Dual-Hit Strategy for Therapeutic Targeting of Pancreatic Cancer in Patient-Derived Xenograft Tumors
Bibliographic record
Abstract
PURPOSE: Paracrine activation of pro-fibrotic hedgehog (HH) signaling in pancreatic ductal adenocarcinoma (PDAC) results in stromal amplification that compromises tumor drug delivery, efficacy, and patient survival. Interdiction of HH-mediated tumor-stroma crosstalk with smoothened (SMO) inhibitors (SHHi) "primes" PDAC patient-derived xenograft (PDX) tumors for increased drug delivery by transiently increasing vascular patency/permeability, and thereby macromolecule delivery. However, patient tumor isolates vary in their responsiveness, and responders show co-induction of epithelial-mesenchymal transition (EMT). We aimed to identify the signal derangements responsible for EMT induction and reverse them and devise approaches to stratify SHHi-responsive tumors noninvasively based on clinically-quantifiable parameters. EXPERIMENTAL DESIGN: Animals underwent diffusion-weighted magnetic resonance (DW-MR) imaging for measurement of intratumor diffusivity. In parallel, tissue-level deposition of nanoparticle probes was quantified as a marker of vascular permeability/perfusion. Transcriptomic and bioinformatic analysis was employed to investigate SHHi-induced gene reprogramming and identify key "nodes" responsible for EMT induction. RESULTS: Multiple patient tumor isolates responded to short-term SHH inhibitor exposure with increased vascular patency and permeability, with proportionate increases in tumor diffusivity. Nonresponding PDXs did not. SHHi-treated tumors showed elevated FGF drive and distinctly higher nuclear localization of fibroblast growth factor receptor (FGFR1) in EMT-polarized tumor cells. Pan-FGFR inhibitor NVP-BGJ398 (Infigratinib) reversed the SHHi-induced EMT marker expression and nuclear FGFR1 accumulation without compromising the enhanced permeability effect. CONCLUSIONS: This dual-hit strategy of SMO and FGFR inhibition provides a clinically-translatable approach to compromise the profound impermeability of PDAC tumors. Furthermore, clinical deployment of DW-MR imaging could fulfill the essential clinical-translational requirement for patient stratification.
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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) | 0.000 | 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 | 0.001 |
| 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".