Association of anticoagulant use with clinical outcomes from crizotinib in <i>ALK</i><scp><i>‐</i></scp> and <i>ROS1</i>‐rearranged advanced non‐small cell lung cancers: A retrospective analysis of <scp>PROFILE</scp> 1001
Bibliographic record
Abstract
BACKGROUND: ROS1- and ALK-rearranged advanced NSCLCs are associated with increased thromboembolic risk. We hypothesized that a prothrombotic phenotype offers an evolutionary advantage to subsets of these cancers. The impact of this phenotype could alter outcomes from targeted therapy. METHODS: In a retrospective analysis of ROS1- and ALK-rearranged NSCLCs treated with crizotinib in a phase 1 trial, we compared progression-free survival (PFS) and objective response rate (ORR) based on the history of anticoagulation use (a possible surrogate of thromboembolism) at baseline (within 90 days before study enrollment) or within 90 days of study treatment. RESULTS: Twelve out of 53 (22.6%) ROS1- and 39 out of 153 (25.5%) ALK-rearranged NSCLCs received anticoagulation before or during the trial. Most ROS1 and ALK patients on anticoagulation received low-molecular-weight heparin (75% and 64.1%, respectively). In the ROS1-rearranged group, the median PFS (95% CI) values were 5.1 (4.4-14.4) and 29.0 (16.5-48.8) months, and the ORR values were 41.7% (95% CI: 15.2 to 72.3) and 80.5% (95% CI: 65.1 to 91.2) among those with and without anticoagulation treatment, respectively. In the ALK-rearranged group, the median PFS (95% CI) was 7.1 (5.4-7.7) and 12.0 (9.4-18.3) months, and the ORR was 41% (95% CI: 25.6 to 57.9) and 74.3% (95% CI: 65.3 to 82.1) among those with and without anticoagulation, respectively. CONCLUSIONS: Anticoagulation (as a potential surrogate of a prothrombotic subset) in ROS1- and ALK-rearranged NSCLCs may be associated with a lower PFS and ORR to crizotinib. CLINICALTRIAL: gov: NCT00585195.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".