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Correcting for Observation Bias in Cancer Progression Modeling
Rudolf Schill1, Maren Klever2, Andreas Lösch3
1Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
Summary
Cancer progression models can be biased by tumor detectability. This study corrects for this bias, revealing new causal links between genetic events in cancer progression and improving tumor detection rates for specific mutations.
Area of Science:
- Genomics
- Cancer Biology
- Computational Biology
Background:
- Tumor progression involves genetic alterations, but their temporal sequence is hard to determine from static data.
- Mutual Hazard Networks (MHNs) model cancer progression by analyzing co-occurrence of genetic events.
- Cross-sectional cancer genomic data may contain a 'collider bias' due to clinical detection factors.
Purpose of the Study:
- To identify and correct for collider bias in cancer progression modeling.
- To enhance Mutual Hazard Networks (MHNs) by accounting for tumor detectability.
- To uncover accurate causal interactions between genetic alterations during tumor development.
Main Methods:
- Developed an enhanced MHN model incorporating genetic events' impact on tumor detectability.
- Derived an efficient tensor formula for the likelihood function.
- Applied the enhanced model to colon and lung adenocarcinoma datasets from the MSK-IMPACT study.
Main Results:
- Identified significantly higher clinical detection rates for TP53-mutated colon adenocarcinoma.
- Observed significantly higher clinical detection rates for EGFR-mutated lung adenocarcinoma.
- The enhanced MHN approach corrected spurious suppressive interactions and revealed promoting effects.
Conclusions:
- Collider bias significantly distorts cancer progression modeling from cross-sectional data.
- Accounting for tumor detectability improves the accuracy of inferring genetic event interactions.
- This enhanced modeling approach provides a more accurate understanding of cancer evolution and identifies key drivers.
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