Back

Pulsed low-dose-rate radiation (PLDR) reduces the tumor-promoting responses induced by conventional chemoradiation in pancreatic cancer-associated fibroblasts

Franco-Barraza, J.; Luong, T.; Wong, J. K.; Raghavan, K.; Handroff, E.; Vendramini-Costa, D. B.; Francescone, R.; Gardiner, J. C.; Meyer, J. E.; Cukierman, E.

2024-01-15 cancer biology
10.1101/2024.01.13.575510 bioRxiv
Show abstract

Pancreatic cancer is becoming increasingly deadly, with treatment options limited due to, among others, the complex tumor microenvironment (TME). This short communications study investigates pulsed low-dose-rate radiation (PLDR) as a potential alternative to conventional radiotherapy for pancreatic cancer neoadjuvant treatment. Our ex vivo research demonstrates that PLDR, in combination with chemotherapy, promotes a shift from tumor-promoting to tumor-suppressing properties in a key component of the pancreatic cancer microenvironment we called CAFu (cancer-associated fibroblasts and selfgenerated extracellular matrix functional units). This beneficial effect translates to reduced desmoplasia (fibrous tumor expansion) and suggests PLDRs potential to improve total neoadjuvant therapy effectiveness. To comprehensively assess this functional shift, we developed the HOST-Factor, a single score integrating multiple biomarkers. This tool provides a more accurate picture of CAFu function compared to individual biomarkers and could be valuable for guiding and monitoring future therapeutic strategies. Our findings support the ongoing NCT04452357 clinical trial testing PLDR safety and TME normalization potential in pancreatic cancer patients. The HOST-Factor will be used in samples collected from this trial to validate its potential as a key tool for personalized medicine in this aggressive disease.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.