Abstract B004: Putting function back into paediatric cancer genomics – modelling the therapeutic implications of novel genomic features
Bibliographic record
Abstract
Abstract One of the great promises of precision oncology programs lies in linking the specific genomic features of a tumour to therapies that directly or indirectly target these features.The ZERO Childhood Cancer Program (ZERO) is Australia’s national precision medicine program for childhood cancer. ZERO has demonstrated that precision guided therapy is associated with improved survival of children diagnosed with high-risk cancers, by identifying therapeutic options that would otherwise go unrecognised. However, there are many roadblocks preventing use of these therapies. One is the situation where a genomic feature is suspected of being a cancer driver and targetable, but has not been previously described. Another is encountering more familiar oncogenic lesions in atypical tumour contexts. This lack of direct clinical experience or preclinical data about the biological and therapeutic significance of the lesions means, the targeted therapies are often not used even when potential for clinical benefit. We are attempting to address this through an experimental program that generates models of individual genomic lesions to establish, at the bench, whether a genomic aberration is oncogenic and how it can be therapeutically targeted. Our focus has been on to novel variants predicted to activate receptor tyrosine kinase signalling found in the ZERO program.We will show we use a variety of approaches, including CRISPR/Cas9 engineering of novelkinase activating lesions in cytokine dependent cell lines to measure whether they are sufficient to transform these cells from cytokine dependence to independence. Further, we compare how driver fusions engineered in the endogenous genome compare, with respect to transforming capacity and responses to targeted therapies, to exogenous over-expression of the same fusions By studying rare or unique variants, we have identified novel mechanisms of tyrosine kinase activation, and identified therapeutic options for individual cancers, so meeting a definition of precision medicine. Further, we contend that understanding the biology of rare or unique variants does more than recover lost therapeutic opportunities for clinicians and families, it also provides important new knowledge about the biology of childhood cancer. Citation Format: Teresa Sadras, Lauren M. Brown, Alice Salib, Pei Y. Liu, Chelsea Mayoh, Vanessa Tyrrell, Mark J. Cowley, Paul G. Ekert. Putting function back into paediatric cancer genomics – modelling the therapeutic implications of novel genomic features [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 B004.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".