CanAssist Breast Risk Stratification in Multifocal/Multicentric Breast Cancer: To Test Each Focus or Not?

Area of Science:

• Oncology
• Genomics
• Artificial Intelligence

Background:

• Multifocal/multicentric breast cancer (MF/MC BC) lacks clear prognostic testing guidelines, impacting adjuvant treatment decisions.
• CanAssist Breast (CAB) is an AI-based proteomic test assessing distant recurrence risk in HR+/HER2- early breast cancer.

Purpose of the Study:

• To evaluate the CAB test's ability to stratify recurrence risk in MF/MC BC.
• To assess the utility of CAB testing in guiding adjuvant chemotherapy recommendations for MF/MC BC.

Main Methods:

• Retrospective analysis of 6126 patients with HR+/HER2- early breast cancer undergoing CAB testing.
• Focused study on 107 MF/MC BC patients with CAB testing on multiple tumor foci.
• Evaluation of concordance and discordance in CAB risk categories across multiple foci.

Main Results:

• Intertumoral heterogeneity was observed in MF/MC BC, with discordant CAB risk categories in 17% of patients.
• A stepwise protocol revealed instances where clinical risk and CAB risk differed, highlighting prognostication complexity.
• Analysis of 75 patients showed 11% with clinically high-risk tumors classified as CAB-low risk, and vice versa.

Conclusions:

• A tiered CAB testing approach is recommended for MF/MC BC.
• Testing additional foci if the initial tumor shows CAB-low risk improves risk stratification.
• This strategy aids personalized adjuvant treatment planning and minimizes undertreatment risk.