Molecular determinants of response to matuzumab in combination with paclitaxel for patients with advanced non-small cell lung cancer
Bibliographic record
Abstract
Antibodies targeting epidermal growth factor receptor (EGFR) have proven to be effective in patients with non-small cell lung cancer (NSCLC) that express EGFR. We recently published a phase I study of weekly matuzumab plus paclitaxel. This therapy was well tolerated and showed clinical responses in the majority of patients. Although matuzumab displays potent antitumor activity in some patients, not all patients respond well to treatment. Whether dysregulation of EGFR-mediated pathways precludes or sensitizes cells to paclitaxel is unknown. We sought to determine molecular predictive factors for therapy response in a phase I/II study patient cohort treated with matuzumab+/-paclitaxel. Twenty-three cases [including one complete response (CR), three partial responses (PR), 10 stable diseases (SD)] were screened using immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), PCR/sequencing and denaturing wave high performance liquid chromatography (D-HPLC) for expression, amplification, and mutation status of EGFR and downstream signaling pathways. All patients with PR or CR displayed an either high overall or single-cell EGFR expression in the majority of cells. In addition, all of the moderate responders, who achieved SD after at least two cycles of therapy, showed diffuse EGFR expression rates and/or strong single-cell EGFR expression. In contrast, 44% of the nonresponders showed low overall or single-cell EGFR expression levels. No low-expressing EGFR cases were present within the responder group. In addition, among patients with a gain-of-function mutation in KRAS primary therapy failure and/or short responses to therapy were observed. Our data suggest that EGFR expression and KRAS mutation status is predictive for clinical response to matuzumab +/- paclitaxel in patients with advanced NSCLC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".