The Landscape of Pancreatic Cancer Therapeutic Resistance Mechanisms
Bibliographic record
Abstract
Pancreatic cancer (pancreatic ductal adenocarcinoma, PDA) is infamously moving to the top of the list as one of the most lethal cancers with an overall 5 year survival rate of 7%. Multiple genomic-based and molecular characterization studies of PDA specimens and established animal models have provided the field with multiple targets and a progression model of this disease. Still, to date, the best therapeutic options are surgery and combination cytotoxic therapies. In general, even in the best case scenario (i.e., an early stage diagnosis and a response to a specific therapy), most of these fortunate patients' PDA cells acquire or exert resistance mechanisms and eventually kill the patient. Herein, we touch on a growing field of investigation that focuses on PDA cell therapeutic resistance mechanisms. We examine extrinsic elements (i.e., the tumor microenvironment, hypoxia) to the intrinsic processes within the cell (i.e., post-transcriptional gene regulation and somatic mutations) that are important for therapeutic efficacy and resistance. Even as better targeted and personalized approaches move through the clinical trial pipeline the discussed resistance mechanisms will most likely play a role in the management of this deadly disease.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".