacting proteins. Specifically, mutations in genes directly implicated in mitochondrial function such as Parkin, PINK1, and SOD1 cause rare Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS), respectively. Furthermore, accumulation of another PD protein, alpha-synuclein, has been linked to mitochondrial impairment. Finally, mitochondrial dysfunction has also been described in atypical forms of PD such as multiple system atrophy. It remains unclear whether mitochondrial dysfunction initiates or is a byproduct of, neurodegeneration. Therefore, conducting a large-scale unbiased study to evaluate whether there is enrichment of variation in nuclearly encoded mitochondrial proteins as well as in tRNA enzymes in neurodegenerative disease cases, can help resolve the role of mitochondria in neurodegeneration.

Professor Sali Farhan

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Improving spatio-temporal image correlation spectroscopy (STICS) computational analysis platform

Spatio-temporal image correlation spectroscopy (STICS) is a fluorescence fluctuation analysis method to measure protein transport and interaction maps from analysis of input data sets from fluorescence microscopy image series of cells. STICS has been used to map dynamics involved in cell migration as well as signaling in cells. The current implementation of STICS is coded in MATLAB but does not have a graphical user interface and is not currently optimized for computational speed. The project would entail improving the STICS implementation via construction of a GUI based implementation, possible porting of the code to another more efficient language, parallelization of code or using GPU implementation to speed up the analysis of microscopy data sets.  

Professor Paul Wiseman

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Gene set enrichment in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a late-onset neurodegenerative disease with an incidence of 2-3 per 100,000 and an estimated lifetime risk of 1 in 472 and 1 in 350 in females and males, respectively. Initially, ALS symptoms consist of voluntary muscle weakness and atrophy leading to respiratory paralysis and death often within three-five years of diagnosis. Genetics is an important risk factor in ALS. In our recent study, we aggregated exome sequencing data from >5,000 ALS patients and >10,000 controls, to discover novel ALS risk factors. We showed a significant exome-wide enrichment of rare protein-truncating variants unique to ALS. This signal originated from constrained genes, which are a unique class of genes under strong purifying selection. Also, we identified a novel gene, DNAJC7, which is a constrained gene, mutated in patients (n=5,095) though completely absent in controls (n=28,910). Moreover, in a patient with a protein-truncating variant, p.Arg156Ter, we observed depleted DNAJC7 protein levels, further supporting the pathogenic role of DNAJC7 haploinsufficiency in ALS. Our approach of first interrogating a subset of genes based on their common biological function has led to a novel genetic discovery. We plan to leverage this same approach and extend it to multiple gene sets that may be enriched in neurodegeneration such as kinases, all heat-shock proteins, genes encoding RNA binding proteins, autophagy proteins, as well as other gene sets. If any gene passes the imposed statistical threshold, we will investigate whether patient cells are available in the Neuro labs or elsewhere, and we will work with our wet lab collaborators to perform the appropriate wet lab experiments based on the mechanism (gain or loss of function).

Professor Sali Farhan

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Large-scale multi-cohort prediction of psychopathology (ADHD, depression) using deep learning

Using deep learning methods, we seek to construct complex prediction models to identify patients at risk of depression/ADHD and determine which modifiable aspect of the environment should be targeted for maximizing improvement (personalized treatment). Our predictive models would consider both environmental and genetic risk/protective factors. We will use DREAM BIG, a consortium of 5 harmonized prenatal cohorts.

Professor Ashley Wazana

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Deep Learning to Predict Disease Progression in Patients with Polycystic Kidney Disease

Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited kidney disease, and the fourth leading cause of kidney failure leading to dialysis or kidney transplantation. There exists significant phenotypic heterogeneity, both within and between families, even among those with the same gene mutation. As a result, clinical risk prediction has focused on estimating total kidney volume (TKV), which reflects the overall burden of cyst development and expansion. Those with higher TKV at a younger age are more likely to be considered for disease modifying therapy. The estimation of TKV by radiologic imaging is practical, but volumetric measurement alone does not sufficiently classify risk, and may lead to inappropriate decisions related to treatment. Recognizing certain patterns of kidney cyst involvement is critical, but this pattern recognition is not standardized and not easily measurable. Deep learning approaches to image analysis of kidneys in patient with ADPKD may provide a more reliable way to assess patients at risk of developing renal progression and subsequently identifying those most amenable to targeted therapy.

Professor Ahsan Alam

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