Identification and In-Depth Analysis of the Novel FGFR2-NDC80 Fusion in a Cholangiocarcinoma Patient: Implication for Therapy
Bibliographic record
Abstract
Fibroblast growth factor receptor 2 (FGFR2) fusions have emerged as a new therapeutic target for cholangiocarcinoma in clinical practice following the United States Food and Drug Administration (FDA) approval of Pemigatinib in May 2020. FGFR2 fusions can result in a ligand-independent constitutive activation of FGFR2 signaling with a downstream activation of multiple pathways, including the mitogen-activated protein (MAPK) cascade. Until today, only a limited number of fusion partners have been reported, of which the most prevalent is BicC Family RNA Binding Protein (BICC1), representing one-third of all detected FGFR2 fusions. Nonetheless, in the majority of cases rare or yet unreported fusion partners are discovered in next-generation sequencing panels, which confronts clinicians with a challenging decision: Should a therapy be based on these variants or should the course of treatment follow the (limited) standard regime? Here, we present the case of a metastasized intrahepatic cholangiocarcinoma harboring a novel FGFR2-NDC80 fusion, which was discussed in our molecular tumor board. The protein NDC80 kinetochore complex component (NDC80) is an integral part of the outer kinetochore, which is involved in microtubule binding and spindle assembly. For additional therapeutic guidance, an immunohistochemical analysis of the predicted fusion and downstream effector proteins was performed and compared to cholangiocarcinoma samples of a tissue microarray. The FGFR2-NDC80 fusion resulted in strong activation of the FGFR2 signaling pathway. These supporting results led to a treatment recommendation of Pemigatinib. Unfortunately, the patient passed away before the commencement of therapy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".