Abstract PR002: Somatic genetic development of Wilms tumor via normal kidneys in predisposed children
Bibliographic record
Abstract
Abstract Ten percent of children with cancer harbor a predisposition mutation, and in children with the kidney cancer, Wilms tumor, the prevalence may be as high as 30%. Whilst clinical management of children with a known predisposition differs from that of children with sporadic Wilms tumors, it is not dependent on the type of predisposition. Some Wilms predispositions are associated with defined histological and clinical features, which suggests differences in the genetic development of such tumors. To investigate this, we assembled a cohort of 137 children with Wilms tumor, of whom 71 were found to have an underlying pathogenic germline or mosaic predisposition variant. Predisposition changes encompassed methylation changes, substitutions, indels, simple rearrangements and more complex ones. Our multi-omic approach detected four novel putative Wilms tumor predisposition genes, and identified causative predisposition variants in children where standard of care assays had failed to do so. We reconstructed the development of 237 neoplasms from zygote via normal kidney tissue, utilizing whole genome sequencing, RNA sequencing and genome wide methylation data, and pursued our findings in an independent validation cohort. We found that tumor development differed between predisposed and sporadic cases, and varied amongst predisposed children according to specific mutations and their developmental timing. These differences pervaded the clonal architecture of normal kidneys, and the repertoire of driver events, such as the possibility of acquiring high risk disease variants. A striking absence of additional driver variants were, in particular, noted for WT1 and TRIM28 predisposed tumors. In 20 children we examined the relationship between multiple neoplasms, including bilateral tumors, recurrences, metastases, and leukemias. The tumors from non-predisposed children shared a common tumor trunk, whereas we in many predisposed children saw an extraordinary propensity for independent tumor generation. In conclusion, we examined the somatic evolution of Wilms tumor via normal kidney tissue in predisposed children, and showed that predisposition variants may constrain the somatic genetic development of Wilms tumor. Our findings suggest that a variant specific approach to the clinical management of these children may merit consideration. Citation Format: Taryn D. Treger, Jenny Wegert, Anna Wenger, Tim H.H. Coorens, Reem Al-Saadi, Paul Kemps, Jonathan Kennedy, Conor Parks, Nathaniel D. Anderson, Angus Hodder, Aleksandra Letunovska, Hyunchul Jung, Toochi Ogbonnah, Mi Trinh, Henry Lee-Six, Guillaume Morcrette, Marry M. van den Heuvel-Eibrink, Jarno Drost, Ruben van Boxtel, Eline Bertrums, Bianca Goemans, Marjolein Jongmans, Roland Kuiper, Evangelia Antoniou, Dirk Reinhardt, Jack Bartram, Ciaran Hutchinson, Gordan Vujanic, Christian Vokuhl, Tanzina Chowdhury, Rhoikos Furtwängler, Norbert Graf, Kathy Pritchard-Jones, Manfred Gessler, Sam Behjati. Somatic genetic development of Wilms tumor via normal kidneys in predisposed children [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr PR002.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".