Precision Oncology Program (POP), an observational study using real-world data and imaging mass cytometry to explore decision support for the Molecular Tumor Board: study protocol
Bibliographic record
Abstract
INTRODUCTION: Precision oncology aims to provide individualised treatment recommendations based on patient-specific characteristics. In this rapidly evolving field with increasing numbers of biomarkers and potential therapeutic targets, there is a growing unmet need for evidence guiding these individualised treatment recommendations. The Precision Oncology Program (POP) harnesses real-world data (RWD) and imaging mass cytometry (IMC) to evaluate the feasibility and utility of integrating different data modalities to inform personalised treatment recommendations. This program uses patient-matched clinicogenomic data and spatial single-cell proteomics analysis to support profiling-driven decision-making for patients with cancer at the Molecular Tumor Board. METHODS AND ANALYSIS: The collaborative POP project recruits patients across all tumour entities and stages at the Comprehensive Cancer Center Zurich (CCCZ). For patients in the POP, a clinically and molecularly matched cohort is identified within the nationwide (US-based) de-identified Flatiron Health-Foundation Medicine clinicogenomic database (CGDB). It assesses whether clinical, genomic and outcome data of the CGDB cohort can inform treatment recommendations. In addition, multiplexed imaging mass cytometry (IMC) is performed in formalin-fixed paraffin-embedded tissue to assess the potential impact of spatial proteomics on personalised treatment decisions. RWD and IMC information is reviewed in the Molecular Tumor Board to assess the potential impact of this information on therapy decisions. However, since this is an observational study, these additional recommendations remain nonprescriptive and will not be forwarded to the treating physician. ETHICS AND DISSEMINATION: The study is registered at ClinicalTrials.gov (NCT06680726) and approved by the Canton of Zurich Ethics Committee (Project ID: 2022-02289). Project-specific informed consent is obtained from all participants. Deceased patients may also be included. In this case, a signed general consent form must be available. Data privacy is ensured by unique patient numbers for pseudo-anonymised data. Study findings will be disseminated through international peer-reviewed journals, conferences, and direct communication with participants and relevant organisations. TRIAL REGISTRATION NUMBER: NCT06680726.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".