Mapping pesticides to cancer risk at the country scale with spatial exposomics
15 hours ago
- #spatial exposomics
- #cancer risk mapping
- #pesticide exposure
- Developed a high-resolution process-based model to map environmental pesticide exposure risk across Peru, incorporating 31 commonly used active ingredients.
- Stratified cancer registry data by developmental lineage (e.g., endoderm-, ectoderm-, mesoderm-derived) rather than organ-based classification, revealing spatial associations with pesticide risk.
- Identified 436 pesticide-associated cancer hotspots using Bayesian spatial modeling, with relative risks ranging from 1.14 to 9.38, indicating a significant increase in cancer incidence linked to environmental pesticide exposure.
- Found that liver cancer clusters in Junín, Peru, particularly among Indigenous populations, show a distinct transcriptomic signature of non-genotoxic pesticide exposure in non-tumour liver tissue.
- Transcriptomic analysis revealed disruption of lineage-specific master transcription factors and core regulatory circuitries in normal liver tissue, suggesting a non-genotoxic mechanism predisposing cells to malignant transformation.
- Highlighted that pesticide-associated cancer risks are concentrated in rural areas with intense agricultural pressure and deforestation, exacerbating socio-ecological inequities.
- Demonstrated that climate variability (e.g., El Niño) can amplify pesticide exposure risk, with higher modeled risk during the 2015 El Niño compared to 2019 neutral conditions.
- Proposed an integrative exposomic framework linking environmental modeling, spatial epidemiology, and molecular validation to assess real-world carcinogenicity of pesticide mixtures.