Management of Non-Metastatic Non-Small Cell Lung Cancer (NSCLC) with Driver Gene Alterations: An Evolving Scenario
Bibliographic record
Abstract
The ever-growing knowledge regarding NSCLC molecular biology has brought innovative therapies into clinical practice; however, the treatment situation in the non-metastatic setting is rapidly evolving. Indeed, immunotherapy-based perioperative treatments are currently considered the standard of care for patients with resectable NSCLC in the absence of EGFR mutations or ALK gene rearrangements. Recently, data have been presented on the use of tyrosine kinase inhibitors (TKIs) in the adjuvant and locally advanced setting for patients with NSCLC harboring such driver gene alterations. The aim of the current work is to review the available evidence on the use of targeted treatments in the non-metastatic setting, together with a summary of the ongoing trials designed for actionable gene alterations other than EGFR and ALK. To date, 3-year adjuvant osimertinib treatment has been demonstrated to improve DFS and OS and to reduce CNS recurrence in resected EGFR-mutated NSCLC in stage IB–IIIA (TNM 7th edition). The use of osimertinib after chemo-radiation in stage III unresectable EGFR-mutated NSCLC showed the relevant PFS improvement. In the ALK-positive setting, 2-year alectinib treatment was shown to clearly improve DFS compared to adjuvant standard chemotherapy in resected NSCLC with stage IB (≥4 cm)–IIIA (TNM 7th edition). Several trials are ongoing to establish the optimal adjuvant TKI treatment duration, as well as neoadjuvant TKI strategies in EGFR- and ALK-positive disease, and (neo)adjuvant targeted treatments in patients with actionable gene alterations other than EGFR or ALK. In conclusion, our review depicts how the current treatment scenario is expected to rapidly change in the context of non-metastatic NSCLC with actionable gene alterations, hence appropriate molecular testing from the early stages has become crucial to establish the most adequate approaches both in the perioperative and the locally advanced disease.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".