Abstract PR003: Canada’s path towards proteome guided therapies and advanced molecular pathology in pediatric precision oncology
Bibliographic record
Abstract
Abstract Molecularly targeted precision treatments have significant potential to improve therapy options for hard-to-treat cancers and reduce late effects in general. Genome sequencing has laid the foundation for precision medicine, yet, clinical success remains moderate. Identifying therapeutic targets at the protein and pathway level holds great promise, but may be considered complex. This is particularly true for smaller centres without local proteomics expertise, raising concern of new access barriers and increased inequity. Here, we present a road map for the nation-wide integration of pre-clinical proteomics evidence into pediatric precision oncology and provide proof of concept for proteome guided therapies. Within the Canadian Pediatric Cancer Consortium - ACCESS (Advancing Childhood Cancer Experience, Science & Survivorship) and the PROFYLE study (Precision Oncology for Young peopLE) we establish a federated proteomics platform and a National Molecular Pathology Board to promote equitable access to proteomics analyses and other innovative but not universally available molecular tests. As proof of concept, we describe the case of an adolescent with metastatic, progressive spindle epithelial tumor with thymus-like differentiation (SETTLE) and evaluate how quantitative proteome profiling can identify treatment options not apparent at the genome or transcript level. Mass spectrometric proteome analysis of macro-dissected tumor and adjacent normal from formalin fixed paraffin embedded sections was completed within two weeks of biopsy and identified key proteins involved in one-carbon metabolism, including SHMT2 and DHFR as possible targets for single or combination therapy. SHMT2 levels were validated and compared across various tumors by immunohistochemistry and the response to SHMT2 inhibition by sertraline was tested in chicken chorioallantoic membrane (CAM) and larval zebrafish xenografts generated from the patient. Following failure of cytotoxic chemotherapy and second-line sorafenib treatment, a monotherapy trial was initiated by the patient. Treatment was stopped after 8 weeks following evidence of a moderate reduction in growth rate but overall progressive disease. Possible combination therapies were evaluated further in the patient-derived models. Significance: Overall, we demonstrate that pre-clinical proteomics analysis and validation can be conducted in a clinically meaningful timeframe and has provided supportive pre-clinical data to support medical decision-making in one case of a rare progressive malignancy. To determine if this is generalizable, we further outline a road map for advancing access to pre-clinical proteomics and innovative molecular assays across Canada. Citation Format: Georgina Barnabas, Tariq Bhat, V. Goebeler, P. Leclair, C. Maxwell, G. Reid, D. L. Senger, J. A. Chan, N. Azzam, N. Melong, J. N. Berman, S Parker, J. Bush, C. Strahlendorf, R. Deyell, C. J. Lim, PROFYLE Program, Philipp F. Lange. Canada’s path towards proteome guided therapies and advanced molecular pathology in pediatric precision oncology [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 PR003.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 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".