Successful Alectinib Treatment for Carcinoma of Unknown Primary with EML4-ALK Fusion Gene: A Case Report
Bibliographic record
Abstract
Gene alteration in anaplastic lymphoma kinase (ALK) is rare, and the efficacy of ALK inhibitors in the treatment of carcinoma of unknown primary (CUP) with ALK alteration remains unclear. The patient was a 56-year-old woman who presented with cervical lymph node swelling. Computed tomography revealed paraaortic, perigastric, and cervical lymph node swelling; ascites; a liver lesion; and a left adrenal mass. A cervical lymph node biopsy was performed, and pathological diagnosis of an undifferentiated malignant tumor was conducted. Finally, the patient was diagnosed with CUP and treated with chemotherapy. To evaluate actionable mutations, we performed a multigene analysis, using a next-generation sequencer (FoundationOne® CDx). It revealed that the tumor harbored an echinoderm microtubule-associated protein-like 4 (EML4) and ALK fusion gene. Additionally, immunohistochemistry confirmed ALK protein expression. Alectinib, a potent ALK inhibitor, was recommended for the patient at a molecular oncology conference at our institution. Accordingly, alectinib (600 mg/day) was administered, and the multiple lesions and symptoms rapidly diminished without apparent toxicity. The administration of alectinib continued for a period of 10 months without disease progression. Thus, ALK-tyrosine kinase inhibitors should be considered in patients with CUP harboring the EML4-ALK fusion gene.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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".