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Enregistrement W4405096089 · doi:10.1182/blood-2024-205727

Topic Modeling of Genotyping of Transcriptomes Reveals Collaboration between BTKC481S-Mutant and Wild Type Cells in Btki-Resistant Chronic Lymphocytic Leukemia

2024· article· en· W4405096089 sur OpenAlexaff
Ramon Massoni-Badosa, Joshua S. Schiffman, Barun Pradhan, Rebecca M. Murray, Andrew Lipsky, Debra Van Egeren, Danny Luan, Shirley Chen, Ronan Chaligné, K. M. Beattie, Alyssa Indart, Nada Chraiki, Neville Dusaj, Erica B. Bhavsar, Chelston Ang, Anna S. Nam, Federico Gaiti, Paulina Chamely, Nathaniel D. Omans, Preeti Trisal, Anita K. Gandhi, Kyu‐Tae Kim, Rafael Schulman, Franco Izzo, Gavriel Mullokandov, Chingiz Underbayev, Jan A. Burger, Adrian Wiestner, Elisa ten Hacken, John N. Allan, Jennifer A. Woyach, Richard R. Furman, Dan A. Landau

Notice bibliographique

RevueBlood · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Lymphocytic Leukemia Research
Établissements canadiensPrincess Margaret Cancer CentreUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésChronic lymphocytic leukemiaGenotypingMutantBiologyTranscriptomeLeukemiaGeneticsCancer researchMolecular biologyGenotypeGeneGene expression

Résumé

récupéré en direct d'OpenAlex

The acquired BTKC481S mutation is the most common resistance mechanism to Bruton's tyrosine kinase (BTK) inhibition in chronic lymphocytic leukemia (CLL). Yet, its variant allele frequency (VAF) is often low (<10%), raising the question of how wild-type (WT) cells survive in the context of low abundance of mutant (MUT) cells. To investigate this, we modeled the clonal dynamics of BTK-MUT and WT cells longitudinally in a cohort of 42 patients resistant to BTK inhibitors (BTKi). In 21 patients, clonal dynamics deviated from a simple model where MUT cells increase at the expense of WT cells. Instead, both MUT and WT cell numbers increased at relapse, suggesting that BTK-MUT and WT cells cooperate to drive resistance to BTKi. To explore the phenotypic variability that may underlie clonal cooperation, we expanded our genotyping of transcriptomes (GoT) profiling of seven CLL samples taken at relapse after BTKi [60,580 cells with a median of 8,713 cells per patient (6,819-9,838)] and carrying low VAFs (3-43%). GoT genotypes a mutation of interest (i.e. BTKC481S) jointly with single-cell transcriptomes of primary samples. This integration allows to link genotype and phenotype, and by comparing BTK-MUT and BTK-WT cells within the same patient, this method eliminates inter-patient variability and confounding factors, thus focusing on changes in expression and intratumoral heterogeneity (ITH). Transcriptomic ITH in CLL is largely driven by the recirculation of CLL cells between peripheral blood and lymphoid tissues. CXCR4hiCD5low CLL cells (i.e. quiescent fraction) migrate to lymph node (LN) proliferation centers (PR) via a CXCL12 gradient. In PR, they receive microenvironmental signals, upregulate mir155 (encoded by MIR155HG), increase BCR signaling, proliferate, and exit as CXCR4lowCD5hi cells (i.e. proliferative fraction). BTKi impairs CXCR4 signaling and homing to LN. Here, we curated gene signatures for the quiescent (CXCR4, CD24), proliferative (CD27, CXCR3, CD52, ITGAX) and MIR155HGhi fractions (MIR155HG, MYC, CD83, CDK4) leveraging published scRNA-seq datasets. We classified cells into the 3 fractions and uncovered an increased quiescent fraction (p=0.058) and a decreased proliferative fraction (p<0.05) in BTK-WT cells compared to BTK-MUT cells across all patients, suggesting that BTK-WT have an impaired homing capacity to LN. We reasoned that cancer cell states are plastic and continuous, requiring tailored methods to account for these features. We employed topic modeling, an algorithm that decomposes each gene expression profile into a set number of topics (k). Topics represent groups of correlated genes, linked to specific biological functions. We could annotate 3/5 topics to the fractions described above in most patients, with an increase of the MIR155HGhi topic (MIR155HG and MYC targets) in BTK-MUT cells (p<0.001). We also identified patient-specific topics that could not be captured through conventional methods. In one patient, we identified a topic enriched in interferon-stimulated genes (IFIT1, IFIT2, IFIT3, IFI44L, STAT1) that was specific to BTK-WT cells (p<0.001) within the quiescent fraction. As interferons protect CLL cells from apoptosis and BTKi sensitive cells are still responsive to interferons [Xia, J Immunol 2020], our data suggests that BTK-WT cells may survive by responding to circulating interferons without homing into LN. In the same patient, we observed a cell state enriched in BTK-MUT cells expressing IL4R, which is associated with resistance to BTKi through the IL4R-IL4 axis. Intriguingly, a cell state specific to BTK-WT cells in the same patient overexpressed Chronic Lymphocytic Leukemia Up-Regulated 1 (CLLU1), a gene that distinguishes CLL from normal B cells and other lymphoid malignancies and is a marker of poor prognosis. IL4R expression was mutually exclusive with CLLU1, suggesting a putative paracrine activation of the IL4 pathway, as CLLU1 was previously proposed to have structural homology to IL4. Notably, we observe subclonal expression of CLLU1 in 6/7 patients. Taken together, we propose two models of clonal collaboration between BTK-WT and BTK-MUT cells, involving paracrine signaling between interferons and CLLU1/IL4. We are currently investigating the relevance of these signaling axes in larger cohorts of longitudinally collected patient samples, including through in vitro functional validation assays.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut 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 enseignants0,503
Score d'incertitude au seuil0,992

Scores Codex et Gemma par catégorie

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

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 Opus0,021
Tête enseignante GPT0,291
Écart entre enseignants0,270 · 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 tête enseignante, 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

Citations1
Publié2024
Routes d'admission1
Résumé présentoui

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