Expression and prognostic significance of kit, protein kinase B, and mitogen-activated protein kinase in patients with small cell lung cancer.
Bibliographic record
Abstract
PURPOSE: The Kit receptor has been proposed as a key molecular target for the treatment of small cell lung cancer (SCLC). Protein kinase B (PKB/Akt) and mitogen-activated protein kinase (MAPK) are intracellular kinases that regulate cell survival and proliferation and may play a role in Kit signal transduction. This study was designed to examine the expression and importance of Kit, PKB/Akt, and MAPK relative to standard clinical prognostic factors in SCLC. EXPERIMENTAL DESIGN: Kit, PKB/Akt, and MAPK expression was assessed by immunohistochemistry in SCLC biopsies. Clinical data were collected on tumor stage, weight loss, hematology, biochemistry, response to treatment, and survival. Univariate and multivariate analyses were performed to evaluate prognostic significance. RESULTS: Biopsies from 42 patients were evaluable, and significant Kit expression (>/=35% cells) was detected in 51% of the tumors, phosphorylated PKB/Akt was detected in 62% of the tumors, and phosphorylated MAPK was detected in 48% of the tumors. Neither Kit nor PKB/Akt expression predicted for survival. Only increased expression of cytoplasmic MAPK was prognostic for survival (median, 1 year for negative staining versus 1.8 years for positive staining; P = 0.0054). CONCLUSIONS: Kit, PKB/Akt, and MAPK are expressed in a high percentage of SCLCs, and our data suggest that MAPK is an independent positive predictive factor in this malignancy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".