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Enregistrement W4405034860 · doi:10.1182/blood-2024-205584

Clonal Hematopoiesis Is Associated with Changes to T Cell Contexture in Solid Tumours

2024· article· en· W4405034860 sur OpenAlexaff
Jahanara Rajwani, Marco M. Buttigieg, Caitlyn Vlasschaert, Michael J. Rauh

Notice bibliographique

RevueBlood · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensQueen's University
Organismes subventionnairesnon disponible
Mots-clésHematologic NeoplasmsHaematopoiesisBiologyCancer researchImmunologyGeneticsStem cellCancer

Résumé

récupéré en direct d'OpenAlex

Introduction: Clonal hematopoiesis of indeterminate potential (CHIP) is an age-related, nonmalignant condition in which hematopoietic stem cells acquire somatic mutations that drive their self-renewal and clonal expansion of progeny blood cells. Despite being nonmalignant, CHIP gives rise to proinflammatory immune cells, and thus contributes to immune dysregulation in a number of diseases. Recently, it has been shown that CHIP is associated with poorer survival outcomes in patients with solid tumours - in part due to alterations in the tumour microenvironment (TME) (reviewed in PMID: 37343201) - as well as differential responses to immunotherapies. Importantly, these therapies impinge on antitumour T cells within the TME. However, while the impact of CHIP on myeloid immune compartments has been extensively studied, the effect on T cell populations is largely unknown. In this study, we explore the impact of CHIP on T cells within the TME of solid tumours. Methods: GATK-Mutect2 and ANNOVAR toolkits were used to make CHIP calls from the peripheral blood (PB) of 1550 patients (10 primary cancer types) from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Similar methods were applied to bulk tumour DNA sequencing for the detection of CHIP mutations that were also found in tumour-infiltrating cells (CHIP_Tum). DESeq2 was used to determine log fold change and adjusted p-value of differentially expressed genes, including exhaustion markers (i.e. PDCD1, CTLA4, HAVCR2 etc). The ranked list was then inputted into Gene Set Enrichment Analysis (GSEA) for the following gene sets: KEGG_antigen_processing_and_presentation, GOBP_t_cell_mediated_cytotoxicity, and a custom gene set of 11 T cell exhaustion markers. Abundance of immune cell populations was estimated with CibersortX. Results: To elucidate the effect of CHIP on T cells within the TME, we first estimated the abundance of T cell populations across all tumour types using CibersortX. Interestingly, we found that the presence of CHIP in PB alone (CHIP_PB) or in PB and tumour (CHIP_Tum) was not associated with significant alterations in CD4 or CD8 populations within the tumour. Notably, however, CHIP_Tum was associated with changes in expression of multiple genes related to T cell exhaustion (i.e. CTLA4, HAVCR2). This was particularly true in brain and colorectal cancers, where there was a trend towards increased expression of multiple exhaustion-related genes with CHIP_Tum (e.g. HAVCR2 in brain, p=0.1377, CTLA4 in colorectal, p=0.1253). GSEA analysis of these two tumour types revealed that the presence of CHIP_Tum, or CHIP_PB characterized by mutations in Tet2 (CHIP_Tet2), was associated with a significant enrichment in genes related to T cell mediated cytotoxicity, antigen processing and presentation, and T cell exhaustion. Conversely, in ovarian cancer, CHIP status was associated with a significant decrease in T cell mediated cytotoxicity and antigen processing and presentation, demonstrating that the influence of CHIP on tumour immune status varies by cancer type. To better understand the potential clinical relevance of these CHIP-associated changes in gene expression, we tested for enrichment of gene expression signatures predictive of anti-PD1 immune checkpoint blockade (ICB) response. Strikingly, CHIP_Tum and CHIP_Tet2 showed significant enrichment of positive ICB response signatures in brain and colorectal cancers, in line with the increased expression of cytotoxicity and exhaustion-related gene signatures in these tumours. However, in ovarian cancer, CHIP_Tet2 was associated with a significant decrease in ICB response signatures in ovarian cancer, again congruent with GSEA results. Conclusion: Our results demonstrate that CHIP status in patients with solid tumours influences expression of markers related to antigen processing and presentation, T cell cytotoxicity and T cell exhaustion within the TME. Further, in some tumour types, CHIP is associated with changes in predicted response to ICB. These data suggest that a combination of CHIP status and T cell-related gene expression signatures may be useful biomarkers to predict response to ICB of some tumours, which is especially relevant in the current era of immunotherapy. Future work will aim to determine whether the influence of CHIP on T cell function is driven by myeloid or lymphoid cells carrying CHIP variants.

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)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,008

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,016
Tête enseignante GPT0,287
Écart entre enseignants0,271 · 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'étudeObservationnel
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

Citations1
Publié2024
Routes d'admission1
Résumé présentoui

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