Heterogeneous responses and resistant mechanisms to crizotinib in <i>ALK</i>‐positive advanced non‐small cell lung cancer
Bibliographic record
Abstract
BACKGROUND: ALK-tyrosine kinase inhibitors (TKIs) have been proven effective for treating ALK-positive non-small cell lung cancer (NSCLC), although patients present with variable responses and disease progression courses. The detailed underlying molecular mechanisms require further investigation to yield a better prognosis. METHODS: Targeted next-generation sequencing (NGS) mutation profiling was performed on samples from 42 NSCLC patients confirmed positive for ALK rearrangements by fluorescence in situ hybridization or immunohistochemistry who experienced disease progression after crizotinib treatment. RESULTS: ALK rearrangements were not confirmed in six patients (14%) with other potential oncogenic drivers identified by NGS, who therefore did not respond to crizotinib and had significantly shorter overall survival (OS) compared to NGS ALK -positive patients. Fifteen ALK activating mutations were detected in 8 out of 26 post-treatment samples (31%), among which ALK L1196M and G1269A were the most common acquired mutations detected in half of the patients with ALK activating mutations. Dynamic monitoring of the genetic evolution in one patient revealed both spatial and temporal heterogeneity of resistant mechanisms during different ALK-TKI treatment courses. Activation of ALK downstream or bypass pathways was detected in patients without ALK activating mutations, such as genetic alterations in PIK3CA, MET, and KRAS. Interestingly, we identified two patients with acquired mutations in the DNA mismatch repair gene POLE, which resulted in a dramatically increased tumor mutation burden, and might contribute to the poor response to crizotinib. CONCLUSIONS: Heterogeneous resistant mechanisms have been identified and correlate to diverse responses to crizotinib. Comprehensive and dynamic mutation profiling is required to better predict clinical outcomes.
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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) | 0.000 | 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) | 0.000 | 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".