Abstract 6715: Cross-species investigation of gene copy number and cancer resistance
Bibliographic record
Abstract
Low cancer incidence rates are observed in large mammals such as elephants and whales, despite their mass and long lifespan hypothetically contributing to a higher lifetime probability of acquiring oncogenic mutations. Few studies have investigated cancer-related genes in these animals. One reported that bowhead whales possess two functional copies of PCNA. Multiple studies have investigated the presence of multiple retrogene copies of TP53 in elephants. Extra copies of genes involved in DNA damage detection and repair could contribute to cancer resistance. Droplet digital PCR (ddPCR) was used to quantify gene copy number for TP53, PCNA, HER2, DLG1, and DLG2 in genomic DNA extracted from frozen skin samples of beluga, narwhal and bowhead whales (n=20 each). Results showed that all 3 whale species had more than one copy of PCNA. Agarose gel PCR showed the simultaneous presence of wild-type PCNA (possessing introns) and variable numbers of pseudogene sequences (lacking introns) within individuals for belugas, narwhals, and bowheads. ddPCR found that elephants did not have increased PCNA copy number, but agarose gel PCR showed the presence of PCNA pseudogenes. Similar patterns were also observed in rhinos, horses, cows and other ungulate species. To investigate copy number loss in cancer, ddPCR was performed on formalin-fixed, paraffin embedded normal and matched tumor tissue of 7 individual belugas from the St. Lawrence estuary, an area that was historically contaminated with industrial carcinogens. Copy number loss was not observed for any investigated tumor suppressor gene in tumor tissue compared to normal tissue. Our results show that bowhead whales are not unique in having multiple copies of PCNA. The presence of incomplete copies with unknown function may influence copy number quantification. Further study is required to understand the significance of the variable PCNA pseudogene copies. Understanding natural copy number variation in tumor suppressor genes may provide insight into risk factors and prevention methods across species. Citation Format: Morla Phan, Thompson Worden, Sarah Adamowicz, Stéphane Lair, Mauricio Seguel, Claire Jardine, Geoffrey Wood, Dirk Steinke. Cross-species investigation of gene copy number and cancer resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6715.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".