Concurrent driver mutations/rearrangements in non-small-cell lung cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: The concept of mutually exclusive oncogenic driver alterations has prevailed over the past decade, but recent reports have stressed the possible occurrence of dual-positive non-small-cell lung cancer (NSCLC) and even triple-positive disease for these oncogenes. This entity presents novel prognostic and therapeutic challenges. The present review highlights the available data in an effort to clarify the clinical and pathological significance of coexisting mutations as well as the subsequent therapeutic consequences. RECENT FINDINGS: Patients with a known driver oncogene can be successfully treated with the appropriate tyrosine kinase inhibitor, which will provide them with significant responses and lesser toxicities compared with cytotoxic therapy. Unfortunately, most patients will eventually progress. Although some resistance mechanisms have been identified, others remain to be determined but the emergence of secondary oncogenes could be part of the answer. SUMMARY: Approximately 20-25% of NSCLC harbor treatable driver mutations/rearrangements; epidermal growth factor receptor mutation, anaplastic lymphoma kinase and ROS-1 gene rearrangements are the main alterations for which a Food and Drug Administration-approved tyrosine kinase inhibitor can be used.Because of recent technological advances, high sensitivity assays with a broad range of genomic targets have become more easily accessible in clinical practice, which has led to an increased detection of coexisting driver alterations in patients with advanced NSCLC. The prognostic/predictive and therapeutic implications of this novel entity are still unsettled for the time being. Randomized trials specifically designed to address this subset of patients will soon be necessary to help determine the optimal therapeutic agent to administer.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".