sharaf501/African-Optimized-Gene-Panel

# African-targeted-Cancer-Panel # Introduction ## Background Africa bears 15% of the global cancer burden but has minimal representation in cancer genomics databases. This disparity creates challenges for precision oncology implementation in African healthcare systems. ## Objectives 1. Design a cost-effective targeted gene panel 2. Maximize clinical actionability for African populations 3. Include pan-cancer and African-specific driver genes 4. Ensure applicability across resource-limited settings ## These script presents the design and validation of an African-specific targeted cancer gene panel optimized for resource-limited settings. ## Notes: - Pan-Cancer Score: The average of (a) frequency score, defined as the gene's pan-cancer mutation frequency divided by the maximum observed frequency, and (b) breadth score, defined as the proportion of the 12 cancer types in which the gene harbors non-silent mutations. - African Relevance Score (30%): Mutation frequencies across TCGA cancer types were weighted by GLOBOCAN 2022 African priority scores (Table 1). The weighted frequencies were summed per gene and converted to percentile ranks to yield a 0–1 scale. - Literature Support Score (20%): The normalized frequency of gene mentions across 1,349 African cancer genomics articles retrieved through per-abstract PubMed mining, with each gene's article count divided by the maximum observed count. - Expert Curation Score (20%): A binary score reflecting presence in the combined Foundation Medicine CDx and MSK-IMPACT gene list (1 if present; 0 if absent). ## Limitations 1. **Data Scarcity**: Limited African genomic data necessitated TCGA and AACR Project GENIE extrapolation 2. **Ethnic Diversity**: Africa's genetic diversity may require regional adaptations 3. **Validation Needed**: Clinical validation in African cohorts required 4. **Actionability Gap**: Limited access to targeted therapies in some regions # Conclusions This study presents a rigorously designed, ev …