Impact of ALK variants on brain metastasis and treatment response in advanced NSCLC patients with oncogenic ALK fusion
Bibliographic record
Abstract
Background: To investigate the impact of ALK variants on the features of brain metastases (BM), the outcome of chemotherapy and targeted therapy using crizotinib, as well as the progression pattern in patients with ALK fusion. Methods: Patients with ALK fusion were retrospectively collected from January 2013 to July 2017 in Shanghai Pulmonary Hospital. ALK rearrangements were identified via ARMS-PCR. ALK variants were identified via Sanger Sequencing. Results: A total of 135 patients and 41 with brain metastasis were identified. Radiological features showed that the patients with ALK variant 1 had a larger BM size compared with patients with ALK non-variant 1 (median tumor size: 16.89 vs. 11.01 mm, P=0.031). Similar time to treatment failure (TTF) was observed in patients with ALK variant 1 and non-variant 1 who received first-line crizotinib (median TTF: 15.7 vs. 13.8 months, HR =0.75, P=0.34). Patients with ALK variant 1 who had baseline BM had significantly shorter TTF than non-variant 1 with baseline BM when treated with first-line crizotinib (median TTF: 9.1 vs. 14.9 months, HR =2.68, P=0.037). In patients treated with chemotherapy, ALK variant 1 was associated with inferior TTF (median TTF: 5.6 vs. 8.1 months, HR =1.66, P=0.039). Progression pattern was similar between ALK variant 1 and non-variant 1. Conclusions: Patients with ALK variant 1 and baseline BM had inferior TTF on first-line crizotinib treatment and presented with more aggressive radiological features. Patients with ALK non-variant 1 had better clinical outcome on first-line chemotherapy.
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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) | 0.000 | 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) | 0.001 | 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".