Evolving Therapeutic Landscape of ROS1-Positive Non-Small Cell Lung Cancer: An Updated Review
Bibliographic record
Abstract
ROS1 gene rearrangements define a distinct molecular subtype of non-small cell lung cancer (NSCLC), occurring in approximately 2% of cases and frequently associated with younger age, non-smoker status, and a high incidence of brain metastases. The discovery of ROS1 as an oncogenic driver has led to the development of targeted tyrosine kinase inhibitors (TKIs). Crizotinib first demonstrated substantial clinical benefit, but its limitations, including poor central nervous system (CNS) penetration and acquired resistance, highlighted the need for next-generation inhibitors. Several agents have since been developed, including entrectinib, lorlatinib, repotrectinib, taletrectinib, and zidesamtinib, each offering improved intracranial (IC) activity and efficacy against resistance mutations, notably ROS1^G2032R. Despite these advances, optimal sequencing strategies remain undefined, and resistance ultimately emerges in most patients. This review provides an updated overview of ROS1 biology, diagnostic approaches, clinical outcomes with currently available TKIs, mechanisms of resistance, and ongoing challenges, emphasizing the rapidly evolving therapeutic landscape.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".