T Cell Receptor Diversity, Cancer and Sex: Insights from 30,000 TCRβ Repertoires
Zahid, H. J.; Taniguchi, R.; Noceda, M. G.; Robbins, H.; Greissl, J.
Show abstract
Immunoediting posits that mutation and immunity jointly shape cancer evolution, yet their population-level interplay remains uncertain. Here we analyze T cell receptor (TCR) {beta} repertoires from 30,000 individuals and find that TCR diversity, essential for recognizing and eliminating malignant cells, declines with age. This immune decline occurs 11 years later in females and coincides with their lower cancer incidence, suggesting a biological connection. To test this link, we formalize immunoediting as a quantitative model of carcinogenesis, relating the measured age-associated decline in TCR diversity to rising cancer incidence. We find that both mutational and immune processes shape cancer risk, with lower incidence in females attributable to delayed immune decline. Extending this analysis across subtypes uncovers structured patterns in cancer incidence that reflect the relative contributions of these processes. Cancers cluster along an emergent immune-mutation axis that aligns with known features of cancer biology and indicates convergent evolutionary dynamics. Together, our results establish a quantitative, population-level framework for immunoediting that connects direct measurements of immune competence to cancer risk, integrating the molecular mechanisms, evolutionary dynamics and incidence patterns of cancer to reveal a fundamental balance between mutation and immunity that underlies carcinogenesis.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Population size interacts with reproductive longevity to shape the germline mutation rate 95%
- Age-related behavioral resilience in smartphone touchscreen interaction dynamics 94%
- Changes in life history and population size can explain relative neutral diversity levels on X and autosomes in extant human populations 94%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.