TY - JOUR
T1 - Imaging-Based Subtypes of Pancreatic Ductal Adenocarcinoma Exhibit Differential Growth and Metabolic Patterns in the Pre-Diagnostic Period
T2 - Implications for Early Detection
AU - Zaid, Mohamed
AU - Elganainy, Dalia
AU - Dogra, Prashant
AU - Dai, Annie
AU - Widmann, Lauren
AU - Fernandes, Pearl
AU - Wang, Zhihui
AU - Pelaez, Maria J.
AU - Ramirez, Javier R.
AU - Singhi, Aatur D.
AU - Dasyam, Anil K.
AU - Brand, Randall E.
AU - Park, Walter G.
AU - Rahmanuddin, Syed
AU - Rosenthal, Michael H.
AU - Wolpin, Brian M.
AU - Khalaf, Natalia
AU - Goel, Ajay
AU - Von Hoff, Daniel D.
AU - Tamm, Eric P.
AU - Maitra, Anirban
AU - Cristini, Vittorio
AU - Koay, Eugene J.
N1 - Funding Information:
We gratefully acknowledge support from the Andrew Sabin Family Fellowship, the Sheikh Ahmed Center for Pancreatic Cancer Research, institutional funds from The University of Texas MD Anderson Cancer Center, the Khalifa Foundation, equipment support by GE Healthcare and the Center of Advanced Biomedical Imaging, Philips Healthcare, and Cancer Center Support (Core) Grant CA016672 from the National Cancer Institute to MD Anderson. Dr. Eugene Koay was supported by NIH (U54CA210181, U54CA143837, U01CA200468, U01CA196403, U01CA214263, R01CA221971, R01CA248917, and R01CA218004) and the Pancreatic Cancer Action Network (16-65-SING).
Publisher Copyright:
© Copyright © 2020 Zaid, Elganainy, Dogra, Dai, Widmann, Fernandes, Wang, Pelaez, Ramirez, Singhi, Dasyam, Brand, Park, Rahmanuddin, Rosenthal, Wolpin, Khalaf, Goel, Von Hoff, Tamm, Maitra, Cristini and Koay.
PY - 2020/12/2
Y1 - 2020/12/2
N2 - Background: Previously, we characterized subtypes of pancreatic ductal adenocarcinoma (PDAC) on computed-tomography (CT) scans, whereby conspicuous (high delta) PDAC tumors are more likely to have aggressive biology and poorer clinical outcomes compared to inconspicuous (low delta) tumors. Here, we hypothesized that these imaging-based subtypes would exhibit different growth-rates and distinctive metabolic effects in the period prior to PDAC diagnosis. Materials and methods: Retrospectively, we evaluated 55 patients who developed PDAC as a second primary cancer and underwent serial pre-diagnostic (T0) and diagnostic (T1) CT-scans. We scored the PDAC tumors into high and low delta on T1 and, serially, obtained the biaxial measurements of the pancreatic lesions (T0-T1). We used the Gompertz-function to model the growth-kinetics and estimate the tumor growth-rate constant (α) which was used for tumor binary classification, followed by cross-validation of the classifier accuracy. We used maximum-likelihood estimation to estimate initiation-time from a single cell (10-6 mm3) to a 10 mm3 tumor mass. Finally, we serially quantified the subcutaneous-abdominal-fat (SAF), visceral-abdominal-fat (VAF), and muscles volumes (cm3) on CT-scans, and recorded the change in blood glucose (BG) levels. T-test, likelihood-ratio, Cox proportional-hazards, and Kaplan-Meier were used for statistical analysis and p-value <0.05 was considered significant. Results: Compared to high delta tumors, low delta tumors had significantly slower average growth-rate constants (0.024 month−1 vs. 0.088 month−1, p<0.0001) and longer average initiation-times (14 years vs. 5 years, p<0.0001). α demonstrated high accuracy (area under the curve (AUC)=0.85) in classifying the tumors into high and low delta, with an optimal cut-off of 0.034 month−1. Leave-one-out-cross-validation showed 80% accuracy in predicting the delta-class (AUC=0.84). High delta tumors exhibited accelerated SAF, VAF, and muscle wasting (p <0.001), and BG disturbance (p<0.01) compared to low delta tumors. Patients with low delta tumors had better PDAC-specific progression-free survival (log-rank, p<0.0001), earlier stage tumors (p=0.005), and higher likelihood to receive resection after PDAC diagnosis (p=0.008), compared to those with high delta tumors. Conclusion: Imaging-based subtypes of PDAC exhibit distinct growth, metabolic, and clinical profiles during the pre-diagnostic period. Our results suggest that heterogeneous disease biology may be an important consideration in early detection strategies for PDAC.
AB - Background: Previously, we characterized subtypes of pancreatic ductal adenocarcinoma (PDAC) on computed-tomography (CT) scans, whereby conspicuous (high delta) PDAC tumors are more likely to have aggressive biology and poorer clinical outcomes compared to inconspicuous (low delta) tumors. Here, we hypothesized that these imaging-based subtypes would exhibit different growth-rates and distinctive metabolic effects in the period prior to PDAC diagnosis. Materials and methods: Retrospectively, we evaluated 55 patients who developed PDAC as a second primary cancer and underwent serial pre-diagnostic (T0) and diagnostic (T1) CT-scans. We scored the PDAC tumors into high and low delta on T1 and, serially, obtained the biaxial measurements of the pancreatic lesions (T0-T1). We used the Gompertz-function to model the growth-kinetics and estimate the tumor growth-rate constant (α) which was used for tumor binary classification, followed by cross-validation of the classifier accuracy. We used maximum-likelihood estimation to estimate initiation-time from a single cell (10-6 mm3) to a 10 mm3 tumor mass. Finally, we serially quantified the subcutaneous-abdominal-fat (SAF), visceral-abdominal-fat (VAF), and muscles volumes (cm3) on CT-scans, and recorded the change in blood glucose (BG) levels. T-test, likelihood-ratio, Cox proportional-hazards, and Kaplan-Meier were used for statistical analysis and p-value <0.05 was considered significant. Results: Compared to high delta tumors, low delta tumors had significantly slower average growth-rate constants (0.024 month−1 vs. 0.088 month−1, p<0.0001) and longer average initiation-times (14 years vs. 5 years, p<0.0001). α demonstrated high accuracy (area under the curve (AUC)=0.85) in classifying the tumors into high and low delta, with an optimal cut-off of 0.034 month−1. Leave-one-out-cross-validation showed 80% accuracy in predicting the delta-class (AUC=0.84). High delta tumors exhibited accelerated SAF, VAF, and muscle wasting (p <0.001), and BG disturbance (p<0.01) compared to low delta tumors. Patients with low delta tumors had better PDAC-specific progression-free survival (log-rank, p<0.0001), earlier stage tumors (p=0.005), and higher likelihood to receive resection after PDAC diagnosis (p=0.008), compared to those with high delta tumors. Conclusion: Imaging-based subtypes of PDAC exhibit distinct growth, metabolic, and clinical profiles during the pre-diagnostic period. Our results suggest that heterogeneous disease biology may be an important consideration in early detection strategies for PDAC.
KW - computed tomography
KW - early detection
KW - mathematical modeling
KW - pancreatic cancer
KW - tumor metabolism
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U2 - 10.3389/fonc.2020.596931
DO - 10.3389/fonc.2020.596931
M3 - Article
C2 - 33344245
AN - SCOPUS:85097678980
SN - 2234-943X
VL - 10
JO - Frontiers in Oncology
JF - Frontiers in Oncology
M1 - 596931
ER -