# Tumor Microenvironment: Insights from Multiparametric MRI in Pancreatic Ductal Adenocarcinoma

## TL;DR

This study explores how MRI and histology can reveal changes in the tumor environment of pancreatic cancer, offering insights to improve treatment.

## Contribution

The study introduces a co-clinical approach combining mpMRI-derived biomarkers with histology to assess tumor microenvironment changes in PDAC.

## Key findings

- Post-treatment ADC and Ktrans values in a preclinical PDAC model showed significant changes correlated with tumor cell percentages.
- Preclinical and clinical QIBs demonstrate potential for evaluating drug combinations targeting tumor and stroma in PDAC.

## Abstract

Pancreatic ductal adenocarcinoma (PDAC) is characterized by a highly heterogeneous tumor microenvironment (TME), enriched with stromal components such as cancer-associated fibroblasts and dense extracellular matrix, which contribute to therapeutic resistance. Multiparametric magnetic resonance imaging (mpMRI) can yield valuable quantitative imaging biomarkers (QIBs) derived from diffusion-weighted (DW) and dynamic contrast–enhanced (DCE) MRI that can be used to assess characteristics of the TME such as cellularity and vascular permeability. Meanwhile, histological staining (Hoechst, hematoxylin and eosin [H&E]) provides insights into the TME spatial organization. Harnessing mpMRI and histology together in PDAC is vital for combating therapeutic resistance and enhancing treatment efficacy. This study establishes a foundation for future co-clinical research to evaluate emerging drug combinations that target both tumor and stroma, thus advancing our understanding of the TME in PDAC.

Background/Objectives: The tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) is characterized by an enriched stroma, hampering the effectiveness of therapy. This co-clinical study aimed to (1) provide insight into early post-treatment changes in the TME using multiparametric magnetic resonance imaging (mpMRI)-derived quantitative imaging biomarkers (QIBs) in a preclinical PDAC model treated with radiotherapy and correlate these QIBs with histology; (2) evaluate the feasibility of obtaining these QIBs in patients with PDAC using clinically approved mpMRI data acquisitions. Methods: Athymic mice (n = 12) at pre- and post-treatment as well as patients with PDAC (n = 11) at pre-treatment underwent mpMRI including diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) data acquisition sequences. DW and DCE data were analyzed using monoexponential and extended Tofts models, respectively. DeepLIIF quantified the total percentage (%) of tumor cells in hematoxylin and eosin (H&E)-stained tissues from athymic mice. Spearman correlation and Wilcoxon signed rank tests were performed for statistical analysis. Results: In the preclinical PDAC model, mean pre- and post-treatment ADC and Ktrans values differed significantly (p < 0.01), changing by 20.50% and 20.41%, respectively, and the median total tumor cells quantified by DeepLIIF was 24% (range: 15–53%). Post-treatment ADC values and relative change in ve (rΔve) showed a significant negative correlation with total tumor cells (ρ = −0.77, p < 0.014 for ADC and ρ = −0.77, p = 0.009 for rΔve). In patients with PDAC, pre-treatment mean ADC and Ktrans values were 1.76 × 10−3 (mm2/s) and 0.24 (min−1), respectively. Conclusions: QIBs in both preclinical and clinical settings underscore their potential for future co-clinical research to evaluate emerging drug combinations targeting both tumor and stroma.