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Enregistrement W4389247802 · doi:10.1182/blood-2023-189567

A High-Risk Subgroup Multiple Myeloma Classification Based on the Detection of PR Minor Subclones

2023· article· en· W4389247802 sur OpenAlexaff
Noémie Leblay, Sungwoo Ahn, Sheri Skerget, Rémi Tilmont, Holly Lee, Mansour Poorebrahim, Daniel Peñaherrera, Elie Barakat, David Jung, Nizar J. Bahlis, Jonathan J. Keats, Paola Neri

Notice bibliographique

RevueBlood · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésMultiple myelomaBone marrowRNAGene signatureGene expressionBiologyGeneTranscriptomeMolecular biologyCancer researchInternal medicineComputational biologyOncologyMedicineImmunologyGenetics

Résumé

récupéré en direct d'OpenAlex

In the last two decades several classifications of Multiple Myeloma (MM) have been proposed based on gene expression signatures. These include the supervised translocation and cyclin D (TC) molecular subgroup approach or unsupervised methods to elucidate the molecular heterogeneity of MM and identify high risk subgroups. In particular, two RNA based gene expression studies have identified 8 to 10 unique subtypes, including the MS, MAF, CD1, CD2, and PR subtypes. The PR subgroup represents a highly proliferative gene signature that is associated with very poor survival outcomes. However, assigning this PR subgroup based on bulk transcriptome signatures can be challenging giving the mixture of genetic backgrounds that can be observed within this subtype. Furthermore, the presence of a minor PR subclone can often be missed with bulk genomic studies leading to the misclassification of a patient as standard risk. We here postulated that minor PR subclones expand under selective therapeutic pressures to become the predominant clones at subsequent relapses and the presence of PR subclones can impact any of the molecular subgroups pertaining poor survival outcomes. Bulk RNA or single cell RNA (scRNAseq) analysis were performed in bone marrow sorted primary MM cells obtained from 718 patients from the CoMMpass trialand 32 relapse/refractory MM patients, respectively. For scRNAseq, serial samples with paired baseline and relapse samples were available in 18 cases. Bone marrow plasma cells samples were separated from bone marrow aspirates using positive sorting selection of CD138 + cells. For bulk RNA-seq we used the mRNA HyperPrep kit (Kapa Biosystems) with RNA from 50,000 cells.For scRNA studiesunbiased mRNA profiling was conducted using the GemCode system (10x Genomics). Cell Ranger and Seurat were used for sample de-multiplexing, barcode processing, single-cell 3' gene and data analysis. Sequencing was performed on Illumina. By performing GSEA at the single cell level using the Zhan gene data set we have classified patients in the appropriate TC groups. FISH data was used to insure accurate calls. For the survival analysis OS was analyzed by the Kaplan-Meier method. In the CoMMpass dataset, with bulk RNA sequencing, the PR subtype contained 7.1% of patients with an admixture of classic genetic subtypes and very poor clinical outcome, with a median OS of 21.3 months. High proliferation index scores were concentrated in this subtype. There was also a strong tendency for patients to transition to the PR subtype at progression, with 25.5% of patients in a non-PR subtype at diagnosis transitioning to PR. Regardless of original subtype, patients that transitioned to the PR subtype rapidly succumbed to their disease with a median OS after the detected progression of 88 days. Using subtype prediction weight we could identify non-PR patients with PR subtype weights above 5% and these patients have a significantly shorter OS of 50 months compared to patients with <5% PR. At the single cell level, investigating the relapsed patients cohort, we have analyzed 92,139 cells and identified 59 clusters. A PR signature (defined as present in ≥15% of cells) was identified in 38% of cases despite baseline favorable prognostic group by FISH cytogenetics (7% HP, 28% t(11;14), and 57% normal FISH). A good concordance with FISH data was observed. In serially relapsed paired samples, all the PR patients maintain their signature overtime and 44% of patients transitioned to PR at subsequent relapse (Fig1A). Survival analyses were performed to evaluate how the presence of minor PR clones at the single cell level relate to clinical outcomes. Notably, the overall survival of patients classified as PR was dramatically reduced with a median OS of 6 months compared to 34 months for non-PR patients (Fig1B). In conclusion, we here show that minor PR clones defined by transcriptional signature through bulk or scRNA may be detectable in patients with variable MM molecular subgroups, including those classified as standard risk by the currently adopted classifications. We also show that the presence of these PR minor clones pertains adverse outcomes with very poor survivals. At relapse, most acquired PR cases originated from standard-risk presentations consistent with a Darwinian clonal evolution. Future classifications should account for the presence of these PR subclones for a better disease prognostication and developments of novel therapeutics.

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

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 Opus0,035
Tête enseignante GPT0,274
Écart entre enseignants0,239 · 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
Publié2023
Routes d'admission1
Résumé présentoui

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