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Purpose <p>Current breast cancer–specific graded prognostic assessment (GPA) models primarily rely on chronological age and clinical disease characteristics, which may inadequately reflect inter-individual vulnerability and physiological reserve. We investigated whether phenotypic biological aging, quantified by PhenoAgeAccel, provides prognostic information beyond chronological age in patients with breast cancer brain metastases (BCBM) and whether integration of biological aging improves the performance of established Breast-GPA models.</p> Methods <p>We retrospectively analyzed 432 consecutive patients with BCBM. Overall survival (OS) was measured from brain metastasis diagnosis. Biological aging was assessed using PhenoAgeAccel derived from routinely available laboratory parameters. Multivariable Cox models evaluated independent prognostic factors, with primary analyses in patients with parenchymal brain metastases. Original and PhenoAge-modified Breast-GPA scores were compared using Harrell’s C-index and milestone survival analyses (3 and 36 months).</p> Results <p>Median OS for the overall cohort was 8.7 months (95% CI 7.4–10.1). Accelerated biological aging (PhenoAgeAccel &gt; 0) was observed in 39.8% of evaluable patients (<i>n</i> = 294) and was associated with significantly shorter OS (median 4.4 vs. 14.6 months; <i>p</i> &lt; 0.001). In multivariable analyses, PhenoAgeAccel remained independently associated with mortality (HR 1.88, 95% CI 1.44–2.46; <i>p</i> &lt; 0.001), whereas chronological age was not. Similar findings were observed in patients with parenchymal brain metastases (HR 1.83, 95% CI 1.35–2.48; <i>p</i> &lt; 0.001). Adding biological aging improved discrimination of Breast-GPA models (C-index up to ~ 0.71) and strengthened prediction of early (3-month) and long-term (36-month) survival status.</p> Conclusions <p>Phenotypic biological aging provides prognostic information beyond chronological age in BCBM and strengthens established GPA-based risk stratification. Integration of biological aging metrics may support more individualized prognostic assessment in clinical practice.</p>

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Biological aging enhances prognostic stratification beyond chronological age in breast cancer brain metastases

  • 期刊论文

  • 发表日期：

    2026年3月3日

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  • Tuğba Önder,
  • Öztürk Ateş

摘要

Purpose

Current breast cancer–specific graded prognostic assessment (GPA) models primarily rely on chronological age and clinical disease characteristics, which may inadequately reflect inter-individual vulnerability and physiological reserve. We investigated whether phenotypic biological aging, quantified by PhenoAgeAccel, provides prognostic information beyond chronological age in patients with breast cancer brain metastases (BCBM) and whether integration of biological aging improves the performance of established Breast-GPA models.

Methods

We retrospectively analyzed 432 consecutive patients with BCBM. Overall survival (OS) was measured from brain metastasis diagnosis. Biological aging was assessed using PhenoAgeAccel derived from routinely available laboratory parameters. Multivariable Cox models evaluated independent prognostic factors, with primary analyses in patients with parenchymal brain metastases. Original and PhenoAge-modified Breast-GPA scores were compared using Harrell’s C-index and milestone survival analyses (3 and 36 months).

Results

Median OS for the overall cohort was 8.7 months (95% CI 7.4–10.1). Accelerated biological aging (PhenoAgeAccel > 0) was observed in 39.8% of evaluable patients (n = 294) and was associated with significantly shorter OS (median 4.4 vs. 14.6 months; p < 0.001). In multivariable analyses, PhenoAgeAccel remained independently associated with mortality (HR 1.88, 95% CI 1.44–2.46; p < 0.001), whereas chronological age was not. Similar findings were observed in patients with parenchymal brain metastases (HR 1.83, 95% CI 1.35–2.48; p < 0.001). Adding biological aging improved discrimination of Breast-GPA models (C-index up to ~ 0.71) and strengthened prediction of early (3-month) and long-term (36-month) survival status.

Conclusions

Phenotypic biological aging provides prognostic information beyond chronological age in BCBM and strengthens established GPA-based risk stratification. Integration of biological aging metrics may support more individualized prognostic assessment in clinical practice.