Purpose: The tumor microenvironment presents with altered extracellular matrix (ECM) and stroma composition, which may affect treatment efficacy and contribute to tissue stiffness. Ultrasound (US) elastography can visualize and quantify tissue stiffness noninvasively. However, the contributions of ECM and stromal components to stiffness are poorly understood. We therefore set out to quantify ECM and stroma density and their relation to tumor stiffness.

Experimental Design: A modified clinical ultrasound system was used to measure tumor stiffness and perfusion during tumor growth in preclinical tumor models. In vivo measurements were compared with collagen mass spectroscopy and automatic analysis of matrix and stromal markers derived from immunofluorescence images.

Results: US elastography estimates of tumor stiffness were positively correlated with tumor volume in collagen and myofibroblast-rich tumors, while no correlations were found for tumors with low collagen and myofibroblast content. US elastography measurements were strongly correlated with ex vivo mechanical testing and mass spectroscopy–based measurements of total collagen and immature collagen crosslinks. Registration of ultrasound and confocal microscopy data showed strong correlations between blood vessel density and T-cell density in syngeneic tumors, while no correlations were found for genetic tumor models. In contrast to collagen density, which was positively correlated with stiffness, no significant correlations were observed for hyaluronic acid density. Finally, localized delivery of collagenase led to a significant reduction in tumor stiffness without changes in perfusion 24 hours after treatment.

Conclusions: US elastography can be used as a potential biomarker to assess changes in the tumor microenvironment, particularly changes affecting the ECM. Clin Cancer Res; 24(18); 4455–67. ©2018 AACR.

Translational Relevance

Only certain patients benefit from immune checkpoint inhibitors, which may be partly due to the immunosuppressive tumor microenvironment. To address this, new therapies targeted at the tumor stroma and the extracellular matrix are currently in development. Ultrasound elastography provides a means to measure tissue stiffness, which may function as a biomarker for therapies targeting the tumor microenvironment. However, the contribution of different matrix proteins to tumor stiffness and their effect on stroma cell distribution are poorly understood. We demonstrate that a modified clinical ultrasound elastography system can be used to track stiffness changes in a range of tumor models. In vivo stiffness measurements were found to be strongly correlated with collagen content, collagen crosslinking, activated fibroblast density, and blood vessel density. This work demonstrates that elastography estimates of tumor stiffness can inform about changes in tumor tissue components and may aid the interpretation of clinical stiffness measurements.

Cancer development typically starts with cells acquiring mutations enabling their uncontrolled proliferation and overcoming intrinsic mechanisms regulating tissue homeostasis. This cell-centered view has been expanded over time to include the tumor microenvironment consisting of different stromal cells, immune cells, and the extracellular matrix (ECM) in which all cells are embedded (1–3). The ECM is comprised of a fibrillary protein network (collagen, elastin), glycoproteins (fibronectin), bound growth factors (VEGF, FGF), and glycosaminoglycans [hyaluronic acid (HA)]. Its major roles include the provision of mechanical stability and functional tissue stratification (4, 5).

Although increased stiffness has been recognized as a biomarker for breast cancer associated with a poor prognosis (6–8), the role of tissue stiffness in tumor initiation (9), tumor invasiveness (10), modulation of cell signaling (11), drug delivery (12), and immune cell infiltration has only recently emerged (13–15). These discoveries have fostered experimental approaches that modify the tumor microenvironment, such as preventing collagen crosslinking by blocking lysyl oxidase to reduce tumor stiffness (11), enzymatic degradation of HA to reduce interstitial pressure and improve drug delivery (12) or genetic ablation of carcinoma associated fibroblasts to alter fibrosis and immune cell infiltration (16). The evaluation of therapies targeted at the tumor microenvironment would benefit from noninvasive biomarkers that are associated with therapeutic efficacy. This is particularly important for drugs targeting the ECM, because their effects cannot be measured noninvasively with currently available techniques (5, 17, 18). Tumor stiffness may function as an integrative biomarker of the tumor microenvironment as it is influenced by many factors, including the stiffness of cancer cells, the fraction of stroma cells in tumors, the amount and state of the ECM, the presence of necrotic areas, and the activity of fibroblasts and immune cells.

To enable noninvasive visualization and quantification of stiffness, magnetic resonance and ultrasound-based elastography have been developed (19, 20). Both techniques have been validated clinically for liver fibrosis and breast cancer staging (6, 21–23). However, a systematic evaluation of tumor stromal components and their contribution to stiffness in either clinical or preclinical tumors is currently lacking. A thorough evaluation of the relationship between tumor stromal components and elastography stiffness estimates is needed to robustly employ stiffness as a noninvasive biomarker for therapies that target stromal components. Only a limited number of preclinical tumor elastography studies have been performed so far (24–29). Furthermore, ultrasound-based estimates of tumor stiffness have not been directly correlated with histologic estimates of ECM proteins or collagen content and crosslinking. Because tumor stiffness is an integrative marker of many processes affecting the tumor microenvironment, careful validation is required to map changes in tumor stiffness to its underlying biology. We therefore tracked changes in tumor stiffness and perfusion over several weeks in four syngeneic (B16F10, EMT6, 4T1, KPR3070) and two genetic (Braf/PTEN, MMTV-Her2) tumor models. In vivo stiffness measurements were compared with ex vivo mechanical testing, mass spectroscopy–based estimates of collagen content, and crosslinking. Furthermore, immunofluorescence of tumor microenvironment markers was performed and confocal microscopy images were spatially registered to in vivo US data for analysis of spatial correspondence between these metrics. Finally, localized delivery of collagenase was performed to evaluate ultrasound elastography detection sensitivity for ECM modifications.

An expanded methods section is available in the Supplementary Material.

Cell lines

Tumor cell lines were selected to cover a broad range of tumor stiffness: B16F10 mouse melanoma (ATCC; CRL-6475), EMT6 mouse mammary carcinoma (ATCC; CRL2755), 4T1 mouse mammary gland carcinoma (ATCC; CRL-2539), and in-house mouse pancreatic ductal adenocarcinoma line KPR3070, derived from Kras.lsl.G12D/wt;Trp53lsl/R270H/wt;Pdx.Cre1 genetically engineered mouse model.

Syngeneic tumor models

All animal procedures were performed according to guidelines from the Institutional Animal Care and Use Committee at Genentech, Inc. Female BALB/c or C57BL/6 (n = 80, n = 45, 20–25 g; The Jackson Laboratory) were inoculated subcutaneously on the right flank with tumor cells (1 × 105 cells/mouse) resuspended in 100 μL Hank's balanced salt solution/Matrigel (BD Biosciences). Additional BALB/c mice were implanted with EMT6 cells + Matrigel (n = 10) or 4T1 cells with or without Matrigel (n = 15, n = 15) in the 5th mammary fat pad (1 × 105 cells/mouse). US imaging was performed 7, 14, and 21 days after inoculation. Animals with tumor ulcerations were euthanized and excluded from the study (see Supplementary Table S1).

Genetically engineered tumor models

BrafLSL.V600E;PTENfl/fl;Tyr.CreER;Rosa26LSL.tdTomato mice were fully backcrossed (>10 generations) into C57BL/6J mice. Licenses were obtained from M. McMahon (UCSF, San Francisco, CA; ref. 30) and H. Wu (UCLA, Los Angeles, CA). At 8 to 12 weeks of age, mice (n = 5) were first anesthetized using 2% isoflurane (Henry Schein). The dorsal skin of the right flank was shaved and 1 μL of 5 mmol/L 4-OH tamoxifen (Sigma) dissolved in ethanol was applied to the shaved skin to induce tumor formation. After application, animals were kept under anesthesia for 3 minutes to allow for ethanol evaporation. Tumors appeared within 4 to 8 weeks, after which weekly ultrasound imaging was performed.

MMTV.huHER2 transgenic animals have been generated in-house and were maintained on a FVB/N background as described previously (31). Following the emergence of palpable tumors (n = 5), weekly ultrasound examinations were performed.

Ultrasound imaging

Mice were anesthetized with 4% sevoflurane (Zoetis), lateral tail veins were cannulated and mice were positioned on their left side for syngeneic tumors, prone for Braf/PTEN, or supine for MMTV-Her2 and EMT6 or 4T1 tumors in the mammary fat pad. Following hair removal, anatomic b-mode images (Siemens Acuson S2000, Siemens Medical Solutions) were acquired for axial and sagittal planes covering the maximum tumor cross sections (14L5SP probe, center frequency 14 MHz, 27% power; 50 μm in plane resolution, 300 μm slice thickness, FOV: 3 × 2 cm2). To estimate tumor stiffness, acoustic radiation force impulse (ARFI) imaging was performed on axial planes using parameters optimized for preclinical tumors. Following ARFI imaging, a 9L4 probe (center frequency 8 MHz, 27% power; 90 μm in plane resolution, 300 μm slice thickness, FOV: 3.5 × 3 cm2) was positioned to match the 14L5SP axial plane. Contrast-enhanced ultrasound (CEUS) imaging was performed following infusion of 40 μL of a microbubble contrast agent (SIMB4-5, Advanced Microbubbles Laboratories, CO, 2.3 ± 0.2 × 109 microbubbles/mL) mixed with 60 μL of heparinized saline. CEUS images were acquired for one minute (30 frames/s) starting prior to microbubble infusion. The entire imaging procedure required approximately 15 minutes per animal.

Mechanical testing

Tumors were excised, core punches (6 or 8 mm diameter, depending on tumor size) were taken, and their height measured using a caliper. A universal testing machine (5943, Instron) was used for unconfined compression of tumor cores. Elastic moduli were calculated for 30% height compression with a linear model fitted to acquired stress–strain curves.

Mass spectroscopy

Tumor tissue was reduced with sodium borohydride, hydrolyzed in hydrochloric acid, reconstituted in hexafluorobutyric acid, and subjected to targeted LC-MS/MS on a 6500+ QTRAP (Sciex). Sample analysis was performed in multiple reaction monitoring mode selecting for specific transitions representing hydroxyproline (HYP), pyridinoline (PYD), deoxypyridinoline (DPD), and dihydroxy lysinonorleucine (DHLNL).

Localized delivery of collagenase

Three weeks after 4T1 inoculation, baseline ultrasound (ARFI + CEUS) was performed followed by injection of 20 μL treatment solutions into the core of tumors and at three subcutaneous locations surrounding the tumor. The control group (n = 12) received M2 media (Sigma), while the collagenase group received 0.25 U/mL collagenase-D (Roche). Follow-up imaging was performed 24 hours after baseline imaging and localized delivery.

IHC and histologic methods

Following immunofluorescence staining, confocal microscopy was performed using a Nikon A1 microscope (Nikon). Stitched images covering the entire tumor cross-sections were acquired using a 20× air objective (0.41 × 0.41 μm2, in plane resolution).

Image analysis

Ultrasound.

Image analysis was performed using custom, in-house software (Matlab R2016a, MathWorks). Tumor boundaries were manually delineated on axial and sagittal b-mode images to estimate tumor volumes by fitting ellipsoids. Four shear wave speed maps were averaged and converted to elastic moduli: |E\ = \ 3\rho {v^2}$|⁠, where material density |\rho $| (1,020 kg/m3) was assumed. A 5% CEUS enhancement threshold was used to define perfused and nonperfused tumor areas. ARFI artifacts in and around fluid filled cysts in MMTV-Her2 tumors were excluded (Supplementary Fig. S1A).

Confocal microscopy.

Images were filtered to reduce noise and thresholds were applied to define stained pixels. Connected objects above a minimum size were used to calculate the percentage of positively stained pixels (collagen, platelet-derived growth factor receptor alpha, and HA). For vessel detection (endomucin/lectin), thresholds were followed by watershed segmentation to separate touching vessels (Supplementary Figs. S2 and S3). Vessel maps were dilated and overlaid on images of alpha smooth muscle actin (SMA) stained tissue to define vessel-associated SMA and nonvessel SMA considered activated fibroblasts/myofibroblasts (Supplementary Fig. S3). To estimate T-cell (CD3) and macrophage density (F4/80), thresholds were applied and objects within single-cell size range were classified as cells (Supplementary Figs. S2F and S4).

Confocal data registration.

Manual regions of interest were drawn on parametric confocal maps to remove nontumor tissue (skin, fat) and maps were registered (translation, rotation and scale, Supplementary Fig. S5) to ultrasound data.

Data analysis.

For multiple regression analysis, tumor masks were applied to all ultrasound and confocal microscopy–derived parametric maps to generate animal-specific averages. Pixel-wise correlations were performed by combining parameter vectors for all animals with the same tumor type. To facilitate the comparison of microenvironment parameters within and across different tumor types, 2D parametric maps were reduced to 1D line representations based on the relative distance between center and rim of individual pixels (Supplementary Fig. S6).

Statistical analysis

Results are shown as mean ± SEM. For the comparisons of different tumor types, two-tailed Wilcoxon rank sum tests were used. To test for linear relations between different parameters, multiple or simple linear regression analysis was performed. No regression lines were plotted for P > 0.05 or r < 0.2. To test for differences in tumor volume, perfusion, or stiffness with time, one-way ANOVA was performed or two-way ANOVA for temporal differences between tumor types was applied. Grubbs outlier tests were performed on ARFI stiffness and hydroxyproline measurements for the combined mass spectroscopy dataset. Based on P < 0.01, six measurements were removed from the dataset (n = 107). Correlation maps were generated using the R corrgram package (v1.12) showing all correlations with P < 0.1. Statistical analysis was performed using R software version 3.2.4. The following significance levels were used: *, P < 0.05; **, P < 0.01; *** P < 0.001.

Tumor stiffness increased with tumor volume for slow growing KPR3070 and 4T1 tumors but remained constant for fast growing B16F10 melanomas

To evaluate the consistency of ARFI stiffness measurements in preclinical tumor models, tumor imaging was repeated within 2 hours. The coefficient of variation for tumor stiffness measurements was 12.5% (Supplementary Fig. S7A, B). Syngeneic tumors frequently develop nonperfused/necrotic regions in the center (core). To account for this, perfusion maps were used to define perfused and nonperfused tumor tissue on stiffness maps (Fig. 1A–D). Although soft (B16F10) tumors presented with increased stiffness around the tumor rim after the first imaging time point, stiff tumors (4T1, KPR3070) showed increased stiffness throughout the tumor including nonperfused cores. (Supplementary Figs. S7 and S8). Tumor growth was fastest for B16F10 melanomas and slowest for KPR3070 tumors (Fig. 1E). As tumors grew, the fraction of perfused tissue declined (Fig. 1F). In perfused tumor tissue, stiffness increased with time for slow-growing KPR3070 and 4T1 tumors. In contrast, fast growing B16F10 showed no stiffness increase (perfused area).

Figure 1.

In vivo stiffness and perfusion imaging in syngeneic tumors. A, Anatomic b-mode ultrasound image showing a subcutaneous 4T1 tumor (dashed line). B, CEUS-derived relative blood flow map with the nonperfused area of a tumor outlined (dotted line). C, ARFI stiffness map with tumor and nonperfused areas outlined (soft: 1 kPa, stiff: 130 kPa). D, Stiffness map restricted to perfused area of the tumor. E and F, Tumor volumes increased while tumor perfusion decreased with time for B16F10 (n = 19), EMT6 (n = 12), 4T1 (n = 14), and KPR3070 (n = 20) tumors (ANOVA, a, P < 0.001; b, P < 0.001). G, Tumor stiffness in perfused areas remained constant with time for B16F10 and EMT6 but increased for 4T1 and KPR3070 (ANOVA, P = n.s.; P = n.s.; c, P < 0.001; d, P < 0.001). H, Tumor stiffness in nonperfused areas was higher compared with perfused areas (comparison of last imaging time points; e, P < 0.05) and increased faster with time (f, P < 0.001). I, Tumor volumes of EMT6 and 4T1 tumors 14 days (EMT6 n = 10, 4T1 n = 11) after orthotopic and 21 days (EMT6 n = 12, 4T1 n = 14) after ectopic implantation were similar, reflecting faster tumor growth at orthotopic sites. J, Volume matched EMT6 tumors had a higher stiffness when grown orthotopically compared with ectopic implantation (P < 0.001). In contrast, no stiffness differences were found for volume matched 4T1 tumors grown at orthotopic or ectopic sites. Data, mean ± SEM.

Figure 1.

In vivo stiffness and perfusion imaging in syngeneic tumors. A, Anatomic b-mode ultrasound image showing a subcutaneous 4T1 tumor (dashed line). B, CEUS-derived relative blood flow map with the nonperfused area of a tumor outlined (dotted line). C, ARFI stiffness map with tumor and nonperfused areas outlined (soft: 1 kPa, stiff: 130 kPa). D, Stiffness map restricted to perfused area of the tumor. E and F, Tumor volumes increased while tumor perfusion decreased with time for B16F10 (n = 19), EMT6 (n = 12), 4T1 (n = 14), and KPR3070 (n = 20) tumors (ANOVA, a, P < 0.001; b, P < 0.001). G, Tumor stiffness in perfused areas remained constant with time for B16F10 and EMT6 but increased for 4T1 and KPR3070 (ANOVA, P = n.s.; P = n.s.; c, P < 0.001; d, P < 0.001). H, Tumor stiffness in nonperfused areas was higher compared with perfused areas (comparison of last imaging time points; e, P < 0.05) and increased faster with time (f, P < 0.001). I, Tumor volumes of EMT6 and 4T1 tumors 14 days (EMT6 n = 10, 4T1 n = 11) after orthotopic and 21 days (EMT6 n = 12, 4T1 n = 14) after ectopic implantation were similar, reflecting faster tumor growth at orthotopic sites. J, Volume matched EMT6 tumors had a higher stiffness when grown orthotopically compared with ectopic implantation (P < 0.001). In contrast, no stiffness differences were found for volume matched 4T1 tumors grown at orthotopic or ectopic sites. Data, mean ± SEM.

Close modal

EMT6 and 4T1 tumors grew faster when implanted orthotopically

Tumor volumes for EMT6 and 4T1 tumors were similar after growing for 2 weeks in the mammary fat pad or 3 weeks in the flank (Fig. 1I), reflecting faster growth at their orthotopic site. There was no difference in perfused tumor stiffness at these time points for 4T1 tumors. However, EMT6 tumors grown orthotopically had a higher stiffness (Fig. 1J) despite faster growth at their orthotopic site. To assess the potential impact of Matrigel on tumor stiffness and perfusion, 4T1 tumor cells were implanted orthotopically with or without Matrigel. Although Matrigel had no significant effect on tumor growth, tumor perfusion was significantly lower for 4T1 tumors implanted with Matrigel (Supplementary Fig. S9A–S9C). Furthermore, there was no significant difference in tumor stiffness for perfused or nonperfused areas of 4T1 tumors implanted with or without Matrigel in the 5th mammary fat pad (Supplementary Fig. S9D and S9E).

Noninvasive ultrasound stiffness measurements correlated with ex vivo mechanical testing

Tumor models could be clearly separated based on ultrasound stiffness measurements (average of perfused and non-perfused) at the final imaging time point (Fig. 2A), leading to a ranking of increasing stiffness from B16F10 to EMT6 to 4T1 and KPR3070. Following in vivo imaging, ex vivo mechanical testing was performed. Ranking tumors based on ex vivo mechanical stiffness measurements matched the ultrasound-based ranking, but ex vivo differences between EMT6 and 4T1 were not statistically significant and KPR3070 tumors were substantially stiffer than 4T1 tumors (Fig. 2B). Mechanical testing of KPR3070 may have led to higher measurement errors due to small tumor volumes (see Discussion). Although regression analysis found a good correlation between mechanical testing and US stiffness, this correlation could be significantly improved by excluding KPR3070 tumors (Fig. 2C and D).

Figure 2.

Comparison of in vivo and ex vivo stiffness measurements. A, Box-plot comparing average in vivo ARFI stiffness (perfused and nonperfused) for different tumor models at the last imaging time point. B, Box-plot showing ex vivo mechanical stiffness measurements for B16F10 (n = 15), EMT6 (n = 12), 4T1 (n = 15), and KPR3070 (n = 15) tumors. C, A good correlation was observed between mechanical stiffness measurements (unconfined compression) and in vivo ARFI-based stiffness measurements. D, The correlation between ARFI and mechanical stiffness measurements could be improved by excluding KPR3070 tumors, which may have higher measurement errors due to their small size. E, A strong linear correlation was observed between tumor HYP content and in vivo ARFI stiffness measurements (B16F10: n = 19, EMT6: n = 17, 4T1: n = 28, KPR3070: n = 19). F, Box-plot for tumor hydroxyproline content across different syngeneic tumors. G, Mature DPD crosslinks normalized to collagen content were not correlated with tumor stiffness. H, Immature DHLNL collagen crosslinks normalized to collagen content were positively correlated with tumor stiffness. Mann–Whitney U test *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Figure 2.

Comparison of in vivo and ex vivo stiffness measurements. A, Box-plot comparing average in vivo ARFI stiffness (perfused and nonperfused) for different tumor models at the last imaging time point. B, Box-plot showing ex vivo mechanical stiffness measurements for B16F10 (n = 15), EMT6 (n = 12), 4T1 (n = 15), and KPR3070 (n = 15) tumors. C, A good correlation was observed between mechanical stiffness measurements (unconfined compression) and in vivo ARFI-based stiffness measurements. D, The correlation between ARFI and mechanical stiffness measurements could be improved by excluding KPR3070 tumors, which may have higher measurement errors due to their small size. E, A strong linear correlation was observed between tumor HYP content and in vivo ARFI stiffness measurements (B16F10: n = 19, EMT6: n = 17, 4T1: n = 28, KPR3070: n = 19). F, Box-plot for tumor hydroxyproline content across different syngeneic tumors. G, Mature DPD crosslinks normalized to collagen content were not correlated with tumor stiffness. H, Immature DHLNL collagen crosslinks normalized to collagen content were positively correlated with tumor stiffness. Mann–Whitney U test *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Close modal

Collagen content and immature collagen crosslinks were correlated with in vivo stiffness measurements

Mass spectroscopy of excised tumors revealed a strong correlation between HYP content (a marker for collagen content) and in vivo ultrasound stiffness measurements (average of perfused and nonperfused; Fig. 2E). HYP content increased from soft B16F10 to stiff KPR3070 tumors matching ultrasound-based stiffness expectations, except for EMT6, which had a higher than expected HYP content (Fig. 2F). Although mature PYD and deoxypyridinoline (DPD) crosslinks were correlated with tumor stiffness when normalized to tumor mass (Supplementary Fig. S10), these correlations disappeared when normalized to collagen content (Fig. 2G). Furthermore, PYD and DPD crosslinks normalized to collagen were not different between tumor models. In contrast to mature crosslinks, immature DHLNL crosslinks were correlated with tumor stiffness even when normalized to collagen content (Fig. 2H). Accordingly, including HYP and DHLNL into a linear regression model for ARFI stiffness increased the multiple correlation coefficient to 0.68 (P < 0.001).

Genetic tumor models were well perfused and homogeneously soft

Unlike syngeneic models, Braf/PTEN melanoma and MMTV-Her2 breast cancer were well perfused and did not develop necrotic areas (Fig. 3A–D; Supplementary Fig. S1). However, MMTV-Her2 tumors developed progressively, which included enlargement of fluid filled cysts. These cysts were excluded from stiffness measurements if they were detectable on b-mode ultrasound images (Supplementary Fig. S1A). Tumor volumes increased linearly with similar rates in both models (Fig. 3E). Although Braf/PTEN tumors were almost completely perfused at the two time points assessed, perfused tumor areas decreased slightly with time in MMTV-Her2 tumors (Fig. 3F). Nonetheless, this decrease may be due to developing cysts, which are not detectable on b-mode images rather than decreased perfusion in viable tumor tissue. In contrast to Braf/PTEN melanoma, where stiffness in perfused tumor tissue did not change with time, a small increase was observed for MMTV-Her2 tumors (Fig. 3G). Similarly, stiffness in the nonperfused tissue of MMTV-Her2 tumors increased with time albeit not significantly (Fig. 3H).

Figure 3.

Stiffness and perfusion in genetically engineered tumor models. A, Anatomic b-mode ultrasound image showing a Braf/PTEN melanoma (dashed line). B, CEUS-derived relative blood flow map showing a well-perfused tumor. C, ARFI stiffness map with tumor area outlined (soft: 1 kPa, stiff: 130 kPa). D, Stiffness map restricted to perfused tumor area. E and F, While tumor volumes increased for Braf/PTEN melanomas (n = 5) and MMTV-Her2 breast cancer (n = 5) at the same rate (p = n.s.), perfusion remained close to 100% for melanoma but declined with time for MMTV-Her2 (ANOVA, aP < 0.001). G, Tumor stiffness in perfused areas remained constant with time for Braf/PTEN but increased slightly for MMTV-Her2 (ANOVA, P = n.s., b, P < 0.05). H, Tumor stiffness in nonperfused areas could only be evaluated for MMTV-Her2 for which no significant increase with time was observed (ANOVA, P = n.s.). Data, mean ± SEM.

Figure 3.

Stiffness and perfusion in genetically engineered tumor models. A, Anatomic b-mode ultrasound image showing a Braf/PTEN melanoma (dashed line). B, CEUS-derived relative blood flow map showing a well-perfused tumor. C, ARFI stiffness map with tumor area outlined (soft: 1 kPa, stiff: 130 kPa). D, Stiffness map restricted to perfused tumor area. E and F, While tumor volumes increased for Braf/PTEN melanomas (n = 5) and MMTV-Her2 breast cancer (n = 5) at the same rate (p = n.s.), perfusion remained close to 100% for melanoma but declined with time for MMTV-Her2 (ANOVA, aP < 0.001). G, Tumor stiffness in perfused areas remained constant with time for Braf/PTEN but increased slightly for MMTV-Her2 (ANOVA, P = n.s., b, P < 0.05). H, Tumor stiffness in nonperfused areas could only be evaluated for MMTV-Her2 for which no significant increase with time was observed (ANOVA, P = n.s.). Data, mean ± SEM.

Close modal

Collagen, vessel, and myofibroblast density were strong predictors of tumor stiffness

Immunofluorescence showed a large number of apoptotic cells in syngeneic tumors, but only few in genetic tumors (Fig. 4). These were typically found in the center of EMT6, 4T1, and KPR3070 tumors in line with a low density of perfused vessels (lectin). In contrast, B16F10 tumors contained pockets of apoptotic cells surrounded by large perfused vessels lacking capillaries throughout the tumor cross-sections. Although collagen (collagen I + III + IV) in soft tumors (B16F10, Braf/PTEN) was primarily found in vessel basement membranes, stiff tumors (4T1, KPR3070) showed increasing amounts of collagen throughout the tumor cross-sections including apoptotic regions in the center. Similarly, soft tumors contained only vascular smooth muscle cells (αSMA+), whereas stiff tumors were characterized by abundant myofibroblasts (nonvascular αSMA+) in the perfused tumor areas. Classical fibroblasts (PDGFR-α+) were primarily localized in the tumor rim often associated with high densities of T cells (CD3+).

Figure 4.

Immunofluorescence images for syngeneic and genetic tumor models. B16F10 tumors had large well-perfused blood vessels, pockets of apoptotic cells throughout tumor cross sections, little collagen, few macrophages and T cells, and only vascular-associated smooth muscle cells. EMT6, 4T1, and KPR3070 tumors had nonperfused cores consisting of apoptotic cells. The concentration of collagen and nonvascular alpha smooth muscle actin positive cells (myofibroblasts) increased from EMT6 to 4T1 and KPR3070 tumor sections. EMT6 tumors often had multiple apoptotic centers in contrast to 4T1 and KPR3070, which typically presented with a single apoptotic core. All three tumor models showed spatial heterogeneous T-cell and macrophage distributions. Braf/PTEN melanomas had a uniform distribution of perfused vessels, T cells, and macrophages with collagen only localized along the periphery and only vascular-associated smooth muscle cells. MMTV-Her2 breast cancers had large fluid-filled cysts. Collagen was found around cysts and blood vessel. Macrophages were found along vessels and cyst boundaries with few T cells distributed throughout areas of high cell density. Alpha smooth muscle actin positive cells were only found around vessels and cysts. DAPI: 4′,6-diamidino-2-Phenylindole, Endom: endomucin, TUNEL: terminal deoxynucleotidyl transferase dUTP nick end labeling, collagen: collagen I + collagen III + collagen IV, PDGFR: platelet-derived growth factor receptor alpha, SMA: alpha smooth muscle actin. Scale bars, 500 μm.

Figure 4.

Immunofluorescence images for syngeneic and genetic tumor models. B16F10 tumors had large well-perfused blood vessels, pockets of apoptotic cells throughout tumor cross sections, little collagen, few macrophages and T cells, and only vascular-associated smooth muscle cells. EMT6, 4T1, and KPR3070 tumors had nonperfused cores consisting of apoptotic cells. The concentration of collagen and nonvascular alpha smooth muscle actin positive cells (myofibroblasts) increased from EMT6 to 4T1 and KPR3070 tumor sections. EMT6 tumors often had multiple apoptotic centers in contrast to 4T1 and KPR3070, which typically presented with a single apoptotic core. All three tumor models showed spatial heterogeneous T-cell and macrophage distributions. Braf/PTEN melanomas had a uniform distribution of perfused vessels, T cells, and macrophages with collagen only localized along the periphery and only vascular-associated smooth muscle cells. MMTV-Her2 breast cancers had large fluid-filled cysts. Collagen was found around cysts and blood vessel. Macrophages were found along vessels and cyst boundaries with few T cells distributed throughout areas of high cell density. Alpha smooth muscle actin positive cells were only found around vessels and cysts. DAPI: 4′,6-diamidino-2-Phenylindole, Endom: endomucin, TUNEL: terminal deoxynucleotidyl transferase dUTP nick end labeling, collagen: collagen I + collagen III + collagen IV, PDGFR: platelet-derived growth factor receptor alpha, SMA: alpha smooth muscle actin. Scale bars, 500 μm.

Close modal

To facilitate a more systematic analysis of ultrasound-based perfusion and stiffness measurements and their biological relevance, automatic image segmentation and registration of confocal images to ultrasound data was performed (Supplementary Fig. S5). As a first analysis level, cross-section averages for different parameters from all tumor models used in this study were combined to generate a correlation matrix (Fig. 5A). Stiffness was positively correlated with collagen, vessel, myofibroblast, and fibroblast density and negatively correlated with tumor volume, relative blood flow, and viability. Collagen density and tumor volume had the strongest positive and negative correlations with stiffness, respectively (Fig. 5B–E). Furthermore, stiffness was also positively correlated with T-cell density, but no correlation was found between stiffness and cell density (Supplementary Figs. S11 and S12). Multiple regression analysis showed that a combination of collagen density, vessel area density, and relative blood flow could best model tumor stiffness (r = 0.76, P < 0.001). Although comparisons of cross-section averages are informative about the relevance of different parameters, they cannot address their spatial distribution within tumors. We therefore performed pixel-wise correlations between T-cell density and different matrix parameters. In KPR3070 tumors, T-cell density was positively correlated with relative blood flow and macrophage density, but only weak correlations were found between T-cell and fibroblast or collagen density (Fig. 5F–I). EMT6 and 4T1 showed similar T-cell distribution patterns, except that collagen was positively correlated with T-cell density in EMT6 tumors. In contrast to these stiffer tumors, soft B16F10, MMTV-Her2, and Braf/PTEN tumors only showed significant correlations between macrophage and T-cell density (data not shown). To further illustrate the spatial distribution of tumor microenvironment parameters and to facilitate the comparison of different tumor models, 2D images (parametric maps) were reduced to a line representation based on the relative center-rim distance of individual pixels (Supplementary Fig. S6). Line plots illustrate that stiffness in EMT6, 4T1, and KPR3070 tumors was higher in the center while soft B16F10 tumors were stiffer in the tumor rim but soft in the center (Fig. 5J). This center versus rim pattern was reversed for blood flow and vessel density but similar for collagen density (Fig. 5K–M). Myofibroblast, fibroblast, macrophage, and T-cell density qualitatively tracked with blood flow and vessel density (Fig. 5N–Q), indicating the importance of perfused vessels for their distribution.

Figure 5.

Systematic comparisons of perfusion, stiffness and histologic tumor microenvironment markers. A, Correlation matrix showing positive correlations between stiffness (Stiff) and vessel density (Vess), lectin-perfused vessel density (Lec), collagen density (Col), myofibroblast density (alpha smooth muscle actin+, SMA), fibroblast density (platelet-derived growth factor receptor-α+, PDG), macrophage density (F4/80+, MAC), and T-cell density (CD3+). Stiffness was negatively correlated with tumor volume (Vol) and relative blood flow (RBF). B–E, Scatter plots showing correlations between tumor stiffness and collagen, tumor volume, vessel density, and myofibroblast density (SMA). F–I, Pixel-wise scatter plots showing correlations between T-cell density and macrophage density, relative blood flow, fibroblast density (PDGFR-α+), and collagen density for KPR3070 tumors (0.2% of data points shown). J–Q, Line representations of tumor microenvironment parameter estimates for B16F10 (n = 7), EMT6 (n = 8), 4T1 (n = 9), KPR3070 (n = 8), Braf/PTEN (n = 3), and MMTV-Her2 (n = 5). 2D parametric maps were reduced to lines based on the relative center to rim position of each pixel. Line plots show the average and standard error of analyzed tumors for each model. Perf: perfused tumor area, VD: vessel diameter, Hyal: hyaluronic acid density, Hydr: hydrazide density, MAC: macrophage density, Viab: viability.

Figure 5.

Systematic comparisons of perfusion, stiffness and histologic tumor microenvironment markers. A, Correlation matrix showing positive correlations between stiffness (Stiff) and vessel density (Vess), lectin-perfused vessel density (Lec), collagen density (Col), myofibroblast density (alpha smooth muscle actin+, SMA), fibroblast density (platelet-derived growth factor receptor-α+, PDG), macrophage density (F4/80+, MAC), and T-cell density (CD3+). Stiffness was negatively correlated with tumor volume (Vol) and relative blood flow (RBF). B–E, Scatter plots showing correlations between tumor stiffness and collagen, tumor volume, vessel density, and myofibroblast density (SMA). F–I, Pixel-wise scatter plots showing correlations between T-cell density and macrophage density, relative blood flow, fibroblast density (PDGFR-α+), and collagen density for KPR3070 tumors (0.2% of data points shown). J–Q, Line representations of tumor microenvironment parameter estimates for B16F10 (n = 7), EMT6 (n = 8), 4T1 (n = 9), KPR3070 (n = 8), Braf/PTEN (n = 3), and MMTV-Her2 (n = 5). 2D parametric maps were reduced to lines based on the relative center to rim position of each pixel. Line plots show the average and standard error of analyzed tumors for each model. Perf: perfused tumor area, VD: vessel diameter, Hyal: hyaluronic acid density, Hydr: hydrazide density, MAC: macrophage density, Viab: viability.

Close modal

Localized delivery of collagenase reduced tumor stiffness

To assess the sensitivity of ultrasound elastography to perturbations of the ECM, localized injections of collagenase were performed (Supplementary Fig. S13A). Injections were performed after baseline ultrasound imaging with follow-up 24 hours thereafter. Tumor perfusion and tumor volume did not change significantly in treatment or control groups (Fig 6B, Supplementary Fig. S13B and S13C). However, collagenase-treated tumors showed a significant decrease in stiffness (Fig. 6A, C, and D). This decrease in tumor stiffness was corroborated by decreased HYP content in collagenase-treated tumors (Fig. 6E), but there was no difference in collagen crosslinks normalized to tumor weights between groups (Supplementary Fig. S14).

Figure 6.

Effect of localized collagenase delivery on perfusion and stiffness of 4T1 tumors. A, Representative stiffness maps before (pre) and 24 hours after (post) control media or collagenase administration. B, Collagenase (n = 12) treatment had no statistically significant effect on tumor perfusion compared with pretreatment or control (n = 12, paired Wilcoxon rank sum tests). C and D, Although control tumors showed a nonsignificant increase in ARFI-based stiffness, collagenase treatment led to a significant decline in stiffness relative to pretreatment and when compared with the control group (paired Wilcoxon rank sum tests: pre vs. post, nonpaired for delta). E, Collagenase treatment (n = 6) led to a significant reduction in HYP content compared with control tumors (n = 6, Wilcoxon rank sum test).

Figure 6.

Effect of localized collagenase delivery on perfusion and stiffness of 4T1 tumors. A, Representative stiffness maps before (pre) and 24 hours after (post) control media or collagenase administration. B, Collagenase (n = 12) treatment had no statistically significant effect on tumor perfusion compared with pretreatment or control (n = 12, paired Wilcoxon rank sum tests). C and D, Although control tumors showed a nonsignificant increase in ARFI-based stiffness, collagenase treatment led to a significant decline in stiffness relative to pretreatment and when compared with the control group (paired Wilcoxon rank sum tests: pre vs. post, nonpaired for delta). E, Collagenase treatment (n = 6) led to a significant reduction in HYP content compared with control tumors (n = 6, Wilcoxon rank sum test).

Close modal

Stiffness can be measured invasively at a cellular, tissue, or organ level. Although cellular measurements have shown that tumor cells are typically softer than normal cells, most tumors present with increased stiffness compared with the surrounding tissue. These changes can be quantified with elastography noninvasively, providing useful information about the tumor microenvironment particularly the ECM and its modification by stromal cells. Ultrasound elastography is the most frequently used technique to assess tumor stiffness due to the ease of use and relatively low cost. When combined with contrast-enhanced ultrasound, perfusion and stiffness can be assessed within one imaging session clinically and preclinically. Magnetic resonance elastography (MRE) offers the advantage of three-dimensional stiffness and viscosity measurements over a wide range of mechanical actuation frequencies with low variability. However, MRE is not as widely available as ultrasound elastography. Other disadvantages of MRE are substantially longer imaging times and higher costs. MRE can be combined with tissue perfusion and vascular permeability quantification within one imaging session. Another technique that has been used preclinically is second harmonic imaging of collagen (32), but this is only a surrogate for stiffness and requires invasive optical windows.

We have shown that ultrasound elastography can reproducibly measure tumor stiffness in syngeneic and genetic tumor models and that in vivo stiffness measurements correlate well with ex vivo mechanical testing. Although reproducibility data for preclinical ultrasound elastography was not previously available, the observed coefficient of variation was similar to estimates from preclinical MRE and clinical US elastography (33, 34). In addition, we found a good correlation between unconstrained mechanical compression and ARFI stiffness measurements. This correlation improved further when KPR3070 tumors were excluded. Mechanical compression required standardization of tumor samples to cylinders of known diameter and height, which were generated by taking core punches from excised tumors. Although diameter-to-height ratios were close to one for most tumors, KPR3070 tumors had ratios close to two due to their size and shape. This limitation is likely responsible for the high mechanical stiffness estimates for KPR3070 tumors. Furthermore, the use of core biopsies to estimate mechanical tumor stiffness introduces sampling errors particularly for soft tumors as most of their mechanical strength is localized in the tumor rim. The ability of elastography techniques to noninvasively measure tumor stiffness in their undisturbed environment should circumvent these problems.

In line with previous publications (25, 26, 35), we found strong correlations between tumor volume and stiffness for the slower growing tumors in this study such as 4T1 and KPR3070. However, tumor volume and stiffness were not correlated in B16F10 or the genetically engineered Braf/PTEN mouse melanoma models. Furthermore, tumor volume and stiffness were only weakly correlated in syngeneic EMT6 and genetically engineered MMTV-Her2 tumor models. These results indicate that stiffness in fast growing syngeneic tumors is likely to reflect cancer cell stiffness, whereas in slow growing syngeneic tumors, stiffness is dominated by ECM and stromal cell changes. Accordingly, tumor models may be grouped into these two general classes and, thus, simplify the interpretation of observed stiffness changes.

The site of tumor implantation may also affect elastic properties of tumors due to local stroma differences. Nonetheless, tumor stiffness for volume-matched 4T1 tumors grown at orthotopic or ectopic sites was not different. In contrast, EMT6 tumors were significantly stiffer when grown orthotopically despite faster growth. This indicates that effects of the implantation site on tumor stiffness are cell line dependent, which may explain the differences observed in the literature (35, 36). Furthermore, different tumor models implanted in the flank led to substantial stiffness differences, highlighting the importance of the tumor line over implantation site. The use of Matrigel as an implantation carrier is an additional variable that may influence tumor stiffness. However, we did not detect any stiffness differences between 4T1 tumors implanted orthotopically with or without Matrigel.

CEUS perfusion estimates were used to identify perfused and nonperfused regions of tumors since these regions may differ in stiffness. Syngeneic tumor models showed a continued decline in perfused tumor area as growth outstripped vascular supply. Stiffness measurements in nonperfused areas were generally higher compared with perfused areas except for day 7, which probably reflects low levels of extracellular matrix deposition in the remaining Matrigel plug. Increased stiffness in nonperfused areas at later time points indicate fibrosis that has not yet progressed to coagulative necrosis, which is associated with matrix destruction and reduced stiffness (25, 26). Perfusion measurements also highlighted the well-perfused nature of genetic compared with syngeneic tumors. Syngeneic B16F10, which despite higher perfusion rates than most syngeneic tumors, did develop nonperfused areas, while Braf/PTEN tumors maintained perfusion throughout the tumor for the duration of the study.

Collagen is the most abundant protein in the ECM, providing most of the mechanical strength in tumors. Collagen cross-linking has been implicated as an important mechanism by which cancer and stromal cells influence the mechanical properties of their environment (11). We found a good correlation between tumor stiffness and HYP, a measure of collagen content in line with published data (11, 16, 37, 38). Furthermore, immature DHLNL collagen crosslinks normalized to collagen content were positively correlated with stiffness, whereas normalized mature DPD and PYD crosslinks were not. The link between collagen accumulation, increased interstitial pressure, and reduced perfusion has been explored in the past (17, 39). However, collagen content and the degree of crosslinking for multiple tumor models have not been compared previously. Tumor stiffness was primarily determined by collagen content and to a smaller degree by immature crosslinks. Interestingly, there was no difference in mature crosslinks normalized to collagen between the tumor models that we analyzed. Inhibition of collagen crosslinking has been shown to reduce tumor growth, metastasis, and invasion (11, 37, 38). Enzymes responsible for collagen crosslinking affect immature crosslinks. It is therefore likely that elastography could track such changes in vivo.

The importance of collagen for tumor stiffness was further corroborated by immunofluorescence and automatic segmentation, which showed strong correlations between collagen density and tumor stiffness on a tumor level and spatially within tumors. In addition, tumor stiffness was correlated with myofibroblast, fibroblast, and vessel density on a tumor level. Positive correlations between microvascular density and tumor stiffness have been reported in the past (27, 35) supporting our findings. These studies also observed positive correlations between cell density and tumor stiffness, which we failed to observe. Such differences may be due to the relatively soft tumor models used for these studies (<7 kPa), which have low amounts of nonvascular-associated ECM increasing the relative importance of cancer cell stiffness for tumor stiffness.

Pixel-wise analysis showed negative correlations between myofibroblast or vessel density and stiffness for stiff tumors such as 4T1 and KPR3070 and positive correlations for soft B16F10 and Braf/PTEN tumors. This pattern may be due to differences in perfusion. Although cores of stiff tumors were nonperfused and apoptotic, soft tumors had perfused cores with higher cell viabilities. Pixel-wise correlations estimated tumor model–specific ultrasound detection sensitivities for perfusion measurements. While approximately 45 perfused blood vessels per square millimeter were required in KPR3070 tumors, 15 were sufficient in B16F10 tumors for CEUS-based detection. One of the limitations of our study is the lack of exogenous fiduciary markers, which may have led to errors during the registration of in vivo ultrasound data to ex vivo histologic data. Pixel-wise correlations between in vivo and ex vivo parameters are particularly affected by such errors. Areas classified as nonperfused based on ultrasound measurements include underperfused hypoxic/necrotic areas (40, 41). Hypoxia leads to fibroblast activation and increased ECM synthesis (42, 43), which may explain the increased stiffness in nonperfused tumor areas.

The relationship between increased tumor stiffness, high interstitial pressure, reduced tumor perfusion, and reduced drug delivery is well documented (17, 39, 44). Limited perfusion of tumor cores may also impede immune cell access and survival (45, 46). More recently, collagen density and crosslinking have been proposed as additional physical barriers impeding T-cell migration (15). Our pixel-wise correlations found strong correlations between perfusion and T-cell density as well as fibroblast and T-cell density. However, collagen density and T-cell density showed only weak negative correlations for KPR3070 and 4T1 tumors and weak positive correlations for EMT6 and B16F10. Furthermore, T-cell density in tumor rims peaked after collagen density started to decline. This indicates that for tumor models analyzed in the current study, blood flow, vessel density, and fibroblast density were important predictors of T-cell density, whereas collagen was not.

Previous studies have shown that MRE and US elastography are able to detect changes in tumor stiffness following cytotoxic or antivascular agent administration in preclinical tumor models (26, 33, 35). However, the ability of elastography to detect direct ECM modifications in vivo has not been documented so far. Our study found that localized collagenase delivery led to a significant reduction in tumor stiffness without affecting tumor volume or perfusion. In agreement with reduced stiffness, mass spectroscopy detected lower collagen concentrations in treated tumors. This indicates that US elastography can detect collagen degradation via reduced mechanical stiffness.

Our findings may help to inform the clinical interpretation of tumor stiffness and stiffness changes following therapeutic interventions, where elastography can provide a biomarker for therapies that target stromal components. However, it will require clinical validation as preclinical tumor models, although informative, do not fully replicate the complexity of human tumors.

In summary, we have shown that ultrasound elastography can reproducibly assess stiffness in syngeneic and genetic preclinical tumor models. Tumor stiffness was strongly correlated with total collagen content and immature collagen crosslinks. Histologic analysis showed that stiffness was correlated with collagen, myofibroblast, fibroblast, and vessel density. Furthermore, T-cell density was primarily determined by vessel and fibroblast density with limited importance for collagen density. We also demonstrated the ability of elastography to detect ECM changes following collagenase delivery. These results show the utility of elastography as a microenvironment biomarker.

J. Riegler is an employee of Genentech. R.A.D. Carano holds ownership interest (including patents) in Roche. No potential conflicts of interest were disclosed by the other authors.

Conception and design: J. Riegler, S. Rosenzweig, A. Castiglioni, S.J. Turley, J. Schartner, R.A.D. Carano

Development of methodology: J. Riegler, S. Rosenzweig, R.A.D. Carano

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): J. Riegler, V. Javinal, A. Castiglioni, C.X. Dominguez, J.E. Long, Q. Li, W. Sandoval, M.R. Junttila, S.J. Turley, J. Schartner

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): J. Riegler, Q. Li, W. Sandoval, S.J. Turley, J. Schartner, R.A.D. Carano

Writing, review, and/or revision of the manuscript: J. Riegler, Y. Labyed, S. Rosenzweig, A. Castiglioni, J.E. Long, Q. Li, M.R. Junttila, S.J. Turley, J. Schartner, R.A.D. Carano

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): R.A.D. Carano

Study supervision: J. Schartner, R.A.D. Carano

We would like to thank Franklin Peal for help with interpretation of histologic findings and Cecile Chalouni for her help with confocal microscopy.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Hanahan
D
,
Weinberg
RA
. 
Hallmarks of cancer: the next generation
.
Cell
2011
;
144
:
646
74
.
2.
Turley
SJ
,
Cremasco
V
,
Astarita
JL
. 
Immunological hallmarks of stromal cells in the tumour microenvironment
.
Nat Rev Immunol
2015
;
15
:
669
82
.
3.
Lu
P
,
Weaver
VM
,
Werb
Z
. 
The extracellular matrix: a dynamic niche in cancer progression
.
J Cell Biol
2012
;
196
:
395
406
.
4.
Hynes
RO
. 
The extracellular matrix: not just pretty fibrils
.
Science
2009
;
326
:
1216
9
.
5.
Cox
TR
,
Erler
JT
. 
Remodeling and homeostasis of the extracellular matrix: implications for fibrotic diseases and cancer
.
Disease Models & Mechanisms
2011
;
4
:
165
78
.
6.
Berg
WA
,
Cosgrove
DO
,
Dore
CJ
,
Schafer
FK
,
Svensson
WE
,
Hooley
RJ
, et al
Shear-wave elastography improves the specificity of breast US: the BE1 multinational study of 939 masses
.
Radiology
2012
;
262
:
435
49
.
7.
Krouskop
TA
,
Wheeler
TM
,
Kallel
F
,
Garra
BS
,
Hall
T
. 
Elastic moduli of breast and prostate tissues under compression
.
Ultrason Imaging
1998
;
20
:
260
74
.
8.
Evans
A
,
Whelehan
P
,
Thomson
K
,
Brauer
K
,
Jordan
L
,
Purdie
C
, et al
Differentiating benign from malignant solid breast masses: value of shear wave elastography according to lesion stiffness combined with greyscale ultrasound according to BI-RADS classification
.
Br J Cancer
2012
;
107
:
224
9
.
9.
Provenzano
PP
,
Inman
DR
,
Eliceiri
KW
,
Knittel
JG
,
Yan
L
,
Rueden
CT
, et al
Collagen density promotes mammary tumor initiation and progression
.
BMC Med
2008
;
6
:
11
.
10.
Swaminathan
V
,
Mythreye
K
,
O'Brien
ET
,
Berchuck
A
,
Blobe
GC
,
Superfine
R
. 
Mechanical stiffness grades metastatic potential in patient tumor cells and in cancer cell lines
.
Cancer Res
2011
;
71
:
5075
80
.
11.
Levental
KR
,
Yu
H
,
Kass
L
,
Lakins
JN
,
Egeblad
M
,
Erler
JT
, et al
Matrix crosslinking forces tumor progression by enhancing integrin signaling
.
Cell
2009
;
139
:
891
906
.
12.
Provenzano
PP
,
Cuevas
C
,
Chang
AE
,
Goel
VK
,
Von Hoff
DD
,
Hingorani
SR
. 
Enzymatic targeting of the stroma ablates physical barriers to treatment of pancreatic ductal adenocarcinoma
.
Cancer Cell
2012
;
21
:
418
29
.
13.
Boissonnas
A
,
Licata
F
,
Poupel
L
,
Jacquelin
S
,
Fetler
L
,
Krumeich
S
, et al
CD8+ tumor-infiltrating T cells are trapped in the tumor-dendritic cell network
.
Neoplasia
2013
;
15
:
85
94
.
14.
O'Connor
RS
,
Hao
X
,
Shen
K
,
Bashour
K
,
Akimova
T
,
Hancock
WW
, et al
Substrate rigidity regulates human T cell activation and proliferation
.
J Immunol
2012
;
189
:
1330
9
.
15.
Salmon
H
,
Franciszkiewicz
K
,
Damotte
D
,
Dieu-Nosjean
MC
,
Validire
P
,
Trautmann
A
, et al
Matrix architecture defines the preferential localization and migration of T cells into the stroma of human lung tumors
.
J Clin Invest
2012
;
122
:
899
910
.
16.
Ozdemir
BC
,
Pentcheva-Hoang
T
,
Carstens
JL
,
Zheng
X
,
Wu
CC
,
Simpson
TR
, et al
Depletion of carcinoma-associated fibroblasts and fibrosis induces immunosuppression and accelerates pancreas cancer with reduced survival
.
Cancer Cell
2014
;
25
:
719
34
.
17.
Netti
PA
,
Berk
DA
,
Swartz
MA
,
Grodzinsky
AJ
,
Jain
RK
. 
Role of extracellular matrix assembly in interstitial transport in solid tumors
.
Cancer Res
2000
;
60
:
2497
503
.
18.
Voytik-Harbin
SL
,
Rajwa
B
,
Robinson
JP
. 
Three-dimensional imaging of extracellular matrix and extracellular matrix-cell interactions
.
Methods Cell Biol
2001
;
63
:
583
97
.
19.
Muthupillai
R
,
Lomas
DJ
,
Rossman
PJ
,
Greenleaf
JF
,
Manduca
A
,
Ehman
RL
. 
Magnetic resonance elastography by direct visualization of propagating acoustic strain waves
.
Science
1995
;
269
:
1854
7
.
20.
Sarvazyan
AP
,
Rudenko
OV
,
Swanson
SD
,
Fowlkes
JB
,
Emelianov
SY
. 
Shear wave elasticity imaging: a new ultrasonic technology of medical diagnostics
.
Ultrasound Med Biol
1998
;
24
:
1419
35
.
21.
Sinkus
R
,
Tanter
M
,
Xydeas
T
,
Catheline
S
,
Bercoff
J
,
Fink
M
. 
Viscoelastic shear properties of in vivo breast lesions measured by MR elastography
.
Magn Reson Imaging
2005
;
23
:
159
65
.
22.
Yin
M
,
Talwalkar
JA
,
Glaser
KJ
,
Manduca
A
,
Grimm
RC
,
Rossman
PJ
, et al
Assessment of hepatic fibrosis with magnetic resonance elastography
.
Clin Gastroenterol Hepatol
2007
;
5
:
1207
13
.
23.
Tsochatzis
EA
,
Gurusamy
KS
,
Ntaoula
S
,
Cholongitas
E
,
Davidson
BR
,
Burroughs
AK
. 
Elastography for the diagnosis of severity of fibrosis in chronic liver disease: a meta-analysis of diagnostic accuracy
.
J Hepatol
2011
;
54
:
650
9
.
24.
Chamming's
F
,
Latorre-Ossa
H
,
Le Frere-Belda
MA
,
Fitoussi
V
,
Quibel
T
,
Assayag
F
, et al
Shear wave elastography of tumour growth in a human breast cancer model with pathological correlation
.
Eur Radiol
2013
;
23
:
2079
86
.
25.
Elyas
E
,
Papaevangelou
E
,
Alles
EJ
,
Erler
JT
,
Cox
TR
,
Robinson
SP
, et al
Correlation of Ultrasound Shear Wave Elastography with Pathological Analysis in a Xenografic Tumour Model
.
Sci Rep
2017
;
7
:
165
.
26.
Dizeux
A
,
Payen
T
,
Le Guillou-Buffello
D
,
Comperat
E
,
Gennisson
JL
,
Tanter
M
, et al
In vivo multiparametric ultrasound imaging of structural and functional tumor modifications during therapy
.
Ultrasound Med Biol
2017
;
43
:
2000
12
.
27.
Jamin
Y
,
Boult
JKR
,
Li
J
,
Popov
S
,
Garteiser
P
,
Ulloa
JL
, et al
Exploring the biomechanical properties of brain malignancies and their pathologic determinants in vivo with magnetic resonance elastography
.
Cancer Res
2015
;
75
:
1216
24
.
28.
Feng
Y
,
Clayton
EH
,
Okamoto
RJ
,
Engelbach
J
,
Bayly
PV
,
Garbow
JR
. 
A longitudinal magnetic resonance elastography study of murine brain tumors following radiation therapy
.
Phys Med Biol
2016
;
61
:
6121
31
.
29.
Pepin
KM
,
Chen
J
,
Glaser
KJ
,
Mariappan
YK
,
Reuland
B
,
Ziesmer
S
, et al
MR elastography derived shear stiffness–a new imaging biomarker for the assessment of early tumor response to chemotherapy
.
Magn Reson Med
2014
;
71
:
1834
40
.
30.
Dankort
D
,
Curley
DP
,
Cartlidge
RA
,
Nelson
B
,
Karnezis
AN
,
Damsky
WE
 Jr.
, et al
Braf(V600E) cooperates with Pten loss to induce metastatic melanoma
.
Nat Genet
2009
;
41
:
544
52
.
31.
Finkle
D
,
Quan
ZR
,
Asghari
V
,
Kloss
J
,
Ghaboosi
N
,
Mai
E
, et al
HER2-targeted therapy reduces incidence and progression of midlife mammary tumors in female murine mammary tumor virus huHER2-transgenic mice
.
Clin Cancer Res
2004
;
10
:
2499
511
.
32.
Brown
E
,
McKee
T
,
diTomaso
E
,
Pluen
A
,
Seed
B
,
Boucher
Y
, et al
Dynamic imaging of collagen and its modulation in tumors in vivo using second-harmonic generation
.
Nat Med
2003
;
9
:
796
800
.
33.
Li
J
,
Jamin
Y
,
Boult
JK
,
Cummings
C
,
Waterton
JC
,
Ulloa
J
, et al
Tumour biomechanical response to the vascular disrupting agent ZD6126 in vivo assessed by magnetic resonance elastography
.
Br J Cancer
2014
;
110
:
1727
32
.
34.
Bota
S
,
Sporea
I
,
Sirli
R
,
Popescu
A
,
Danila
M
,
Costachescu
D
. 
Intra- and interoperator reproducibility of acoustic radiation force impulse (ARFI) elastography–preliminary results
.
Ultrasound Med Biol
2012
;
38
:
1103
8
.
35.
Juge
L
,
Doan
BT
,
Seguin
J
,
Albuquerque
M
,
Larrat
B
,
Mignet
N
, et al
Colon tumor growth and antivascular treatment in mice: complementary assessment with MR elastography and diffusion-weighted MR imaging
.
Radiology
2012
;
264
:
436
44
.
36.
Wang
H
,
Nieskoski
MD
,
Marra
K
,
Gunn
JR
,
Trembly
SB
,
Pogue
BW
, et al
Elastographic assessment of xenograft pancreatic tumors
.
Ultrasound Med Biol
2017
;
43
:
2891
903
.
37.
Chen
Y
,
Terajima
M
,
Yang
Y
,
Sun
L
,
Ahn
YH
,
Pankova
D
, et al
Lysyl hydroxylase 2 induces a collagen cross-link switch in tumor stroma
.
J Clin Invest
2015
;
125
:
1147
62
.
38.
Baker
AM
,
Bird
D
,
Lang
G
,
Cox
TR
,
Erler
JT
. 
Lysyl oxidase enzymatic function increases stiffness to drive colorectal cancer progression through FAK
.
Oncogene
2013
;
32
:
1863
8
.
39.
Eikenes
L
,
Bruland
OS
,
Brekken
C
,
Davies Cde
L
. 
Collagenase increases the transcapillary pressure gradient and improves the uptake and distribution of monoclonal antibodies in human osteosarcoma xenografts
.
Cancer Res
2004
;
64
:
4768
73
.
40.
Gerling
M
,
Zhao
Y
,
Nania
S
,
Norberg
KJ
,
Verbeke
CS
,
Englert
B
, et al
Real-time assessment of tissue hypoxia in vivo with combined photoacoustics and high-frequency ultrasound
.
Theranostics
2014
;
4
:
604
13
.
41.
Shi
Y
,
Oeh
J
,
Hitz
A
,
Hedehus
M
,
Eastham-Anderson
J
,
Peale
FV
 Jr.
, et al
Monitoring and targeting anti-VEGF induced hypoxia within the viable tumor by (19)F-MRI and multispectral analysis
.
Neoplasia
2017
;
19
:
950
59
.
42.
Ryan
HE
,
Poloni
M
,
McNulty
W
,
Elson
D
,
Gassmann
M
,
Arbeit
JM
, et al
Hypoxia-inducible factor-1alpha is a positive factor in solid tumor growth
.
Cancer Res
2000
;
60
:
4010
5
.
43.
Corpechot
C
,
Barbu
V
,
Wendum
D
,
Kinnman
N
,
Rey
C
,
Poupon
R
, et al
Hypoxia-induced VEGF and collagen I expressions are associated with angiogenesis and fibrogenesis in experimental cirrhosis
.
Hepatology
2002
;
35
:
1010
21
.
44.
Minchinton
AI
,
Tannock
IF
. 
Drug penetration in solid tumours
.
Nat Rev Cancer
2006
;
6
:
583
92
.
45.
Johansson
A
,
Hamzah
J
,
Payne
CJ
,
Ganss
R
. 
Tumor-targeted TNFalpha stabilizes tumor vessels and enhances active immunotherapy
.
Proc Natl Acad Sci U S A
2012
;
109
:
7841
6
.
46.
Sun
J
,
Zhang
Y
,
Yang
M
,
Zhang
Y
,
Xie
Q
,
Li
Z
, et al
Hypoxia induces T-cell apoptosis by inhibiting chemokine C receptor 7 expression: the role of adenosine receptor A(2)
.
Cell Mol Immunol
2010
;
7
:
77
82
.