Scaling for African Inclusion in High-Throughput Whole Cancer Genome Bioinformatic Workflows

Area of Science:

• Genomic Medicine
• Bioinformatics
• Cancer Research

Background:

• Sub-Saharan Africa faces the highest cancer mortality rates globally.
• African populations are largely excluded from global cancer genomic research and precision oncology benefits.
• Limited availability of whole cancer genome databases for African patients hinders research.

Purpose of the Study:

• To identify and adapt bioinformatic workflows for large-scale analysis of African cancer genomic data.
• To demonstrate scalable computational strategies for analyzing African tumor genomes.
• To provide guidance for improving future African-inclusive cancer genomics research.

Main Methods:

• Comprehensive literature review to identify existing African cancer genome databases.
• Selection and adaptation of a bioinformatic workflow emphasizing cohort-level data and scalability.
• Implementation of high-level parallelism using data or genomic interval chunking strategies.

Main Results:

• Only five whole cancer genome databases including Sub-Saharan African patients were identified, covering breast, esophageal, prostate, and Burkitt lymphoma.
• Studies reveal higher tumor genome instability and African-specific cancer drivers in these populations.
• Demonstrated scalability of the selected workflow using African genomic data through parallel processing.

Conclusions:

• Scalable bioinformatic workflows are crucial for analyzing large-scale African genomic resources.
• Enhancements in genomic techniques and prioritization of African datasets are recommended.
• This work offers practical computational guidance to lower barriers for African-inclusive cancer research, addressing mortality disparities.