Saturation transfer properties of tumour xenografts derived from prostate cancer cell lines 22Rv1 and DU145

Histopathology is currently the most reliable tool in assessing the aggressiveness and prognosis of solid tumours. However, developing non-invasive modalities for tumour evaluation remains crucial due to the side effects and complications caused by biopsy procedures. In this study, saturation transfer MRI was used to investigate the microstructural and metabolic properties of tumour xenografts in mice derived from the prostate cancer cell lines 22Rv1 and DU145, which express different aggressiveness. The magnetization transfer (MT) and chemical exchange saturation transfer (CEST) effects, which are associated with the microstructural and metabolic properties in biological tissue, respectively, were analyzed quantitatively and compared amongst different tumour types and regions. Histopathological staining was performed as a reference. Higher cellular density and metabolism expressed in more aggressive tumours (22Rv1) were associated with larger MT and CEST effects. High collagen content in the necrotic regions might explain their higher MT effects compared to tumour regions.


Results
Saturation transfer effects were measured in mouse tumour xenografts derived from DU145 (n = 34) and 22Rv1 (n = 32) PCa cell lines. Z-spectra at high B 1 (3 and 6 µT) and low B 1 (0.5 and 2 µT) were collected from tumour and necrotic regions of these two types of tumours. The MT and CEST effects were isolated by fitting to the twopool MT model 14 to the MT-sensitive Z-spectra data. The four independent model parameters are the transverse relaxation time, T 2 , of the free pool (T 2,F ), the exchange rate of magnetization from the MT to the free pool (R MT ), the equilibrium magnetization of the MT pool relative to the free pool (M 0,MT ), and the T 2 of the MT pool (T 2,MT ). Previously, we found through phantom studies that there is a constant negative correlation between R MT and the solute pool size M 0,MT (see Supplementary Fig. S3). Therefore, instead of analyzing them separately, their product is used to represent the MT effect. Results are compared between tumours from these cell lines and between the active tumour and necrotic/apoptotic regions. Segmentation results. All tumours were automatically segmented into muscle and three intratumoural regions: active tumour (henceforth shortened to tumour), necrotic/apoptotic (shortened to necrotic), and blood/ edema using an automatic segmentation algorithm developed by our group 21 . Representative T 2 -weighted anatomical images, segmentation masks, and histopathology stains, which were of consistent quality across mice, are shown in Fig. 1. The masks generated by the segmentation pipeline were used as regions of interest for quantitative analysis of tumour and necrotic regions. A similar figure containing all the tumours can be found in Supplementary Figs. S1 and S2.
Z-spectrum analysis. Figure 2 shows the ROI-averaged Z-spectra for intratumoural regions in the two tumour types. The saturation transfer effects in the tumour regions are higher (i.e., there is lower signal) in 22Rv1 compared to DU145. This is also seen in the necrotic regions. Both high B 1 s (3 and 6 μT) Z-spectra (Fig. 2a,c) exhibit strong MT effects, which can be observed as a deviation from the sigmoidal curve around 50 ppm. The Z-spectra with the lowest B 1 (0.5 μT) of both 22Rv1 and DU145 tumour regions (Fig. 2b) have peaks at 3.5, 2.0, and − 3.3 ppm, which are the resonance frequency offsets of the amide, guanidinium, and aliphatic groups, respectively. These peaks are still present with a B 1 of 2 μT (Fig. 2d), albeit they are more difficult to appreciate because of their increased width. There are also differences between the two types of tumours at lower B 1 values, where both necrosis and tumour regions of the 22Rv1 tumours (blue lines in Fig. 2b,d) have higher saturation transfer effects than their counterparts in the DU145 tumours (red lines in Fig. 2b,d) at 2 μT (solid lines), but such differences are less pronounced at 0.5 μT (dashed lines).
Quantitative MT parameters and CEST/rNOE maps. One representative of each type of tumour is shown in Fig. 3 (MT model parameters) and Fig. 4 (CEST/rNOE contributions to the Z-spectra at three frequency offsets). Based on the segmentation of these two tumours in Fig. 3a, the necrotic region (marked with arrows) of the DU145 tumour displays much higher free pool transverse relaxation time, T 2 , (T 2,F ; Fig. 3b) than other regions. Both tumour and necrosis (marked with arrows) regions in the 22Rv1 tumour show higher MT effects (R MT M 0,MT ) than their counterparts in DU145 (Fig. 3c). Furthermore, within the 22Rv1 tumour, the MT effect is higher in the necrotic region than the tumour region. High MT effect in the muscle region is also seen in both tumours.  Fig. 4, the necrotic region (marked with arrows) in the both 22Rv1 and DU145 tumours display much lower CEST effects than their tumour regions at frequency offsets 3.5 and 2.0 ppm. Moreover, the CEST effect for the tumour regions is smaller for the DU145 tumour xenograft.
Isolating the effects of the free water, MT, CEST, and rNOE pools. In Figs. 5 and 6, the observed T 1 and T 2 (T 1,obs and T 2,obs , respectively) and estimated two-pool MT model parameters of tumour and necrotic regions in both tumour types are shown. T 1,obs and T 2,obs are both significantly lower in the necrotic region of the 22Rv1 (2000 ± 50 ms and 44 ± 4 ms, respectively) as compared to the DU145 necrotic region (2500 ± 200 ms and 70 ± 20 ms), shown in Fig. 5a. These differences were all significant with p < 0.001.    Fig. 6. The goodness of fit is shown in Supplementary  Fig. S5 and Table S1. The intrinsic transverse relaxation time T 2 of the free pool (T 2,F ) is significantly different between 22Rv1 tumour and necrotic regions (47 ± 6 vs. 39 ± 5 ms) and between 22Rv1 and DU145 necrotic regions (39 ± 5 vs. 69 ± 15 ms). The product of the exchange rate of magnetization from the MT to the free pool (R MT ) and equilibrium MT pool size relative to that of water (M 0,MT ), has three significant differences: between 22Rv1 tumour and necrotic regions (1.8 ± 0.2 vs. 2.3 ± 0.6 Hz), between 22Rv1 and DU145 tumour regions (1.8 ± 0.2 vs. 1.2 ± 0.1 Hz), and between 22Rv1 and DU145 necrotic regions (2.3 ± 0.6 vs. 1.1 ± 0.4 Hz) and is shown in Fig. 6b. The intrinsic T 2 relaxation time of the MT pool (T 2,MT ) has four significant differences: between 22Rv1 tumour and necrotic regions (7.8 ± 0.1 vs. 7.4 ± 0.1 µs), between DU145 tumour and necrotic regions (8.2 ± 0.2 vs. 7.9 ± 0.4 µs), between 22Rv1 and DU145 tumour regions (7.8 ± 0.1 vs. 8.2 ± 0.2 µs), and between  www.nature.com/scientificreports/ 22Rv1 and DU145 necrotic regions (7.4 ± 0.1 vs. 7.9 ± 0.4 µs) and is shown in Fig. 6c. These differences were all significant to p < 0.001. The CEST (3.5 and 2.0 ppm) and rNOE (− 3.3 ppm) contributions to the saturation effect at low B 1 (0.5 and 2 μT), calculated using AREX with the extrapolated MT-only spectra as the Z-spectrum reference and minimizing the confounding effects of T 1 at these B 1 s, are shown in Fig. 7 corresponding to the resonances of the amide (3.5 ppm), guanidinium (2.0 ppm), and aliphatic (− 3.3 ppm) groups. The spectra for MTR REX and AREX are shown in the supplementary info (see Supplementary Fig. S8). The tumour regions have significantly higher (p < 0.001) CEST effects compared to their necrotic regions at all CEST offsets (3.5 and 2.0 ppm) in both tumour types. Both the tumour and necrotic regions of 22Rv1 tumours display significantly higher (p < 0.001) CEST effects at all three offsets than their counterparts in DU145 tumours.
Histopathology. H&E, TUNEL, and Masson's trichrome histopathology stains were performed on each tumour and representative cases are shown in Fig. 8. In each tumour, one zone was picked in the tumour region as well as in the necrotic region and the magnified images of these areas are also included. In the tumour regions (H&E section, Fig. 8) cells in the DU145 xenograft appear to be larger and also have a lower cellular density compared to the cells in the 22Rv1 tumour regions. The major difference between the necrotic regions (brown stain in the TUNEL assay; Fig. 8) of the two tumour types is that this region in the 22Rv1 xenograft consists primarily of apoptotic cell debris including the cell membranes and other cell contents, while that in DU145 is mostly  www.nature.com/scientificreports/ extracellular matrix. In both tumour types, there is more connective tissue (light blue in the Masson's trichrome stain; Fig. 8) in the necrotic regions than tumour regions. In DU145 tumours, both the tumour and necrotic regions demonstrate more connective tissue deposition throughout the tumour compared to their counterparts in the 22Rv1 tumours.

Discussion
In this study, the saturation transfer properties of tumours derived from two PCa cell lines (22Rv1 and DU145), commonly used in translational research, were compared. DU145 was derived from a brain metastasis in a patient with metastatic castrate-resistant PCa 22 and the 22Rv1 was a subline obtained from the CWR22 xenograft that was derived from a patient with castrate-sensitive prostate cancer 23 . The 22Rv1 subline was obtained following serial passage of xenografts in castrated mice to recapitulate the in vivo development of castrate-resistance. While Data is shown at offsets of 3.5, 2.0, and − 3.3 ppm. There are consistent significant differences between 22Rv1 tumour and necrotic regions and between DU145 tumour and necrotic regions. The contributions are calculated using the apparent exchange rate (AREX) formula. Similar differences also exist in the comparison with a B 1 of 0.5 μT (see Supplementary Fig. S6). The CEST and rNOE contribution calculated using conventional subtraction method have, however, failed to reveal the differences between these cell lines (see Supplementary  Fig. S7). ***p < 0.001. www.nature.com/scientificreports/ there has been no previous study that compares the aggressiveness between these two types of xenografts, we have observed in our experiments that the 22Rv1 tumours display a higher growth rate compared to the DU145 tumours on average, which may suggest that the former has a higher proliferation rate, and behaves more aggressively than the latter. Z-spectrum measurements revealed that 22Rv1 xenografts had higher saturation transfer effects than DU145 in both the tumour and necrotic regions, especially at high saturation amplitudes (B 1 s of 3 and 6 μT), where the Z-spectrum is mainly affected by MT. A two-pool model fit indicated a stronger MT effect in 22Rv1 tumours. The MT effect is defined here as the product of the exchange rate and the volume fraction, R MT M 0,MT , fit using the two-pool model, and commonly reported as a product due to coupling 14 . Both the tumour and necrotic regions in the 22Rv1 tumours had significantly higher R MT M 0,MT compared to these regions in DU145 tumours. Moreover, the intrinsic transverse relaxation time of the free pool, T 2,F , which is associated with mobility of free water molecules, was longer in the necrotic regions of the DU145 tumours than the 22Rv1 tumours. Histopathology showed higher cell membrane content due to higher cellular density in both the tumour and necrotic regions of 22Rv1 tumours as compared to their counterparts in DU145. The larger MT effect in the 22Rv1 xenografts could be explained by higher cellular density, leading to increased concentration of phospholipid bilayers in cells and organelle membranes, which are the major contributors to the MT effect 24 . In addition, the higher mobility of water molecules in the DU145 tumours, due to decreased cellular density, could contribute to the higher T 2,F .
Shorter T 2 relaxation times of the macromolecular pool, T 2,MT , in tumour as compared with necrotic regions in both tumour types (7.8 ± 0.1 vs. 8.2 ± 0.2 µs in the 22Rv1 tumours and 7.4 ± 0.1 vs. 7.9 ± 0.4 µs in the DU145 tumours), could be related to the differences in the type of macromolecules responsible for the MT effect in these different regions. Interestingly, the necrotic regions exhibited higher MT effects than their tumour counterparts. This phenomenon could be further explained by qualitative analysis of the macromolecular content in different tumour types and regions based on histopathology.
Masson's trichrome stain is sensitive to connective tissue with high collagen content, which is known to be a major macromolecule contributing to the MT effect 25 . Indeed, collagen-rich tissues have the highest MT effect among all tissues in the body 17 . As shown by the Masson's trichrome stain, the necrotic regions contain more connective tissue than tumour in both tumour types. Several studies have also demonstrated the high connective tissue content of necrotic regions using Masson's trichrome stain 26,27 , but, to our knowledge, there has been no direct comparison of the connective tissue content between different tumour types. Both the tumour and necrotic regions of the DU145 tumours had a more fibrous appearance (more collagen) on histology and lower cellular density (less lipid) than 22Rv1 tumours, but had lower MT effects than 22Rv1. Therefore, it is speculated that the higher MT effects in both of tumour and necrotic regions of 22Rv1 resulted primarily from their higher cellular density, and therefore higher lipid content, rather than collagen content. On the other hand, as shown in the TUNEL stains, the cellular density did not vary significantly between tumour and necrotic regions within the same tumour type. Therefore, it could be assumed that collagen content plays a more dominant role in contributing to the MT effects within a tumour. A previous study 28 has pointed out that decreased collagen content in the extracellular matrix was related to increased necrotic foci and higher tumour grade, which accords with our hypothesis that 22Rv1 tumours are more aggressive due to their high growth rate. This study also revealed that high collagen synthesis was often noticed in the host tissue around the tumour to serve as a barrier to impede tumour invasion, which could explain the high MT effect in the muscle region shown in Fig. 3.
To evaluate the CEST contribution, we used a modification of the AREX metric developed by Windschuh et al. 29 . The original multi-pool AREX method requires the acquisition of the entire Z-spectrum with low B 1 , which is time consuming, and typically assumes two CEST pools and one rNOE pool to calculate the contribution from each pool. The adapted method relies on the acquisition of Z-spectra with high B 1 and a T 1,obs map to extrapolate the MT reference and acquisition of Z-spectrum point(s) with low B 1 only at offsets of interest 21 . This is faster, but means that the AREX values calculated in this paper potentially contain contributions from the overlapping spectral contributions of multiple chemical groups. When using relatively low B 1 amplitudes, such as 0.5 µT, the labeling will not be perfectly efficient due to influences from the solute exchange rate and T 2 , which varies for each of the CEST and NOE pools. In our experiments, AREX is not independent from B 1 due to labeling efficiency being less than unity. However, comparisons between the two tumour types for the AREX metric at any given B 1 and solute pool are expected to be largely unaffected by variation in labeling efficiency. Calculations using the B 1 used in this study and the solute exchange rates and T 2 s found in literature 30 , show that a 10% increase in either the solute exchange rate or T 2 of the three pools considered affects the efficiency by 0.03 or less and a 20% increase affects efficiency by 0.06 or less.
For both tumour types, the necrotic regions had a significantly lower CEST effect compared to tumour, which is consistent with necrotic regions having decreased metabolism. The tumour regions in 22Rv1 tumours displayed significantly higher CEST effects compared to those of the DU145 tumours. This can be explained by the higher cellular density of the 22Rv1 yielding a higher metabolite concentration and therefore stronger CEST effects. Although in this study the effect of extracellular/intracellular pH on CEST was not investigated, previous studies have pointed out that increased extracellular pH could lead to hyperintensity of amide signal (3.5 ppm) 31 and the increased intracellular pH has been correlated with increased amide exchange rate in glioblastoma tissue 32 . NOE has also been proved to be pH insensitive 33 .
In this study, saturation transfer properties showed great potential in assessing solid tumours by providing information relating to intratumoural microstructure including cellular density, cell membrane integrity, and intratumoural tissue composition, which have been related to tumour aggressiveness and prognosis 34,35 . Furthermore, the intratumoural metabolic properties identified using CEST could guide tumour treatment 36,37 . This study, for the first time, analyzed and compared the saturation transfer properties between two types of prostate tumours, which are speculated to have different aggressiveness. We discovered that the 22Rv1 tumours which are potentially more aggressive are characterized with higher MT (higher cellularity) and higher CEST effects Scientific Reports | (2020) 10:21315 | https://doi.org/10.1038/s41598-020-78353-8 www.nature.com/scientificreports/ (higher metabolism) than DU145 tumours. This comparison provided important pioneering references for future preclinical studies in identifying the stage and malignancy of solid tumours with a non-invasive modality.

Methods
Animal model. Two prostate adenocarcinoma cell lines were used in this study: DU145 22  For the DU145 tumours, saturation transfer-weighted images were acquired using one 490 ms block RF saturation pulse per k-space line and single-slice FLASH acquisition (TR = 500 ms; TE = 3 ms; flip angle = 30°; FOV = 20 mm × 20 mm; slice thickness = 1 mm; matrix = 64 × 64; bandwidth = 50 kHz; dummy scans = 1) as in our previous work 41 . For the 22Rv1 tumours, saturation transfer-weighted images were acquired using one 4900 ms block RF saturation pulse and four-shot, centrically encoded, single-slice RARE acquisition (TR = 5000 ms; TE eff = 4.75 ms; flip angle = 90°; same FOV and matrix as FLASH; RARE factor = 16; bandwidth = 50 kHz; dummy scans = 1), which produced Z-spectra identical to those from the FLASH sequence (see Supplementary Fig. S6), but with a much shorter acquisition time. The cumulative saturation time when acquiring the centre of k-space was approximately 16 s for RARE and 10 s for FLASH, which was sufficient for the system to reach steady-state saturation. The use of four-shot RARE instead of FLASH reduced the acquisition time from 32 to 20 s per frequency offset.
To allow for correction of system instability in post-processing, reference scans at Δω = 667 ppm were acquired before and after and also interleaved between every five Z-spectrum measurements 41,42 . For the DU145 tumours, the scan time for the Z-spectra including reference scans with B 1 = 0.5 and 2 µT was 44 min/spectrum; 3 and 6 µT, 8.5 min/spectrum; and 0.1 µT, 15 min. For the 22Rv1 tumours, the scan time was shortened to 28 min/ spectrum, 5.5 min/spectrum, and 9.5 min, respectively.
To evaluate the longitudinal relaxation time T 1 , five inversion recovery RARE scans (TR = 10,000 ms; TE eff = 10 ms; TI = 30, 110, 390, 1400, 5000 ms; same FOV and matrix as FLASH; RARE factor = 4; bandwidth = 77 kHz; 2 min each) were also acquired for a T 1 map 43 . The T 2 maps were calculated using a T 1 map and WASSR. The total acquisition time including scout and shimming was 2-2.5 h per animal.
Histopathology. Tumours were excised for histopathological assessment immediately after scanning. Each tumour was isolated and marked with a suture on the proximal margin for subsequent alignment with MRI, formalin fixed for 24-48 h, and then stored in 70% ethanol. Tumours were trimmed for sectioning in the region that corresponded as closely as possible to the MRI slice. Tissues were paraffin embedded, sectioned at 10 µm, and mounted on slides. Three types of histological section were prepared: H&E staining for structural detail, a TUNEL assay using 3,3′-diaminobenzidine (DAB) chromogen and haematoxylin counter staining for necrosis, and a Masson's trichrome stain for distinguishing connective tissue content. The tissue section that best correlated with the MRI slice was imaged using an Axio Imager 2 (version M2, Carl Zeiss Canada Ltd., Toronto, ON) 44 microscope with the Stereo Investigator (MBF Bioscience, Williston, VT) stereology system at magnification 20 × and 60 × for details 42 . MRI data pre-processing. For each animal, images were registered using a rigid body transformation to the first reference image acquired with B 1 = 0.5 µT. In order to avoid misregistration of low SNR images acquired with saturation near the water resonance, Z-spectrum images with less than 50% of the mean signal of the reference scan were registered using the transformation matrix of the last image with sufficient SNR, typically an interleaved reference scan. Baseline drift correction of all Z-spectrum scans consisted of fitting a straight line to the interleaved reference scans. This was followed by spectrum-wise B 0 correction of the WASSR and Z-spectrum images with low B 1 (0.5 and 2 µT). The correction consisted of fitting one Lorentzian (corresponding to the DE contribution) to the WASSR Z-spectrum at frequency offsets between ± 0.5 ppm and a sum of two Lorentzians (corresponding to the DE and MT contributions) to the low B 1 Z-spectra. The spectra were recentred to the peak position of the DE Lorentzian and linearly interpolated. High B 1 images (which were largely Scientific Reports | (2020) 10:21315 | https://doi.org/10.1038/s41598-020-78353-8 www.nature.com/scientificreports/ MT sensitive) were acquired with logarithmically spaced offsets ranging from 3 to 300 ppm. Thus, B 0 correction was not required for these spectra. The T 2 maps were calculated using a T 1 map and WASSR. First, the T 1 map (T 1,obs ) was calculated from the inversion recovery scans by fitting to the inversion recovery RARE signal equation 43 . Then, a T 2 map (T 2,obs ) was calculated from the T 1 map and WASSR Z-spectrum using the steady-state direct water saturation signal intensity as in previous work 42 : where R 1 = 1/T 1,obs , R 2 = 1/T 2,obs , and ω 1 = γB 1 . T 1 and T 2 values were normalized by 4000 and 300 ms, respectively, which were values selected as being slightly higher than the highest values typically seen in tumour regions to match the range of the saturation transfer images prior to segmentation.
The pre-processing above was performed in MATLAB (Release 2018b, The MathWorks, Natick, MA) 45 . Subsequent processing was performed in Python (version 3.7) 46  Image erosion was used to remove edge voxels, which can be contaminated by partial volume effects. Voxels with a B 0 shift of greater than 0.5 ppm were excluded, so only well-shimmed voxels were used. Erosion was performed using the binary_erosion function in SciPy using a rank 2 structuring element where all elements are neighbours.
Automatic segmentation. The segmentation pipeline, developed in our lab 21 , is shown in Fig. 9 and used T 1 and T 2 maps and Z-spectrum images acquired at high B 1 (3 and 6 µT) as input (Fig. 9a). These were concatenated to generate an observation matrix for each tumour type. For each observation matrix, an independent component analysis (ICA) was performed using the FastICA algorithm 50 . ICA is a linear transformation from the original feature space to a new one such that the new features are mutually independent (Fig. 9b). Transformation into three independent components (ICs) was chosen based on our previous work 21 . In this study, the ICs of each dataset were sorted in order of increasing mutual information between each component and the average of all protocol images, calculated using the normalized_mutal_info_score function in scikit-learn normalized to the arithmetic mean of the ICs and average images, and labelled IC 1 , IC 2 , and IC 3 . The ICs were then weighted (IC 1 :IC 2 :IC 3 weightings of 2:3:1 were used for 22Rv1 images and 1:3:2 for DU145, which resulted in segmentation masks that visually best matched histology) and input to a Gaussian mixture model (GMM) 51 , which is a probabilistic model that identifies clusters with Gaussian distributions within IC space (Fig. 9c). For each GMM cluster, the fitting estimated a weighting, along with a mean in the three-dimensional IC space, and a full covariance matrix (i.e., each Gaussian may adopt any position and shape). Based on our previous study, the optimal number clusters was five. All clusters were associated with histology results and assigned to active tumour, necrosis/apoptosis, muscle, muscle/connective tissue and blood/edema.
After GMM fitting, the following label assignment algorithm was applied to the DU145 segmentation masks as in our previous work 21 : (1) the cluster with the largest absolute value of the GMM mean of IC 1 was labelled blood/edema; (2) each dataset was reflected about the IC 1 = 0, IC 2 = 0, and IC 3 = 0 planes, as required, such that the blood/edema cluster was in the first octant since ICA does not identify the sign of the source signals; (3) of the remaining clusters, the one with the smallest (i.e., most negative) GMM mean of IC 2 was labelled muscle; the second smallest, muscle/connective; the second largest, necrosis/apoptosis; and the largest, active tumour. A similar algorithm was applied to the 22Rv1 masks except that the clusters in step 3 were assigned in the following order: active tumour, muscle/connective, necrosis/apoptosis, and muscle. The segmentation results were visually connected to histology stains (H&E and TUNEL).
Quantitative MT model fitting. T 1 maps and Z-spectra with B 1 = 0.1, 3, and 6 µT were fitted to a twopool MT model using a super-Lorentzian lineshape for the semisolid macromolecular pool for the tumour and necrosis/apoptosis voxels 52 . The four free parameters are the T 2 of the free pool (T 2,F ), the exchange rate of magnetization from the MT to the free pool (R MT ), the equilibrium magnetization of the MT pool relative to the free pool (M 0,MT ), and the T 2 of the MT pool (T 2,MT ). Since the parameters R MT and M 0,MT are coupled, their product was used for further analysis and termed the MT effect. All parameters were fitted for the tumour and necrosis/ apoptosis regions of individual mice and then averaged together. The difference between the MT effect between tumour and necrosis/apoptosis regions over all mice were compared using unpaired Student's t-tests.

Isolation of CEST and rNOE contributions.
Since Z-spectra are sensitive to direct water saturation, MT, CEST, and rNOE at different offset ranges, it is necessary to isolate each of them to reduce confounds. Based on the method introduced by Heo et al. 53 , the extrapolated semi-solid magnetization transfer reference (EMR) was calculated using the MT model parameters, which represents the MT effect. Adapting the technique described by Windschuh et al. 29 , the T 1 -corrected apparent exchange-dependent relaxation (AREX) metric for CEST and rNOE contributions from each tumour and necrosis/apoptosis regions was calculated as follows: www.nature.com/scientificreports/ www.nature.com/scientificreports/ where the measured Z-spectrum (B 1 s of 0.5 and 2 µT were each used) is denoted Z lab , the extrapolated MT reference is Z EMR , and T 1,obs is the measured T 1 . The difference between the mean CEST-only contribution at 3.5 (amide CEST), 2.0 (guanidinium CEST), and − 3.3 ppm (aliphatic rNOE) between tumour and necrosis/ apoptosis regions over all mice were compared using unpaired Student's t-tests.

Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.