The continually evolving landscape of novel therapies in oncogene-driven advanced non-small-cell lung cancer
Bibliographic record
Abstract
Non-small-cell lung cancer (NSCLC) is a highly heterogeneous disease that is frequently associated with a host of known oncogenic alterations. Advances in molecular diagnostics and drug development have facilitated the targeting of novel alterations such that the majority of NSCLC patients have driver mutations that are now clinically actionable. The goal of this review is to gain insights into clinical research and development principles by summary, analysis, and discussion of data on agents targeting known alterations in oncogene-driven, advanced NSCLC beyond those in the epidermal growth factor receptor (EGFR) and the anaplastic lymphoma kinase (ALK). A search of published and presented literature was conducted to identify prospective trials and integrated analyses reporting outcomes for agents targeting driver gene alterations (except those in EGFR and ALK) in molecularly selected, advanced NSCLC. Clinical efficacy data were extracted from eligible reports and summarized in text and tables. Findings show that research into alteration-directed therapies in oncogene-driven, advanced NSCLC is an extremely active research field. Ongoing research focuses on the expansion of new agents targeting both previously identified targets (particularly hepatocyte growth factor receptor (MET), human epidermal growth factor receptor 2 (HER2), and Kirsten rat sarcoma viral oncogene homolog (KRAS)) as well as novel, potentially actionable targets (such as neuregulin-1 (NRG1) and phosphatidylinositol 3-kinase (PI3K)). The refinement of biomarker selection criteria and the development of more selective and potent agents are allowing for increasingly specific and effective therapies and the expansion of clinically actionable alterations. Clinical advances in this field have resulted in a large number of regulatory approvals over the last 3 years. Future developments should focus on the continued application of alteration therapy matching principles and the exploration of novel ways to target oncogene-driven NSCLC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".