Monoclonal Antibodies and Other Targeted Therapies for Pancreatic Cancer
Bibliographic record
Abstract
Pancreatic cancer continues to be a challenging disease to treat because of its aggressive nature, advanced stage at the time of diagnosis, and limited treatment options that are available. Traditional cytotoxic chemotherapy provides modest benefit to patients with pancreatic adenocarcinoma. Recently, a FOLFIRINOX regimen revealed improved response in overall and progression-free survival over single-agent gemcitabine in metastatic pancreatic cancer, but there is still much needed advancement in the systemic treatment of pancreatic cancer. There is a growing interest in the development of novel agents, while our understanding of molecular pathogenesis of pancreatic adenocarcinoma continues to expand. With identification of various molecular pathways in pancreatic cancer tumorigenesis, potential targets for drug development have been pursued with the use of monoclonal antibodies and small-molecule inhibitors. Although preclinical studies with multiple targeted therapies demonstrated encouraging results in pancreatic cancer, only erlotinib, an epidermal growth factor receptor inhibitor, showed a marginal survival benefit in a phase III clinical trial, when combined with gemcitabine. As further signaling pathways and their importance in pancreatic cancer tumorigenesis are better understood, further clinical trials will need to be designed to study these targeted agents as single agents, in combination with other novel agents or in combination with cytotoxic chemotherapy. In this review, we present the current knowledge on targeted therapy in pancreatic adenocarcinoma and its application in clinical practice.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".