# An unfavorable biologic profile associated with decreased overall survival and cancer-specific survival in non-metastatic breast cancer: A latent class analysis

## TL;DR

A biological risk profile based on aging markers predicts worse survival in breast cancer patients, regardless of chronological age, and could help tailor treatment strategies.

## Contribution

A novel biological risk profile using latent class analysis of aging markers improves cancer-specific survival prediction beyond chronological age.

## Key findings

- An unfavorable biological profile combining high MCP-1, high Chitinase, and low IGF-1 is strongly linked to poorer overall survival.
- The profile identifies biologically frail patients in both young and old age groups, outperforming chronological age as a risk predictor.
- The profile is specifically associated with cancer-specific death, suggesting its potential for personalized treatment decisions.

## Abstract

•Latent Class Analysis (LCA) integrates multiple aging markers (IGF-1, MCP-1, Chitinase) into a single, robust Biological Risk Profile.•This Unfavorable Biological Profile transcends chronological age, identifying biologically frail patients in both "Old" (≥70) and "Young" (≤60) cohorts.•The integrated profile is a strong and specific predictor of Cancer-Specific Death, outperforming individual biomarkers and chronological age.•This measurable aging signature may help clinicians evaluate patients’ individual expectancy to tailor (adjuvant) treatment strategies.

Latent Class Analysis (LCA) integrates multiple aging markers (IGF-1, MCP-1, Chitinase) into a single, robust Biological Risk Profile.

This Unfavorable Biological Profile transcends chronological age, identifying biologically frail patients in both "Old" (≥70) and "Young" (≤60) cohorts.

The integrated profile is a strong and specific predictor of Cancer-Specific Death, outperforming individual biomarkers and chronological age.

This measurable aging signature may help clinicians evaluate patients’ individual expectancy to tailor (adjuvant) treatment strategies.

Chronological age is an imperfect proxy for risk assessment in geriatric oncology. There is an urgent need for an objective, easily measurable biological aging signature to refine patient stratification and personalize therapeutic decisions.

We analyzed a panel of seven aging-related biomarkers (including markers of inflammation, anabolic reserve, and telomere status) in 244 nonmetastatic breast cancer patients from two age groups (“Old”, ≥70 years, N = 162; “Young”, ≤60 years, N = 82). We used Latent Class Analysis (LCA) to integrate these markers and identify distinct biological risk profiles. These profiles were then evaluated for their association with Overall Survival (OS) and Cancer-Specific Death (CSD) via Competing Risk Analysis.

LCA identified two patient profiles. The Unfavorable Biologic Profile (56.1% of the cohort) was defined by a triad of high MCP-1, high Chitinase activity, and low IGF-1. This profile was strongly associated with poorer OS (Age-adjusted HR=1.82, p = 0.018). Crucially, 15% of chronologically “Young” patients were assigned to this high-risk profile, while 23% of “Old” patients were assigned to the Favorable Profile. Furthermore, the Unfavorable Profile was more strongly and specifically associated with CSD (Subdistribution HR: 2.05, p = 0.012) than with Non-Cancer Death.

Our results delineate an unfavorable, trans-chronological biological profile that identifies patients with low host reserve, largely driven by inflammaging and catabolism. This integrated signature provides a robust, objective screening tool to identify biologically frail patients, validating the need for Comprehensive Geriatric Assessment (CGA) and biomarker-guided therapeutic de-escalation (e.g., avoiding adjuvant chemotherapy) to improve individualized outcomes in oncology.Trial registration: BS32220096117.