Allysine-Targeted Molecular MRI Enables Early Prediction of Chemotherapy Response in Pancreatic Cancer.
Hua MaShadi A EsfahaniShriya KrishnaBahar AtaeiniaIris Yuwen ZhouNicholas J RotileJonah Weigand-WhittierAvery T BoiceAndrew S LissKenneth K TanabePeter CaravanPublished in: Cancer research (2024)
Neoadjuvant therapy is routinely used in pancreatic ductal adenocarcinoma (PDAC), but not all tumors respond to this treatment. Current clinical imaging techniques are not able to precisely evaluate and predict the response to neoadjuvant therapies over several weeks. A strong fibrotic reaction is a hallmark of a positive response, and during fibrogenesis, allysine residues are formed on collagen proteins by the action of lysyl oxidases. Here, we report the application of an allysine-targeted molecular MRI probe, MnL3, to provide an early, noninvasive assessment of treatment response in PDAC. Allysine increased 2- to 3-fold after one dose of neoadjuvant therapy with FOLFIRINOX in sensitive human PDAC xenografts in mice. Molecular MRI with MnL3 could specifically detect and quantify fibrogenesis in PDAC xenografts. Comparing the MnL3 signal before and 3 days after one dose of FOLFIRINOX predicted subsequent treatment response. The MnL3 tumor signal increased by 70% from day 0 to day 3 in mice that responded to subsequent doses of FOLFIRINOX, whereas no signal increase was observed in FOLFIRINOX-resistant tumors. This study indicates the promise of allysine-targeted molecular MRI as a noninvasive tool to predict chemotherapy outcomes. Significance: Allysine-targeted molecular MRI can quantify fibrogenesis in pancreatic tumors and predict response to chemotherapy, which could guide rapid clinical management decisions by differentiating responders from nonresponders after treatment initiation.
Keyphrases
- locally advanced
- rectal cancer
- contrast enhanced
- magnetic resonance imaging
- squamous cell carcinoma
- radiation therapy
- diffusion weighted imaging
- cancer therapy
- lymph node
- computed tomography
- magnetic resonance
- endothelial cells
- stem cells
- type diabetes
- machine learning
- systemic sclerosis
- weight loss
- quantum dots
- artificial intelligence
- combination therapy