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Enregistrement W4417019216 · doi:10.1182/blood-2025-352

Novel immune gene expression signature risk-stratifies Mantle Cell Lymphoma outcomes independent of TP53 status

2025· article· en· W4417019216 sur OpenAlexaff
Jean L. Koff, Veronica Russell, Rachel Kositsky, David L. Jaye, Michael C. Churnetski, Katelin Baird, Colin B. O’Leary, Christopher R. Flowers, Sirpa Leppä, Shaoying Li, Jessie Xu, Mette Ølgod Pedersen, Anne Ortved Gang, Kikkeri N. Naresh, Rex Au-Yeung, Jennifer R. Chapman, Amy Chadburn, Izidore S. Lossos, Sarah L. Ondrejka, Abner Louissaint, Eric Tse, Cassandra Love, Devang Thakkar, Clayton Parker, Choon Kiat Ong, Andrew Evans, Emily F. Mason, Yan Jiong, Magdalena Czader, Andrew M. Evens, Dina Sameh Soliman, Yuri Fedoriw, Sandeep S. Davé, Jonathon B. Cohen

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMantle cell lymphomaGene signatureProportional hazards modelHazard ratioLymphomaGeneCancerGene expression profilingDNA sequencing

Résumé

récupéré en direct d'OpenAlex

Abstract Background: The prognostic impact of specific genomic changes in mantle cell lymphoma (MCL) is not well characterized beyond altered TP53, which is recognized as a high-risk marker and commonly assessed at diagnosis, and the “proliferation signature” developed using gene expression in fresh frozen tissues (Rosenwald et al, Cancer Cell 2003). To bridge this knowledge gap, we applied comprehensive tumor sequencing to investigate how genomic abnormalities affect prognosis in patients (pts) with MCL with and without TP53 alterations. Methods: The Atlas of Blood Cancer Genomes project is an international collaborative effort including 25 sites for collecting and sequencing all blood cancers (Love et al, ASH 2021). We recruited MCL pts with detailed clinical data and subjected their tumors to whole exome, whole transcriptome, and targeted sequencing. TP53-aberrant cases (i.e., mutation or deletion) were identified from sequencing and clinical pathology reports. Progression-free survival (PFS) and overall survival (OS) were measured using the Kaplan-Meier method, with statistical comparisons by the log-rank test. Risk for a gene signature was defined as the coefficient from the respective Cox proportional hazard model. Results: RNA and DNA sequencing were performed successfully for 252 pts with MCL. Clinical features and treatment regimens were consistent with prior disease descriptions and have been reported previously (Koff et al, ASH 2022). In the entire cohort, median PFS was 38 months, and median OS was not reached. As expected, TP53 abnormalities were associated with inferior OS compared to TP53-wildtype (WT; 5-year OS 48% and 79%, respectively, p<0.001). A novel gene expression signature (“immune signature”) was determined by identifying genes that displayed prognostic ability independent of the previously described MCL proliferation signature (Rosenwald et al, Cancer Cell 2003; Scott et al, J Clinical Oncology 2017). This signature was distinct from the proliferation signature both in terms of included genes and ability to discriminate between risk groups; the correlation plot between the immune and proliferation signature scores showed a low R2 value of 0.01. The immune signature appears to derive from differences in tumor microenvironment (TME) CD8+ T and T follicular helper cells: pts designated as high-risk by the immune signature demonstrated lower proportions of these TME subsets as assessed by CIBERSORT (p=0.001). For the cohort with tumors sequenced prior to treatment (n=208), 5-year OS was 45% for pts with a high-risk immune score (bottom quartile), 80% for pts with intermediate-risk immune score (middle 2 quartiles), and 91% for low-risk immune score (top quartile, p <0.001), with improved discrimination compared to the proliferation signature (56%, 79%, and 80% for high-, intermediate-, and low-risk proliferation scores, respectively; p=0.03). The immune signature also risk-stratified outcomes in MCL subgroups with and without TP53 alteration. For pts with TP53-WT (n=74), low-risk immune score predicted 5-year OS of 86%, while high-risk immune score was associated with 5-year OS of 38% (p<0.001), compared to 72% vs 82% for low-risk and high-risk proliferation scores (p=0.16). For pts with aberrant TP53 (n=43), low-risk immune score had 5-year OS of 77%, and high-risk immune score had 5-year OS of 9% (p<0.001), compared to 35% vs 52% for low-risk and high-risk proliferation scores (p=0.28). Application of the immune signature also further stratified pts deemed high-risk by the proliferation score (n=52): within this group, low-risk immune score associated with 5-year OS of 76%, and high-risk immune score predicted 5-year OS of 29% (p<0.001). Similar stratification was also observed when the immune signature was applied to pts with low- and intermediate-risk proliferation scores (n=156; 5-year OS of 88% vs 57% for low-risk vs high-risk immune score respectively, p<0.001). Conclusions: In this largest-ever study of MCL's genomic landscape, we identify a novel gene expression signature that stratifies risk within and across existing prognostic groups, including TP53-altered cases. Our findings support development of the immune signature as a tool that can be used in routine clinical practice to improve risk stratification of all MCL patients at diagnosis. Additional study is warranted to define therapeutic implications of differential TME T cell subset composition in MCL.

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 enseignants0,001
Score d'incertitude au seuil0,005

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)0,0010,000

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 Opus0,008
Tête enseignante GPT0,241
Écart entre enseignants0,234 · 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

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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