Abstract B035: Stratified Medicine Paediatrics2 – advancing precision medicine in paediatric cancer
Bibliographic record
Abstract
Abstract Stratified Medicine Paediatrics (SMPaeds) was the UK’s first national profiling platform and successfully provided an infrastructure to triage patients for targeted treatments and clinical trials by identifying molecular abnormalities in relapsed childhood cancer. The molecular panels provided as part of SMPaeds have now been implemented into the National Health Service (NHS) Test directory as routine Standard of Care (SoC). Paired whole genome sequencing (PWGS) has also been available for children with cancer in the UK since 2021, although the uptake of this molecular assay at diagnosis and relapse has varied nationally. In centres that have routinely implemented WGS, a clinical benefit has been demonstrated. However, due to the costs and prolonged turnaround time, its utility is limited to 1-2 timepoints for any patient. SMPaeds recruited >800 patients, with a turnaround time of 21-28 days for reporting results to clinicians via the NMTB. A germline cancer predisposition was identified in 8% of patients, leading to a change or refinement in diagnosis in 13%. A pathogenic event was found in 72% of patients, the NMTB provided a treatment recommendation in 37% and 16% entered a clinical trial. These results are comparable with other international precision medicine programmes for paediatric cancer (NCI-COG Ped MATCH, INFORM, MAPPYACTS and ZERO). There is still significant unmet need in paediatric cancer, with a proportion of patients not having actionable variants identified in tumour tissue, and their response/resistance to recommended treatment being unmonitored molecularly. SMPaeds2 will build on the success of SMPaeds. The main objective of this programme is to identify novel and potentially actionable drivers of aggressive disease, treatment resistance factors and developing less invasive “liquid biopsies” for improved patient selection and monitoring in future clinical trials. SMPaeds2 will also develop assays amenable to serial sampling to monitor patients through their treatment journey. Patients aged 0-21 years and older patients with paediatric cancers at relapse will be eligible. Tumour tissue, paired blood samples (and CSF if applicable) will be collected to study genetic, epigenetic, proteomic, metabolomic and immunologic events to make informed recommendations for relapse treatment. The utility of liquid biopsies will be established within cancer subtypes as less invasive diagnostic assays. Serial liquid biopsies to extract circulating tumour DNA (ctDNA) will then be used to monitor response to therapy and emergence of treatment resistance to allow early, molecularly guided alterations to treatment. Expanding beyond single assay genomic testing, SMPaeds2 will provide a comprehensive genetic, epigenetic and immunologic understanding of each childhood cancer patient to improve precision diagnostics and therapies. The programme will also extend to research-based platforms including spatial transcriptomics, and detection of minimal residual disease in blood and CSF. Citation Format: Aditi Vedi, Sally George, Thomas Jacques, Isidro Cortes-Ciriano, Michael Hubank, Andrew Beggs, Maggie Cheang, Nuria Porta, Ciaran Hutchinson, Louise Hopkins, Amina Bukhari, Regan Barfoot, Steve Clifford, David O'Connor, Timothy Ritzmann, Amos Burke, John Anderson, Darren Hargrave, Louis Chesler. Stratified Medicine Paediatrics2 – advancing precision medicine in paediatric cancer [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 B035.
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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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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".