Portrait de Sébastien Lemieux

Sébastien Lemieux

Membre académique associé
Professeur agrégé, Université de Montréal, Département de biochimie et de médecine moléculaire
Université de Montréal
Sujets de recherche
Biologie computationnelle
Modélisation moléculaire
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Biographie

Microbiologiste de formation, Sébastien Lemieux s'est tourné vers la bio-informatique en 1997 et a réalisé des études de maîtrise et de doctorat à l'Université de Montréal sous la direction de François Major. Après avoir obtenu son doctorat en 2002, le jeune chercheur s'est dirigé vers le secteur privé et a effectué un stage postdoctoral chez Elitra Canada (maintenant Merck & Co.) sous la supervision de Bo Jiang. Il y a acquis des compétences en analyse de séquences et en analyse de données de microréseaux d'ADN, ainsi qu'en intégration informatique de données expérimentales.

Il a finalement rejoint les rangs de l'Institut de recherche en immunologie et en cancérologie (IRIC) en 2005. En 2018, il a été nommé professeur agrégé au Département de biochimie et médecine moléculaire de la Faculté de médecine de l'Université de Montréal.

Étudiants actuels

Serena Chan
Maîtrise recherche - UdeM
Site web
Léa Kaufmann
Maîtrise recherche - UdeM
Site web
Github

Publications

High IL1R1 expression predicts poor survival and benefit from stem cell transplant in intermediate-risk acute myeloid leukemia from the Leucegene cohort
Guillaume Richard-Carpentier
Francois Béliveau
Sandrine Lacoste
Banafsheh Khakipoor
Véronique Lisi
Michael Vladovsky
Miriam Marquis
Jean-François Spinella
Patrick Gendron
Sébastien Lemieux
Vincent-Philippe Lavallee
Guy Sauvageau
Josée Hébert
There is an unmet clinical need to identify patients with acute myeloid leukemia and intermediate-risk cytogenetics who benefit from allogen… (voir plus)eic hematopoietic stem cell transplantation in first remission, especially among those without FLT3 -ITD mutation. We analyzed transcriptomic data from the Leucegene cohort composed of 316 patients with acute myeloid leukemia and intermediate-risk cytogenetics who have been treated with intensive chemotherapy. We evaluated associations between gene expression and overall survival or relapse-free survival and we analyzed the interaction between gene expression and allogeneic hematopoietic stem cell transplantation to identify biomarkers that predict the benefit of stem cell transplantation in this subgroup of patients. We identified high IL1R1 expression ( IL1R1 high ) as a prognostic and predictive marker in the Leucegene cohort. IL1R1 high (≥ 2.0 transcripts per million) was associated with older age, monocytic differentiation, higher frequency of FLT3 -ITD and RUNX1 mutations and lower frequency of IDH1 / 2 and bZIP CEBPA mutations. Patients with IL1R1 high had lower 5-year overall survival (10% vs 38%, p  < 0.01), and higher 5-year cumulative incidence of relapse (76% vs 59%, p  < 0.01) than those with low IL1R1 expression. IL1R1 high was independently associated with overall survival in multivariable analyses including age, white blood cell count at diagnosis and NPM1 , FLT3 -ITD, bZIP CEBPA , RUNX1 , ASXL1 and DNMT3A mutations (HR 1.78, p  < 0.01). Importantly, in landmark analysis, hematopoietic stem cell transplantation in first remission significantly improved 5-year overall survival in patients with IL1R1 high (67% vs 27%, HR 0.33, p  < 0.01), but not in patients with IL1R1 low (62% vs 54%, HR 0.72, p  = 0.31), especially among those without FLT3 -ITD mutation (48% vs 50%, HR 0.93, p  = 0.85). In patients who proceeded to allogeneic hematopoietic stem cell transplantation, the 5-year overall survival was 60% in patients with IL1R1 high compared to 56% in patients with IL1R1 low confirming that the worse prognosis associated with high expression of IL1R1 was abrogated by stem cell transplantation. IL1R1 expression is a candidate marker to identify patients with intermediate-risk cytogenetics acute myeloid leukemia at high risk of relapse who benefit from allogeneic hematopoietic stem cell transplantation in first remission.
2025-10-22
Biomarker Research (publié)
doi.org
EZH2 Inhibition Induces an Integrated Stress Response Driving Glutamine-Dependent Vulnerability in TNBC
Lucas Porras
Marina Fukano
Ann-Sophie Gironne
Elise Quadri
Gabriel Alzial
Hugo Philippeau
Yousef Aleassa
Anie Monast
Faustine Gorse
Myriame Saint-Arnaud
Mariana De Sa Tavares Russo
Sylvie Mader
Daina Avizonis
Sébastien Lemieux
Morag Park
Geneviève Deblois
EZH2, the catalytic subunit of Polycomb Repressive Complex II, is highly expressed and associated with poor prognosis in triple-negative bre… (voir plus)ast cancer (TNBC). Despite inducing significant changes in chromatin profiles and gene expression, EZH2 inhibition in TNBC models has limited impact on growth, suggesting adaptive compensatory mechanisms. Here, we demonstrate that EZH2 inhibition induces accumulation of double-stranded RNA and misfolded proteins in TNBC, activating an integrated stress response (ISR) via the PKR/PERK-eIF2α pathway. We identify Activating Transcription Factor 4 (ATF4) as a key effector upon EZH2 inhibition, driving metabolic changes characterized by increased amino acid uptake and glutamine dependency. Targeting this ISR-ATF4-mediated metabolic response using glutaminase inhibitor in combination with EZH2 inhibition significantly impairs TNBC cell proliferation and tumor progression. These findings reveal a stress-driven metabolic adaptation that enables TNBC survival upon EZH2 blockade, highlighting inhibition of this pathway as a strategy to enhance the efficacy of EZH2 inhibitors in TNBC.
2025-09-08
bioRxiv (prépublication)
doi.org
A Transparent and Generalizable Deep Learning Framework for Genomic Ancestry Prediction
Camille Rochefort-Boulanger
Matthew Scicluna
Raphaël Poujol
Jean-Christophe Grenier
Pierre Luc Carrier
Sébastien Lemieux
Julie G Hussin
1 Accurately capturing genetic ancestry is critical for ensuring reproducibility and fairness in genomic st… (voir plus)udies and downstream health research. This study aims to address the prediction of ancestry from genetic data using deep learning, with a focus on generalizability across datasets with diverse populations and on explainability to improve model transparency. We adapt the Diet Network, a deep learning architecture proven effective in handling high-dimensional data, to learn population ancestry from single nucleotide polymorphisms (SNPs) data using the populational Thousand Genomes Project dataset. Our results highlight the model’s ability to generalize to diverse populations in the CARTaGENE and Montreal Heart Institute biobanks and that predictions remain robust to high levels of missing SNPs. We show that, despite the lack of North African populations in the training dataset, the model learns latent representations that reflect meaningful population structure for North African individuals in the biobanks. To improve model transparency, we apply Saliency Maps, DeepLift, GradientShap and Integrated Gradients attribution techniques and evaluate their performance in identifying SNPs leveraged by the model. Using DeepLift, we show that model’s predictions are driven by population-specific signals consistent with those identified by traditional population genetics metrics. This work presents a generalizable and interpretable deep learning framework for genetic ancestry inference in large-scale biobanks with genetic data. By enabling more widespread genomic ancestry characterization in these cohorts, this study contributes practical tools for integrating genetic data into downstream biomedical applications, supporting more inclusive and equitable healthcare solutions.
2025-08-29
bioRxiv (prépublication)
doi.org
Predictive Gene Discovery with EPCY: A Density-Based Alternative to DE analysis
Éric Olivier Audemard
Jean-François Spinella
Vincent-Philippe Lavallee
Josée Hébert
Guy Sauvageau
Sébastien Lemieux
Identifying predictive genes from high-throughput data remains a key challenge in biomedical research. Most current approaches rely on stati… (voir plus)stical tests to select differentially expressed genes (DEGs), which may not align with the goal of predicting outcomes. We present EPCY, a method that ranks genes based on their predictive power using cross-validated classifiers and density estimation, without relying on null hypothesis testing. Applied to both bulk and single-cell RNA sequencing datasets, EPCY consistently outperforms benchmark DEG-based methods in selecting robust candidate genes. It also demonstrates greater stability across varying cohort sizes, enabling reproducible gene prioritization even in large, heterogeneous datasets. EPCY provides interpretable predictive scores, facilitating candidate selection aligned with downstream validation goals.
2025-08-10
bioRxiv (prépublication)
doi.org
Tumor antigens preferentially derive from unmutated genomic sequences in melanoma and non-small cell lung cancer
Anca Apavaloaei
Qingchuan Zhao
Leslie Hesnard
Maxime Cahuzac
Chantal Durette
Jean-David Larouche
Marie-Pierre Hardy
Krystel Vincent
Sylvie Brochu
Jean-Philippe Laverdure
Joël Lanoix
Mathieu Courcelles
Patrick Gendron
Mathieu Lajoie
Maria Virginia Ruiz Cuevas
Eralda Kina
Julie Perrault
Juliette Humeau
Gregory Ehx
Sébastien Lemieux … (voir 5 de plus)
Ian R. Watson
Daniel E. Speiser
Michal Bassani-Sternberg
Pierre Thibault
Claude Perreault
Melanoma and non-small cell lung cancer (NSCLC) display exceptionally high mutational burdens. Hence, immune targeting in these cancers has … (voir plus)primarily focused on tumor antigens (TAs) predicted to derive from nonsynonymous mutations. Using comprehensive proteogenomic analyses, we identified 589 TAs in cutaneous melanoma (n = 505) and NSCLC (n = 90). Of these, only 1% were derived from mutated sequences, which was explained by a low RNA expression of most nonsynonymous mutations and their localization outside genomic regions proficient for major histocompatibility complex (MHC) class I-associated peptide generation. By contrast, 99% of TAs originated from unmutated genomic sequences specific to cancer (aberrantly expressed tumor-specific antigens (aeTSAs), n = 220), overexpressed in cancer (tumor-associated antigens (TAAs), n = 165) or specific to the cell lineage of origin (lineage-specific antigens (LSAs), n = 198). Expression of aeTSAs was epigenetically regulated, and most were encoded by noncanonical genomic sequences. aeTSAs were shared among tumor samples, were immunogenic and could contribute to the response to immune checkpoint blockade observed in previous studies, supporting their immune targeting across cancers.
2025-05-21
Nature Cancer (publié)
doi.org
Replication of a GWAS signal near HLA-DQA2 with AML using a disease-only cohort and external population-based controls
Rose Laflamme
Véronique Lisi
Josée Hébert
Guy Sauvageau
Sébastien Lemieux
Vincent-Philippe Lavallee
Guillaume Lettre
2025-04-30
Blood Neoplasia (publié)
doi.org
IL1RAP is an immunotherapeutic target for normal karyotype triple-mutated acute myeloid leukemia
Arnaud Metois
Marie-Eve Bordeleau
Louis Theret
Azadeh Hajmirza
Ossama Moujaber
Jean-François Spinella
Jalila Chagraoui
Nadine Mayotte
Isabel Boivin
Éric Audemard
Léo Aubert
Véronique Lisi
Banafsheh Khakipoor
Azer Farah
Éric Bonneil
Alma Robert
Julie Lippens
Anna Moraitis
Francois Béliveau
Albert Feghaly … (voir 10 de plus)
Geneviève Boucher
Richard Marcotte
Patrick Gendron
Pierre Thibault
Sébastien Lemieux
Guillaume Richard-Carpentier
Vincent-Philippe Lavallee
Josée Hébert
Philippe P. Roux
Guy Sauvageau
Surface antigens of potential clinical significance remain under-characterized in AML. The European Leukemia Network classifies normal karyo… (voir plus)type AML (NK-AML) mutated for NPM1 (NPM1c) as a distinct entity associated with favorable outcomes if not associated with FLT3-ITD mutation. A subset of NPM1c NK-AML shows additional mutations in 2 genes: FLT3 (FLT3-ITD) and DNMT3 A. These leukemias, also referred to as NK triple mutated AML (NKt-AML), are particularly difficult to eradicate with current treatment options. Therefore, novel therapies are necessary that use proteins specifically expressed at the surface. In order to identify surface antigens for immunotherapy in NKt-AML, an extensive multi-omic analysis was conducted on primary AML samples. Surface proteome enrichment was performed on 100 primary AML samples, twelve of which were NKt-AML. Transcriptome analysis was carried out on the 691 primary AML samples, and single-cell RNA sequencing was conducted on 23 primary AML samples. Herein, using multi-omics data from the Leucegene collection, we identify IL1RAP as a promising antigen for this AML subgroup. We demonstrate that IL1RAP is expressed at the surface of primitive AML cells reminiscent of leukemic stem cells in NKt-AML primary human AML specimens, while showing relatively low expression levels in normal bone marrow HSCs. Furthermore, results indicate that elevated IL1RAP expression associates with poor overall and relapse-free survival in the Leucegene cohort of AML patients and predicts nonresponse to hematopoietic stem cell transplantation. Finally, we show that IL1RAP protein is internalized following exposure to specific antibodies, suggesting that IL1RAP represents an interesting target for antibody–drug conjugate development in NKt-AML. IL1RAP exhibits preferential expression within NKt-AML, correlating with diminished overall survival rates and diminished responsiveness to hematopoietic stem cell transplantation. Moreover, internalization of IL1RAP presents a promising avenue for immunotherapeutic intervention. The online version contains supplementary material available at 10.1186/s40364-025-00769-z.
2025-04-13
Biomarker Research (publié)
doi.org
Replication of a GWAS signal near
<i>HLA-DQA2</i>
with acute myeloid leukemia using a disease-only cohort and external population-based controls
Rose Laflamme
Véronique Lisi
Josée Hébert
Guy Sauvageau
Sébastien Lemieux
Vincent-Philippe Lavallee
Guillaume Lettre
Acute myeloid leukemia (AML) is the most common type of acute leukemia in adults. Its risk factors include rare and highly penetrant somatic… (voir plus) mutations. Genome-wide association studies (GWAS) have also identified four common inherited variants associated with AML risk, but these findings have not yet been confirmed in many independent datasets. Here, we performed a replication study with 567 AML cases from the Leucegene cohort and 1,865 controls from the population-based cohort CARTaGENE (CaG). Because genotypes were generated using different technologies in the two datasets (e.g. low- vs. high-coverage whole-genome sequencing), we applied stringent quality-control filters to minimize type I errors. We showed using data reduction methods (e.g. principal component analysis [PCA] and uniform manifold approximation and projection [UMAP]) that our approach successfully integrated the Leucegene and CaG genetic data. We replicated the association between cytogenetically normal (CN)-AML and rs3916765, a variant located near HLA-DQA2 (odds ratio [95% confidence interval] = 1.88 [1.21-2.93], P- value=0.005). The effect size of this association was stronger when we restricted the analyses to AML patients with NPM1 mutations (odds ratios >2.35). We found HLA- DOB to be the most significantly upregulated gene in Leucegene participants with the CN-AML protective A-allele at rs3916765. We further found that several HLA class II genes are also differentially expressed albeit at lower statistical significance. Our results confirm that a common genetic variant at the HLA locus associates with AML risk, providing new opportunities to improve disease prognosis and treatment.
2024-09-26
medRxiv (prépublication)
doi.org
Immunotherapeutic targeting of surfaceome heterogeneity in AML
Marie-Eve Bordeleau
Éric Audemard
Arnaud Metois
Louis Theret
Véronique Lisi
Azer Farah
Jean-François Spinella
Jalila Chagraoui
Ossama Moujaber
Léo Aubert
Banafsheh Khakipoor
Laure Mallinger
Isabel Boivin
Nadine Mayotte
Azadeh Hajmirza
Éric Bonneil
Francois Béliveau
Sybille Pfammatter
Albert Feghaly
Geneviève Boucher … (voir 9 de plus)
Patrick Gendron
Pierre Thibault
Frederic Barabe
Sébastien Lemieux
Guillaume Richard-Carpentier
Josée Hébert
Vincent-Philippe Lavallee
Philippe P. Roux
Guy Sauvageau
2024-05-31
Cell Reports (publié)
doi.org
Identification of Acute Myeloid Leukemia Cell Surface Therapeutic Targets Using Single Cell RNA Sequencing Supported By Surface Proteomics
Véronique Lisi
Banafsheh Khakipoor
Azer Farah
Marie-Eve Bordeleau
Éric Audemard
Arnaud Metois
Louis Theret
Jean-François Spinella
Jalila Chagraoui
Ossama Moujaber
Laure Mallinger
Isabel Boivin
Nadine Mayotte
Azadeh Hajmirza
Éric Bonneil
Francois Béliveau
Albert Feghali
Geneviève Boucher
Patrick Gendron
Frederic Barabe … (voir 6 de plus)
Sébastien Lemieux
Guillaume Richard-Carpentier
Josée Hébert
Philippe Roux
Guy Sauvageau
Vincent-Philippe Lavallee
Background: Acute myeloid leukemia (AML) comprises diverse genomic subgroups and remains hard to treat in most patients. Desp… (voir plus)ite breakthroughs in the therapeutic arsenal in recent years, clinical usage of therapeutic antibodies or chimeric antigen receptor T (CAR-T) cells has been lagging in contrast to other hematological malignancies. In fact, CD33 represents the only antibody-based strategy approved for this disease to date, highlighting the need to identify new promising targets. AML cells span a wide range of aberrant myeloid differentiation programs, complexifying the identification, by bulk genomics, of targets expressed in the most immature leukemic cells. Aims and Methods: To identify the expression landscape of surface proteins in immature leukemic cells, we performed single-cell RNA sequencing (scRNA-seq, 10x 3' Reagent Kits) of primary human AML cells from 20 specimens of the Leucegene cohort enriched in intermediate and adverse genetic backgrounds ( KMT2A-rearranged n=5, chromosome 5 and/or 7 deletions (abn5/7, n=5) complex karyotype (n=4), NPM1/DNMT3A/FLT3-ITD triple-mutant (n=3) and others (n=3)). A Random Forest classifier was developed to unbiasedly classify AML cells into distinct differentiation stages using normal bone marrow-derived scRNA-seq data from the Human Cell Atlas (HCA) consortium. Genes were scored based on their probability of coding for proteins expressed at the cell surface using the SPAT algorithm developed by our group (https://doi.org/10.1101/2023.07.07.547075), retaining high score ones. To validate surface expression, we concomitantly analyzed the surface proteome (hereafter named surfaceome) of 100 primary human AML samples from the Leucegene cohort, including all 20 samples profiled by scRNA-seq. Results: After quality control, we profiled and characterized 103 690 high quality cells (mean of 5185 cells/sample). We trained a Random Forest classifier to annotate cells in a two step process, first identifying plasma cells based on a restricted list of genes abundantly expressed in these cells and subsequently assigning the remaining cells to one of 33 cell types. We performed a five-fold cross validation of the model and subsequently determined the accuracy of our classifier to be 92% on the test subset of the HCA data. Applied to our AML cell collection, a total of 35 053 cells (34%) were unbiasedly classified as Hematopoietic Stem Cell (HSC)-like, corresponding to the most phenotypically immature leukemic cells in each patient sample (ranging from 4 to 74 %). Accordingly, HSC-like AML cells preferentially express genes associated with normal HSCs, such as CD34, FAM30A, and SPINK2, and globally lack expression of mature lineages defining genes, further validating our classifier. The proportion of HSC-like cells varied among AML subgroups, and was lowest in KMT2A-r AML (median 19%) and highest in abn5/7 samples (46%). Integration of our AML atlas using Harmony algorithm preserved differentiation hierarchies across samples, with most cell types, including HSC-like cells, occupying a defined area in the low dimensional embedding. To identify new surface antigens specifically expressed in immature leukemic cells, we compared the high (≥8) SPAT score gene expression profile of AML HSC-like cells with that of normal HSC cells (HCA), and identified 60 genes significantly overexpressed in AML immature cells. Of those, 39 genes were also detected at the protein level by the surfaceome analysis, supporting their predicted expression at the cell surface in AML samples. 59% of these 39 genes (n=23) were detected in over 80% of the specimens analyzed by the surfaceome, and thus are nearly universally expressed in our AML cohort. To identify targets of therapies that could be repurposed, we next evaluated the relevance of our findings by querying the Thera-SAbDab database. Most interestingly, 8 of the 39 AML specific HSC markers are targeted by therapeutic antibodies FDA-approved or in clinical trials for the treatment of AML (n=4, IL3RA, FLT3, CD37 and TNFRSF10B) or other indications (n = 4). Conclusion Our genetically diverse AML single-cell atlas, supported by mass spectrometry, enables the identification of both subset-specific and pan-AML surface protein genes. These represent potential targets for antibody based strategy development or therapy repurposing in AML.
2023-11-01
Blood (publié)
doi.org
BamQuery: a proteogenomic tool to explore the immunopeptidome and prioritize actionable tumor antigens
Maria Virginia Ruiz Cuevas
Marie-Pierre Hardy
Jean-David Larouche
Anca Apavaloaei
Eralda Kina
Krystel Vincent
Patrick Gendron
Jean-Philippe Laverdure
Chantal Durette
Pierre Thibault
Sébastien Lemieux
Claude Perreault
Gregory Ehx
MHC-I-associated peptides (MAPs) derive from selective yet highly diverse genomic regions, including allegedly non-protein-coding sequences,… (voir plus) such as endogenous retroelements (EREs). Quantifying canonical (exonic) and non-canonical MAPs-encoding RNA expression in malignant and benign cells is critical for identifying tumor antigens (TAs) but represents a challenge for immunologists. We present BamQuery, a computational tool attributing an exhaustive RNA expression to MAPs of any origin (exon, intron, UTR, intergenic) from bulk and single-cell RNA-sequencing data. We show that non-canonical MAPs (including TAs) can derive from multiple different genomic regions (up to 35,343 for EREs), abundantly expressed in normal tissues. We also show that supposedly tumor-specific mutated MAPs, viral MAPs, and MAPs derived from proteasomal splicing can arise from different unmutated non-canonical genomic regions. The genome-wide approach of BamQuery allows comprehensive mapping of all MAPs in healthy and cancer tissues. BamQuery can also help predict MAP immunogenicity and identify safe and actionable TAs.
2023-08-14
Genome Biology (publié)
doi.org
Transposable elements regulate thymus development and function
Jean-David Larouche
Céline M Laumont
Assya Trofimov
Krystel Vincent
Leslie Hesnard
Sylvie Brochu
Caroline Côté
Juliette F Humeau
Éric Bonneil
Joël Lanoix
Chantal Durette
Patrick Gendron
Jean-Philippe Laverdure
Ellen R Richie
Sébastien Lemieux
Pierre Thibault
Claude Perreault
Abstract Transposable elements (TE) are repetitive sequences representing ∼45% of the human and mouse genomes and are high… (voir plus)ly expressed by medullary thymic epithelial cells (mTEC). In this study, we investigated the role of transposable elements (TE), which are highly expressed by medullary thymic epithelial cells (mTEC), on T-cell development in the thymus. We performed multi-omic analyses of TEs in human and mouse thymic cells to elucidate their role in T cell development. We report that TE expression in the human thymus is high and shows extensive age- and cell lineage-related variations. TEs interact with multiple transcription factors in all cell types of the human thymus. Two cell types express particularly broad TE repertoires: mTECs and plasmacytoid dendritic cells (pDC). In mTECs, TEs interact with transcription factors essential for mTEC development and function (e.g., PAX1 and RELB) and generate MHC-I-associated peptides implicated in thymocyte education. Notably, AIRE, FEZF2, and CHD4 regulate non-redundant sets of TEs in murine mTECs. Human thymic pDCs homogenously express large numbers of TEs that lead to the formation of dsRNA, triggering RIG-I and MDA5 signaling and explaining why thymic pDCs constitutively secrete IFN ɑ/β. This study illustrates the diversity of interactions between TEs and the adaptive immune system. TEs are genetic parasites, and the two thymic cell types most affected by TEs (mTEcs and pDCs) are essential to establishing central T-cell tolerance. Therefore, we propose that the orchestration of TE expression in thymic cells is critical to prevent autoimmunity in vertebrates.
2023-07-13
bioRxiv (prépublication)
doi.org