High Frequency Spectral Ultrasound Imaging to Detect Metastasis in Implanted Biomaterial Scaffolds

Abstract

For most cancers, metastasis is the point at which disease is no longer curable. Earlier detection of metastasis, when it is undetectable by current clinical methods, may enable better outcomes. We have developed a biomaterial implant that recruits metastatic cancer cells in mouse models of breast cancer. Here, we investigate spectral ultrasound imaging (SUSI) as a non-invasive strategy for detecting metastasis to the implanted biomaterial scaffolds. Our results show that SUSI, which detects parameters related to tissue composition and structure, identified changes at an early time point when tumor cells were recruited to scaffolds in orthotopic breast cancer mouse models. These changes were not associated with acellular components in the scaffolds but were reflected in the cellular composition in the scaffold microenvironment, including an increase in CD31 + CD45-endothelial cell number in tumor bearing mice. In addition, we built a classification model based on changes in SUSI parameters from scaffold measurements to stratify tumor free and tumor bearing status. Combination of a linear discriminant analysis and bagged decision trees model resulted in an area under the curve of 0.92 for receiver operating characteristics analysis. With the potential for early non-invasive detection, SUSI could facilitate clinical translation of the scaffolds for monitoring metastatic disease.

Keywords: Cancer; Cancer diagnostics; Metastasis detection; Pre-metastatic niche; Spectral ultrasound imaging.

Figure 1:. SUSI detects changes in scaffolds from mouse-tumor bearing mice with micrometastasis relative to tumor-free.

(A) Grayscale images of scaffolds from tumor free and tumor bearing mice overlaid with parameter values for mid-band fit, slope, ASD, and AAC. (B) SUSI parameters for control and tumor bearing scaffolds including grayscale, mid-band fit, slope, ASD, and AAC. (C) The number of pixels lower than the lower bound of the 95% confidence interval of the sample for each SUSI parameter. N = 6 scaffolds/condition. Error bars s.e.m. *p<0.05 via two-sided t-test. Scale bar indicates 1 mm.

Figure 2:. SUSI detects changes in mouse- and human-tumor bearing mice relative to tumor-free at early stages of metastasis

(A) Grayscale images of scaffolds taken from control and 231-BR tumor bearing mice at day 5 post-inoculation overlaid with parameter values for mid-band fit, slope, ASD and AAC. (B) The number of pixels lower than the 95% confidence interval for each sample and each SUSI parameter (grayscale p=0.001, mid-band fit p=0.002, slope p=0.001, ASD p=0.001, and AAC p=0.003) for control and day 5 231-BR tumor bearing scaffolds. (C) Grayscale images of scaffolds taken from control and 4T1 tumor bearing mice at day 5 post-inoculation overlaid with parameter values for mid-band fit, slope, ASD and AAC. (D) The number of pixels lower than the 95% confidence interval for each sample and each SUSI parameter (grayscale p=0.03, mid-band fit p=0.03, slope p=0.02, ASD p=0.04, and AAC p=0.06) for control and day 5 4T1 tumor bearing scaffolds. N = 4 scaffolds for control and N = 6 scaffolds for tumor bearing for NSG mice bearing 231-BR tumors. N=6 scaffolds/condition for balb/c mice bearing 4T1 tumors. Error bars s.e.m. *p<0.05 via two-sided t-test. Scale bar indicates 1 mm.

Figure 3:. SUSI detects changes in cellular composition with tumor progression and metastasis.

(A) qRT-PCR data showing normalized gene expression for ECM associated genes including Col1a1, Col4a1, Fn1, Lox, and Mmp2 for scaffolds from control and day 15 4T1 tumor bearing mice (N = 3 scaffolds/condition). (B) Flow cytometric analysis of CD31+CD45− endothelial cells in scaffolds from control and day 15 4T1 tumor bearing mice (N = 4 scaffolds/condition). (C) SUSI analysis of decellularized scaffolds taken from control and day 5 4T1 tumor bearing mice showing fold change from control of the number of pixels under 95% confidence interval for each sample for each SUSI parameter including grayscale, mid-band fit (MBF), ASD, and AAC (N ≥ 4 scaffolds/condition). (D) SUSI analysis of scaffold-derived cells in a collagen gel taken from control and day 5 4T1 tumor bearing mice showing fold change from control of the number of pixels under 95% confidence interval for each sample and each SUSI parameter including grayscale, MBF, ASD, and AAC (N ≥ 4 scaffolds/condition). (E) SUSI analysis of spleen-derived cells in a collagen gel taken from control and day 5 4T1 tumor bearing mice showing fold change from control of the number of pixels under 95% confidence interval for each sample and each SUSI parameter including grayscale, MBF, ASD, and AAC (N ≥ 4 scaffolds/condition). For all plots error bars s.e.m. and *p<0.05 via two-sided t-test.

Figure 4:. SUSI parameters are able to classify tumor free and tumor bearing mice with good sensitivity and specificity.

(A) Schematic of method used to classify samples as tumor free (TF) or tumor bearing (TB). A training cohort of n=6 TF and n=8 TB mice were used to build a linear discriminant model and a bagged decision trees model. The test cohort (n=14 TF and n=18 TB) were tested using the models for classification. (B) Heatmap with unsupervised hierarchical clustering of test cohort data normalized across each parameter. (C) Classification of test cohort data and score given by each model indicating prediction of either TF status (score of 0) or TB status (score of 1). (D) Receiver Operating Characteristic (ROC) curve for tumor status classification showing the classification accuracy for the combined score including both bagged decision trees and linear discriminant analysis models and each model alone