As cancer becomes an increasingly important cause of mortality in sub-Saharan Africa, nations must improve their capacity to diagnose and treat the malignancies that most affect their populations. Breast cancer is the most common cancer and cause of cancer-related mortality for women in Cameroon, where two thirds of patients present with stage III or IV disease, and five-year survival rates are less than 30%. A longstanding collaboration between the Cameroonian Ministry of Public Health, the University of Buea, and the University of California, Los Angeles (the MBLA partnership) plans to conduct key stakeholder interviews in order to assess the feasibility and acceptability of implementing a breast cancer screening program that is initially offered to high-risk women. Risk-based screening aims to equitably target early screening efforts while ensuring that diagnostic and treatment capacity are sufficient to manage lesions identified through screening. Such a program requires a breast cancer risk prediction model that is applicable and acceptable to Cameroonian women and feasible to evaluate through a community-based screening program. Studies in the United States, Asia, and Nigeria demonstrate that breast cancer risk prediction models perform best when they are ethnic group-specific, but no breast cancer risk prediction model has been developed for Cameroonian women. The African Breast Cancer case-control Study (ABCS) contains breast cancer risk factor information for women from Nigeria, Uganda, and a small subset of women from Cameroon. Traditional approaches to risk prediction will likely suffer from small sample size in models trained on Cameroon data only or from bias in models trained and validated on the full, ethnically diverse dataset.
In Aim 1, a subgroup-specific cross-validation method will be incorporated into the Super Learner ensemble prediction algorithm to develop a breast cancer risk prediction model that incorporates all ABCS data but is optimized for Cameroonian women.
Aim 2 addresses MBLA members? concerns that certain risk factors from ABCS will be difficult to evaluate by community survey. Targeted learning methods will be used to define metrics for comparing risk prediction models including and excluding these variables so that Cameroonian stakeholders can evaluate whether to include these risk factors in their breast cancer risk prediction model.
In Aim 3, an R shiny app will be developed, tested, and optimized in order to facilitate use of a Cameroon-specific breast cancer risk model in a future screening program. The methods developed and tested in this project could help to optimize cancer risk prediction models for other ethnic groups with limited data in sub-Saharan Africa and globally. This research will be conducted under the mentorship of the MBLA collaboration, UC Berkeley?s leaders in the field of targeted learning, and the UCSF Global Cancer Program. By providing protected time for training, research, and career development, this grant will facilitate the applicant?s progress towards becoming a breast surgical oncologist researching methods of improving access to cancer care in Africa.
In response to rising rates of breast cancer in Cameroon, a partnership between the Cameroonian Ministry of Public Health, the University of Buea, and the University of California, Los Angeles plans to evaluate the feasibility and acceptability of implementing a risk-based breast cancer screening program in Cameroon. This project aims to develop a method for defining a breast cancer risk prediction model optimized for Cameroonian women that makes use of information from a large, ethnically diverse case-control dataset and to provide Cameroonian stakeholders with information needed to select which risk factors to include in a Cameroon- specific breast cancer risk model. These methods could be used to define optimal cancer risk prediction models for ethnic groups with limited data in sub-Saharan Africa and globally.
