Clinicopathologic, Genomic, and Immunophenotypic Landscape of <i>ATM</i> Mutations in Non–Small Cell Lung Cancer
Bibliographic record
Abstract
PURPOSE: ATM is the most commonly mutated DNA damage and repair gene in non-small cell lung cancer (NSCLC); however, limited characterization has been pursued. EXPERIMENTAL DESIGN: Clinicopathologic, genomic, and treatment data were collected for 5,172 patients with NSCLC tumors which underwent genomic profiling. ATM IHC was performed on 182 NSCLCs with ATM mutations. Multiplexed immunofluorescence was performed on a subset of 535 samples to examine tumor-infiltrating immune cell subsets. RESULTS: A total of 562 deleterious ATM mutations were identified in 9.7% of NSCLC samples. ATM-mutant (ATMMUT) NSCLC was significantly associated with female sex (P = 0.02), ever smoking status (P < 0.001), non-squamous histology (P = 0.004), and higher tumor mutational burden (DFCI, P < 0.0001; MSK, P < 0.0001) compared with ATM-wild-type (ATMWT) cases. Among 3,687 NSCLCs with comprehensive genomic profiling, co-occurring KRAS, STK11, and ARID2 oncogenic mutations were significantly enriched among ATMMUT NSCLCs (Q < 0.05), while TP53 and EGFR mutations were enriched in ATMWT NSCLCs. Among 182 ATMMUT samples with ATM IHC, tumors with nonsense, insertions/deletions, or splice site mutations were significantly more likely to display ATM loss by IHC (71.4% vs. 28.6%; P < 0.0001) compared with tumors with only predicted pathogenic missense mutations. Clinical outcomes to PD-(L)1 monotherapy (N = 1,522) and chemo-immunotherapy (N = 951) were similar between ATMMUT and ATMWT NSCLCs. Patients with concurrent ATM/TP53 mutations had significantly improved response rate and progression-free survival with PD-(L)1 monotherapy. CONCLUSIONS: Deleterious ATM mutations defined a subset of NSCLC with unique clinicopathologic, genomic, and immunophenotypic features. Our data may serve as resource to guide interpretation of specific ATM mutations in 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".