Department of Neurology & Neurosurgery Montreal Neurological Institute McGill University Montreal, Quebec
Functional and pathological markers of dementia: Evaluating disease progression and response to therapy
Dr. Edith Hamel’s research focuses on the interactions between neurons, astrocytes and blood vessels that assure a proper blood supply to activated brain areas, a phenomenon commonly referred to “neurovascular coupling”. These interactions are at the basis of several brain imaging techniques that use hemodynamic signals to map changes in brain activity under physiological and pathological conditions. An important part of her research is dedicated to the understanding of the relationships between cerebrovascular alterations and cognitive failure. Particularly, she investigates how pharmacotherapy or life style habits can impact cerebrovascular reactivity, brain perfusion and cognitive performance in animal models of vascular cognitive impairment and dementia (VCID) or Alzheimer's disease. She uses behavioral testing, laser doppler flowmetry, classic biochemical and anatomical techniques as well as imaging of optical intrinsic signals, laser speckle, electrophysiological recordings and electrical or optogenetic stimulation of specific brain neurons to investigate their hemodynamic responses.
John E. Dick PhD FRS Senior Scientist and Canada Research Chair, Princess Margaret Cancer Centre, University Health Network. Professor, Dept of Molecular Genetics, University of Toronto. Director, Translational Research Initiative in Leukemia, Ontario Institute for Cancer Research.
Tracking the arc of AML and B-ALL from the cell of origin to the origin of relapse in a single blood sample
Individual cancer cells exhibit functional heterogeneity of many cancer hallmarks including the capacity for sustaining long-term clonal maintenance, a stemness property involving self-renewal. Our studies established that only rare AML cells possessed such leukemia stem cell (LSC) properties and that AML is a cellular hierarchy. LSC were found to be highly relevant to human disease as gene signatures specific to LSC were much more predictive of patient response to therapy and overall survival compared to the bulk non-LSC AML cells. We have recently shown that the genetic and hierarchy models of heterogeneity are not mutually exclusive as often posited, but highly integrated. Through sequencing of purified populations of normal blood cells, AML cells, and xenografts from paired diagnosis/relapse AML samples, we tracked the full arc of leukemia development in humans: from the cell of origin (an HSC) that acquires the first genetic mutation; to pre-leukemic clonal expansion of HSC; the generation of genetically diverse LSC; and finally to the cellular origin of relapse (rare LSC subclones) within the diagnosis sample. Similar studies have been undertaken in B-ALL where relapse-fated subclones were proven to be partially resistant to clinically used drugs. By showing that pre-existing LSC survive therapy and cause relapse, the key role that stemness plays in human cancer is evident. The ability to track the cellular origin of relapse will enable biomarker development leading to improved methods for disease management and monitoring in AML. Finally, the existence of clonally expanded pre-leukemic HSPC in the diagnosis blood sample of many AML samples predicted that it should be possible to identify individuals who are at risk for progressing to AML long before AML develops. We have undertaken genotyping studies of normal individuals who have enrolled in the large cohort EPIC who eventually developed AML and compared them to the enrollment sample of others who never progressed to AML. We have found a clear signature that is able to predict with high accuracy those individuals who have age related clonal hematopoiesis (ARCH) who progress to AML, almost 10 years prior to AML development. This is distinct from individuals who have benign ARCH who never progress. These new findings offer the potential for future intervention to permit AML prevention trials.
Wednesday November 7th, 2018 School of Medicine, Room 032A 11:00 - 12:00
Michael M. Hoffman, Ph.D. Scientist, Princess Margaret Cancer Centre Assistant Professor, Department of Medical Biophysics, Assistant Professor, Department of Computer Science University of Toronto
Michael Hoffman creates predictive computational models to understand interactions between genome, epigenome, and phenotype in human cancers. He implemented the genome annotation method Segway, which simplifies interpretation of large multivariate genomic datasets, and was a linchpin of the NIH ENCODE Project analysis. He is a principal investigator at the Princess Margaret Cancer Centre and Assistant Professor in the Departments of Medical Biophysics and Computer Science, University of Toronto. He was named a CIHR New Investigator and has received several awards for his academic work, including the NIH K99/R00 Pathway to Independence Award, and the Ontario Early Researcher Award
Identifying transcription factor binding using open chromatin, transcriptome, and methylation data
First, we will discuss a new method to discover transcription factor motifs and identify transcription factor binding sites in DNA with covalent modifications such as methylation. Just as transcription factors distinguish one standard nucleobase from another, they also distinguish unmodified and modified bases. To represent the modified bases in a sequence, we replace cytosine (C) with symbols for 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC). Similarly, we adapted the well-established position weight matrix model of transcription factor binding affinity to an expanded alphabet. We created an expanded-alphabet genome sequence using genome-wide maps of 5mC and 5hmC in mouse naive T cells. Using this sequence and expanded-alphabet position weight matrices, we reproduced various known methylation binding preferences, including the preference of ZFP57 and C/EBPβ for methylated motifs and the preference of c-Myc for unmethylated motifs. Using these known binding preferences to tune model parameters enables discovery of novel modified motifs.
Second, we will discuss a new method, Virtual ChIP-seq, which predicts binding of individual transcription factors in new cell types using an artificial neural network that integrates ChIP-seq results from other cell types and chromatin accessibility data in the new cell type. Virtual ChIP-seq also uses learned associations between gene expression and transcription factor binding at specific genomic regions. This approach outperforms methods that use transcription factor sequence preferences in the form of position weight matrices, predicting binding for 36 transcription factors (Matthews Correlation Coefficient > 0.3).
Monday, November 26th, 2018 10:30 – 11:30 am Bioscience Complex, Room 103