Serial Functional and Genomic Analyses Illuminate Clonal Evolution in Metastatic NSCLC with 12-Year Survival
Bibliographic record
Abstract
Background: Non-small cell lung cancer (NSCLC) is the most common form of lung cancer and a leading cause of cancer-related death. Despite therapeutic advances, long-term survival in stage IV disease is uncommon. Tumor analyses that combine genomic and functional platforms may provide the opportunity to monitor clonal dynamics and guide therapy selection. Case Presentation: We report a 67-year-old woman with metastatic poorly differentiated lung adenocarcinoma, who achieved four durable remissions and survived nearly 12 years. Serial studies using ex vivo analysis of programmed cell death (EVA/PCD) functional-profiling-guided therapeutic choices were correlated with next-generation sequencing (NGS). Molecular events included the emergence of a BRAF V600E mutation responsive to dabrafenib plus trametinib and the acquisition of an EGFR exon 19 deletion responsive to Osimertinib. EVA/PCD identified activity for targeted agents and revealed synergy for vinorelbine plus Osimertinib not predicted by genomic profiling, which provided additional response. Discussion: This case highlights clonal evolution in NSCLC and illustrates how serial tissue analyses correlating phenotypic and genomic events can offer therapeutic interventions to provide long-term survival. Conclusions: The integration of functional and genomic profiling may improve personalized treatment in NSCLC by interrogating tumor heterogeneity and clonal evolution to inform rational therapeutic selection.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".