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

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

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population