Testing whether biology adds signal to a clinical risk score
M2‑BLAIPI studies whether biologically informed metabolic features can strengthen risk stratification in Burkitt lymphoma beyond what clinical variables alone provide.
Survival depends on more than the clinic can currently measure
Burkitt lymphoma is an aggressive B-cell malignancy and a major cause of childhood cancer mortality in malaria-endemic regions of sub-Saharan Africa. Despite cost-effective chemotherapy, survival in low- and middle-income countries remains substantially lower than in high-income settings.
The Burkitt Lymphoma International Prognostic Index (BL‑IPI) is built on clinical variables and therefore does not reflect the biological heterogeneity that contributes to differing outcomes.
One name, several biologically distinct diseases
Recent genomic work has shown that Burkitt lymphoma divides into distinct genetic subgroups defined by recurrent mutation patterns affecting MYC signaling, B-cell receptor signaling, chromatin remodeling, and tumor suppressor regulation.
Tumor metabolism is a downstream functional consequence of these oncogenic programs, which makes metabolic pathway activity a candidate bridge between molecular alterations and clinical behavior.
Three questions the project is built to answer
Signatures
Are specific metabolic pathway activity patterns associated with Burkitt lymphoma genetic subgroups?
Added value
Can metabolic and molecular features add prognostic information beyond the clinical variables in BL‑IPI?
Context
How should endemic context be represented when modeling risk in the settings where the disease is most common?
From pathway biology to a benchmarked model
Pathway signatures
Identify metabolic pathway signatures associated with genetic subgroups, separating subgroup biology from EBV status.
Feature engineering
Develop literature-derived metabolic features and composite scores linking oncogenic drivers to phenotypes.
Risk modeling
Develop and evaluate a multivariate risk-modeling framework, benchmarked against clinical prognostic tools.
A biologically interpretable path to better risk tools
Metabolic pathway features may offer an interpretable way to improve on clinical-only prognostic tools, and integrating endemic context matters for the populations most affected by the disease. The project is designed to be open and reproducible so its methods can be reviewed, reused, and extended.
Toward decision support where it is needed most
The long-term aspiration is a decision-support framework that could help oncologists in low- and middle-income countries, integrate with existing staging systems, and contribute to reducing survival disparities in endemic settings.
This describes the project's direction. It is not a claim of present clinical readiness, and no clinical tool is being offered here.