Efficacy of immune-checkpoint inhibitors (ICI) in non-small cell lung cancer (NSCLC) patients harboring activating molecular alterations.
Bibliographic record
Abstract
172 Background: Two revolutions recently occur in the treatment of advanced NSCLC: the development of targeted therapies and of ICI). Both strategies have been shown to outperform chemotherapy. Patients with molecular alterations are usually considered as poor candidate for immunotherapy. Here, we aimed to analyze the efficacy of immunotherapy in NSCLC patients with oncogenic addiction. Methods: We conducted a retrospective multicentric study, on patients treated with ICI and carrying an activating molecular abnormality: EGFR, ALK, KRAS, ROS1, HER2, BRAF, MET and RET. We have collected anonymized data, which were evaluated for clinical characteristics and outcome (progression free survival duration of ICI treatment and overall survival since initiation of ICI). Results: 209 patients were registered from 7 centers in France and Switzerland. 198 patients had adenocarcinoma (95.2%), 6 large cell carcinoma (2.9%), 161 were former or current smokers (81.3%), 99 were female (47.4%), median age at diagnosis was 59 yrs. (range 30-79). 33 patients had EGFR mutations (15.8%), 132 KRAS mutation (63.2%), 6 ALK rearrangement (2.9 %), 2 ROS1 rearrangement (1 %), 5 HER2 mutation (2.4 %), 12 BRAF mutations (5.7 %), 7 MET alteration (3.3 %), 3 RET rearrangements (1.4 %), and 9 had concomitant multiple molecular alterations (4.3%). After validated treatment 200 patients were treated with PD1 inhibitors (nivolumab, 97.5% and pembrolizumab, 2.5%), 4 with anti CTLA4 (tremelimumab), 5 with anti PDL1 (4 atezolizumab). The median PFS was 2.8 m. [95%CI 2.3;3.5] for the whole population 2.1 for EGFR [1.7;2.8], 3.5 for KRAS [2.5;4.9], 2.7 for BRAF [1.5;NR] and not estimable for other alterations. Median exposure to ICI was 1.89 m. [0;18.5]. The median overall survival for the whole population was 13.0 m. [9.4; 15.6], for EGFR 13.3 m. [4.7; NR], KRAS 11.3 m. [8.2; 16], BRAF 10.7 m. [1.5; NR] and not yet estimable for the other alterations. Conclusions: PFS (2.8 m.) and OS (13 m.) are very similar from the ones observed in pretreated unselected NSCLC patients. ICI is associated with better PFS in patients with KRAS mutation than in EGFR mutated patients. Analysis of outcome in other molecular subgroups is ongoing.
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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 | 0.001 | 0.001 |
| 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".