Jiang, JueSamaha, GeorginaWillet, Cali E.Chew, TracyHayes, Vanessa M.Jaratlerdsiri, Weerachai2025-09-042025-09-042025-08Jiang, J.; Samaha, G.; Willet, C.E.; Chew, T.; Hayes, V.M.; Jaratlerdsiri, W. Scaling for African Inclusion in High-Throughput Whole Cancer Genome Bioinformatic Workflows. Cancers 2025, 17, 2481. https://doi.org/10.3390/cancers17152481.2072-6694 (online)10.3390/cancers17152481http://hdl.handle.net/2263/104200SUPPLEMENTARY MATERIAL : Supplementary Table S1: Benchmarking on steps for optimised computational configurations.Sub-Saharan Africa is experiencing the highest mortality rates for several cancer types. While cancer research globally has entered the genomic era and advanced the deployment of precision oncology, Africa has largely been excluded and has received few benefits from tumour profiling. Through a thorough literature review, we identified only five whole cancer genome databases that include patients from Sub-Saharan Africa, covering four cancer types (breast, esophageal, prostate, and Burkitt lymphoma). Irrespective of cancer type, these studies report higher tumour genome instability, including African-specific cancer drivers and mutational signatures, suggesting unique contributory mechanisms at play. Reviewing bioinformatic tools applied to African databases, we carefully select a workflow suitable for large-scale African resources, which incorporates cohort-level data and a scalable design for time and computational efficiency. Using African genomic data, we demonstrate the scalability achieved by high-level parallelism through physical data or genomic interval chunking strategies. Furthermore, we provide a rationale for improving current workflows for African data, including the adoption of more genomic techniques and the prioritisation of African-derived datasets for diverse applications. Together, these enhancements and genomic scaling strategies serve as practical computational guidance, lowering technical barriers for future large-scale African-inclusive research and ultimately helping to reduce the disparity gap in cancer mortality rates across Sub-Saharan Africa. SIMPLE SUMMARY Africa faces the highest mortality rates across eight cancer types. However, cancer studies are biased toward European populations, leading to major concerns that cancer treatments may be ineffective for African patients. Providing a systematic review of African-inclusive whole cancer genome studies, African-derived tumours reveal distinct clinically relevant drivers, molecular taxonomies, and overall increased genomic instability, highlighting challenges associated with non-African-derived computational workflows. We provide a rationale for parallelism strategies to accelerate the processing steps of those distinctly intensive data, allowing for required scalability. Advocating for further resources that capture the rich African ancestral diversity, a concerted global effort will be required to improve and ultimately standardise bioinformatic workflows, thereby enhancing health outcomes for African cancer patients.en© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).AfricaComputational workflowParallelismCancer genomicsWhole-genome sequencing (WGS)Article