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

Abstract

Objectives: This study aimed to identify the tumor mutation burden (TMB) value in Egyptian breast cancer (BC) patients. Moreover, to find the best TMB prediction model based on the expression of estrogen (ER), progesterone (PR), human epidermal growth factor receptor 2 (HER-2), and proliferation index Ki-67. Methods: The Ion AmpliSeq Comprehensive Cancer Panel was used to determine TMB value of 58 Egyptian BC tumor tissues. Different machine learning models were used to select the optimal classification model for prediction of TMB level according to patient's receptor status. Results: The measured TMB value was between 0 and 8.12/Mb. Positive expression of ER and PR was significantly associated with TMB ≤ 1.25 [(OR =0.35, 95% CI: 0.04-2.98), (OR = 0.17, 95% CI= 0.02-0.44)] respectively. Ki-67 expression positive was significantly associated with TMB >1.25 than those who were Ki-67 expression negative (OR = 9.33, 95% CI= 2.07-42.18). However, no significant differences were observed between HER2 positive and HER2 negative groups. The optimized logistic regression model was TMB = -27.5 -1.82 ER -0.73 PR + 0.826 HER2 + 2.08 Ki-67. Conclusion: Our findings revealed that TMB value can be predicted based on the expression level of ER, PR, HER-2, and Ki-67.

Somatic Mutations Alter Interleukin Signaling Pathways in Grade II Invasive Breast Cancer Patients: An Egyptian Experience

“…Fifty-five grade II invasive BC patients were included in this study. Data for somatic mutations in 45 BC patients were already available from a previous study [ 17 ]. In addition, the study included data for ten new BC patients’ somatic mutations.…”

“…So, we developed a cohort study to explore the landscape of somatic mutations in Egyptian BC patients and identify the most frequently detected somatic mutations [ 16 ]. In another study on 58 BC patients, we identified all the somatic, non-silent, base substitution, and indel mutations per megabase of the examined genome to calculate the tumor mutation burden (TMB) and established an Egyptian TMB prediction model based on the expression level of ER, PR, HER-2, and Ki-67 [ 17 ]. In the present study, we used Ion AmpliSeq Comprehensive Cancer Panel-targeted sequencing to study the involvement of somatic mutations in interleukin signaling pathways associated with grade II invasive BC Egyptian patients to broaden our understanding of their role in promoting carcinogenesis and developing personalized therapy in Egypt.…”

Artificial intelligence-based prediction for cancer-related outcomes in Africa: Status and potential refinements

“…The performances of the set of models proposed for use in African oncological settings so far were satisfactory to excellent upon validation [ 9 - 12 , 14 , 18 ] ( Table 1 ). However, to fully actualize the potential of AI-based prediction for cancer outcomes in Africa, inquiries into the impact and efficiency of the models in comparison to the current standards of management are paramount.…”