Clinical prognostic factors associated with progression-free survival in pts with metastatic EGFR-mutant non-small cell lung cancer treated with gefitinib first-line therapy.
Bibliographic record
Abstract
e20695 Background: The current standard treatment of Lung Cancer (LC) are third generation tyrosine kinase inhibitors (TKI) targeting tumors with epidermal growth factor receptor mutations (EGFRmut). The Mexican Institute of Social Security provides care to 86.4% of the total population in the metropolitan area of Monterrey in which the High Specialty Medical Unit 25 (UMAE 25) serves as the LC state reference center. In our institution, access to TKI is limited. This study aims to evaluate the pattern of 1L therapy in pts diagnosed with metastatic non-small cell lung cancer (mNSCLC) with EGFRmut at UMAE 25, determine the progression-free survival (PFS) with gefitnib, and analyze clinical prognostic factors that impact PFS. Methods: Electronic records of pts with mNSCLC EGFRmut treated at UMAE 25 between January 2020 and July 2024 were reviewed. Clinical factors were considered including site of metastasis, high tumor burden (HTB), number of disease-related symptoms (DRS), treatment factors and type of EGFRmut.PFS was analyzed for those treated with gefitinib. As disease progressed, resistance mut T790M and tumor burden were assessed; variables pertaining to treatment access were determined: time from diagnosis to TKI initiation, use of bridging chemotherapy, and CNS radiotherapy techniques. Results: Sixty pts with mNSCLC EGFRmut were identified with the following characteristics: age(mean) 60.7 years, 65% female, 68.3% ECOG ≤1, 66.7% non-smokers, 13.3% presenting ≥3 symptoms, 30% high tumor burden, 30% CNS metastases, 50% exon 19 deletions, 43.3% L858R mut, 3.4% other. Gefitinib was the predominant 1L therapy (65%, n=39), the remaining received afatinib (n=4), erlotinib (n=1), osimertinib (n=5), unspecified (n=11). PFSm for pts treated with 1L gefitinib was 8.5 mo. The main factor that influenced PFSm was the presence of DRS, pts with ≥3 experienced disease progression (PD) earlier (2.2 mo vs. 8.4 mo, p<0.001), significantly increasing the risk of PD or death (HR 28.14, p=0.007, 95% CI: 2.446–323.777). Pts that harbored the L858R mut progressed earlier than those with exon 19 deletions (7.7 vs. 9.3 mo, p<0.001). For those who presented both ≥3 symptoms and ECOG 2, there was a significantly higher risk of PD or death (HR 9.47, p=0.001, 95% CI: 2.559–35.093). Upon PD, HTB increased from 30% to 50%, 16.6% progressed to CNS. Bridging chemotherapy before TKI was required for 57.1% of pts due to delayed access to gefitinib, with an average delay of 12 weeks. 52.9% of pts who progressed on gefitinib, developed the T790M resistance mutation. Conclusions: The number of DRS, EGFRmut type, and functional status are the clinical factors with the highest impact on PFSm in pts with mNSCLC EGFRmut treated with 1L gefitinib. This type of analysis in centers with limited access to therapies can help evaluate strategies to improve the quality of service.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".