Androgen Receptor Influenced Recurrence Score Correlation in Hormone Positive and HER2 Negative Breast Cancer Indian...

Area of Science:

• Oncology
• Biomarkers
• Medical Informatics

Background:

• Breast cancer recurrence poses a significant challenge, impacting patient outcomes and treatment strategies.
• Accurate prediction of recurrence risk is essential for personalized treatment decisions and improving disease-free survival.
• Existing clinical assays for recurrence prediction are effective but costly, limiting accessibility.

Purpose of the Study:

• To evaluate the applicability of open-access breast cancer recurrence prediction tools in Indian patients.
• To investigate the influence of androgen receptor (AR) expression on recurrence risk prediction.
• To compare the performance of Magee equations (ME) and other online tools, incorporating AR IHC data.

Main Methods:

• Analysis of histopathologic and immunohistochemistry (IHC) data, including ER/PR, HER2, and Ki67, from 55 Indian breast cancer patients.
• Application of open-access recurrence prediction tools: Magee equations (ME), CTS5 Calculator, and Predict Breast cancer.
• Correlation analysis between AR IHC expression and recurrence scores (RSs) generated by different prediction tools.

Main Results:

• A statistically significant correlation was observed between androgen receptor (AR) expression and average Magee equations (ME) scores.
• No significant correlations were found between AR expression and other tested prediction tools.
• Magee equations (ME) demonstrated greater relevance and informativeness compared to other online tools.

Conclusions:

• Magee equations (ME) show promise as a relevant and informative tool for breast cancer recurrence prediction.
• Integrating androgen receptor (AR) IHC expression analysis with ME may enhance prediction accuracy and feasibility.
• This combined approach could benefit underserved breast cancer patient populations by providing a more accessible risk assessment.