Driving Best Practices Throughout the Treatment Journey for Patients with NSCLC with Actionable Alterations: A Podcast Discussion
Bibliographic record
Abstract
Non-small cell lung cancer (NSCLC) treatment has been revolutionized by the advent of targeted therapies for tumors harboring specific actionable alterations. Targeted agents are now approved for use in patients with advanced NSCLC with various drivers including ALK rearrangements, BRAF V600E mutations, EGFR mutations, ERBB2 mutations, KRAS G12C mutations, MET exon 14 skipping alterations, NTRK fusions, RET rearrangements, and ROS1 rearrangements. Importantly, the availability of these agents has raised the clinical question of how to optimally sequence their use alongside chemotherapy and/or immunotherapy strategies, which are indicated for broader populations. Key considerations include (i) evidence for better outcomes when first-line treatment is initiated following availability of molecular profiling data; (ii) the decreasing proportion of patients able to receive therapy in each successive treatment line; (iii) the efficacy of targeted agents demonstrated in either single-arm trials or head-to-head comparisons with chemotherapy and/or immunotherapy, as compared with evidence for poor or modest efficacy of immunotherapy in patients with tumors with actionable drivers; (iv) real-world data showing better outcomes of patients with tumors with actionable alterations who received targeted therapies compared with those who did not; (v) the generally favorable safety profile of targeted therapies, as well as the potential for increased toxicity when immunotherapy precedes certain targeted agents; and (vi) patient-centric factors including the greater ease of administration of oral targeted therapies over intravenous chemotherapy or immunotherapy strategies. In line with these considerations, guidelines typically recommend most targeted agents approved for first-line use as initial therapy over chemotherapy and/or immunotherapy. In this podcast, the authors discuss the current therapeutic landscape of NSCLC with actionable alterations and provide their perspectives on treatment algorithms, and how to optimally sequence therapies for patients with tumors harboring actionable alterations, using patient cases to illustrate key principles. Several targeted therapies are now available for the treatment of patients with advanced non-small cell lung cancer that has certain genetic alterations. These medicines specifically target the genetic alterations in cancer cells that increase their ability to grow and spread. Targeted therapies are approved for patients whose tumors have changes in genes such as ALK, BRAF, EGFR, and others. In this podcast, three oncologists engage in a discussion on the optimal use of targeted therapies alongside other treatments like chemotherapy and immunotherapy. Their discussion highlights the importance of analyzing the tumor’s genetic makeup before treatment to determine if targeted therapy is an option. Cancer guidelines typically recommend using targeted therapies as the first treatment option in patients with non-small cell lung cancer with specific genetic alterations, if approved for this use. Since many patients will receive only one therapy, informed selection of the initial treatment is especially important. The authors also consider the data from clinical trials showing the effectiveness and manageable side-effect profile of targeted therapies. In contrast, there is evidence that immunotherapies may be less effective in patients whose tumors have certain genetic alterations. Real-world data support the importance of using targeted therapies in patients who are eligible. In addition, targeted therapies are often taken as oral tablets, which may be preferred by patients to intravenous treatment with chemotherapy or immunotherapy. Finally, the authors use example patient cases to show important factors to consider when choosing between targeted therapies and other types of treatment.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.015 | 0.032 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".