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Enregistrement W2906428280 · doi:10.1182/blood-2018-99-118691

A Single-Cell Transcriptional Analysis of Tumour Cells and the Immune Microenvironment during Disease Evolution in a Transgenic Mouse Model of Myeloma

2018· article· en· W2906428280 sur OpenAlexaffabout
Danielle Croucher, Marta Chesi, Zhihua Li, Victoria M. Garbitt, Meaghen E. Sharik, Daniel D. Waller, Michaël Sébag, P. Leif Bergsagel, Trevor J. Pugh, Suzanne Trudel

Notice bibliographique

RevueBlood · 2018
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensMcGill University Health CentreMcGill UniversityPrincess Margaret Cancer CentreUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésImmune systemBiologyMultiple myelomaTumor microenvironmentPlasma cellPathogenesisMonoclonal gammopathy of undetermined significanceBone marrowCancer researchTranscriptomeGene expression profilingImmunologyGene expressionGeneMonoclonalMonoclonal antibodyAntibodyGenetics

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction: Multiple Myeloma (MM) is consistently preceded by pre-malignant asymptomatic monoclonal gammopathies (AMG). To date, our understanding of the pathogenesis of progression to MM remains incomplete. Genetic analyses of AMG cells compared to MM-derived plasma cells (PCs) have found few differences, suggesting that progression may be mediated in part by tumour-extrinsic mechanisms. To comprehensively examine the cellular and molecular complexities of MM pathogenesis, we performed an unbiased single cell RNA-sequencing (scRNA-seq) analysis of tumour cells as well as immune cells from the tumour microenvironment (TME) derived from transgenic mice transitioning from AMG to MM. Methods: We employed the Vk*MYC immune-competent mouse model of MM (C57BL/6/KaLwRij), which is a clinically and biologically faithful model of untreated disease that similarly progresses from AMG to MM with age. We established an age-based cohort of Vk*MYC mice to recapitulate a range of MM disease stages, generated single-cell suspensions from flushed bone marrow and subjected these cells to scRNA-seq profiling (10x Genomics). Results: Across 12 samples profiled to date, our scRNA-seq dataset contains 82,853 high-quality cells, expressing 17,922 genes. We employed dimensionality reduction and unsupervised graph-based clustering to visualize and group transcriptionally-similar cell populations, which revealed 42 clusters. Expression of known marker genes and computed correlation scores with bulk gene expression reference datasets enabled annotation of cell types, revealing both malignant cells and non-malignant immune cell populations. We first focused on single cell T/NK profiles in our data given the emerging utility of immune checkpoint inhibitors that target these populations. Although we did not observe numerical differences in the proportion of CD8+ T cells across disease stages, analysis of immune checkpoint receptor genes revealed increased expression of Pdcd1 (PD-1) and Lag3 in CD8+ T cells from mice with disease. Co-expression of LAG3 and PD-1 proteins was also confirmed using a Vk*MYC transplantable model, with a positive correlation between disease burden (%CD138+/B220- cells) and %PD1+LAG3+ CD8+ T cells by flow cytometry. Consistent with reports of PD-1 and LAG3 co-expression on non-functional exhausted T cells, CD8+ T cells from diseased mice demonstrated elevated T cell exhaustion scores in our scRNA-seq dataset. These observations suggest that T cell exhaustion may be mediated by multiple immune checkpoint receptors during disease evolution. Although combinatorial treatment with PD-1 and LAG3 antibodies failed to induce tumour regression in mice with established disease, the addition of cyclophosphamide (Cy) to these antibodies resulted in marked improvement in survival of the mice compared to Cy alone, presumably by promoting immunogenic cell death. Studies exploring the combination of LAG3 and PD-1 antibodies as a strategy to inhibit transition from AMG to MM in the Vk*MYC mice are ongoing and will be reported. We also performed subclustering analysis of 5,228 Sdc1+ (CD138) PCs in our scRNA-seq dataset revealing 11 distinct clusters, with evidence of inter- and intra-tumoural heterogeneity across all Vk*MYC mice. Differential gene expression analysis revealed a non-malignant PC (nPC) cluster as supported by lower Myc transgene and Ccnd2 expression. Moreover, this cluster was predominantly comprised of cells from age-matched control mice or mice with earlier disease. Single-cell chromosomal copy number analysis revealed loss of Chr5 in the majority of tumour cells from MM mice, but not in the nPC cluster. Loss of Chr5 was observed in tumor subclones from all AMG mice suggesting that it is an early and potentially unifying event in Vk*MYC mice during disease progression. Further, the data support the establishment of intratumoural heterogeneity early in disease evolution. Conclusions: Our approach of using scRNA-seq to characterize the pathogenesis of disease evolution in MM has enabled simultaneous measurement of intratumoural heterogeneity and immune cell phenotypes in the TME. In turn, this has provided insights into mechanisms that may contribute to transition from AMG to MM, including induction of T cell exhaustion and loss of mouse Chr5. Ongoing and future work aims to evaluate whether these mechanisms can be exploited therapeutically in pre-malignant AMG. Disclosures Sebag: Amgen Canada: Membership on an entity's Board of Directors or advisory committees; Janssen Inc.: Membership on an entity's Board of Directors or advisory committees; Celgene Canada: Membership on an entity's Board of Directors or advisory committees; Takeda Canada: Membership on an entity's Board of Directors or advisory committees. Pugh:Prosigna: Honoraria; N/A: Patents & Royalties: Hybrid-capture sequencing for determining immune cell clonality; N/A: Patents & Royalties: Combined hybrid-capture DNA sequencing for disease detection; Boehringer Ingelheim: Research Funding; Chrysalis Biomedical Advisors: Honoraria; Merck: Honoraria; DynaCare: Consultancy.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,015
Tête enseignante GPT0,225
Écart entre enseignants0,210 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2018
Routes d'admission2
Résumé présentoui

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