Analysis of the Genomic Landscape in ALK+ NSCLC Patients Identifies Novel Aberrations Associated with Clinical Outcomes
Bibliographic record
Abstract
Abstract Rearrangements in the anaplastic lymphoma kinase (ALK) gene are found in approximately 5% of non–small cell lung carcinoma (NSCLC). Here, we present a comprehensive genomic landscape of 11 patients with ALK+ NSCLC and investigate its relationship with response to crizotinib. Using whole-exome sequencing and RNAseq data, we identified four rare ALK fusion partners (HIP1, GCC2, ERC1, and SLC16A7) and one novel partner (CEP55). At the mutation level, TP53 was the most frequently mutated gene and was only observed in patients with the shortest progression-free survival (PFS). Of note, only 4% of the genes carrying mutations are present in more than 1 patient. Analysis of somatic copy number aberrations (SCNA) demonstrated that a gain in EML4 was associated with longer PFS, and a loss of ALK or gain in EGFR was associated with shorter PFS. This study is the first to report a comprehensive view of the ALK+ NSCLC copy number landscape and to identify SCNA regions associated with clinical outcome. Our data show the presence of TP53 mutation as a strong prognostic indication of poor clinical response in ALK+ NSCLC. Furthermore, new and rare ALK fusion partners were observed in this cohort, expanding our knowledge in ALK+ NSCLC.
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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 | 0.000 |
| 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) | 0.001 | 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".