Molecular profiling and target actionability for precision medicine in neuroendocrine neoplasms: real-world data
Bibliographic record
Abstract
BACKGROUND: Key molecular alterations (MA) of neuroendocrine neoplasm (NEN) of various grade/primaries have been described but the applicability of molecular profiling (MP) for precision medicine in NEN remains to be demonstrated. METHODS: We conducted a retrospective study of all patients with metastatic NEN who had MP on tumour tissue at Gustave Roussy. The primary objective was to assess the clinical applicability of MP by evaluating the growth modulator index (GMI) as the primary end-point. RESULTS: MPs were obtained in 114 out of 156 eligible patients, including 12% NET-G1, 42% NET-G2, 13% NET-G3 and 35% neuroendocrine carcinoma (NEC). Primary sites were lung/thymus (40%), pancreas (19%), gastro-intestinal (16%), head&neck (10%), unknown (10%) and others (10%) with synchronous metastases in 61% of the patients. Most frequent MA were: MEN1 (25%), PTEN (13%), TP53 (11%) and TSC2 (9%), in neuroendocrine tumour (NET), and TP53 (50%) and RB1 (18%) in NEC. ESMO Scale for Clinical Actionability of Molecular Targets (ESCAT) classification of these MA were: I(5%), III(20%), IV(23%), X(27%); a putative actionable MA was identified in 48% patients. Median TMB was 5.7 Mut/Mb, with 3 TMB > 10 and 1 MSI NET. No MA was found in 26% patients. Molecularly matched treatment was administered to 19 patients (4 NEC, 15 NET): immunotherapy (n = 3), tipifarnib (n = 1), NOTCHi (n = 1), EGFRi (n = 2), HER2i (n = 1) and everolimus (n = 11). Overall, 67% of patients had a clinical benefit defined as a GMI over 1.3 with a 78% disease control rate. CONCLUSION: We report 48% of NEN with a putative actionable MA of which 35% received molecularly matched treatment, with a clinical benefit in 67% of the cases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".