Sébastien Lemieux

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Biography

Sébastien Lemieux trained as a microbiologist but turned to bioinformatics in 1997, completing his MSc and PhD at Université de Montréal under the supervision of François Major. After obtaining his PhD in 2002, he headed to the private sector for postdoctoral training at Elitra Canada (now Merck & Co) under the supervision of Bo Jiang. There he acquired skills in sequence analysis and the analysis of DNA microarray data, as well as in the integration of experimental data with computational techniques.

Lemieux joined Université de Montréal in 2005, first at the Institute for Research in Immunology and Cancer (IRIC). In 2018, he was appointed associate professor in the Department of Biochemistry and Molecular Medicine of the Faculty of Medicine.

Current Students

Serena Chan

Master's Research - Université de Montréal

serena.chan@mila.quebec

Lena Herbold

Master's Research - Université de Montréal

lena.herbold@mila.quebec

Github

Léa Kaufmann

Master's Research - Université de Montréal

lea.kaufmann@mila.quebec

Github

Publications

Unified gene expression signature of novel NPM1 exon 5 mutations in acute myeloid leukemia

Véronique Lisi

Ève Blanchard

Michael Vladovsky

Éric Audemard

Albert Ferghaly

Josée Hébert

Guy Sauvageau

Vincent-Philippe Lavallee

Visual Abstract

2022-07-17

Blood Advances (published)

Monoallelic Heb/Tcf12 Deletion Reduces the Requirement for NOTCH1 Hyperactivation in T-Cell Acute Lymphoblastic Leukemia

Diogo F. T. Veiga

Mathieu Tremblay

Bastien Gerby

Sabine Herblot

André Haman

Patrick Gendron

Juan Carlos Zúñiga-Pflücker

Josée Hébert

Joseph Paul Cohen

Trang Hoang

Early T-cell development is precisely controlled by E proteins, that indistinguishably include HEB/TCF12 and E2A/TCF3 transcription factors,… (see more) together with NOTCH1 and pre-T cell receptor (TCR) signalling. Importantly, perturbations of early T-cell regulatory networks are implicated in leukemogenesis. NOTCH1 gain of function mutations invariably lead to T-cell acute lymphoblastic leukemia (T-ALL), whereas inhibition of E proteins accelerates leukemogenesis. Thus, NOTCH1, pre-TCR, E2A and HEB functions are intertwined, but how these pathways contribute individually or synergistically to leukemogenesis remain to be documented. To directly address these questions, we leveraged Cd3e-deficient mice in which pre-TCR signaling and progression through β-selection is abrogated to dissect and decouple the roles of pre-TCR, NOTCH1, E2A and HEB in SCL/TAL1-induced T-ALL, via the use of Notch1 gain of function transgenic (Notch1ICtg) and Tcf12+/- or Tcf3+/- heterozygote mice. As a result, we now provide evidence that both HEB and E2A restrain cell proliferation at the β-selection checkpoint while the clonal expansion of SCL-LMO1-induced pre-leukemic stem cells in T-ALL is uniquely dependent on Tcf12 gene dosage. At the molecular level, HEB protein levels are decreased via proteasomal degradation at the leukemic stage, pointing to a reversible loss of function mechanism. Moreover, in SCL-LMO1-induced T-ALL, loss of one Tcf12 allele is sufficient to bypass pre-TCR signaling which is required for Notch1 gain of function mutations and for progression to T-ALL. In contrast, Tcf12 monoallelic deletion does not accelerate Notch1IC-induced T-ALL, indicating that Tcf12 and Notch1 operate in the same pathway. Finally, we identify a tumor suppressor gene set downstream of HEB, exhibiting significantly lower expression levels in pediatric T-ALL compared to B-ALL and brain cancer samples, the three most frequent pediatric cancers. In summary, our results indicate a tumor suppressor function of HEB/TCF12 in T-ALL to mitigate cell proliferation controlled by NOTCH1 in pre-leukemic stem cells and prevent NOTCH1-driven progression to T-ALL.

2022-03-23

Frontiers in Immunology (published)

CAMAP: Artificial neural networks unveil the role of codon arrangement in modulating MHC-I peptides presentation

Tariq Daouda

Maude Dumont-Lagacé

Albert Feghaly

Yahya Benslimane

Rebecca Panes

Mathieu Courcelles

Mohamed Benhammadi

Lea Harrington

Pierre Thibault

François Major

Yoshua Bengio

Étienne Gagnon

Claude Perreault

MHC-I associated peptides (MAPs) are small fragments of intracellular proteins presented at the surface of cells and used by the immune syst… (see more)em to detect and eliminate cancerous or virus-infected cells. While it is theoretically possible to predict which portions of the intracellular proteins will be naturally processed by the cells to ultimately reach the surface, current methodologies have prohibitively high false discovery rates. Here we introduce an artificial neural network called Codon Arrangement MAP Predictor (CAMAP) which integrates information from mRNA-to-protein translation to other factors regulating MAP biogenesis (e.g. MAP ligand score and transcript expression levels) to improve MAP prediction accuracy. While most MAP predictive approaches focus on MAP sequences per se, CAMAP’s novelty is to analyze the MAP-flanking mRNA sequences, thereby providing completely independent information for MAP prediction. We show on several datasets that the integration of CAMAP scores with other known factors involved in MAP presentation (i.e. MAP ligand score and mRNA expression) significantly improves MAP prediction accuracy, and further validate CAMAP learned features using anin-vitroassay. These findings may have major implications for the design of vaccines against cancers and viruses, and in times of pandemics could accelerate the identification of relevant MAPs of viral origins.

2020-12-31

PLoS Computational Biology (published)

Factorized embeddings learns rich and biologically meaningful embedding spaces using factorized tensor decomposition

Claude Perreault

The recent development of sequencing technologies revolutionized our understanding of the inner workings of the cell as well as the way dise… (see more)ase is treated. A single RNA sequencing (RNA-Seq) experiment, however, measures tens of thousands of parameters simultaneously. While the results are information rich, data analysis provides a challenge. Dimensionality reduction methods help with this task by extracting patterns from the data by compressing it into compact vector representations. We present the factorized embeddings (FE) model, a self-supervised deep learning algorithm that learns simultaneously, by tensor factorization, gene and sample representation spaces. We ran the model on RNA-Seq data from two large-scale cohorts and observed that the sample representation captures information on single gene and global gene expression patterns. Moreover, we found that the gene representation space was organized such that tissue-specific genes, highly correlated genes as well as genes participating in the same GO terms were grouped. Finally, we compared the vector representation of samples learned by the FE model to other similar models on 49 regression tasks. We report that the representations trained with FE rank first or second in all of the tasks, surpassing, sometimes by a considerable margin, other representations. A toy example in the form of a Jupyter Notebook as well as the code and trained embeddings for this project can be found at: https://github.com/TrofimovAssya/FactorizedEmbeddings. Supplementary data are available at Bioinformatics online.

2020-06-30

Bioinform. (published)

Glucocorticoid sensitivity in a subset of human RUNX1-mutated acute myeloid leukemia

Frédéric Leduc

Maude Wur

Geneviève Boucher

Rose-Marie Rébillard

Valérie Bordeleau

Julie Bergeron

Nadine Mayotte

Anne-Marie Côté

Véronique Lisi

Elliot D. Drobetsky

Tarik Möröy

Lambert Busque

Jean-Sébastien Delisle

Martin Sauvageau

Josée Hébert

Guy Sauvageau

Purpose: RUNX1-mutated (RUNX1mut) acute myeloid leukemia (AML) is associated with adverse outcome, highlighting the urgent need for a bett… (see more)er genetic characterization of this AML subgroup and for the design of efficient therapeutic strategies for this disease. Toward this goal, we further dissected the mutational spectrum and gene expression profile of RUNX1mut AML and correlated these results to drug sensitivity to identify novel compounds targeting this AML subgroup. Experimental Design: RNA-sequencing of 47 RUNX1mut primary AML specimens was performed and sequencing results were compared to those of RUNX1 wild-type samples. Chemical screens were also conducted using RUNX1mut specimens to identify compounds selectively affecting the viability of RUNX1mut AML. Results: We show that samples with no remaining RUNX1 wild-type allele are clinically and genetically distinct and display a more homogeneous gene expression profile. Chemical screening revealed that most RUNX1mut specimens are sensitive to glucocorticoids (GCs) and we confirmed that GCs inhibit AML cell proliferation through their interaction with the glucocorticoid receptor (GR). We observed that specimens harboring RUNX1 mutations expected to result in low residual RUNX1 activity are most sensitive to GCs, and that coassociating mutations as well as GR levels contribute to GC sensitivity. Accordingly, acquired glucocorticoid sensitivity was achieved by negatively regulating RUNX1 expression in human AML cells. Conclusions: Our findings show the profound impact of RUNX1 allele dosage on gene expression profile and glucocorticoid sensitivity in AML, thereby opening opportunities for preclinical testing which may lead to drug repurposing and improved disease characterization. Clin Cancer Res; 23(22); 6969–81. ©2017 AACR.

2017-08-30

Clinical Cancer Research (unknown)