Abstract 3010: Epigenetic age acceleration in older adults with hematologic malignancies
Notice bibliographique
Résumé
Abstract Background: Older adults with hematologic malignancy (HM) are a growing demographic with heterogenous health needs. Chronologic age, performance status and comorbidities impact the selection and intensity of treatments but may not accurately predict treatment toxicity or resiliency. Here, we conducted longitudinal measures of clinical frailty and epigenetic age to explore the relationships between physiologic aging and fitness. Methods: Newly diagnosed patients >60 years with HM were enrolled in a prospective longitudinal study with biomarkers procured at baseline (pre-therapy) and end-of-study (EOS). EOS was defined as the earliest of the following events: progression, transplant, or 1-year from baseline. Peripheral blood was collected, CD3+ T-cells isolated, and DNA extracted using a column-based commercial nucleic acid kit. Peripheral blood T lymphocytes samples were also procured from healthy controls using identical methods. PBTL was extracted, bisulfite converted, and methylation was quantified using the Illumina 850K EPIC methylation array at the TruDiagnostic core laboratory. Raw data was processed using the minfi pipeline, followed by normalization, batch correction, and KNN-based imputation using the GMQN and impute R packages. Principal-component (PC) PhenoAge measures were calculated using the processed methylation values. Measured clinical phenotypes of fitness included ECOG, independent activities of daily living, physical health scores, comorbidities, cognition (BOMC) and objective measures of function (SPPB). Results: 94 patients with HM (acute leukemia n=35, lymphoma n=23, CLL = 6, plasma cell n=30) with PBTL and clinical fitness data were evaluated. The median chronologic age was 70.55 years (range 28.71 yrs, IQR = 10.84). mPhenoAge was higher in patients with HM (Median = 49.59 yrs, Range = 71.245, IQR = 12.52) than healthy donors (Median = 30.46; Range = 75.58, IQR = 32.83). Patients with acute leukemia had the highest mPhenoAge (57.10 yrs, Range = 61.97, IQR = 30.03; p=3e-04). mPhenoage was lower in patients with ECOG 0 vs ECOG 1 (p=0.011). Patients with Acute leukemia also showed a decrease in mPhenoage at EOS (p=0.034), which was not seen in other HMs. Patient IADLs (p=0.032) and comorbidities (p=0.009) were positively correlated with Epigenetic Age Acceleration (EAA). Patient cognitive score (p=0.053) negatively correlated with EAA. mPhenoAge decreased more in patients who received hypomethylating therapies than those who received high-dose multi-drug chemotherapy, low-dose multi-drug chemotherapy or targeted agents. Conclusions: Physiologic age, as measured by PBTL mPhenoAge, is increased among older adults with HMs relative to healthy controls. PBTL mPhenoAge is highest among HM patients with acute leukemia and changes are associated with clinical phenotypes. PBTL mPhenoAGE decreased with hypomethylating agents in patients with acute leukemia. Citation Format: Ashley E. Rosko, Kaitlyn Dvorak, Jennifer Woyach, Varun Dwaraka, Ryan Smith, Ryann Finlayson, Alice Mims, Christin Burd. Epigenetic age acceleration in older adults with hematologic malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3010.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».