Abstract IA007: Evolutionary cancer epidemiology
Bibliographic record
Abstract
Abstract What are the implications of evolution for cancer epidemiology? Cancers develop through a process of cellular evolution, constrained and shaped by the evolution of the organisms in which they grow. Furthermore, diet, exercise, exposures and the constitutive genotypes of the hosts impact the cellular evolution that drives neoplastic progression and cancer’s response to interventions. An evolutionary approach to cancer epidemiology helps to bridge scales from the molecular causes of cancer and cellular dynamics up to population level phenomena. Until recently, there has been little consideration of the implications of evolution for cancer epidemiology. In April 2025, we held the first workshop on evolutionary cancer epidemiology. We identified at least six ways in which organismal evolution impacts cancer epidemiology: 1) Organismal evolution has led to the evolution of cancer genes in the germline, 2) population bottlenecks have led to the spread of cancer risk alleles like BRCA1 mutations, 3) some species have evolved extremely low cancer rates which may illuminate new mechanisms of cancer suppression, while species with extremely high cancer rates may help us learn about cancer susceptibility, 4) organismal evolution has likely involved tradeoffs between cancer risk and other selective pressures, 5) changes in our environments have led to evolutionary mismatches between our ancestral environments and modern environments, and 6) co-evolution of human biology with microbes have impacted cancer incidence. We also identified at least five ways in which cell level evolution impacts cancer epidemiology: 1) Multiscale models of cell level dynamics help explain cancer incidence patterns, 2) exposures change the selective pressures on somatic cells, 3) the germline genotype also affects the selective pressures and mutational processes in somatic cells, 4) the stochastic and spatially heterogeneous nature of cell level evolution impact the success of cancer screening and biomarkers, and 5) many exposures leave a signature of their role in the types of mutations they cause, allowing us to infer their role in any given tissue. Previous work on all of these topics have demonstrated the power and utility of this approach. The inclusion of evolutionary theory and tools in cancer epidemiology holds the promise of improving cancer risk prediction, prevention, disparities, and management. It may also help to understand the rise in early onset cancers and help to improve cancer screening and clinical trial design. The critical barrier to progress is only a lack of cross-training and collaborations between evolutionary biologists and cancer epidemiologists. We hope to rectify that. Citation Format: Carlo Maley, Katherine Lawson-Michod, Hannah Carter, Kathleen Curtius, Kathleen Houlahan, Nic Fisk, Li Liu, Joseph Lachance, James DeGregori, Charles Swanton, Zachary T. Compton, Athena Aktipis, Robert Hiatt, Arcadi Navarro. Evolutionary cancer epidemiology [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Cancer Evolution: The Dynamics of Progression and Persistence; 2025 Dec 4-6; Albuquerque, NM. Philadelphia (PA): AACR; Cancer Res 2025;85(23_Suppl):Abstract nr IA007.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.052 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".