Bio

Professional Education


  • Doctor of Philosophy, Universite De Sherbrooke (2016)
  • Bachelor of Science, Universite De Sherbrooke (2010)

Publications

All Publications


  • A human lung tumor microenvironment interactome identifies clinically relevant cell-type cross-talk. Genome biology Gentles, A. J., Hui, A. B., Feng, W., Azizi, A., Nair, R. V., Bouchard, G., Knowles, D. A., Yu, A., Jeong, Y., Bejnood, A., Forgó, E., Varma, S., Xu, Y., Kuong, A., Nair, V. S., West, R., van de Rijn, M., Hoang, C. D., Diehn, M., Plevritis, S. K. 2020; 21 (1): 107

    Abstract

    Tumors comprise a complex microenvironment of interacting malignant and stromal cell types. Much of our understanding of the tumor microenvironment comes from in vitro studies isolating the interactions between malignant cells and a single stromal cell type, often along a single pathway.To develop a deeper understanding of the interactions between cells within human lung tumors, we perform RNA-seq profiling of flow-sorted malignant cells, endothelial cells, immune cells, fibroblasts, and bulk cells from freshly resected human primary non-small-cell lung tumors. We map the cell-specific differential expression of prognostically associated secreted factors and cell surface genes, and computationally reconstruct cross-talk between these cell types to generate a novel resource called the Lung Tumor Microenvironment Interactome (LTMI). Using this resource, we identify and validate a prognostically unfavorable influence of Gremlin-1 production by fibroblasts on proliferation of malignant lung adenocarcinoma cells. We also find a prognostically favorable association between infiltration of mast cells and less aggressive tumor cell behavior.These results illustrate the utility of the LTMI as a resource for generating hypotheses concerning tumor-microenvironment interactions that may have prognostic and therapeutic relevance.

    View details for DOI 10.1186/s13059-020-02019-x

    View details for PubMedID 32381040

  • Sparse discriminative latent characteristics for predicting cancer drug sensitivity from genomic features. PLoS computational biology Knowles, D. A., Bouchard, G., Plevritis, S. 2019; 15 (5): e1006743

    Abstract

    Drug screening studies typically involve assaying the sensitivity of a range of cancer cell lines across an array of anti-cancer therapeutics. Alongside these sensitivity measurements high dimensional molecular characterizations of the cell lines are typically available, including gene expression, copy number variation and genomic mutations. We propose a sparse multitask regression model which learns discriminative latent characteristics that predict drug sensitivity and are associated with specific molecular features. We use ideas from Bayesian nonparametrics to automatically infer the appropriate number of these latent characteristics. The resulting analysis couples high predictive performance with interpretability since each latent characteristic involves a typically small set of drugs, cell lines and genomic features. Our model uncovers a number of drug-gene sensitivity associations missed by single gene analyses. We functionally validate one such novel association: that increased expression of the cell-cycle regulator C/EBPdelta decreases sensitivity to the histone deacetylase (HDAC) inhibitor panobinostat.

    View details for DOI 10.1371/journal.pcbi.1006743

    View details for PubMedID 31136571

  • GFPT2-expressing cancer-associated fibroblasts mediate metabolic reprogramming in human lung adenocarcinoma. Cancer research Zhang, W., Bouchard, G., Yu, A., Shafiq, M., Jamali, M., Shrager, J. B., Ayers, K., Bakr, S., Gentles, A. J., Diehn, M., Quon, A., West, R. B., Nair, V., van de Rijn, M., Napel, S., Plevritis, S. K. 2018

    Abstract

    Metabolic reprogramming of the tumor microenvironment is recognized as a cancer hallmark. To identify new molecular processes associated with tumor metabolism, we analyzed the transcriptome of bulk and flow-sorted human primary non-small cell lung cancer (NSCLC) together with 18FDG-positron emission tomography scans, which provide a clinical measure of glucose uptake. Tumors with higher glucose uptake were functionally enriched for molecular processes associated with invasion in adenocarcinoma (AD) and cell growth in squamous cell carcinoma (SCC). Next, we identified genes correlated to glucose uptake that were predominately overexpressed in a single cell-type comprising the tumor microenvironment. For SCC, most of these genes were expressed by malignant cells, whereas in AD they were predominately expressed by stromal cells, particularly cancer-associated fibroblasts (CAFs). Among these AD genes correlated to glucose uptake, we focused on Glutamine-Fructose-6-Phosphate Transaminase 2 (GFPT2), which codes for the Glutamine-Fructose-6-Phosphate Aminotransferase 2 (GFAT2), a rate-limiting enzyme of the hexosamine biosynthesis pathway (HBP), which is responsible for glycosylation. GFPT2 was predictive of glucose uptake independent of GLUT1, the primary glucose transporter, and was prognostically significant at both gene and protein level. We confirmed that normal fibroblasts transformed to CAF-like cells, following TGF-beta treatment, upregulated HBP genes, including GFPT2, with less change in genes driving glycolysis, pentose phosphate pathway and TCA cycle. Our work provides new evidence of histology-specific tumor-stromal properties associated with glucose uptake in NSCLC and identifies GFPT2 as a critical regulator of tumor metabolic reprogramming in AD.

    View details for PubMedID 29760045

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