Prediction of therapeutic outcome and survival in a transgenic mouse model of pancreatic ductal adenocarcinoma treated with dendritic cell vaccination or CDK inhibitor using MRI texture: a feasibility study

Abstract

There is a lack of a well-established approach for assessment of early treatment outcomes for modern therapies for pancreatic ductal adenocarcinoma (PDAC) e.g. dinaciclib or dendritic cell (DC) vaccination. Here, we developed multivariate models using MRI texture features to detect treatment effects following dinaciclib drug or DC vaccine therapy in a transgenic mouse model of PDAC including 21 LSL-Kras^G12D ; LSL-Trp53^R172H ; Pdx-1-Cre (KPC) mice used as untreated control subjects (n=8) or treated with dinaciclib (n=7) or DC vaccine (n=6). Support vector machines (SVM) technique was performed to build a linear classifier with three variables for detection of tumor tissue changes following drug or vaccine treatments. Besides, multivariate regression models were generated with five variables to predict survival behavior and histopathological tumor markers (Fibrosis, CK19, and Ki67). The diagnostic performance was evaluated using accuracy, area under the receiver operating characteristic curve (AUC) and decision curve analyses. The regression models were evaluated with adjusted r-squared (R_adj ²). SVM classifier successfully distinguished changes in tumor tissue with an accuracy of 95.24% and AUC of 0.93. The multivariate models generated with five variables were strongly associated with histopathological tumor markers, fibrosis (R_adj ²=0.82, P<0.001), CK19 (R_adj ²=0.92, P<0.001) and Ki67 (R_adj ²=0.97, P<0.001). Furthermore, the multivariate regression model successfully predicted survival of KPC mice by interpreting tumor characteristics from MRI data (R_adj ²=0.91, P<0.001). The results demonstrated that MRI texture features had great potential to generate diagnosis and prognosis models for monitoring early treatment response following dinaciclib drug or DC vaccine treatment and also predicting histopathological tumor markers and long-term clinical outcomes.

Keywords: Dendritic cell vaccine; dinaciclib; machine learning; magnetic resonance imaging; pancreatic ductal adenocarcinoma; texture analysis.

Figure 1

The quantitative MRI texture features to characterize underlying tumor structure. Representative MRI images for PDAC tumors from untreated control and treatment (DC vaccine and dinaciclib) groups were presented in (A). The heatmap representation demonstrates the association between computed texture features identified following correlation analysis (B). The z-scores of candidate features and diagnostic accuracies (ACC) of each variable in a univariate model were presented in (C). Multivariable models are performed to improve early detection of treatment effects on tumor tissues due to observed lower accuracy with univariate models.

Figure 2

The diagnostic performance of the generated classification model to detect changes in the tumor microenvironment. (A) represents posterior probabilities of each sample in control and treatment (dinaciclib and DC vaccine) groups that were computed with support vector machines classification model. Only one KPC mice used as untreated control subject was misclassified. The receiver operating curves (ROC) for the developed model are given in (B). The model was utilized to generate a ROC curve with different groups (DC vs. control, dinaciclib vs. control, Treatment (DC and dinaciclib vs. control). (C) visualizes the generated separation surface (green) to differentiate pancreatic tumors according to the status of treatment. (D) shows the benefit curve of the generated classification model according to decision curve plot analysis (solid line: prediction model, dashed line: assume all subjects are treated, dotted-dashed line: assume no subjects were treated). Abbreviations: wv_rln: run-length nonuniformity of vertical wavelet coefficients; wa_m: mean of approximate wavelet coefficients; wh_m: mean of horizontal wavelet coefficients.

Figure 3

Survival behavior and prediction analysis of the samples. Post enrolment survival behavior of the KPC mice was demonstrated with Kaplan-Meier plot (A). The prediction performance of the generated multivariable model for survival of the PDAC mice (B). The model had a strong correlation with observed survival behavior of KPC mice including untreated control and treated with DC vaccine or dinaciclib (R_adj ²=0.91). The statistical assessment of post-treatment survival (C). The survival of KPC mice treated with DC vaccine had significantly longer (P=0.035) while there was no significant improvement for KPC mice treated with dinaciclib (P=0.55).

Figure 4

Histopathological analysis of the tumor tissues with trichrome staining (A), CK19 (B) and Ki67 (C) immunostainings for assessment of treatment effects. The subjects treated with DC vaccine had significantly lower fibrosis (P<0.01) and higher CK19^* (P<0.01) area while there were no significant differences for Ki67^* (P > 0.05) cells. Besides, the subjects treated with dinaciclib had significantly higher CK19^* area (P=0.05) and lower number Ki67^* (P=0.03) cells while no significant difference was observed in fibrosis pepercentage (P > 0.05).

Figure 5

Performance of the regression models that predict histopathological tumor markers (fibrosis, CK19^*, and Ki67^*). The generated models had a strong correlation with the measured values of the histopathological tumor markers, (A) fibrosis performance (R_adj ²=0.82), (B) CK19^* area (R_adj ²=0.92), and (C) number of Ki67^* cells (R_adj ²=0.97). Moreover, the residuals of the models were given in (D-F) for fibrosis percentage, CK19^* area, and the number of Ki67^* cells, respectively.