Abstract 3771: Oncogenes and tumour-suppressors drive differential retinoblastoma evolution
Bibliographic record
Abstract
Abstract Unlike any other tumour type, retinoblastomas can be driven by only two tumour-initiating events: bi-allelic loss of the tumour suppressor RB1 or amplification of the MYCN oncogene. These mutations drive morphologically indistinguishable tumours that arise at different ages, providing a unique model system to evaluate the influence of initiating mutation on tumour evolution. We performed high-resolution copy number analysis of 101 retinoblastoma and whole genome sequencing of 23. These data reveal that different initiating mutations cause probabilistic changes in somatic point and copy number mutations, but large changes in genomic rearrangements and mitochondrial number. Independent of the initiating mutation, retinoblastomas harbour multiple subclonal populations that preferentially accrue point mutations after subclonal diversification. These subclonal structures suggest caution when choosing chemotherapeutic strategies for primary treatment. Overall, initiating mutations influence evolutionary trajectory more than specific driver mutations: RB1- and MYCN-driven tumours harbour distinct mutational processes, sequences of mutation acquisition and patterns of subclonal diversification. These data show how tumour-initiating mutations drive clinical behaviour by subtly biasing multiple evolutionary processes. Validation in other tumour types is underway. Citation Format: Adriana Salcedo, John D. Watson, Hilary Racher, Diane Rushlow, Shadrielle Melijah G. Espiritu, Doroto H. Sendorek, Stephenie D. Prokopec, Donco Matveski, Fouad Yousif, Julie Livingstone, Brenda L. Gallie, Paul C. Boutros. Oncogenes and tumour-suppressors drive differential retinoblastoma evolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3771.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".