<sup>90</sup>Y-FAPI-46 Theranostics Leads to Near-Complete Metabolic Response in 3 Patients with Solitary Fibrous Tumors
Bibliographic record
Abstract
Solitary fibrous tumor (SFT) is a rare soft-tissue sarcoma with limited treatment options, especially in advanced or metastatic cases. Fibroblast activation protein α (FAPα) is overexpressed in certain sarcomas, including SFTs, making it a promising target for diagnostics and radiopharmaceutical therapy (RPT). We present the cases of 3 patients with metastatic SFTs who, after exhausting standard treatments, underwent molecular profiling and showed elevated FAPα expression. Methods: Messenger RNA and protein expression of FAPα were examined in biopsy samples from 3 patients participating in the Molecularly Aided Stratification for Tumor Eradication Research program, a multicenter observational study focused on biology-driven stratification of adults with advanced cancer. Messenger RNA expression levels were quantified as transcripts per million, with RNA extraction, sequencing, and data processing performed using established protocols. Protein expression was assessed and stained with FAPα immunohistochemistry using a recombinant anti-FAPα antibody. Following the recommendation of the molecular tumor board, these patients received 90Y-labeled fibroblast activation protein inhibitor (FAPI)-46 RPT because of the high uptake observed in 68Ga-FAPI-46 PET/CT scans. Results:90Y-FAPI-46 RPT led to substantial clinical benefits, including metabolic resolution and symptom relief, with disease control confirmed using RECIST and PERCIST. Treatment was well-tolerated, with only minor adverse events observed. Conclusion: Our findings underscore the utility of FAPα screening as a predictive biomarker and the potential of FAP-targeted RPT as a viable treatment for advanced SFT.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".