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Published on: May 18, 2020
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Computational Model Predicts Patient Outcomes in Luminal B Breast Cancer Treated with Endocrine Therapy and CDK4/6
Leonard Schmiester1, Fara Brasó-Maristany2, Blanca González-Farré2,3
1Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway.
Summary
A new computational biomarker accurately predicts breast cancer patient response to CDK4/6 inhibitors and endocrine therapy. This tool aids in selecting personalized treatments for better patient outcomes.
Area of Science:
- Oncology
- Computational Biology
- Biomarker Development
Background:
- Endocrine therapy combined with CDK4/6 inhibition is a standard treatment for hormone receptor-positive breast cancer.
- Predicting patient response to this combination therapy remains a clinical challenge.
- There is a need for predictive biomarkers to guide treatment decisions and improve outcomes.
Purpose of the Study:
- To develop and validate a computational biomarker for predicting response to CDK4/6 inhibition plus endocrine therapy in breast cancer patients.
- To identify key gene expression signatures associated with treatment response.
- To enable personalized treatment strategies for breast cancer.
Main Methods:
- A mechanistic mathematical model was developed using publicly available breast cancer cell line data.
- The model incorporates protein signaling and drug mechanisms of action.
- Patient-specific response scores were generated based on the expression of six genes: CCND1, CCNE1, ESR1, RB1, MYC, and CDKN1A.
- The model was validated in five independent cohorts (148 patients) with early-stage or advanced breast cancer.
Main Results:
- The computational biomarker demonstrated significant associations with patient outcomes across all five validation cohorts.
- The model accurately predicted high Ki67 levels (AUCs ranging from 0.80 to 0.81) and high PAM50 risk of relapse (AUC of 0.78).
- Patient stratification based on the model's predictions was significantly associated with progression-free survival (PFS) (HR = 2.92 and HR = 2.16).
Conclusions:
- Mathematical modeling provides an accurate method for predicting patient outcomes in CDK4/6 inhibitor plus endocrine therapy.
- This computational biomarker represents a significant step towards personalized treatment selection for breast cancer patients.
- The findings are particularly relevant for patients with Luminal B breast cancer.
