Measuring the functional status of tumor vasculature, including blood flow fluctuations and changes in oxygenation, is important in cancer staging and therapy monitoring. Current clinically approved imaging modalities suffer long procedure times and limited spatiotemporal resolution. Optoacoustic tomography (OT) is an emerging clinical imaging modality that may overcome these challenges. By acquiring data at multiple wavelengths, OT can interrogate hemoglobin concentration and oxygenation directly and resolve contributions from injected contrast agents. In this study, we tested whether two dynamic OT techniques, oxygen-enhanced (OE) and dynamic contrast-enhanced (DCE)-OT, could provide surrogate biomarkers of tumor vascular function, hypoxia, and necrosis. We found that vascular maturity led to changes in vascular function that affected tumor perfusion, modulating the DCE-OT signal. Perfusion in turn regulated oxygen availability, driving the OE-OT signal. In particular, we demonstrate for the first time a strong per-tumor and spatial correlation between imaging biomarkers derived from these in vivo techniques and tumor hypoxia quantified ex vivo. Our findings indicate that OT may offer a significant advantage for localized imaging of tumor response to vascular-targeted therapies when compared with existing clinical DCE methods.

Significance: Imaging biomarkers derived from optoacoustic tomography can be used as surrogate measures of tumor perfusion and hypoxia, potentially yielding rapid, multiparametric, and noninvasive cancer staging and therapeutic response monitoring in the clinic.

Graphical Abstract:http://cancerres.aacrjournals.org/content/canres/78/20/5980/F1.large.jpg. Cancer Res; 78(20); 5980–91. ©2018 AACR.

Angiogenesis, the growth of new blood vessels from surrounding host vasculature, can be a rate-limiting process in tumor development and progression. The resulting tumor vasculature is often chaotic and tortuous, leading to high intratumoral heterogeneity in vascular density and function (1). A high density of tumor vasculature does not necessarily translate into efficient oxygen and nutrient transport (2). Diffusion-limited hypoxia emerges early in tumor development, as rapidly proliferating cancer cells experience a gradient of hypoxia with increasing distance from the nearest perfused blood vessel (3). Perfusion-limited (or “cycling”) hypoxia occurs in cells close to blood vessels that experience rapid spatiotemporal fluctuations in local oxygen delivery due to highly variable blood flow (4). Hypoxia in solid tumors has been associated with both chemo- and radioresistance (4), as well as poor prognosis (5, 6). Furthermore, antiangiogenic and vascular disrupting therapies are under active clinical development, with highly variable rates of success (7, 8). A rapid test to probe the functional status of the tumor vasculature, including blood flow fluctuations and changes in oxygenation, could therefore improve cancer patient management, for example, in distinguishing benign from malignant tumors, in monitoring response to chemo- and radiotherapy, and in aiding development of novel vascular-targeted therapies (1).

Noninvasive imaging of tumor vascular function in the clinic usually requires administration of an exogenous untargeted contrast agent followed by longitudinal imaging of wash-in and wash-out kinetics, referred to as dynamic contrast-enhanced (DCE) imaging (9). DCE MRI has been broadly applied to interrogate tumor perfusion by tracking the dynamics of an injected gadolinium-based small-molecule contrast agent (10). Unfortunately, an increasing number of reports suggesting long-term toxicity of gadolinium chelates (11) may limit future use and typical voxel sizes of approximately 2 × 2 × 4 mm3 (12) limit interrogation of spatial heterogeneity (5). To avoid the use of contrast agents, label-free MRI techniques that are sensitive to perfusion such as arterial spin labeling (ASL) may be used; however, ASL can suffer from low signal-to-noise ratio, typically requiring voxel sizes of over approximately 3 × 3 × 4 mm3 in patients (13). A more established MRI technique is blood oxygen level-dependent (BOLD) MRI, sensitive to deoxyhemoglobin content, which can reach submillimeter in-plane spatial resolution and temporal resolution of < 10 seconds clinically (14). The BOLD signal appears to reflect tumor perfusion and hypoxia, based on correlations with immunohistochemistry (15), and can be applied, for example, to indicate prognosis in chemo- and radiotherapy (14, 16). Despite this promise, attempts to directly correlate the BOLD and DCE-MRI signals have shown no significant relationship (17), and it has been suggested that some ambiguity remains in the biological interpretation of the BOLD signal (14, 18).

Considering other clinical imaging modalities, PET contrast agents are available clinically for visualization of vascular function (e.g., H215O) and hypoxia (e.g., 18F-MISO). Although these approaches benefit from the exquisite sensitivity of PET, difficulties arise from the fundamental spatial resolution limits (19) and the requirement to administer a radiopharmaceutical, which is a particular challenge for short-half-life agents such as 15O (t1/2 ∼ 2 minutes; ref. 20). A more cost-effective option may be possible with diffuse optical spectroscopic imaging, which measures concentrations of oxy- and deoxyhemoglobin as surrogate markers of hypoxia and is in clinical trials (21), though this all-optical imaging approach suffers from very poor spatial resolution (∼1 cm). A cost-effective and high-resolution solution could be available using DCE ultrasound with gas-filled microbubbles as an exogenous contrast agent (22), yet safety concerns related to injection of microbubbles have been raised in patients (23). Thus, there remains a need for cost-effective, noninvasive imaging of tumor vascular function with high spatiotemporal resolution, ideally available without contrast agent administration.

Optoacoustic tomography (OT) is an emerging imaging modality (24) that is currently in clinical trials (25). OT reveals the distribution of tissue optical absorption in real time (26). Because the optical absorption spectra of oxy- and deoxyhemoglobin are distinct, acquiring OT data at multiple wavelengths makes it possible to derive imaging biomarkers that relate to total hemoglobin concentration (THb) and oxygenation (SO2). These imaging biomarkers provide complementary hemodynamic information to those measured clinically with DCE-based techniques and also the label-free MRI-based techniques introduced above. OT has been shown to monitor the evolution of tumor vasculature during disease development (27, 28) and to detect response to vascular-targeted therapies (29, 30). OT has also been combined with DCE ultrasound (31, 32), showing relationships between hemoglobin parameters and perfusion metrics. For these reasons, OT has already been deployed in clinical studies in breast, ovarian, and prostate cancers among others, achieving localized imaging at depths of up to 7 cm with spatial resolution of 500 μm or better and wavelength tuning rates of up to 100 Hz (25). Importantly, numerous clinical trials are underway world-wide, which are beginning to show great promise for the technology (despite the aforementioned depth limitations) for detecting tumor vascularization and differentiating benign and malignant lesions, particularly in the breast (33–35).

In addition to the “static” measurements of hemoglobin concentration and oxygenation available with existing OT, new techniques have recently emerged that directly report on vascular maturity and function. Inspired by clinically approved oxygen-enhanced (OE) MRI methods, OE-OT (36) measures the change in hemoglobin oxygenation following a change in respiratory gas from air to 100% oxygen. Contrary to the static measurement of oxygenation, these “dynamic” OE-OT biomarkers have been shown to correlate with histopathologic analysis of tumor vascular function and substantially outperform the static biomarkers in terms of robustness and repeatability (36). DCE-OT is also available, using the clinically approved fluorescent agent indocyanine green (ICG) as an untargeted blood pool agent (37). Taking multiwavelength OT data over time makes it possible to separate ICG signals from oxy- and deoxyhemoglobin, giving the potential to extract spatially resolved relationships between tumor oxygenation and tumor perfusion in a clinical setting using nontoxic, noninvasive OT imaging.

The purpose of the present study was to evaluate the potential of OT to be used for rapid, multiparametric, noninvasive assessment of tumor vascular function, hypoxia, and necrosis. Here, we perform coregistered OE-OT and DCE-OT in two tumor models, showing for the first time a quantitative spatial per-pixel correlation between OT metrics derived in vivo and the histopathologic assessment of vascular maturity and tissue hypoxia ex vivo. Furthermore, we resolved the key determinants of OE-OT response in terms of oxygen delivery via the blood supply and oxygen demand in the tissue. Our findings suggest that OE-OT– and DCE-OT–derived imaging biomarkers can be used as surrogate measures of tumor perfusion and hypoxia. We also note that OE-OT may provide a label-free alternative to DCE approaches for evaluating tumor perfusion that can be readily implemented into the imaging protocol of the emerging clinical optoacoustic technology thanks to its negligible toxicity risk.

Animal experiments

All animal procedures were conducted in accordance with project (70-8214) and personal licenses (IDCC385D3) issued under the United Kingdom Animals (Scientific Procedures) Act, 1986, and were approved locally under compliance form number CFSB0671. Subcutaneous tumors were established in male BALB/c nude mice (Charles River). Note that 1.5 × 106 PC3 prostate adenocarcinoma cells (donated by Dr. Jason Carroll's lab at the CRUK Cambridge Institute in July 2015) suspended in a mixture of 50 μL PBS and 50 μL matrigel (354248; Corning) were inoculated subcutaneously in both lower flanks of 9 mice (resulting in 18 tumors). Also note that 1 × 106 K8484 mouse pancreatic adenocarcinoma cells suspended in 100 μL PBS were inoculated subcutaneously in both lower flanks of a further 4 mice (resulting in 7 tumors). The K8484 cells (38) were derived from a pancreatic adenocarcinoma of a transgenic mouse model (39, 40) and were kindly donated in October 2017 by Professor Duncan Jodrell's lab at the CRUK Cambridge Institute, providing validation of the findings in a model of distinct morphology to the prostate tumors. Authentication of PC3 cells using Genemapper ID v3.2.1 (Genetica) by STR Genotyping (1/2015) showed 94% match. No authentication was performed in K8484 cells. Both cell types were mycoplasma tested by RNA-capture ELISA prior to use (PC3, March 13, 2017; K8484, September 05, 2017). Cells were used at 4 passages from thawing from frozen stocks. Tumor growth was monitored by calipers, and imaging was performed when tumors reached approximately 8 mm in any linear dimension.

To investigate the effect of vascular disruption on the optoacoustic measurements and evaluate its relationship with vascular maturity, 1.5 × 106 PC3 prostate adenocarcinoma cells were inoculated as described above in a further 8 mice (resulting in 16 tumors). In 4 animals, both tumors could not be visualized in a single imaging slice, resulting in 4 tumors being excluded, leaving 12 tumors for analysis. Combretastatin 4A phosphate (CA4P, C7744; Sigma-Aldrich), a potent Vascular Disrupting Agent with well-established efficacy in preclinical models, was used (30, 41, 42). When tumors reached approximately 8 mm linear dimension, mice were randomly allocated into two groups: treated (CA4P, 8 mL/kg of 12.5 mg/mL solution dosed intraperitoneally to achieve a dose of 100 mg/kg, n = 7 tumors, 4 mice) and vehicle (PBS 8 mL/kg intraperitoneally, n = 5 tumors, 4 mice).

OT

A commercial multispectral OT (MSOT) system (inVision 256-TF; iThera Medical GmbH) was used for this study (43). Briefly, a tunable optical parametric oscillator pumped by an Nd:YAG laser provides excitation pulses with a duration of 9 ns for wavelengths ranging from 660 to 1,300 nm at a repetition rate of 10 Hz, wavelength tuning speed of 10 ms, and a peak pulse energy of 90 mJ at 720 nm. Ten arms of a fiber bundle provide uniform illumination of a ring-shaped light strip of approximately 8 mm width. For ultrasound detection, 256 toroidally focused ultrasound transducers with a center frequency of 5 MHz (60% bandwidth), organized in a concave array of 270 degree angular coverage and a radius of curvature of 4 cm, are used.

Mice were prepared for OT according to our standard operating procedure (44). Briefly, mice were anaesthetized using <3% isoflurane, placed on a heat pad, and a catheter (home-made with 30G needle) was placed in the tail vein and fixed in place using tissue glue (TS1050071F; TissueSeal). The mouse was subsequently moved into a custom animal holder (iThera Medical) wrapped in a thin polyethylene membrane, with ultrasound gel (Aquasonic Clear; Parker Labs) used to couple the skin to the membrane. The holder was placed within the imaging chamber of the MSOT system filled with degassed heavy water (617385; Sigma-Aldrich) maintained at 36°C, with the end of the catheter line available outside of the imaging chamber for contrast agent injection. Heavy water was used due to other studies performed in parallel on the MSOT system and is not essential to the study described here because the optical absorption of water and heavy water is similar in the spectral range interrogated.

Mice were allowed to stabilize their physiology for 15 minutes within the system prior to initialization of the scan, and their respiratory rate was then maintained in the range 70 to 80 bpm with approximately 1.8% isoflurane concentration for the entire scan. The respiration rate was monitored by observing the breathing motion of the animal using a video feed from an optical camera positioned within the imaging chamber and counting the breaths over a minute using a stopwatch. We first performed OE OT (OE-OT; ref. 36), in which the breathing gas was switched manually from medical air (21% oxygen) to pure oxygen (100% oxygen), using separate flow meters (according to the schedule in Supplementary Fig. S1). A single slice was chosen for imaging showing the largest cross-sectional area of the tumors on both flanks where possible. Images were acquired in the single slice using 10 wavelengths (700, 730, 750, 760, 770, 800, 820, 840, 850, and 880 nm) and an average of 6 pulses per wavelength; an entire single slice multiwavelength data acquisition was 5.5 seconds in duration. In the CA4P- and vehicle-treated mice, where imaging was performed twice, the imaging slice in the second session was chosen to be as close as possible to the first one by visual alignment to the reconstructed images of the first scan.

Following OE-OT, the breathing gas was switched back to medical air and after 10 minutes allowed for equilibration, the DCE-OT was initiated in the same imaging slice. Images were acquired using 5 wavelengths (700, 730, 760, 800, and 850 nm) and an average of 10 pulses. After 1 minute of continuous imaging to establish the baseline signal, a bolus of ICG (40 nmol/20 g mouse in PBS; ref. 45) was injected intravenously through the catheter, followed by a pulse of PBS to flush the line. OT was continued for a further 15 minutes to sample the enhancement curve.

All mice underwent the full OT procedure at least once. Mice receiving CA4P or vehicle were imaged at 48 hours before treatment to ensure clearing of the injected contrast agent and then again at 4 hours after treatment.

Histopathologic tumor staining

Following the last OT procedure, mice were immediately sacrificed by cervical dislocation while still under anesthesia. The tumors were then excised, taking care for the orientation to be preserved, and cut in half along the imaging plane. The top- and left-hand sides of the tumors were marked with green and red tissue marking dyes (RCD-0727-3, RCD-0727-5, Cell Path) to later indicate the orientation of histopathologic sections relative to the in vivo imaging procedure. One half was then fixed in neutral buffered 10% formalin for 24 hours prior to paraffin embedding. Fixed blocks were sectioned at 3-μm thickness at 4 separate levels within the tumor spaced by 500 μm apart. Hematoxylin and eosin (H&E) staining and immunohistochemistry were performed. Adjacent sections from each of the 4 levels were stained with CD31 (anti-mouse; BD Biosciences, 553370) to indicate vessel density; alpha smooth muscle actin (ASMA; anti-mouse; Abcam, ab5694) to indicate smooth muscle coverage; and CAIX (anti-human, BioScience Slovakia, AB1001) to indicate hypoxic regions. We also performed pimonidazole staining for hypoxia in a representative tumor to qualitatively assess the reliability of CAIX staining for hypoxia visualization in the PC3 model. Note that 60 mg/kg pimonidazole hydrochloride (Hypoxyprobe) in PBS was injected intraperitoneally 60 minutes before sacrifice. IHC staining (Mab-1 antibody, 4.3.11.3, Hypoxyprobe) was performed on the sections. Spatial colocalization between CAIX and pimonidazole staining was observed (Supplementary Fig. S2). All immunostainings were performed with DAB as substrate. All sections were digitized at 20x with an Ariol System (Aperio Technologies Ltd.).

OT image analysis

All OT analysis was performed in MATLAB 2017b (Mathworks) using custom software. OT images were reconstructed using an acoustic backprojection algorithm (iThera Medical) with an electrical impulse response correction, to account for the frequency-dependent sensitivity profile of the transducers (46), and a speed-of-sound adjustment, to focus the images. Images were reconstructed with a pixel size of 75 μm × 75 μm, which is approximately equal to half of the in-plane resolution of the InVision 256-TF, to facilitate region drawing. It should be noted that the out-of-plane resolution of this system is approximately 0.9 mm (47). Regions of interest (ROI) were drawn manually around the tumor area (excluding the skin) and a healthy, well-vascularized tissue region around the spine, in the 800 nm (isosbestic) image taken from the first frame of the OE-OT scan. The reconstructed images were downsampled to 200 μm × 200 μm pixel size for further analysis, to improve response classification, as described below.

For OE-OT analysis, a pseudoinverse matrix inversion (pinv function in MATLAB 2017b) was used for spectral unmixing of the relative weights of oxy-[HbO2] and deoxyhemoglobin [Hb] independently in each pixel. Because OT is not able to accurately measure the absolute SO2 without the precise knowledge of optical energy distribution, we denote the approximate oxygenation metric derived in this study as the apparent SO2MSOT rather than absolute SO2. SO2MSOT was computed as the ratio of HbO2 to total hemoglobin THb = [HbO2 + Hb]. Average SO2MSOT was calculated in each pixel for air and oxygen breathing periods and denoted SO2MSOT(Air) and SO2MSOT(O2), respectively. The amplitude of response to the oxygen gas ΔSO2MSOT = SO2MSOT(O2) – SO2MSOT(Air) was calculated for each pixel (illustrated in Fig. 1A). The variability of the signal was also assessed by calculating the standard deviation SDOE of the SO2MSOT values between the individual scans acquired during air breathing. Each pixel was classified as responding to the oxygen challenge if ΔSO2MSOT exceeded 2 × SDOE (see Supplementary Fig. S3). A small fraction of pixels showed artifactual negative Hb or HbO2 levels due to low signal and were classified as nonresponding. The OE responding fraction (RF) was subsequently calculated for each tumor and scan as the ratio of the number of tumor pixels classified as responding to the total number of pixels in the tumor ROI.

Figure 1.

Tumor OE-OT and DCE-OT responses are strongly correlated. A strong spatial relationship was observed between the response maps for OE-OT (A) and DCE-OT (B) in both PC3 (top) and K8484 (bottom) tumors. OE and DCE kinetic curves (C and D) were used to quantify metrics, as denoted, that were then compared in correlation analyses on a per-pixel basis in each tumor. E, Exemplar per-pixel correlations for each tumor type. F, When comparing correlations extracted from the entire tumor cohort (each data point represents one tumor), significantly stronger correlations were observed in the dynamic OE-OT metric ΔSO2MSOT than either static metrics of SO2MSOT(Air) and SO2MSOT(O2). Red dashed horizontal line, no correlation (correlation coefficient of 0). Data in A to E are exemplars taken from one representative tumor for each type. Data in F are taken from the entire tumor cohort (n = 30 PC3; n = 7 K8484). ***, P < 0.001 by paired two-tailed t test. Boxes between 25th and 75th percentiles; line at median. a.u., arbitrary unit.

Figure 1.

Tumor OE-OT and DCE-OT responses are strongly correlated. A strong spatial relationship was observed between the response maps for OE-OT (A) and DCE-OT (B) in both PC3 (top) and K8484 (bottom) tumors. OE and DCE kinetic curves (C and D) were used to quantify metrics, as denoted, that were then compared in correlation analyses on a per-pixel basis in each tumor. E, Exemplar per-pixel correlations for each tumor type. F, When comparing correlations extracted from the entire tumor cohort (each data point represents one tumor), significantly stronger correlations were observed in the dynamic OE-OT metric ΔSO2MSOT than either static metrics of SO2MSOT(Air) and SO2MSOT(O2). Red dashed horizontal line, no correlation (correlation coefficient of 0). Data in A to E are exemplars taken from one representative tumor for each type. Data in F are taken from the entire tumor cohort (n = 30 PC3; n = 7 K8484). ***, P < 0.001 by paired two-tailed t test. Boxes between 25th and 75th percentiles; line at median. a.u., arbitrary unit.

Close modal

DCE-OT analysis was performed similarly. The same ROIs as for the corresponding OE-OT scans were used, because the imaging was performed in the same slice and the movement of the anaesthetized animal between the scans was negligible. After down-sampling the reconstructed image, linear spectral unmixing as above was performed for HbO2, Hb, and ICG. The amplitude of ICG enhancement, ΔICG, was quantified as the difference between the average baseline ICG signal and the maximum signal recorded in the first 3 minutes after injection (illustrated in Fig. 1A) to capture the perfusion rather than accumulation effect of the dye. Variability of the ICG signal was also measured as the SD of the individual images acquired before contrast agent injection (SDDCE). Each pixel was then classified as enhancing when ΔICG exceeded 2 × SDDCE (see Supplementary Fig. S3), with artifactual negative pixels classified as nonenhancing. DCE RF was computed accordingly for each tumor.

Correlations between OE and DCE signals were calculated for each tumor on a per-pixel basis. The results presented are from all mice (n = 12+18 tumors). The small fraction of pixels showing artifactual SO2MSOT or ICG signal values was excluded from the correlation analysis. Spearman rank correlation coefficient was calculated (MATLAB) and quoted, due to the apparent nonlinear monotonic relationship between the metrics.

Histopathologic image analysis and data coregistration

For each tumor, 4 sections were analyzed (see “Histopathologic Tumor Staining”). Necrosis was identified from H&E sections using a Convolution Neural Network (CNN) approach. The schematic of the CNN layer architecture is presented in Supplementary Fig. S4. The viable and necrotic patches of H&E sections for training the model were identified manually. A threshold of 0.5 was applied to the necrosis score maps to discriminate the necrotic from viable regions as the probabilistic output had a range from 0 to 1, meaning that values below or above 0.5 have a higher likelihood of being viable or necrotic tissue, respectively. The necrotic fraction was quantified as the ratio of the total necrotic area to total tumor area across a whole section. Model performance was assessed by qualitative comparison with H&E sections, an example of which is demonstrated in Supplementary Fig. S5A and S5B, and quantitative comparison to results of manual segmentation, performed in Imagescope (Aperio Technologies Ltd.). A strong, significant correlation was observed between the model and manual quantification (r = 0.75; P < 0.0001, see Supplementary Fig. S6).

Hemorrhagic areas were identified in H&E sections based on their color. Quantification of hemorrhagic fraction was performed automatically using Halo (Indica Labs) image analysis software. Analysis of CD31 and ASMA coverage was also performed using Halo software, quantifying the CD31-positive area (to measure the amount of vasculature), as well as the CD31-positive areas that were also positive for ASMA in adjacent sections to identify mature vasculature with smooth muscle coverage (2). The fraction of area positive for both CD31 and ASMA to the CD31-positive area was quoted as a metric. CAIX analysis was performed using custom code written in MATLAB. The areas of CAIX-positive staining were identified based on color deconvolution (48) of the antibody, cell nuclei, and background. The correct colors for the 3 classes were computed by manually outlining example areas in two sections. CD31/ASMA analysis was performed in the CA4P/vehicle-treated cohort (n = 12), to increase the vascular maturity range probed. The CAIX and necrosis analysis was performed in the other cohort only (n = 18), as the different experimental protocol (2 imaging sessions for CA4P cohort, 1 for untreated cohort) made the histology datasets not eligible to be combined.

To evaluate the relationship between OT metrics of vascular function and histopathologic assessment of tissue hypoxia, point set registration was performed with points determined by the applied tissue marking dyes (49).

The ΔSO2MSOT and ΔICG OT images were then compared spatially with CAIX sections. Kernel density estimation, assuming a bimodal intensity distribution with low and high values, was applied on pooled CAIX stain intensity values across all tumors to obtain a discriminative threshold for binarization of stain intensity in individual sections. The binarized CAIX and necrosis maps were overlaid, and the necrotic areas were excluded from the analysis. Mean ΔSO2MSOT and ΔICG values in CAIX-positive and -negative viable regions were then calculated. The differences between the ΔSO2MSOT and ΔICG values in the CAIX-positive and -negative regions were then extracted for each tumor, with a value significantly different from 0 taken as a measure of differential response. This informed on spatial colocalization and co-occurrence of high/low ΔSO2MSOT with negative/positive CAIX regions. It should be noted that this approach to image coregistration is subject to human error in tissue handling and sectioning, which leads to a high rate of exclusion for the analysis. Out of the 18 tumors analyzed in the cohort, 8 had to be excluded due to failure of the registration, arising from distorted or torn tissue sections, resulting in misplaced tissue marking dyes, which in turn obstructed accurate registration of the image pairs.

Statistical analysis

All errors are quoted as the SEM unless otherwise stated. All statistical analyses were performed in OriginPro 9 (OriginLab). Paired two-tailed t test compared different metrics in each tumor and changes in parameters due to CA4P treatment; unpaired two-tailed t test assuming equal variances compared tumors in different cohorts. One-tailed t test was used to assess whether the differences in OT parameters between low and high CAIX staining regions are significantly above 0 for the coregistered histopathology and OT image analysis. Only the last scan immediately before sacrifice was used for correlations with histology, and the Pearson rank test was performed to assess the significance. P < 0.05 was considered statistically significant.

OE and DCE-OT responses are strongly correlated

Using our intrinsically coregistered OE-OT and DCE-OT data, we first sought to examine the spatial correlations between the tumor OE and DCE responses. The spatial distribution (Fig. 1A and B) was examined, and amplitudes of these responses, ΔSO2MSOT (Fig. 1C) and ΔICG (Fig. 1D), respectively, were compared on a per-pixel basis. Highly significant correlations (P value < 10−6 in all cases) were observed between these two metrics (Fig. 1E). The correlation deviates from linearity for the extreme values, suggesting Spearman rank correlation coefficient as a more informative estimate of the relationship (Fig. 1E). Although very strong correlations between OE and DCE response were observed in both tumor cohorts (Spearman r = 0.64 ± 0.02, n = 30 PC3 tumors; Spearman r = 0.65 ± 0.07, n = 7 K8484 tumors), no correlation was observed to the static metrics of SO2MSOT(Air) or SO2MSOT(O2) measured at baseline, indicating that these metrics are not sensitive to tumor perfusion [Spearman r = –0.16 ± 0.05 and r = –0.11 ± 0.05 for SO2MSOT (Air) and SO2MSOT(O2), respectively for PC3, n = 30 PC3 tumors, r = –0.04 ± 0.09 and r = 0.25 ± 0.09 respectively for K8484]. The correlations for all tumors analyzed are summarized in Fig. 1F.

ICG retaining pixels in DCE-OT show weak or no OE-OT response

Examining the DCE-OT data in greater depth, it was clear that two classes of pixels showing distinct ICG kinetics were present. The first such group, referred to as “clearing,” consistently showed an obvious enhancement peak followed by exponential clearance of the contrast down to a plateau (Fig. 2A and B, blue). The second such group, referred to as “retaining,” showed an enhancement after injection, but displayed no clearance; the level of signal either remained high and stable over the duration of the experiment, or even increased gradually (Fig. 2A and B, red). As might be expected based on established differences in vascular maturity (36), clearing regions tended to be more prevalent in the rim than the core of the tumor, with the fraction of the rim occupied by clearing pixels being significantly higher than the corresponding fraction of the core (0.51 ± 0.04 vs. 0.39 ± 0.05, P = 0.002, n = 30 PC3 tumors; 0.44 ± 0.13 vs. 0.16 ± 0.06, P = 0.01, n = 7 K8484 tumors).

Figure 2.

Two distinct classes of DCE kinetics also possess different OE kinetics. Spatially distinct regions were segmented showing ICG clearance or retention (A) following injection, according to the DCE-OT response kinetics (B). The retaining regions show little or no OE-OT response (C), reflecting the poorer vascular function in these areas. Response maps of OE-OT (D) and DCE-OT (E) are also shown, with the clearing, retaining, and nonenhancing regions denoted in light blue, red, and black ROIs, respectively. These further indicate that the strongest OE response occurs in clearing regions, as suggested in C. Data shown are from a representative PC3 tumor. a.u., arbitrary unit.

Figure 2.

Two distinct classes of DCE kinetics also possess different OE kinetics. Spatially distinct regions were segmented showing ICG clearance or retention (A) following injection, according to the DCE-OT response kinetics (B). The retaining regions show little or no OE-OT response (C), reflecting the poorer vascular function in these areas. Response maps of OE-OT (D) and DCE-OT (E) are also shown, with the clearing, retaining, and nonenhancing regions denoted in light blue, red, and black ROIs, respectively. These further indicate that the strongest OE response occurs in clearing regions, as suggested in C. Data shown are from a representative PC3 tumor. a.u., arbitrary unit.

Close modal

Interestingly, the OE-OT responses of these two distinct classes of DCE response also showed significant differences (Fig. 2C). Retaining regions demonstrate weaker OE response and have a significantly lower OE RF than clearing regions (0.55 ± 0.05 vs. 0.25 ± 0.02, P < 10−5, n = 30 PC3 tumors). The retaining regions also show a weaker correlation between the ΔSO2MSOT and ΔICG (Fig. 2D and E) than the clearing regions (0.57 ± 0.03 vs. 0.44 ± 0.04, P = 0.007).

Tumor DCE-OT signal is driven predominantly by vascular maturity, whereas OE-OT is also strongly related to hypoxia and necrosis

The relationships observed between OE-OT and DCE-OT suggested that similar vascular characteristics may underpin their responses. We next broadly explored the correlations between the in vivo OT responses and the ex vivo histopathologic analysis relating to vascular maturity (ASMA coverage of CD31-positive blood vessels), hypoxia (CAIX positivity), and tumor viability (necrosis assessed with H&E) on a per-tumor basis in PC3 tumors, where our study was sufficiently well powered to identify significant correlations.

Vascular maturity (Fig. 3A) showed a significant positive correlation with both OE RF (OE RF, r = 0.58, P = 0.048, n = 12 PC3 tumors; Fig. 3B) and DCE RF (r = 0.78, P = 0.002, n = 12 PC3 tumors; Fig. 3C). As could be expected given the direct influence of vascular maturity on vessel function and subsequently on perfusion, a higher and more significant correlation was observed for DCE RF than for OE RF.

Figure 3.

OE-OT and DCE-OT RFs show a significant positive correlation with vascular maturity. A, Overlaid CD31- and ASMA-stained sections were used to evaluate the fraction of blood vessels positive for ASMA (red on CD31-stained section). The OE RF (B) and DCE RF (C) both show a significant correlation to tumor vascular maturity (B), with DCE showing a stronger relationship. The analysis includes tumors treated with combretastatin A4 phosphate (red points, n = 7 PC3 tumors), which show clearly lower ASMA coverage than vehicle-treated tumors (black points, n = 5 PC3 tumors). *, P < 0.05 and **, P < 0.01 show the strength of the correlation assessed using a Pearson rank test. Line of best fit with 95% confidence intervals is also shown in the graphs.

Figure 3.

OE-OT and DCE-OT RFs show a significant positive correlation with vascular maturity. A, Overlaid CD31- and ASMA-stained sections were used to evaluate the fraction of blood vessels positive for ASMA (red on CD31-stained section). The OE RF (B) and DCE RF (C) both show a significant correlation to tumor vascular maturity (B), with DCE showing a stronger relationship. The analysis includes tumors treated with combretastatin A4 phosphate (red points, n = 7 PC3 tumors), which show clearly lower ASMA coverage than vehicle-treated tumors (black points, n = 5 PC3 tumors). *, P < 0.05 and **, P < 0.01 show the strength of the correlation assessed using a Pearson rank test. Line of best fit with 95% confidence intervals is also shown in the graphs.

Close modal

A significant negative correlation was found between hypoxia (based on CAIX-positive area fraction, Supplementary Fig. S7A) and OE RF (Fig. 4A; Supplementary Fig. S7B; r = –0.68, P = 0.002, n = 18 PC3 tumors). The negative correlation between hypoxia and DCE RF was weaker (Fig. 4A; r = –0.49, P = 0.04, n = 18 tumors). A negative correlation was also observed between OE RF and the tumor necrotic fraction (Fig. 4B; Supplementary Fig. S8A and S8B; r = –0.56, P = 0.016, n = 18 tumors); however, no significant relationship was seen for DCE RF (Fig. 4B; Supplementary Fig. S8B; r = –0.42, P = 0.08, n = 18 tumors).

Figure 4.

OE-OT is also strongly related to tumor hypoxia and necrosis. A, Representative CAIX-stained sections were used to quantify the extent of tumor hypoxia, to which OE-OT RF shows a strong inverse correlation, whereas the DCE-OT RF shows a weaker relationship. B, H&E-stained sections were used to quantify the extent of tumor necrosis (green line outlines necrotic area), to which OE-OT again showed a strong inverse correlation, whereas DCE-OT response was not significant. Analysis shown from 18 PC3 tumors. n.s., not significant; *, P < 0.05; **, P < 0.01. Line of best fit with 95% confidence intervals is shown in the graphs where significant relationships are identified.

Figure 4.

OE-OT is also strongly related to tumor hypoxia and necrosis. A, Representative CAIX-stained sections were used to quantify the extent of tumor hypoxia, to which OE-OT RF shows a strong inverse correlation, whereas the DCE-OT RF shows a weaker relationship. B, H&E-stained sections were used to quantify the extent of tumor necrosis (green line outlines necrotic area), to which OE-OT again showed a strong inverse correlation, whereas DCE-OT response was not significant. Analysis shown from 18 PC3 tumors. n.s., not significant; *, P < 0.05; **, P < 0.01. Line of best fit with 95% confidence intervals is shown in the graphs where significant relationships are identified.

Close modal

Given the strong relationship between hypoxia and OE-OT response, we investigated further by examining the spatial colocalization of high ΔSO2MSOT and low CAIX signals. Taking each CAIX-stained section (Fig. 5A) and H&E-stained section (Fig. 5B), we performed an image coregistration and down-sampled the spatial resolution of the CAIX image (binarized into high and low staining regions) to match that of the in vivo OT image (Fig. 5C). The resulting coregistered CAIX data were then compared with ΔICG (Fig. 5D) and ΔSO2MSOT (Fig. 5E) on a per-pixel basis. Where successful coregistration was possible (n = 10 PC3 tumors, see Materials and Methods), the analysis revealed that areas of low CAIX staining (considered to reflect low tissue hypoxia) were associated with a notably higher mean ΔSO2MSOT level than the areas of high CAIX staining (Fig. 5E). The difference between average ΔSO2MSOT taken in low CAIX compared with that in high CAIX regions was significantly higher than 0 when compared across all tumors (difference = 0.014 ± 0.005 vs. 0, P = 0.007, n = 10 PC3 tumors). The average ΔICG was also significantly higher in normoxic than in hypoxic tumor regions in line with the per-tumor findings (difference = 0.42 ± 0.17 vs. 0, P = 0.018, n = 10 PC3 tumors).

Figure 5.

Spatial coregistration allows comparison of OE-OT and DCE-OT response in hypoxic tumor tissue. CAIX-stained sections (A) were binarized into low and high stain areas. This information was overlaid with necrosis map obtained from H&E sections (B), then coregistered and downsampled (C) for comparison with the optoacoustic images. DCE-OT ΔICG (D) and OE-OT ΔSO2MSOT (E) could then be compared with the degree of CAIX staining in viable areas. The box plots show that for the analyzed tumors, the difference between ΔICG (D) and ΔSO2MSOT (E) between areas of low and high CAIX hypoxia staining is significantly higher than 0 (indicated with red dashed line). Images in A to C are from a representative PC3 tumor. Analysis in D and E is presented from 10 PC3 tumors. *, P < 0.05; **, P < 0.01 by one-tailed t test (deviation from 0). Box between 25th and 75th percentiles; line at median.

Figure 5.

Spatial coregistration allows comparison of OE-OT and DCE-OT response in hypoxic tumor tissue. CAIX-stained sections (A) were binarized into low and high stain areas. This information was overlaid with necrosis map obtained from H&E sections (B), then coregistered and downsampled (C) for comparison with the optoacoustic images. DCE-OT ΔICG (D) and OE-OT ΔSO2MSOT (E) could then be compared with the degree of CAIX staining in viable areas. The box plots show that for the analyzed tumors, the difference between ΔICG (D) and ΔSO2MSOT (E) between areas of low and high CAIX hypoxia staining is significantly higher than 0 (indicated with red dashed line). Images in A to C are from a representative PC3 tumor. Analysis in D and E is presented from 10 PC3 tumors. *, P < 0.05; **, P < 0.01 by one-tailed t test (deviation from 0). Box between 25th and 75th percentiles; line at median.

Close modal

OE-OT and DCE-OT are highly sensitive to treatment with a vascular disrupting agent

Having established relationships between OE-OT and DCE-OT imaging biomarkers with vascular function, hypoxia, and necrosis, we then sought to evaluate their utility in detecting response to a vascular disrupting agent. Imaging studies were performed both before and 4 hours after administration of the vascular disrupting agent combretastatin A4 phosphate. The 4-hour time point was chosen so as to observe the induced vascular disruption prior to the development of substantial tumor necrosis (30).

The dramatic effect of the treatment on tumor vasculature was confirmed histologically, through induction of hemorrhage (Supplementary Fig. S9A and S9B) and change in vascular maturity (Supplementary Fig. S9C). As desired, this was not followed by induction of significant necrosis in the treated tumors (necrotic fraction = 0.28 ± 0.09, n = 6 vs. 0.18 ± 0.06, n = 5 treated vs. vehicle, P = 0.38). The induced vascular disruption was qualitatively observed in maps of ΔSO2MSOT and ΔICG (Supplementary Fig. S10A) as well as in the quantification of the kinetic responses in both cases (Supplementary Fig. S10B and S10C). As expected, treated tumors were dominated by ICG retaining areas (as defined in Fig. 2). Clear changes in the spatial distribution of responding pixels could be observed in both OE-OT and DCE-OT images in drug-treated tumors (Fig. 6A), but not in the vehicle-treated ones (Supplementary Fig. S11). These changes were reflected in the responding fractions in vehicle- and CA4P-treated tumors (Fig. 6B). OE RF showed a significant decrease between 48 hours before and 4 hours after treatment (0.47 ± 0.05 vs. 0.16 ± 0.03, P = 0.0005, n = 7 PC3 tumors). No significant change was seen in vehicle-treated control animals (0.38 ± 0.06 vs. 0.30 ± 0.05, P = 0.44, n = 5 PC3 tumors). Similarly, DCE RF showed a significant decrease (0.63 ± 0.05 vs. 0.32 ± 0.02, P = 0.0001, n = 7 PC3 tumors), whereas control did not (0.74 ± 0.06 vs. 0.68 ± 0.05, P = 0.34, n = 5 PC3 tumors).

Figure 6.

OE and DCE RFs show similarly high sensitivity in detecting changes in vascular function. Both OE RF and DCE RF show a drastic drop due to vascular shutdown caused by the treatment, as seen in representative enhancement maps from a PC3 tumor (A) and in box plots representing the cohort (n = 7 treated; n = 5 vehicle PC3 tumors). n.s., not significant; ***, P < 0.001 by paired two-tailed t test. Box between 25th and 75th percentile; line at median.

Figure 6.

OE and DCE RFs show similarly high sensitivity in detecting changes in vascular function. Both OE RF and DCE RF show a drastic drop due to vascular shutdown caused by the treatment, as seen in representative enhancement maps from a PC3 tumor (A) and in box plots representing the cohort (n = 7 treated; n = 5 vehicle PC3 tumors). n.s., not significant; ***, P < 0.001 by paired two-tailed t test. Box between 25th and 75th percentile; line at median.

Close modal

The balance of oxygen supply and demand in solid tumors can be a key determinant of prognosis and response to therapy. The aim of this work was to evaluate the potential of imaging biomarkers accessible using OT to be used in rapid, multiparametric, and noninvasive assessment of tumor vascular function and monitoring response to therapy.

We first examined the relationship between the two OT imaging biomarkers under study: ΔSO2MSOT, accessible without the introduction of a contrast agent using OE-OT; and ΔICG, requiring administration of the clinically approved and nontoxic contrast agent ICG and imaged through a DCE-OT technique. These dynamic biomarkers were strongly spatially correlated in both tumor models examined, suggesting that perfusion is a strong determinant of response in both techniques. The kinetics of the DCE-OT response also showed strong differences between “clearing” and “retaining” regions, the latter of which have been previously described as associated with the enhanced permeability and retention effect in areas of immature and leaky vasculature (50, 51). These regions also showed distinct OE-OT responses, with greater ΔSO2MSOT seen in clearing regions.

We then established how these OT imaging biomarkers were connected with ex vivo measurements of vascular function, as well as tumor hypoxia and necrosis; these relationships are summarized in Fig. 7. Vascular maturity leads to changes in vascular function that affect tumor perfusion, modulating the DCE-OT signal. Perfusion in turn regulates oxygen availability, driving the OE-OT signal. Insufficient oxygen supply leads to tissue hypoxia and eventually necrosis (Fig. 7, bottom row; ref. 4). These relationships, and hence our understanding of the OE and DCE-OT signals, were directly confirmed by the correlations observed between our in vivo OT and ex vivo histopathologic measurements (Fig. 7, middle row). The strength of the correlation reflected how closely the individual measurement is linked with the underlying physiologic process (Fig. 7, top row), overall revealing a complex, yet consistent network of relationships in the tumor vascular microenvironment. These findings indicate that the response for DCE-OT is driven most strongly by perfusion and vascular function, which would be expected given that ICG shows strong serum binding in vivo. The response for OE-OT appears to also be governed strongly by perfusion and vascular function but is further modulated by the tumor oxygen demand. The strong and significant relationships observed between the OE RF and hypoxia area on a per-tumor basis were also confirmed on a spatial per-pixel basis.

Figure 7.

Summary of the relationships governing tumor physiology that have been established with OT imaging biomarkers. The physiologic relationships underpin the correlations that we observed between the in vivo and ex vivo measurements of physiologic processes. As indicated with the color bar, the more disconnected the physiologic parameters are from each other, the weaker the observed correlations. *, P < 0.05; **, P < 0.01.

Figure 7.

Summary of the relationships governing tumor physiology that have been established with OT imaging biomarkers. The physiologic relationships underpin the correlations that we observed between the in vivo and ex vivo measurements of physiologic processes. As indicated with the color bar, the more disconnected the physiologic parameters are from each other, the weaker the observed correlations. *, P < 0.05; **, P < 0.01.

Close modal

Treatment with a potent vascular disrupting agent, combretastatin A4 phosphate, was used to induce vascular shutdown, causing a dramatic perfusion drop, resulting in a significant decrease in DCE RF, as expected. Interestingly, the OE RF showed equally high sensitivity to the vascular shutdown, indicating that it could be used as an alternative to the contrast agent–based DCE methods for detecting response to vascular-targeted therapies. Both of our surrogate biomarkers were able to sensitively detect response to the vascular-targeted therapy.

In line with our previous findings (36), static OT biomarkers such as SO2MSOT(O2) and SO2MSOT(Air) showed little relationship to perfusion or hypoxia. Similar relationships were examined previously comparing tumor oxygenation assessed using static OT with DCE ultrasound (31, 32) or pimonidazole staining (52). Although some spatial relationships were noted, particularly in relation to the necrotic tumor core, these studies were limited respectively by a lack of histologic validation, poor sensitivity of the optoacoustic imaging approach applied, and small numbers of biological replicates.

There remain some limitations to the presented work that must be addressed in future studies. From a biological perspective, vascularization of subcutaneous models differs from that of orthotopic xenografts and spontaneous tumors (53) and may not be representative of the vascular function found clinically in solid tumors. These findings should therefore be validated in orthotopic and transgenic tumor models prior to application of dynamic OT metrics in studies of cancer biology or in the clinic. In our subcutaneous PC3 model, good colocalization was observed between CAIX and pimonidazole staining, which we took as an indication that CAIX staining indeed reflected hypoxia in this model. Although CAIX staining is well-documented to be regulated by the activation of hypoxia-inducible factor (54) and has been widely used for ex vivo hypoxia identification, nonspecific effects can be observed in some models; therefore, if our findings are to be further validated in other tumor models, it would be prudent to use multiple methods to assess hypoxia ex vivo.

Some further limitations exist in the efficient clinical translation of OT and associated imaging biomarkers. Penetration depths of up to 3 to 7 cm (25) have been reported in patients, enabling access to superficial cancer sites, such as those in the breast or head and neck. With the ongoing development of endoscopic probes, imaging organs such as the prostate (55) is also expected to be possible, yet access to some deep-seated organs will remain limited even with these technological advances. The localized nature of OT means that it would be most appropriately placed in the patient management pathway after diagnosis or identification of a suspicious lesion using another imaging technique. Light attenuation at depth in tissue poses an additional challenge for signal quantification. Methods available to perform light fluence correction of OT data have received only limited validation in vivo (47). Future work is required to directly relate OT data to absorbed optical energy density and enable absolute quantification if desired. However, qualitative features derived from clinical optoacoustic images have also shown significant prognostic value (56).

In summary, we have shown that noninvasive and nontoxic OE-OT and DCE-OT techniques can be used to interrogate tumor vascular function, hypoxia, and necrosis. The comprehensive histopathologic validation of the OT imaging biomarkers presented here indicates that despite the aforementioned technical challenges that face the technology, OT is capable of providing a unique and rapid insight into the tumor vascular microenvironment. Although DCE-OT requires administration of a contrast agent, OE-OT provides a completely noninvasive, label-free measurement; our findings indicate that the oxygen challenge approach could be used as a safe alternative for exogenous contrast injection as it has been used clinically with no associated risk (57). OT is already being tested, with promising results in numerous clinical trials in patients with cancer (33–35), despite some technical limitations of the technology. In the future, the low cost, portability, and simplicity of OT may offer significant advantage for localized imaging of tumor response to vascular-targeted therapies when compared with existing clinical DCE methods, particularly in the neoadjuvant setting.

M.R. Tomaszewski reports receiving other commercial research support from iThera Medical. S.E. Bohndiek reports receiving other commercial research support from iThera Medical and PreXion Inc. No potential conflicts of interest were disclosed by the other authors.

Conception and design: M.R. Tomaszewski, S.E. Bohndiek

Development of methodology: M.R. Tomaszewski, J. Joseph, I. Quiros-Gonzalez, S.E. Bohndiek

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): M.R. Tomaszewski

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): M.R. Tomaszewski, M. Gehrung, I. Quiros-Gonzalez, S.E. Bohndiek

Writing, review, and/or revision of the manuscript: M.R. Tomaszewski, J. Joseph, I. Quiros-Gonzalez, J.A. Disselhorst, S.E. Bohndiek

Study supervision: J.A. Disselhorst, S.E. Bohndiek

We would like to thank the CRUK CI Core Facilities for their support of this work, in particular, the Biological Resource Unit, Histopathology, and Biorepository. We would also like to thank Emma Brown for helpful comments on the draft article.

Data associated with this article can be found online at https://doi.org/10.17863/CAM.23164. This work was supported by Cancer Research UK (C47594/A16267 and C14303/A17197) and the EPSRC-CRUK Cancer Imaging Centre in Cambridge and Manchester (C197/A16465 and C8742/A18097).

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.

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Supplementary data