Abstract 3010: Epigenetic age acceleration in older adults with hematologic malignancies
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".