Assessing the role of the EGF receptor in the development and progression of pancreatic cancer
Bibliographic record
Abstract
Natalie Cook,1 Kristopher K Frese,2 Malcolm Moore11Division of Medical Oncology and Hematology, 2Ontario Cancer Institute, Princess Margaret Cancer Centre, Toronto, ON, CanadaAbstract: The prognosis for patients with pancreatic cancer remains poor despite increased knowledge of its molecular genetics and histopathological progression. Pancreatic cancer is a complicated, heterogeneous tumor with several common genetic alterations. Aberrant epidermal growth factor receptor (EGFR) expression can be found in chronic pancreatitis and in preinvasive and invasive pancreatic cancer. Therefore targeting this receptor, through monoclonal antibodies or downstream inhibition of tyrosine kinase activity, has the potential to produce encouraging results. Despite this, the majority of targeted therapies against EGFR have not performed as expected in clinical trials of pancreatic cancer. Understanding mechanisms of resistance and identification of pertinent biomarkers of efficacy will likely lead to further optimization of EGFR-directed treatment. In this article, we discuss the role of EGFR in the development and progression of pancreatic cancer, mechanisms of action of EGFR-directed agents, and the future of epidermal growth factor targeted-therapy and research in pancreatic cancer.Keywords: epidermal growth factor receptor, therapies, resistance
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".