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Enregistrement W4310096555 · doi:10.1182/blood-2022-167804

High Metabolic Tumor Volume Is Associated with Higher Toxicity and Decreased Efficacy of BCMA CAR-T Cell Therapy in Multiple Myeloma

2022· article· en· W4310096555 sur OpenAlexaff
Ricardo Villanueva, Doris K. Hansen, Rolf Petter Tonseth, Kenneth L. Gage, Zhouping Wei, Gabriel De Avila, Rachid Baz, Ariel Grajales‐Cruz, Omar Castañeda Puglianini, Brandon Blue, Jason Brayer, Kenneth H. Shain, Melissa Alsina, Hien Liu, Taiga Nishihori, Meghan Menges, Frederick L. Locke, Yoganand Balagurunathan, Ciara L. Freeman

Notice bibliographique

RevueBlood · 2022
Typearticle
Langueen
DomaineMedicine
ThématiqueCAR-T cell therapy research
Établissements canadiensBC Cancer Agency
Organismes subventionnairesnon disponible
Mots-clésMedicineMultiple myelomaBone marrowChimeric antigen receptorInternal medicineOncologyLenalidomideCohortChemotherapyCancerImmunotherapy

Résumé

récupéré en direct d'OpenAlex

Background Recently, two chimeric antigen receptor T-cell (CAR-T) constructs have been FDA approved for relapsed/refractory multiple myeloma (RRMM) after exposure to at least 4 prior lines of therapy. High tumor burden has been associated with inferior outcomes, reduced CAR-T expansion and higher rates of toxicity. Patients relapsing after multiple lines of therapy more frequently have oligosecretory and extra-medullary disease that may not be well captured by serum-based markers of disease or bone marrow infiltration, respectively. Functional imaging with FDG-PET is well established as a useful tool in patients with myeloma but its prognostic impact in highly refractory patients or those proceeding with CAR-T has not been described. Metabolic tumor volume (MTV) capturing all metabolically active disease can be useful as a more global assessment of disease burden. We sought to evaluate tumor burden as measured by MTV in a cohort of patients presenting for anti-BCMA CAR-T with RRMM. Methods We identified all patients presenting to our center treated with anti-BCMA CAR-T with available restaging imaging performed within 60 days prior to lymphodepleting chemotherapy (LD-chemo). Baseline skull to midthigh with or without leg/whole-body 18F-FDG PET/CT scans were evaluated for MTV using a custom tool implemented on MIM PACS version 7.1 (MIM Software, Cleveland, OH) as previously reported (Dean et al, Blood Adv 2020). Briefly, images were semi-automatically analyzed to identify abnormal regions with reference to average metabolic activity of normal liver defined by PET SUV. Metabolically active volume at the lesion level were converged based on PERCIST criteria (41% of SUVmax). Summation of the metabolically active volumes across the human body is reported as the Metabolically Tumor Volume (MTV), measured in ml. Patient image scans were reviewed centrally by a radiologist blinded to outcomes, who removed any false positive uptake unrelated to metabolically active disease. Patients with the following FISH cytogenetics were considered high risk (HR): t(4;14), t(14;16) and deletion 17p/monosomy 17 whereas the remainder were standard risk (SR). The high and low tumor volume groups were selected based on the median MTV value in cohort. Baseline laboratory tests (e.g. beta2 microglobulin (b2M), C-reactive protein (CRP)) and repeat bone marrow biopsy were obtained prior to LD-chemo. Soluble BCMA (sBCMA) was measured by ELISA on patients with available serum samples (R&D Systems, MN, USA #DY193). All analyses were conducted in Stata (16.1, StataCorp LLC, College Station, TX). Results The study cohort consisted of 66 patients. Median age was 65 years (range 36 - 81), 55% were male, 55% had ECOG 0-1 prior to LD-chemo and 65% received bridging therapy. 25 (38%) were categorized as HR based on FISH cytogenetics. Patients had received a median of 6 prior lines of therapy and median interval between PET and date of CART infusion was 14.5 days (range 2-54). Median MTV was 26.3ml (0.26-1073.87ml) and only 3/66 (5%) had no measurable PET-avid lesions captured by this algorithm. Patients with high MTV (>26) were more likely to have elevated baseline CRP >0.5mg/dL (p=0.007) and receive bridging therapy (60% vs 40%, p=0.02). MTV correlated with sBCMA levels on day -6 (r=0.82, p=0.003), and with baseline b2M (r=0.47, p=0.005), but MTV did not correlate well with percentage plasma cells in pre-CAR-T bone marrow biopsy (r=0.12, p=0.3). The risk of ³G2 CRS was significantly higher (44% vs 12%, p=0.02) and any grade ICANS was numerically higher (27% vs 12%, p=0.1) in those with high vs low MTV, respectively. Finally, fewer patients with high MTV achieved CR by D30 (19% vs 31%, p=0.2) and with limited follow up 12/14 (86%) of deaths had occurred in patients with high baseline MTV (p=0.003). Conclusion These preliminary data confirm the added value of MTV calculated from baseline imaging as part of standard of care in patients with heavily pre-treated relapsed myeloma presenting for anti-BCMA CAR-T therapy. MTV correlated highly with sBCMA measured at day-6 and may be more representative for risk assessment than basing burden on bone marrow plasma cell percentage, which has not demonstrated a consistent relationship in predicting adverse events to date. High MTV appears to be associated with an increased risk of CAR-T specific adverse events and inferior outcomes and warrants further investigation. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

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 seuil0,003

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 savante0,0000,000
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 Opus0,019
Tête enseignante GPT0,253
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

Citations16
Publié2022
Routes d'admission1
Résumé présentoui

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