A phase IV study using multi-omics to identify mechanisms of response and resistance to crizotinib in ALK+ advanced non-small cell lung cancer (NSCLC) patients with distinct progression free survival outcomes.
Bibliographic record
Abstract
e20588 Background: Crizotinib is the current first-line standard of care for ALK+ NSCLC with response rates reaching 65% [1] . However, most patients progress within 1 or 2 years and mechanisms of resistance remain unknown for approximately 30% of patients [2] . [1] Shaw et al. Crizotinib versus chemotherapy in advanced ALK-positive lung cancer. NEJM 368: 2385-2394, 2013. [2] Doebele et al. Mechanisms of resistance to crizotinib in patients with ALK gene rearranged non-small cell lung cancer. Clin Cancer Res 2012; 18:1472-1482 Methods: Eligible patients had locally advanced or metastatic ALK+ NSCLC. Patients were asked to undergo a repeat tumor biopsy at time of progression and serial bloods and archived primary tumor were collected. Responses were assessed by RECIST 1.1. ctDNA and protein expression on serial blood will be evaluated over the course of treatment. Tumors were profiled using exome sequencing and targeted genomic analysis to identify novel mutations associated with response. Results: 24 patients with stage IV adenocarcinoma received crizotinib as first-line (n = 22) and second-line (n = 2) treatment. Mean age was 60 y (range 41-80). 4% were smokers, 33.5% were former and 62.5% were never-smokers. At data cutoff (Jan 26, 2017), 16 patients have discontinued treatment (14 due to progression and 2 withdrew). The median PFS markedly differed in non-responders (patients with immediate progression) vs responders (patients with initial response or stable disease) (1.8 vs. 27 months). Profiling was performed to identify the genomic traits in tissue and blood that correlate to treatment response. Conclusions: We identified a previously unrecognized subset of patients treated with crizotinib with an exceptionally long PFS. We also observed a subset of patients intrinsically resistant to crizotinib despite harboring ALK+. There are no known predictive biomarkers which can identify patients that will have a long-lasting response or who may not derive benefit from crizotinib. The multi-omics profiling applied to these groups may provide novel insights into mechanisms of response and resistance to crizotinib. Clinical trial information: NCT02041468.
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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".