No Correlation between KRAS Status and Advanced Pancreatic Adenocarcinoma Survival
Bibliographic record
Abstract
Erlotinib plus gemcitabine is one of the standard chemotherapies for unresectable pancreatic cancer. Pancreatic cancer has the highest frequency of KRAS gene mutations among human cancers, and some studies suggest that KRAS status might be a predictive biomarker for anti-epidermal growth factor receptor treatment. However, the reliability of this biomarker has not been confirmed. Here, we evaluated the impact of KRAS mutations in pancreatic cancer patients treated with first line gemcitabine-based chemotherapy. 23 patients treated with gemcitabine-based chemotherapy whose KRAS status could be examined from primary or metastatic lesions were enrolled. KRAS mutations were analyzed by sequencing codons 12 and 13. We retrospectively evaluated the correlation between KRAS status, and prognosis and treatment efficacy. Patient characteristics were as follows: median age 68 years, male/female=6/17, PS 0/1=9/14, TNM stage III/IV=1/22, and gemcitabine alone/erlotinib plus gemcitabine=13/10. Among the 23 patients, KRAS codon 12 was mutated in 15, one of whom also had mutation on codon 13. Median progression-free survival (PFS) and overall survival (OS) of all patients were 4.3 months (95% confidence interval (CI): 3.1 to 5.4) and 8.1 months (95% CI: 5.9 to 10.0; events in 96%), respectively. KRAS status showed no association with PFS (p=0.310), OS (p=0.934), or the efficacy of treatment with (p=0.833) or without erlotinib (p=0.478). Thus, in this study, there was no correlation between KRAS status and the efficacy of first line chemotherapy with gemcitabine with or without erlotinib. Identification of a rationale for personalized medicine in pancreatic cancer will require further exploratory prospective studies.
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 | 0.001 | 0.002 |
| 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) | 0.002 | 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".