Sébastien Lemieux

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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

serena.chan@mila.quebec

Lena Herbold

Maîtrise recherche - UdeM

lena.herbold@mila.quebec

Github

Léa Kaufmann

Maîtrise recherche - UdeM

lea.kaufmann@mila.quebec

Github

Publications

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 Beliveau

Albert Feghali

Geneviève Boucher

Patrick Gendron

Frederic Barabe … (voir 6 de plus)

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)

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

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)

Abstract 2987: BamQuery: a new 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

Claude Perreault

Gregory Ehx

Abstract MHC class I-associated peptides (MAPs), collectively referred to as the immunopeptidome, have a pivotal role in cancer immunosurvei… (voir plus)llance. While MAPs were long thought to be solely generated by the degradation of canonical proteins, recent advances in the field of proteogenomics (genomically-informed proteomics) evidenced that ∼10% of them originate from allegedly noncoding genomic sequences. Among these sequences, endogenous retroelements (EREs) are under intense scrutiny as a possible source of actionable tumor antigens (TAs). With the increasing number of cancer-oriented immunopeptidomic and proteogenomic studies comes the need to accurately attribute an RNA expression level to each MAP identified by mass-spectrometry. Here, we introduce BamQuery (BQ), a computational tool to attribute an exhaustive RNA expression to MAPs of any genomic origin (exon, intron, UTR, intergenic) from bulk and single-cell RNA-sequencing data. By using BQ on large datasets of published MAPs identified by mass spectrometry, we show that many of them can arise from more than one genomic region. Indeed, 27% of MAPs reported as deriving from protein-coding exons (canonical MAPs) could also arise from non-canonical genomic regions, sometimes with greater probability, and 61% of non-canonical MAPs could arise from more than a single genomic origin (334 possible regions on average per non-canonical MAP; up to 35,343 for EREs). The consideration of all these origins evidenced an unsuspected high RNA expression in normal human tissues of (i) published neoantigens/TAs (mutated or not); (ii) MAPs derived from proteasomal splicing, supposedly not genomically templated, and (iii) MAPs derived from viruses. In particular, the high expression of candidate immunotherapeutic targets such as TAs highlights the relevance of BamQuery and the necessity of using it to validate such antigens before translating their usage in clinical trials. We also demonstrate that BamQuery can be used to directly identify safe and actionable TAs as well as to predict their immunogenicity through our freely accessible web portal (https://bamquery.iric.ca/search). Therefore, BQ could become an essential tool in any TA prioritization pipeline in the near future. Citation Format: 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, Sebastien Lemieux, Claude Perreault, Gregory Ehx. BamQuery: a new proteogenomic tool to explore the immunopeptidome and prioritize actionable tumor antigens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2987.

2023-04-03

Cancer Research (publié)

Abstract 2993: Unmutated tumor antigens are abundant and contribute to tumor control in melanoma

Anca Apavaloaei

Qingchuan Zhao

Leslie Hesnard

Krystel Vincent

Marie‐Pierre Hardy

Chantal Durette

Joël Lanoix

Jean‐Philippe Laverdure

Jean‐David Larouche

Maria Virginia Ruiz Cuevas

Gregory Ehx

Pierre Thibault

Claude Perreault

Abstract Recognition of MHC-I-associated tumor antigens (TAs) by CD8+ T cells is central to antitumor immunity. Owing to the elevated tumor … (voir plus)mutational burden (TMB) in melanoma, the marked efficacy of immune checkpoint blockade (ICB) has been attributed to the recognition of mutated TAs. However, recent reports showed that response to ICB in melanomas with low TMB is associated with CD8+ T-cell reactivity against melanocyte lineage-associated antigens (LSAs). Here, we systematically evaluated the contribution of all TA classes, i.e., mutated and unmutated, canonical and non-canonical, to the antigenic landscape of melanoma. We characterized the TAs from melanoma biopsies and patient-derived cell lines using proteogenomics. Out of 79450 MHC-I-associated peptides (MAPs) identified from 19 samples, we found 557 unmutated TAs classified as tumor-specific (TSA), tumor-associated (TAA), or LSAs. These TAs most often derived from annotated open-reading frames, followed by ncRNAs and intergenic regions. By contrast, only 6 MAPs were mutated and tumor-specific, which could be partially explained by a decreased expression of mutations within MAP-generating genomic regions. While the number of unmutated TAs with predicted presentation (TApres) in melanoma patients was similar between responders and non-responders pre-ICB, non-responders showed marks of inefficient antigen presentation. In consequence, only responders lost TApres upon treatment, in tandem with an expansion in tumor-infiltrating lymphocytes. These results reveal a previously underappreciated contribution of unmutated TAs to tumor control in melanoma and suggest that enhancing their recognition could improve the ICB efficacy in non-responders. Citation Format: Anca Apavaloaei, Qingchuan Zhao, Leslie Hesnard, Krystel Vincent, Marie-Pierre Hardy, Chantal Durette, Joël Lanoix, Jean-Philippe Laverdure, Jean-David Larouche, Maria Virginia Ruiz Cuevas, Grégory Ehx, Sébastien Lemieux, Pierre Thibault, Claude Perreault. Unmutated tumor antigens are abundant and contribute to tumor control in melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2993.

2023-04-03

Cancer Research (publié)

Toward computing attributions for dimensionality reduction techniques

Matthew Scicluna

Jean-Christophe Grenier

Raphaël Poujol

Julie G. Hussin

We describe the problem of computing local feature attributions for dimensionality reduction methods. We use one such method that is well es… (voir plus)tablished within the context of supervised classification—using the gradients of target outputs with respect to the inputs—on the popular dimensionality reduction technique t-SNE, widely used in analyses of biological data. We provide an efficient implementation for the gradient computation for this dimensionality reduction technique. We show that our explanations identify significant features using novel validation methodology; using synthetic datasets and the popular MNIST benchmark dataset. We then demonstrate the practical utility of our algorithm by showing that it can produce explanations that agree with domain knowledge on a SARS-CoV-2 sequence dataset. Throughout, we provide a road map so that similar explanation methods could be applied to other dimensionality reduction techniques to rigorously analyze biological datasets. We have created a Python package that can be installed using the following command: pip install interpretable_tsne. All code used can be found at github.com/MattScicluna/interpretable_tsne.

2022-12-31

Bioinformatics Advances (publié)

Identification of Novel Cell Surface Therapeutic Targets for KMT2A-Rearranged Acute Myeloid Leukemia

Louis Theret

Marie-Eve Bordeleau

Azadeh Hajmirza

Arnaud Metois

Ossama Moujaber

Éric Audemard

Jean-François Spinella

Jalila Chagraoui

Léo Aubert

Azer Farah

Véronique Lisi

Éric Bonneil

Isabel Boivin

Nadine Mayotte

Tara MacRae

Pierre Thibault

Vincent-Philippe Lavallee

Josée Hébert

Guy Sauvageau … (voir 1 de plus)

Philippe P Roux

2022-11-14

Blood (publié)

IL1R1 Expression Predicts the Benefit from Allogeneic Hematopoietic Stem Cell Transplantation in Patients with Acute Myeloid Leukemia and Intermediate-Risk Cytogenetics

Guillaume Richard-Carpentier

Francois Beliveau

Sandrine Lacoste

Jean-François Spinella

Michael Vladovsky

Patrick Gendron

Vincent-Philippe Lavallee

Guy Sauvageau

Josée Hébert

2022-11-14

Blood (publié)

Targeting PLZF Enhances Glucocorticoid Sensitivity in Acute Myeloid Leukemia

Azadeh Hajmirza

Laura Simon

Jalila Chagraoui

Nadine Mayotte

Jean-François Spinella

Tara MacRae

Bernhard Lehnertz

Thierry Bertomeu

Jasmin Coulombe-Huntington

Geneviève Boucher

Mike Tyers

Josée Hébert

Guy Sauvageau

2022-11-14

Blood (publié)

The tumor-specific antigen landscape of acute myeloid leukemia

Gregory Ehx

Krystel Vincent

Jean-David Larouche

Chantal Durette

Jean-Philippe Laverdure

Joël Lanoix

Éric Bonneil

Marie-Pierre Hardy

Caroline Coté

Nandita Noronha

Leslie Hesnard

Qingchuan Zhao

Céline M Laumont

Pierre Thibault

Claude Perreault

2022-09-30

Molecular Immunology (publié)

Abstract 25 ECTmatch: Optimizing Small-Scale Cord Blood Banking Through HLA Analysis

Maude Dumont-Lagacé

Albert Feghaly

Gabrielle Thauvette

Marie-Christine Meunier

William Lemieux

Diane Fournier

Lucie Richard

Guy Sauvageau

Sandra Cohen

Abstract Introduction Cord blood (CB) banks have had to rely on large inventories of CB units to try to serve the largest possible proportio… (voir plus)n of the population, all the while prioritizing collection of non-Caucasian ethnic groups. However, due to the high linkage disequilibrium of HLA genes and the high frequency of several HLA alleles in the population, CB banks contain hundreds of CB units that could be matched to the same patients, making the inventory somewhat redundant from a clinical standpoint. Objective ExCellThera developed ECTmatch, an algorithm dedicated to optimizing the selection of CB units based on in-depth HLA analysis in order to maximize the efficiency of the bank to suitably match the largest proportion of subjects within a small pool of donors. Methods The performance of ECTmatch was evaluated in a simulation aiming to select 100 CB units from the Héma-Québec CB bank that satisfied an arbitrary minimal cell content criteria of 120 × 107 TNC and 6 × 106 CD34+ cells (n = 2,987). Selection was performed to optimize matching for the Quebec population, with a minimal HLA-match of 5/8 for HLA-A, -B, -C, and -DRB1. Results ECTmatch provides a suitably matched donor for 71.5% of the Quebec population, compared with only 45.0% (±2.4%) with random selection. Because patients who require a CB transplant tend to have rarer HLAs, the performance of ECTmatch was evaluated for this specific subset of patients (n = 62). Again, ECTmatch outperformed random selection, by providing a donor for 54.8% of patients, compared with only 29.9% with random selection. Finally, while ECTmatch was developed to optimize CB selection specifically for the Quebec population, it still outperformed random selection for subjects from the other Canadian provinces or the USA. Discussion By selecting CB units based on HLA profiles, ECTmatch allows the creation of a highly useful inventory with a very low number of CB units. This approach to small-scale CB banking can be adapted to different population subsets and could be used to select a subset of CB units for pre-release for immediate clinical availability or for the creation of a pre-expanded CB inventory with maximal population coverage.

2022-08-31

Stem Cells Translational Medicine (publié)

Two types of human TCR differentially regulate reactivity to self and non-self antigens

Assya Trofimov

Philippe Brouillard

Jean-David Larouche

Jonathan Séguin

Jean-Philippe Laverdure

Ann Brasey

Gregory Ehx

Denis-Claude Roy

Lambert Busque

Silvy Lachance

Claude Perreault

Based on analyses of TCR sequences from over 1,000 individuals, we report that the TCR repertoire is composed of two ontogenically and funct… (voir plus)ionally distinct types of TCRs. Their production is regulated by variations in thymic output and terminal deoxynucleotidyl transferase (TDT) activity. Neonatal TCRs derived from TDT-negative progenitors persist throughout life, are highly shared among subjects, and are reported as disease-associated. Thus, 10%–30% of most frequent cord blood TCRs are associated with common pathogens and autoantigens. TDT-dependent TCRs present distinct structural features and are less shared among subjects. TDT-dependent TCRs are produced in maximal numbers during infancy when thymic output and TDT activity reach a summit, are more abundant in subjects with AIRE mutations, and seem to play a dominant role in graft-versus-host disease. Factors decreasing thymic output (age, male sex) negatively impact TCR diversity. Males compensate for their lower repertoire diversity via hyperexpansion of selected TCR clonotypes.

2022-08-31

iScience (publié)

Proteogenomics and Differential Ion Mobility Enable the Exploration of the Mutational Landscape in Colon Cancer Cells

Zhaoguan Wu

Éric Bonneil

Michael Belford

Cornelia Boeser

Maria Virginia Ruiz Cuevas

Jean-Jacques Dunyach

Pierre Thibault

The sensitivity and depth of proteomic analyses are limited by isobaric ions and interferences that preclude the identification of low abund… (voir plus)ance peptides. Extensive sample fractionation is often required to extend proteome coverage when sample amount is not a limitation. Ion mobility devices provide a viable alternate approach to resolve confounding ions and improve peak capacity and mass spectrometry (MS) sensitivity. Here, we report the integration of differential ion mobility with segmented ion fractionation (SIFT) to enhance the comprehensiveness of proteomic analyses. The combination of differential ion mobility and SIFT, where narrow windows of ∼m/z 100 are acquired in turn, is found particularly advantageous in the analysis of protein digests and typically provided more than 60% gain in identification compared to conventional single-shot LC–MS/MS. The application of this approach is further demonstrated for the analysis of tryptic digests from different colorectal cancer cell lines where the enhanced sensitivity enabled the identification of single amino acid variants that were correlated with the corresponding transcriptomic data sets.

2022-08-21

Analytical Chemistry (publié)