Tumor Mutation Burden Prediction Model in Egyptian Breast Cancer patients based on Next Generation Sequencing

Area of Science:

• Oncology
• Genomics
• Biomarker Research

Background:

• Tumor mutation burden (TMB) is an emerging biomarker in cancer research.
• Predicting TMB can aid in treatment selection for breast cancer (BC).
• Understanding TMB in diverse populations like Egyptian BC patients is crucial.

Purpose of the Study:

• To determine the tumor mutation burden (TMB) in Egyptian breast cancer (BC) patients.
• To develop a predictive model for TMB using key biomarkers: estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), and Ki-67.
• To identify the optimal machine learning model for TMB prediction based on receptor status.

Main Methods:

• Tumor tissues from 58 Egyptian BC patients were analyzed for TMB using the Ion AmpliSeq Comprehensive Cancer Panel.
• Machine learning models were employed to predict TMB levels based on ER, PR, HER-2, and Ki-67 expression.
• Logistic regression was used to establish the final predictive model.

Main Results:

• TMB values ranged from 0 to 8.12 mutations per megabase (Mb).
• Positive ER and PR expression were associated with lower TMB (≤1.25/Mb).
• Positive Ki-67 expression was significantly associated with higher TMB (>1.25/Mb), while HER-2 status showed no significant association.
• An optimized logistic regression model was developed: TMB = -27.5 -1.82 ER - 0.73 PR + 0.826 HER2 + 2.08 Ki-67.

Conclusions:

• TMB in Egyptian breast cancer patients can be effectively predicted using a combination of ER, PR, HER-2, and Ki-67 expression levels.
• This predictive model offers a potential non-invasive method to estimate TMB.
• The findings contribute to personalized medicine approaches in breast cancer treatment.