Gene expression based prognostic models for predicting patient outcome in lung cancer

My primary research focus involves novel approaches to constructing gene expression-based signatures for predicting patient outcome in lung cancer. This is joint work with Paul Boutros and Syed Haider from the Ontario Institute for Cancer Research.

Integrating gene interaction networks and functional genomics data from lung cancer

Together with Anna Goldenberg and Sara Mostafavi from the University of Toronto, we are developing a new statistical model to integrate gene interaction networks and functional genomics (gene expression) data in lung cancer patients. Of the thousands of differentially expressed genes detected between tumor and healthy lung tissues, only a few genes are thought to be responsible for initiation and development of lung cancer. Our goal is to find the small set of 'culprit' genes whose changes in expression, together with their relative position in gene interaction networks, can explain the much larger set of differentially expressed genes observed over the entire genome.

Identifying genes important in juvenile arthritis development and progression

Together with Ang Cui (The University of Toronto), Rae Yeung (The Hospital for Sick Children) and Alan Rosenberg (The University of Saskatchewan), we are analyzing gene expression profiles from juvenile idiopathic arthritis (JIA) patients to find genes responsible and indicative of onset and progression of this heterogeneous group of autoimmune diseases.

Modeling population dynamics of hematopoietic stem cell systems

Together with Peter Zandstra and Wendy Qiao from the University of Toronto, we are developing an in silico tool to track the dynamics of cell populations throughout cell culture. in silico cell population tracking tools have several advantages over the traditional experimental procedure, flow cytometry. Most importantly, in silico models can facilitate identification of new cell populations that may not yet have defined cell surface markers. Also, flow cytometry method utilizes the expression of cell surface markers whose expression is very dynamic and may be affected by culture conditions and treatment during cell processing.

ISOLATE: Finding the site of origin of carcinomas of unknown origin

One of the most deadly cancer diagnoses is the carcinoma of unknown primary origin. Without the knowledge of the site of origin, treatment regimens are limited in their specificity and result in high mortality rates. Though supervised classification methods have been developed to predict the site of origin based on gene expression data, they require large numbers of previously classified tumors for training, in part because they do not account for sample heterogeneity, which limits their application to well-studied cancers.

We present ISOLATE, a new statistical method that simultaneously predicts the primary site of origin of cancers and addresses sample heterogeneity, while taking advantage of new high-throughput sequencing technology that promises to bring higher accuracy and reproducibility to gene expression profiling experiments. ISOLATE makes predictions de novo, without having seen any training expression profiles of cancers with identified origin. Compared with previous methods, ISOLATE is able to predict the primary site of origin, de-convolve and remove the effect of sample heterogeneity and identify differentially expressed genes with higher accuracy, across both synthetic and clinical datasets. Methods such as ISOLATE are invaluable tools for clinicians faced with carcinomas of unknown primary origin.

Inferring the ancestral regulome of the vertebrate lineage

Vertebrates share the same general body plan and organs, possess related sets of genes, and rely on similar physiological mechanisms, yet show great diversity in morphology, habitat and behavior. Alteration of gene regulation is thought to be a major mechanism in phenotypic variation and evolution, but relatively little is known about the broad patterns of conservation in gene expression in non-mammalian vertebrates.

We measured expression of all known and predicted genes across twenty tissues in chicken, frog and pufferfish. By combining the results with human and mouse data and considering only ten common tissues, we have found evidence of conserved expression for more than a third of unique orthologous genes. We find that, on average, transcription factor gene expression is neither more nor less conserved than that of other genes. Strikingly, conservation of expression correlates poorly with the amount of conserved nonexonic sequence, even using a sequence alignment technique that accounts for non-collinearity in conserved elements. Many genes show conserved human/fish expression despite having almost no nonexonic conserved primary sequence.

In conclusion, there are clearly strong evolutionary constraints on tissue-specific gene expression. A major challenge will be to understand the precise mechanisms by which many gene expression patterns remain similar despite extensive cis-regulatory restructuring.

This work was a joint collaboration with Timothy Hughes, Esther Chan, Michael Brudno, and Yee Whye Teh.