Simultaneous in vivo optical quantification of key metabolic and vascular endpoints reveals tumor metabolic diversity in murine breast tumor models

Abstract

Therapeutically exploiting vascular and metabolic endpoints becomes critical to translational cancer studies because altered vascularity and deregulated metabolism are two important cancer hallmarks. The metabolic and vascular phenotypes of three sibling breast tumor lines with different metastatic potential are investigated in vivo with a newly developed quantitative spectroscopy system. All tumor lines have different metabolic and vascular characteristics compared to normal tissues, and there are strong positive correlations between metabolic (glucose uptake and mitochondrial membrane potential) and vascular (oxygen saturations and hemoglobin concentrations) parameters for metastatic (4T1) tumors but not for micrometastatic (4T07) and nonmetastatic (67NR) tumors. A longitudinal study shows that both vascular and metabolic endpoints of 4T1 tumors increased up to a specific tumor size threshold beyond which these parameters decreased. The synchronous changes between metabolic and vascular parameters, along with the strong positive correlations between these endpoints suggest that 4T1 tumors rely on strong oxidative phosphorylation in addition to glycolysis. This study illustrates the great potential of our optical technique to provide valuable dynamic information about the interplay between the metabolic and vascular status of tumors, with important implications for translational cancer investigations

Graphical Abstract

1 |. INTRODUCTION

Interest in metabolism and the vascular microenvironment continues to grow in a broad range of disciplines including neuroscience [1], cardiovascular biology [2] and the field of cancer research [3]. At the most basic level, lack of oxygen promotes glycolysis to allow biological systems to survive acute stress [4]. Under conditions of chronic stress, such as persistent hypoxia, biological cells can undergo long-term changes that allow their metabolism and supporting vasculature to pre-emptively adapt to a more hostile environment [4]. For example, it is well known that tumors can employ high rates of glycolysis in the presence of oxygen to maintain a proliferative phenotype [5]. It is also becoming evident that some tumors can switch their metabolism between glycolysis and oxidative phosphorylation to survive hostile conditions [6]. These “adaptable” tumors with capacity to rely on both glycolytic and mitochondrial metabolism under a range of oxygen conditions promote negative outcomes such as increased recurrence [7], migration [8] and metastatic propensity [9]. Several studies have shown that 4T1 tumors, which are highly metastatic, are able to more readily adapt metabolically to microenvironmental changes such as hypoxia and fuel source deprivation compared to their sibling nonmetastatic tumors, 67NR and 4T07 [10–12].

Oxygen saturation (SO₂) within the tumor microenvironment influences metabolism by affecting substrate availability; by the same token, metabolic needs affect vascular parameters by affecting substrate demand [4]. It is commonly known that hypoxia plays an important role in tumor cells that evade traditional therapies including chemotherapy [13] and radiotherapy [14]. Tumors also rely on angiogenesis to maintain a proliferative, glycolytic phenotype, and impaired angiogenesis is a characteristic of residual disease [15, 16]. Residual disease can readopt a proliferative, glycolytic phenotype if angiogenesis is restored [17, 18]. Taken together, glycolysis, oxidative phosphorylation, angiogenesis and hypoxia all play a key role in understanding tumor biology.

Well-accepted metabolic characterization tools include the Seahorse Assay [19], metabolomics [20], positron emission tomography (PET) [21], and magnetic resonance (MR) based spectroscopy techniques [22, 23]. While these techniques have been widely adopted, there are some limitations to these metabolic tools. The Seahorse Assay and metabolomics are limited to in vitro or ex vivo samples, and therefore cannot report on the modulation of metabolism in vivo or the effect of variations in SO₂ or hemoglobin concentration ([Hb]) on tumor cell metabolism within the tumor microenvironment. Immunohistochemistry (IHC) [24] can be used to perform metabolic imaging and report the effect of hypoxia on tumor cell metabolism [25], while bioluminescence is required to capture all of relevant information [26]. PET and MR based spectroscopy techniques can provide in vivo imaging, however, multiple systems are required to capture all of relevant endpoints and they are inherently expensive.

A cost-effective alternative to the prior methods described above is to use optical techniques. Optical techniques can leverage endogenous contrast or be coupled with appropriate indicators to provide quantitative parameters related tumor metabolism and its associated vasculature in vivo [27–30]. For example, the autofluorescence of reduced nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) [29, 31] provide insights into the reduction-oxidation state in the electron transport chain, as NADH fluorescence is increased in tumors reliant on glycolysis, whereas increased FAD fluorescence corresponds to more oxidative tumors [32]. Several novel optical techniques including multiphoton microscopy [31] and label-free autofluorescence-multiharmonic microscopy [33] have been developed to quantify FAD and NADH fluorescence from a wide array of cellular and extracellular components in living tissue. Exogenous indicators, however, have been used when the goal is to quantify specific substrate uptake (eg, glucose) and metabolic output (mitochondrial metabolism) in a direct and explicit manner. The fluorescent analog to fluoro-deoxy-glucose (FDG), 2-[N-(7-nitrobenz-2-oxa-1, 3-diazol-4-yl) amino]-2-deoxy-D-glucose (2-NBDG) has been extensively validated in cell and animal model studies to report on glucose uptake [34–38] analogous to widely accepted PET imaging [39]. A key indicator of mitochondrial metabolism is the mitochondrial membrane potential (MMP) maintained by the electron transport chain [40]. Tetramethylrhodamine ethyl ester (TMRE) has been used extensively to visualize MMP in cell culture and preclinical model studies to study oxidative phosphorylation [41–44]. Moreover, measuring both SO₂ and MMP can discern between oxidative phosphorylation vs. nonmetabolic proton gradient changes [45]. Our lab has developed novel optical techniques to quantify glucose uptake and MMP using 2-NBDG [46, 47] and TMRE [48, 49] in different tumor models. Most recently, we developed a novel strategy to measure 2-NBDG and TMRE simultaneously using intravital microscopy in a window chamber tumor model [48]. To translate simultaneous quantification of key metabolic endpoints to solid tumor models that are often used for therapeutic studies [50], we have also demonstrated the capability of a quantitative spectroscopy system to quantify 2-NBDG and TMRE fluorescence along with key vascular parameters (SO₂ and [Hb]) on preclinical solid tumor model [51].

In this report, we utilized our optical spectroscopy technique to investigate the metabolic and vascular phenotypes of three sibling breast tumors lines that have different metastatic potentials (highly metastatic 4T1, micrometastatic 4T07, and nonmetastatic 67NR) [52]. We observed that all three types of small tumors have increased 2-NBDG uptake, TMRE uptake, and [Hb], but decreased SO₂ relative to normal tissue. Among the three tumor lines, 4T1 tumors had both the highest TMRE and 2-NBDG uptake; 67NR tumors had comparable TMRE uptake to 4T1 tumors while 4T07 tumors had comparable 2-NBDG uptake to 4T1 tumors. We also observed a strong positive correlation between TMRE and 2-NBDG for the highly metastatic 4T1 tumors but not for micrometastatic (4T07) and nonmetastatic tumors (67NR). To further understand how the metabolic characteristics of the 4T1 tumor changes with tumor size, we carried out longitudinal in vivo optical spectroscopy studies. We observed that both SO₂ and [Hb] increased up to a specific tumor size threshold beyond which they decreased. TMRE and 2-NBDG uptake also showed a similar trend. The synchronous changes between metabolic and vascular endpoints, along with the strong positive correlations between these endpoints suggest that 4T1 tumors likely rely on oxidative phosphorylation in addition to glycolysis. Our reported study demonstrates the potential of our optical spectroscopy to provide dynamic, quantitative and simultaneous measurements of both metabolism and the associated vascular endpoints of solid tumors under a variety of conditions in vivo. Our optical technique may potentially serve as an important tool for translational cancer studies.

2 |. METHODS

2.1|. Flank tumor model and study design

All in vivo experiments described in this report were performed according to a protocol approved by Duke University Institutional Animal Care and Use Committee (IACUC). Female athymic nude mice (nu/nu, NCI, Frederick, Maryland) between 8 and 10-week-old were used for these studies. All mice were housed in an on-site housing facility with ad libitum access to food and water and standard 12-hour light/dark cycles. A total of 30 animals were assigned to a (1) nontumor group (normal, n = 6); (2) nonmetastatic tumor group (67NR, n = 5); (3) micrometastatic tumor group (4T07, n = 10); and (4) metastatic tumor group (4T1, n = 9). Mice assigned to tumor groups received a subcutaneous injection of 4T1, 4T07 or 67NR cells (10⁵ cells in 100 μL serum-free medium) in the right flank under anesthesia with inhaled isoflurane (1%−1.5% v/v) in room air. Mice assigned to the nontumor group did not receive any injection. After tumor cell injection, mice were returned to the cage and monitored daily for 2 weeks. On day 10 after the tumor injection or the tumor diameter approximately reached to 6 mm, the tumors were characterized using the quantitative optical spectroscopy platform under isoflurane anesthesia. To understand how the metabolic characteristics of the 4T1 tumor changes with size, a separate group of animals (4T1 tumor, n = 4) was studied longitudinally for 2 weeks during tumor growth. Specifically, mice received optical measurements under isoflurane anesthesia on day 4, day 9, and day 14 posttumor cell injection. Tumor length and width were measured using calipers on these specified days to estimate tumor volume using the formula (V = (W × W × L)/2) introduced by Faustino-Roch et al [53]. Before each set of optical measurements, mice were fasted for 6 hours to minimize variance in metabolic demand [30]. All animals received a tail-vein injection of TMRE (100 μL of 75 μM) first and then a tail-vein injection of 2-NBDG (100 μL of 6 mM 2-NBDG) with 20-minute delay between the two, as described previously [48, 51]. The solute of the fluorescent probe solutions is phosphate-buttered saline (PBS). Because there was a 20 minute delay between TMRE injection and 2-NBDG injection, the 2-NBDG₆₀ (2-NBDG uptake at 60 minutes post 2-NBDG injection) and TMRE₈₀ (TMRE uptake at 80 minutes post TMRE injection) that measured at the same time point were used to report final 2-NBDG and TMRE uptake.

2.2 |. Diffuse reflectance and fluorescence spectroscopy measurements

The optical spectroscopy platform described in detail previously [30, 51] was used to perform diffuse reflectance and fluorescence measurements in a darkroom. All diffuse reflectance and fluorescence spectra on each set of experiments were calibrated using a reflectance puck and fluorescence standard puck as described in detail in our former publications [30, 51]. Optical measurements on mice were obtained by placing the fiber probe gently on the flank site of interest. The optical measurement site was fixed on the center of tumor mass, thus there was only one set of spectrum per measurement. The measured spot size was 2-mm in diameter, which was determined by the probe size as reported previously [30, 51]. Baseline diffuse reflectance and fluorescence spectra were measured from the tissue region of interest prior to any fluorescent probe injection. Diffuse reflectance spectra were acquired from 400 to 650 nm with an integration time of 3.8 ms. Two sets of fluorescence emission spectra were acquired at each time point to reflect 2-NBDG fluorescence (488 nm excitation, 520–600 nm emission) and TMRE fluorescence (555 nm excitation, 565–650 nm emission). Integration time of 2 seconds and 5 seconds were used for 2-NBDG and TMRE fluorescence measurements, respectively, to ensure sufficient signal to noise ratio. In order to quantify both 2-NBDG and TMRE uptake on the same tissue site, our previously established sequential injection protocol [48, 51] was used. In addition, the optical measurements on each animal were acquired continuously for a period of 80 minutes to characterize the kinetic profile of both 2-NBDG and TMRE uptake.

2.3 |. Data analysis

All in vivo optical spectral data were processed using our previously developed scalable inverse Monte Carlo (MC) model [30] to extract tissue absorption spectra, and corrected fluorescence of 2-NBDG and TMRE [51]. The extracted absorption spectra were further fitted with a linear combination of the extinction spectra of oxy-hemoglobin and deoxy-hemoglobin to quantify SO₂ and [Hb] [54]. The MC model analyzed 2-NBDG and TMRE fluorescence (without absorption and scattering distortions) at different time points were used to create kinetic uptake curves. Measurement at the last time point was used to calculate final 2-NBDG or TMRE uptake. The fluorescence intensities, SO₂, and [Hb] among different animal groups were compared using a two-way Analysis of Variance (ANOVA) test. A P value of 0.05 or less was considered to be statistically significant. Pearson’s correlation coefficients and P values were calculated to assess relationship between variables. K-means clustering was used to generate centroids for clustering analysis. MATLAB (Mathworks, Natick, Massachusetts) was used to perform all statistical analysis.

3 |. RESULTS

3.1 |. Metabolic and vascular characteristics of the three murine breast cancer tumor phenotypes

The metabolic and vascular features of the different tumor types and the normal flank were characterized using the quantitative optical spectroscopy platform. All of the flank tumors had a diameter of approximately 6 mm. For the purposes of this analysis, they were classified as small tumors (<150 mm³) [11]. Figure 1A,B show baseline [Hb] and baseline SO₂ levels calculated from the absorption spectra. Consistent with previous studies [55, 56], baseline SO₂ levels are significantly lower in all tumors compared with normal tissue (P < 0.005). Average baseline [Hb] was higher in 4T1 and 67NR tumors compared with normal tissues (P < 0.05), while baseline [Hb] in 4T07 tumors was comparable to baseline [Hb] in normal tissue. Figure 1C illustrates that the average 2-NBDG₆₀ in 4T1 and 4T07 tumors is statistically higher compared to that of normal tissue (P < 0.01 for 4T1 and P < 0.001 for 4T07), while the average 2-NBDG₆₀ in 67NR tumors is not statistically higher than that of normal tissue. The TMRE₈₀ uptake shown in Figure 1D reveals that the average TMRE₈₀ in 4T1 and 67NR tumors is statistically higher than that of normal tissue (P < 0.001 for 4T1 and P < 0.05 for 67NR), while the average TMRE₈₀ uptake in 4T07 tumors is not statistically higher than that in normal tissue. In summary, our optical data shows that 4T07 tumors appear glycolytic (higher 2-NBDG uptake), 67NR tumors appear oxidative (higher TMRE uptake and decreased SO₂), and 4T1 tumors appear to show high levels of both glycolysis and oxidative phosphorylation (higher 2-NBDG and TMRE uptake and decreased SO₂) compared to normal tissue. All representative reflectance spectra, MC corrected fluorescence spectra, and the averaged fluorophore uptake kinetic curves were shown in Supporting Information Figure S1 in Appendix S1.

FIGURE 1.

Baseline SO₂ is significantly lower, while baseline [Hb] is higher in tumors compared with normal tissue. Final 2-NBDG uptake and final TMRE uptake are higher in tumors compared with normal tissue. (A) Baseline [Hb] levels and (B) baseline SO₂ levels across tissue types. (C) Average 2-NBDG₆₀ and (D) TMRE₈₀ across tissue types. There was a 20-minute delay between TMRE injection and 2-NBDG injection, thus the 2-NBDG₆₀ and TMRE₈₀ uptake were measured at the same time point. All measured tumors have a diameter of approximately 6 mm. For the purposes of this analysis, they were classified as small tumors. Comparison of baseline SO₂, baseline [Hb], mean intensity of 2-NBDG₆₀ or TMRE₈₀ across animal groups was performed with a two-way ANOVA test using the MATLAB (Mathworks, Natick, Massachusetts) statistics toolbox. Only statistically significant P values (<0.05) were shown in the figures. N = 5–10 mice/group. Error bars in the graphs represent SEs

3.2 |. Optically measured physiological parameters capture the interplay between tumor metabolism and vasculature

Near-simultaneous measurement of key vascular and metabolic endpoints on the same tissue site enables us to investigate the relationship between tumor metabolism and the associated vasculature. Figure 2A,B show scatter plots of metabolic endpoints along with baseline SO₂ and [Hb] represented as different sized symbols for all four types of tissues (nontumor, 67NR, 4T07 and 4T1) in one graph. The 2-NBDG₆₀ and TMRE₈₀ values were normalized to their own global highest value, that is, the highest 2-NBDG₆₀ and TMRE₈₀ across all normal and tumor mice. Each tissue group is clustered at a different region in the scatter plot, showing that each has a different cluster of correlations between the measured parameters. Among the three tumor lines, 4T1 tumors have the highest TMRE and 2-NBDG uptake; 67NR tumors have comparably high TMRE uptake to that of 4T1, while 4T07 tumors have high 2-NBDG uptake that is comparable to 4T1 tumors. Normal tissues have relatively low 2-NBDG and TMRE uptake compared to all three tumor types. Figure 2A also shows that normal tissues have the lowest TMRE and 2-NBDG values and the highest SO₂ values. Figure 2B shows that the variation of baseline [Hb] values is similar across both normal and tumor tissues. Figure 2C-F show scatter plots of 2-NBDG₆₀ and TMRE₈₀. There is strong positive correlation between glucose uptake (2-NBDG) and mitochondrial metabolism (TMRE) for 4T1 tumors (r = 0.89, P = 0.001), while no obvious correlation was observed for the other tumor types or normal tissues. Figure S2 in Appendix S1 shows the correlations among metabolic and vascular endpoints for the four tissue types. Strong positive correlations between metabolic endpoints (TMRE and 2-NBDG) and vascular endpoints (baseline SO₂ and [Hb]) were observed for 4T1 tumors but not for the nonmetastatic 67NR tumors or normal tissue. In the micrometastatic 4T07 tumor line, a significant positive correlation was found only between 2-NBDG uptake and baseline [Hb] (r = 0.77, P = 0.01) but not among other endpoints.

FIGURE 2.

Near-simultaneous optical quantification of baseline SO₂, baseline [Hb], final 2-NBDG uptake, and final TMRE uptake reflects tumor metabolic heterogeneity. 2-NBDG₆₀ and TMRE₈₀ values were normalized to the global highest value, that is, the highest 2-NBDG₈₀ and TMRE₈₀ intensity across all normal and tumor mice. (A) Scatter plots of metabolic endpoints along with baseline SO₂ for all normal animals and tumor animals in one graph. Baseline SO₂ levels are represented by marker size. Larger markers indicate increased SO₂. (B) Scatter plots of metabolic endpoints along with baseline [Hb] for all normal animals and tumor animals in one graph. Baseline [Hb] is represented by marker size. Larger markers indicate increased [Hb]. K-means clustering was used to generate the centroids shown in (A) and (B). Correlations between 2-NBDG uptake and TMRE uptake for (C) normal tissue, (D) 67NR tumors, (E) 4T07 tumors, and (F) 4T1 tumors. All measured tumors have a diameter of approximately 6 mm. For the purposes of this analysis, they were classified as small tumors. Pearson’s correlation coefficients and P values were calculated using the MATLAB (Mathworks, Natick, Massachusetts) statistics toolbox. N = 5–10 mice/group

3.3 |. Longitudinal optical quantification of tumor metabolism and vasculature captures metabolic and vascular changes with tumor size

To further understand how the metabolic characteristics of the 4T1 tumors changes with size, we performed a set of longitudinal in vivo studies from which both metabolic and vascular parameters were quantified on 4T1 tumors during tumor growth. Figure 3A shows the averaged tumor volumes measured at different days following inoculation, for a total of 14 days. Tumors were classified as tiny tumors (<20 mm³), small tumors (20–150 mm³), and medium tumors (150–400 mm³) according to their sizes using the criteria reported previously [11]. Figure 3B,C show scatter plots of vascular and metabolic endpoints for all tumors and normal tissues. Figure 3B shows that baseline SO₂ and [Hb] increased as tumors progressed to a small size but then decreased relative to a normal baseline when it reached a medium size. Figure 3C shows that both 2-NBDG and TMRE uptake showed a discernible increase when tumor size progressed to tiny, small and medium sizes with small sized tumors showing the highest metabolic endpoints relative to baseline. In both plots there is a positive correlation between SO₂ and [Hb] and between TMRE and 2-NBDG. Figure 3D,E show scatter plots of the change in vascular and metabolic endpoints as a function of tumor size change (delta). Figure 3D shows that there is an initial change in [Hb] followed by an increase in SO₂ as tumors progress from a tiny to small size. Figure 3E shows that 2-NBDG increase followed by an increase in TMRE as tumor size progresses from a tiny to small size. All parameters decrease as tumor progress from a small to medium size.

FIGURE 3.

Longitudinal monitoring of vascular and metabolic endpoints captures metabolic changes with tumor volume for a metastatic tumor line (4T1). (A) Averaged 4T1 tumor volumes measured at different days post-tumor cells injection. (B)-(C) Scatter plot of vascular endpoints along with tumor volume (B), and scatter plot of metabolic endpoints along with tumor volume (C). Tumor volumes are represented by marker size and color. Larger markers indicate increased tumor volume. Blue marks represent normal tissues (six independent animals), Green markers represent tiny tumors (<20 mm³), red makers represent small tumors (20–150 mm³), and black markers represent medium tumors (150–400 mm³). (D)-(E) Scatter plot of vascular endpoints changes along with tumor volume changes (D), and scatter plot of metabolic endpoints changes along with tumor volume changes (E). Tumor volumes increases are represented by marker size and color. Larger markers indicate increased tumor volume. Different marker colors represent different tumor growth periods. Blue symbols represent tumor volume change for the period from day 0 to day 4, pink symbols represent tumor volume change for the period from day 4 to day 9, and black symbols represent tumor volume change for the period from day 9 to day 14. Pearson’s correlation coefficients and P values were calculated using the MATLAB (Mathworks, Natick, Massachusetts) statistics toolbox. Four animals were used for the longitudinal study, however, the tail vein injections in one animal were not successful on day 4 and day 14 thus only three pairs of data were explored

4 |. DISCUSSION

Quantifying metabolic and vascular endpoints at a systems-level is of great interest to cancer biologists studying therapeutic efficacy, as both tumor metabolism and the vascular microenvironment could affect the fate of a tumor and its response to therapeutic and environmental stress [6]. In this report, we utilized our optical spectroscopy technique to investigate the metabolic and vascular characteristics of three sibling breast tumors lines of varying metastatic potential. We observed that all small tumors showed increased average glucose uptake and oxidative metabolism compared to normal tissues, which suggests that these tumors rely on both glycolytic and mitochondrial metabolism as reported previously [49]. Among the three tumor lines, the average glucose uptake values in metastatic (4T1) and micrometastatic (4T07) tumors were higher than that in nonmetastatic (67NR) tumors, while the MMP values in metastatic (4T1) and nonmetastatic (67NR) tumors were higher than that in micrometastatic (4T07) tumors. These clustering results suggest that metabolic reprogramming occurs during initial tumor growth [10, 57]. It should be noted that the trends of glucose uptake within these tumor lines quantified by our optical platform match well with published studies including Seahorse Assay measurement [12], magnetic resonance spectroscopy [12], metabolomics quantification [10], and FDG-PET imaging [11]. These published results from other groups along with our in vivo optical data all reveal that both tumorigenicity and metastasis is directly correlated with increased glycolysis. Previous in vitro cell Seahorse Assay studies showed that the metastatic tumors (4T1 and 4T07) have significantly increased oxygen consumption rate (OCR) compared to their nonmetastatic counterpart (67NR) [12]. However, independent metabolomics studies reported that all the three types of tumors have significantly increased tricarboxylic acid cycle (TCA) metabolites compared with normal cells, and nonmetastatic tumors (67NR) have increased glutathione (GSH) levels (higher antioxidant activity) compared with metastatic tumors (4T1 and 4T07) [10]. Interestingly, our optically measured in vivo mitochondrial metabolism showed increased MMP in all three tumor lines compared to that in normal tissue, while the MMP values among the three tumor lines were comparable. Differences between in vivo optically measured average MMP values and published in vitro cell OCR results or metabolomics data are likely due to the inherent differences of microenvironments in cell culture media, ex vivo tissue, and actual in vivo tissues. Nevertheless, our optically measured MMP on these tumor lines along with the published metabolomics data [10] may suggest that the gain of the general metastatic ability highly correlates with further enhancement of both glycolysis and TCA cycle metabolism as reported previously [58]. Our optical data also showed that even within these three sibling cell lines, there is high diversity in their metabolic profiles. Specifically, 4T07 tumors tend to have increased glucose uptake (the Warburg effect), 67NR tumors tend to have increased mitochondrial metabolism, while 4T1 tumors tend to have both increased glucose uptake and mitochondrial metabolism (adaptable) [10–12].

Both glucose uptake and mitochondrial metabolism showed little spread in normal tissues. Conversely, glucose uptake and mitochondrial metabolism were highly variable between tumors within the same subgroup. There was considerable variance in vascular endpoints for tumors, which is likely because tumor hypoxia is heterogeneous both spatially and temporally [59]. In contrast, baseline SO₂ and [Hb] values for normal animals fall within a narrow range. This heterogeneous distribution of tumor metabolism and vasculature suggests that the vascular microenvironment influences tumor metabolism. The correlations between metabolic (TMRE and 2-NBDG) and vascular endpoints (SO₂ and [Hb]) illustrated that the dependence of glucose uptake and MMP on vascular endpoints is correlated with metastatic potential. Strong positive correlations between the metabolic endpoints and vascular parameters for 4T1 tumors suggest that the metabolism of this highly metastatic tumor line is highly reliant on substrate availability [12]. In micrometastatic tumors (4T07), only glucose uptake is positively and significantly correlated with [Hb]. In nonmetastatic tumors and normal tissues, no obvious correlation exists between metabolic and vascular endpoints. These unique relationships may reveal that tumor metastasis is highly reliant on in both metabolic pathways and on the vascular microenvironment. To further elucidate the role of metabolism and vascular microenvironment in tumorigenesis and metastasis, pharmacological inhibitors may be used. For example, treatment of nonmetastatic tumors with hypoxia-inducible factor-1 (HIF-1) inhibitors [60] could be used to determine if hypoxia and enhanced glycolysis promote tumorigenesis, while treatment of metastatic tumors with mitochondrial inhibitors [61] might help decrease metastatic ability.

In the longitudinal study, the changes of the optically measured vascular parameters reflect changes in tumor angiogenesis and hypoxia [62, 63]. Tumor angiogenesis leads to increased [Hb] in the early stage of tumor growth followed by an increase in SO₂. Both SO₂ and [Hb] levels in 4T1 tumors increased when the tumor volume increased up to a specific tumor size threshold beyond which they decreased. TMRE and 2-NBDG uptake also showed a similar trend. The synchronous changes between metabolic and vascular endpoints suggest that 4T1 tumors rely on oxidative phosphorylation in addition to glycolysis. This is further supported by the strong positive correlation between SO₂ and [Hb] and between TMRE and 2-NBDG in 4T1 tumors with different sizes. As discussed above, our optical data may suggest that strong oxidative phosphorylation in addition to enhanced glycolysis might be highly correlated with tumor metastasis, as supported by another group’s study, which showed that breast cancer cells with the potential to form brain metastases may use aerobic glycolysis coupled with oxidative phosphorylation to generate energy for tumor cell growth [64]. Consistent with our optically measured data, a previous MRI and PET imaging study showed that 4T1 tumor glucose uptake decreased dramatically when tumor size reached to a medium range (150–400 mm³) [11]. The decrease in glucose uptake as 4T1 tumors increase in size may be explained by the increase in tumor necrosis as evidenced by prior Haemotoxylin and Eosin (H&E) examinations [11], in which visible necrosis in small 4T1 tumors and significant necrosis in medium and large 4T1 tumors were observed. Similarly, the decrease in mitochondrial metabolism as 4T1 tumors increase in size may also be correlated with tumor necrosis. Several other independent studies have shown that 4T1 tumor cells have significantly increased TCA activity and oxidative phosphorylation [10, 12, 64]. It has been reported that the MYC gene, which induces mitochondrial biogenesis and glutamine mitochondrial metabolism, might be responsible for this increased oxidative phosphorylation [65]. However, it remains unclear whether the increase in TCA cycle activity in 4T1 tumors is driven primarily by glucose or by glutamine metabolism. Our optically measured synchronous changes of MMP and glucose uptake, and the strong positive correlation between MMP and glucose uptake suggests that the increase in TCA cycle activity in 4T1 tumors might be driven primarily by glucose metabolism though additional studies need to be required to confirm this finding. If this is the case, HIF-1 inhibitors could be ideal therapeutic targets [66] for the treatment of this type of metastatic tumor as HIF-1 inhibitors will decrease aerobic glycolysis, angiogenesis, and potentially downstream TCA cycle activity.

In summary, our study presents several important findings: (1) Metabolic reprogramming occurs during initial tumor growth. The gain of general metastatic ability is highly correlated with further enhancement of glycolysis and mitochondrial metabolism; (2) Tumor metastasis might be highly reliant on both metabolic pathways and on the vascular microenvironment; (3) 4T1 tumors rely on oxidative phosphorylation in addition to glycolysis, and the increase in mitochondrial metabolism in 4T1 tumors might be driven primarily by glucose metabolism; (4) All of above findings underscore the importance of in vivo simultaneous quantification of tumor metabolism and vascular microenvironment for future therapeutic investigations.

5 |. CONCLUSION

This study demonstrates that quantitative optical spectroscopy is able to capture tumor metabolic phenotypes within a wide dynamic range in murine flank tumor models. This capability will enable studies to understand how the interplay between metabolism and the associated vasculature underlies cancer progression, metastasis and resistance to therapies. We also demonstrated that our optical technique is able to perform in vivo longitudinal studies in solid tumor models at a length scale that complements existing methods, and thus it can potentially facilitate novel interdisciplinary studies in cancer pharmacology.