Purpose: Pancreatic ductal adenocarcinoma (PDAC) is a heterogeneous disease with variable presentations and natural histories of disease. We hypothesized that different morphologic characteristics of PDAC tumors on diagnostic computed tomography (CT) scans would reflect their underlying biology. Experimental Design: We developed a quantitative method to categorize the PDAC morphology on pretherapy CT scans from multiple datasets of patients with resectable and metastatic disease and correlated these patterns with clinical/pathologic measurements. We modeled macroscopic lesion growth computationally to test the effects of stroma on morphologic patterns, hypothesizing that the balance of proliferation and local migration rates of the cancer cells would determine tumor morphology. Results: In localized and metastatic PDAC, quantifying the change in enhancement on CT scans at the interface between tumor and parenchyma (delta) demonstrated that patients with conspicuous (high-delta) tumors had significantly less stroma, higher likelihood of multiple common pathway mutations, more mesenchymal features, higher likelihood of early distant metastasis, and shorter survival times compared with those with inconspicuous (low-delta) tumors. Pathologic measurements of stromal and mesenchymal features of the tumors supported the mathematical model's underlying theory for PDAC growth. Conclusions: At baseline diagnosis, a visually striking and quantifiable CT imaging feature reflects the molecular and pathological heterogeneity of PDAC, and may be used to stratify patients into distinct subtypes. Moreover, growth patterns of PDAC may be described using physical principles, enabling new insights into diagnosis and treatment of this deadly disease.
|Original language||English (US)|
|Number of pages||12|
|Journal||Clinical Cancer Research|
|State||Published - Dec 1 2018|
Bibliographical noteFunding Information:
We thank Chris Wogan for her thoughtful editing of the manuscript. We also thank Mark Hurd, Amalia Gonzalez, Alexander Ondari, Christopher Bristow, and Stephanie Kerps for their research support. We gratefully acknowledge partial support from the Andrew Sabin Family Fellowship, Center for Radiation Oncology Research, the Sheikh Ahmed Center for Pancreatic Cancer Research, institutional funds from The University of Texas MD Anderson Cancer Center, equipment support by GE Healthcare and the Center of Advanced Biomedical Imaging, Philips Healthcare, Project Purple, and Cancer Center Support (Core) Grant CA016672 from the National Cancer Institute to MD Anderson. E. Koay was also supported by NIH grants U54CA210181-01, U54CA143837, and U01CA196403, the Pancreatic Cancer Action Network (14-20-25-KOAY), and the Radiological Society of North America (RSD1429). J. Fleming was supported by the Lustgarten Foundation (989161) and Viragh Family Foundation. This work was also supported by NIH T32CA009599. V. Cristini was supported by NSF DMS- 1716737, NIH 1U01CA196403, 1U01CA213759, 1R01CA226537, 1R01CA222007, the Rochelle and Max Levit Chair in the Neurosciences, and the University of Texas System STAR Award. J. Lowengrub was supported by NSF DMS-1714973 and NIH grants 1U54CA217378-01A1, P50GM76516, and P30CA062203. This research was partly performed in the Flow Cytometry & Cellular Imaging Facility.
K.A. Reiss reports receiving commercial research support from Bristol-Myers Squibb, Clovis Oncology, Eli Lilly Oncology, and TesaroBio. R.A. Wolff receives royalty payments from McGraw-Hill for acting as co-editor of the MD Anderson Manual of Medical Oncology. C.H. Crane reports receiving speakers bureau honoraria from Celgene. E.P. Tamm reports receiving commercial research grants from General Electric. No potential conflicts of interest were disclosed by the other authors.
© 2018 American Association for Cancer Research.