Retrospective analysis of real-world data to evaluate actionability of a comprehensive molecular profiling panel in solid tumor tissue samples (REALM study)
Bibliographic record
Abstract
INTRODUCTION: Considering the growing interest in matched cancer treatment, our aim was to evaluate the ability of a comprehensive genomic profiling (CGP) assay to propose at least one targeted therapy given an identified genomic alteration or signature (actionability), and to collect the treatment modifications based on the CGP test results in clinical practise for solid tumors. METHODS: This retrospective, multicentre French study was conducted among 25 centres that participated in a free of charge program between 2017 and 2019 for a tissue CGP test. Data were collected on the patient, disease, tumor genomic profile, treatment suggested in the report (related to the genomic profile results) and subsequent therapeutic decisions according to the physician's declaration. RESULTS: Among the 416 patients, most had lung cancer (35.6%), followed by biliary tract cancer (11.5%) or rare cancers (11.1%); 75% had a metastatic disease. The actionability was 75.0% (95% CI [70.6%-78.9%]) for all patients, 85.1% and 78.4%, respectively in lung cancer and metastatic patients. After exclusion of clinical trial suggestions, the actionability decreased to 62.3% (95% CI [57.5%-66.8%]). Treatment modification based on the test results was observed in 17.3% of the patients and was more frequent in metastatic disease (OR = 2.73, 95% CI [1.31-5.71], p = 0.007). The main reasons for no treatment modification were poor general condition (33.2%) and stable disease or remission (30.2%). The genomic-directed treatment changes were performed mostly during the first six months after the CGP test, and interestingly a substantial part was observed from six to 24 months after the genomic profiling. CONCLUSION: This French study provides information on the real-life actionability of a CGP test based on tissue samples, and trends to confirm its utility in clinical practice across the course of the disease, in particularly for patients with lung cancer and/or advanced disease.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".