Preliminary Clinical Application of RGD-Containing Peptides as PET Radiotracers for Imaging Tumors

Abstract

Angiogenesis is a common feature of many physiological processes and pathological conditions. RGD-containing peptides can strongly bind to integrin αvβ3 expressed on endothelial cells in neovessels and several tumor cells with high specificity, making them promising molecular agents for imaging angiogenesis. Although studies of RGD-containing peptides combined with radionuclides, namely, ¹⁸F, ⁶⁴Cu, and ⁶⁸Ga for positron emission tomography (PET) imaging have shown high spatial resolution and accurate quantification of tracer uptake, only a few of these radiotracers have been successfully translated into clinical use. This review summarizes the RGD-based tracers in terms of accumulation in tumors and adjacent tissues, and comparison with traditional ¹⁸F-fluorodeoxyglucose (FDG) imaging. The value of RGD-based tracers for diagnosis, differential diagnosis, tumor subvolume delineation, and therapeutic response prediction is mainly discussed. Very low RGD accumulation, in contrast to high FDG metabolism, was found in normal brain tissue, indicating that RGD-based imaging provides an excellent tumor-to-background ratio for improved brain tumor imaging. However, the intensity of the RGD-based tracers is much higher than FDG in normal liver tissue, which could lead to underestimation of primary or metastatic lesions in liver. In multiple studies, RGD-based imaging successfully realized the diagnosis and differential diagnosis of solid tumors and also the prediction of chemoradiotherapy response, providing complementary rather than similar information relative to FDG imaging. Of most interest, baseline RGD uptake values can not only be used to predict the tumor efficacy of antiangiogenic therapy, but also to monitor the occurrence of adverse events in normal organs. This unique dual predictive value in antiangiogenic therapy may be better than that of FDG-based imaging.

Introduction

Angiogenesis, a well-known process by which new blood vessels sprout from the pre-existing capillaries, includes vascular endothelial cell activation, the degradation of vascular basement membrane during the activation, proliferation and migration of endothelial cells, and the construction of new blood vessels and vascular networks. It is associated with a long list of physiological and pathological events (1, 2). Under normal physiological conditions, such as embryonic development, wound healing, and the menstrual cycle, angiogenesis serves primarily to ensure adequate nutrient and oxygen supply and waste removal. Of course, normal angiogenesis also occurs in pathological conditions, such as tissue damage after reperfusion of ischemic tissue or cardiac failure (3, 4). Unlike the normal angiogenesis described above, both solid and hematologic tumors feature a high dependence on persistent abnormal angiogenesis (5–7). Solid tumors initially form as avascular masses that rely on the vascular system in the host microenvironment, but upon reaching a diameter of 1–2 mm, these tumors exhibit excessive dysfunctional angiogenesis to obtain essential oxygen and nutrients (8–11). The initial recognition of angiogenesis as an interesting process in oncology began in the early 1970s, when Drs. Folkman and Denekamp put forward the idea that highly immature neovascularized tumors are vulnerable via their blood supply. The immature vessels are characterized by an undifferentiated endothelium and a lack of smooth muscles, and act as the vehicles for peripheral sprouting of new capillaries, invasion and distant metastasis (12–15). These mechanisms are facilitated by the synergistic effects of transmission signals from the extracellular matrix (ECM) to endothelial cells, among which integrin binding to endothelial cell recognition sites is the first process to occur. Importantly, the tripeptide sequence of Arg-Gly-Asp (RGD) was identified as a common recognition site in the ECM and blood, and can bind to a variety of adhesion proteins and integrins (16).

The contribution of the RGD tripeptide sequence to cell attachment was first recognized in fibronectin by Pierschbacher and Ruoslahti in 1984 and later confirmed in more ligands, namely, vitronectin, fibrinogen, decorsin, von Willebrand factor, thrombospondin, disintegrins, laminin, entactin, tenascin, prothrombin, osteopontin, bone sialoprotein, adenovirus penton base protein, and collagens (16–18). Combinations of different alpha (α) and beta (β) subunits, such as αvβ3, αvβ5, αvβ8, α5β1 and α8β1, act as receptors for many of the above-mentioned proteins, mainly acting on angiogenesis, inflammation, thrombosis, lung development, cell differentiation, and other pathophysiological processes (19–22). Among these, αvβ3 is highly expressed on angiogenic endothelial cells and several tumor cells, but is expressed only at low levels by resting endothelial cells of normal tissues (23–26). The αvβ3 expression level has also been reported to be a promising factor in tumor diagnosis, staging and therapy (27–29). Due to the high specific affinity between RGD and αvβ3, radiolabeled RGD peptides have been intensively studied for their ability to represent αvβ3 expression density and the tumor angiogenesis state noninvasively (30, 31). Combinations of RGD-containing peptides with radionuclides such as ¹⁸F, ⁶⁴Cu, and ⁶⁸Ga, have been introduced successfully in positron emission tomography/computed tomography (PET/CT) imaging, among which ¹⁸F has near ideal decay characteristics for PET (t_1/2 = 110 min, β⁺ = 0.64 MeV). A linear RGD peptide was first labeled with ¹⁸F via solid-phase synthesis in 2001, but this radiotracer showed nonspecific accumulation within tumor tissue and early breakdown (32). Then, the cyclic pentapeptide cyclo (Arg-Gly-Asp-D-Phe-Val) (denoted as cyclic RGD peptide) was prepared and found to be a highly potent, stable and selective inhibitor of integrin signaling (33, 34). In addition, based on the polyvalency effect, different types of dimeric and multimeric cyclic RGD peptides were prepared to improve αvβ3-binding affinities for intense tumor accumulation and increased tumor/background ratios compared with the monomeric compounds (35–37). During the optimization of radiotracers, strategies such as cyclization, glycosylation, pegylation, multimerization, and insertion of spacers/linkers have been adopted to optimize the in vitro and in vivo behaviors of radiolabeled RGD peptides (38–41). However, few of these RDG peptide labeling methods have been successfully translated from the bench to clinical use.

Therefore, in this review, we focus on the preliminary clinical applications of PET radionuclide-conjugated RGD-containing peptides that target RGD-binding integrins (mainly αvβ3 integrin), discussing their distribution characteristics and potential use in diagnosis, delineation of tumor subvolume, evaluation response of chemoradiotherapy or antiangiogenic therapy, and the comparative results achieved with traditional ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) imaging agent in the context of functional tumor imaging.

Comparative Characteristics of RGD-Based and FDG Tracers in Clinical Application

Monomeric and multimeric RGD-based PET tracers that have been investigated in preliminary clinical studies are listed in Supplementary Table 1 and Figure 1 . ¹⁸F-Galacto-RGD, which is a glycosylated RGD-peptide, was initially reported by Haubner et al. in 2005 and was the first monomeric integrin-specific PET tracer used in patients (56). It is well tolerated, as all studies reported neither drug-related adverse events nor important clinical indicators. The dose distribution results were consistent for a variety of tumor types, with similar evidence of predominant renal excretion, fast blood elimination, rapid tumor tracer accumulation, and low background concentration in most organs, namely, the lung, muscle tissue and brain, allowing imaging of tumor lesions with good contrast (56–58). A relatively small amount of ¹⁸F-Galacto-RGD is also excreted via the hepatobiliary pathway, and thus, in some patients, tracer uptake is seen in the gallbladder. Similar biodistribution results were observed for the other monomeric radionuclide tracers such as ¹⁸F-fluciclatide (denoted as ¹⁸F-AH111585) (59–61) and ¹⁸F-RGD-K5 (43). Consistent with the polyvalency effect caused by dimerization and multimerization of cyclic RGD peptides, series of RGD radiotracers were found to show improved radiosynthesis yields, relatively higher tumor integrin-specific accumulation and favorable in vivo pharmacokinetic properties compared with monomeric ones in both preclinical and clinical studies (44, 45, 59, 62–68). However, the multistep, time-consuming and low-yield synthetic procedures make an automated production process, which is mandatory for routine clinical use, extremely difficult, and thus, the commercial clinical viability of this tracer is limited. Chen et al. successfully established a simplified labeling procedure for ¹⁸F-fluoride-aluminum complex-labeled RGD tracer ¹⁸F-AlF-NOTA-PRGD2 (denoted as ¹⁸F-Alfatide) and ¹⁸F-AlF-NOTA-E[PEG4-c(RGDfk)]2 (denoted as ¹⁸F-Alfatide II) in one single step of radiosynthesis (46, 54, 69). Upon successful introduction of a simple, one-step, lyophilized kit for ¹⁸F-alfatide preparation, with which complete radiosynthesis including purification can be accomplished within 30 min with a radiochemical purity of more than 95%, the potential for clinical transformation of this radiotracer was greatly improved. The pharmacokinetics and imaging properties of ¹⁸F-Alfatide were comparable to those of ¹⁸F-FP-PEG3-E[c(RGDyK)]2 (denoted as ¹⁸F-FPPRGD2) (54, 70). In addition, the positive effect of cyclic mono or multimeric peptides was also successfully developed into ⁶⁸Ga-labeled RGD analogs, which have shown promise for imaging integrin αvβ3 expression (48, 71–79).

Figure 1.

Examples of RGD-based radiotracers [¹⁸F-Galacto-RGD (42), ¹⁸F-AH111585 (42), ¹⁸F-RGD-K5 (43), ¹⁸F-FPRGD2 (44), ¹⁸F-FPPRGD2 (45), ¹⁸F-Alfatide (46), ¹⁸F-Alfatide II (47), ⁶⁸Ga-NOTA-RGD (48), ⁶⁸Ga-DOTA-RGD (49), ⁶⁸Ga-NODAGA-RGD (50), ⁶⁸Ga-BBN-RGD (51), ⁶⁸Ga-NOTA-3P-TATE-RGD (52), ⁶⁸Ga-DOTA-RGD2 (53), ⁶⁸Ga-NOTA-PRGD2 (54) and ⁶⁸Ga-NODAGA-RGD2 (55)] for imaging tumor angiogenesis in clinical application.

Unlike the phenomenon of decreasing standard uptake values (SUVs) in normal organs over time, tumor retention of RGD-based PET tracers did not stop increasing until a plateau period was reached, which showed no obvious fluctuation within nearly 40–60 min after tracer injection. It appeared as either a distinct increase in primary tumors and lung, pleura, bone, and lymph node metastases compared with surrounding normal tissue or as moderate or reduced accumulation in the cases of kidneys, bladder, liver, spleen, and intestine. Sixty minutes after injection was recommended as the appropriate imaging time for optimal lesion-to-background contrast, according to the data from RGD-based fluorine-labeled monomeric or dimeric radiotracer studies (44, 45, 57, 59, 60). For biodistribution in normal tissues, RGD tracers can be used to image brain tumors or brain metastases with a clear background, due to their inability to pass through the healthy blood–brain barrier (BBB) but ability to cross the damaged BBB created by the tumor (43, 46, 61). Primary high uptakes were shown in organs such as the kidneys, bladder, liver, spleen and intestine organs, along with diffuse uptake in the bilateral ventricles and thyroid. High uptake in the kidneys and bladder resulted predominantly from urinary excretion. The uptake values of liver and spleen were thought to be physiological, high liver background may result from the densely vascularized nature of the organ and metabolism of RGD-based imaging tracers by the liver (60). Prominent accumulation of RGD peptide ligands in the liver reported in mice and humans also supported this assertion (80, 81). The uptake values in the intestine maybe associated with physiological expression of αvβ3 on intestinal smooth muscle cells, because the uptake of these tracers did not change significantly during an observation period of 90 min (57, 58, 82). While an initial ¹⁸F-FPPRGD2 increase followed by a pattern of decreasing uptake in the intestine was also observed, this may reflect initial dimeric RGD accumulation was cleared from hepatobiliary excretion (45, 83). Overall, this finding indicates the unsuitability of RGD imaging for hepatocellular carcinoma (HCC) tumors or diseases with liver metastases.

In addition to integrin αvβ3 expression, glucose metabolism is also believed to correlate with tumor aggressiveness and progression (84, 85). In tumor lesions, RGD-based tracer uptake is significantly lower than that of FDG, as commonly observed in studies comparing FDG and RGD tracers (52, 63, 65, 66, 86–92), but this difference was altered in some cases, such as with the higher maximum SUV (SUV_max) and tumor-to-blood ratio (TBR) of ⁶⁸Ga-NOTA-3P-TATE-RGD compared with ¹⁸F-FDG in neuroendocrine tumor (NET) cases and also in both human epidermal growth factor receptor 2 negative [HER2 (–)] and estrogen receptor positive [ER (+)] breast cancer (52, 88). One possible reason responsible for this difference may be related to the different binding mechanisms of the tracers. ¹⁸F-FDG mainly depends on the hypermetabolism of glucose in malignant tumor cells, whereas RGD PET imaging predominantly binds to avβ3 highly expressed in tumor neovascular endothelial cells. Also, within each lesion, the number of endothelial cells expressing integrin αvβ3 is smaller than the abundant number of tumor cells containing glucose transporters (93). In addition, both tracers showed strong uptake in inflammatory lesions similar to malignancies. RGD was thought to be more beneficial than FDG for differentiating inflammation, as it demonstrated significantly lower retention than FDG after comparable high uptake levels in inflammation (56, 94–96). With regard to the correlation between RGD and FDG data, inconsistent results were observed when tumor lesions overall were considered. Moderate or significant correlations between RGD and FDG parameters were reported in locally advanced rectal cancer (LARC) and squamous cell carcinoma of head and neck (HNSCC) (87, 88, 90, 97), and for subgroups of tumors, namely, non-small cell lung cancer (NSCLC), triple-negative breast cancer (TNBC), and FDG-avid tumors (86, 89). However, other studies failed to demonstrate a correlation between RGD and FDG uptake (65, 91, 98–100).

These results overall may indicate the coexistence of interrelated pathophysiological phenomena within the tumor, such as cell proliferation and neoangiogenesis, but the lack of a significant correlation between RGD and FDG uptake suggests that these imaging modalities provide complementary rather than similar information (101). RGD imaging offers indispensable value in terms of evaluating tumor angiogenesis and planning αvβ3-based therapy. As the next logical step, many research groups focused on exploring the clinical characteristics of RGD-based imaging agents.

Preliminary Diagnostic Value of RGD-Containing Peptide Tracers

In a pan-cancer study with 9 patients (5 with malignant melanomas, 2 with sarcomas, 1 with osseous metastasis from renal cell carcinoma [RCC], and 1 with villonodular synovitis), ¹⁸F-Galacto-RGD SUVs showed inter- and intra-individual variances ranging from 1.2 to 10.0 (56). Consistently, Beer et al. reported the heterogeneous accumulation of ¹⁸F-Galacto-RGD from 1.2 to 9.0 in 19 patients. Distribution volume values for tumor tissue (1.5 ± 0.8) were 4 times higher than those for muscle tissue (0.4 ± 0.1), suggesting intense specific receptor binding in tumor tissues and only minimal free and bound (specific or nonspecific) tracer in muscle tissue (57). Similarly, studies of other imaging agents such as ¹⁸F-AH111585 and ⁶⁸Ga-NODAGA-E[(cRGDyK)₂] (denoted as ⁶⁸Ga-NODAGA-RGD2) also reported heterogeneous tracer uptake in tumor and metastatic sites and superior tumor-to-background ratios in patients with malignant disease (58, 61, 76, 86, 102). However, the sensitivity of ¹⁸F-Galacto-RGD (76%) for lesion identification was lower than that of conventional staging with contrast-enhanced CT and ¹⁸F-FDG PET in a comparative study (86). This is not surprising because, unlike the glycolytic effect of entire tumor cell, αvβ3 is expressed at elevated levels on angiogenic blood vessels and on malignant tumors (103, 104). Interestingly, the related research demonstrated high inter- and intraindividual variances in tracer accumulation in different types of lesions, indicating great diversity in receptor expression.

According to the literature, the expression patterns of αvβ3 are specific on malignant carcinomas and benign tumors or inflammatory tissues (95, 105, 106). In benign lesions (e.g., pigmented villonodular synovitis, neurofibroma, and inflammatory tissue), αvβ3 expression is located only on the vasculature. However, in malignant lesions, αvβ3 is expressed not only on the endothelial cells of neovasculature but also on the surface of some tumor cells. In HNSCC, αvβ3 is located predominantly on the neovasculature but shows some low expression on tumor cells (56, 102, 107). Immunohistochemical analyses confirmed αvβ3 expression predominantly on microvessels (5/5) and, to a lesser extent, on tumor cells (3/5) in invasive ductal breast cancer (IDC) (28, 108). In malignant melanoma and clear cell RCC (ccRCC), αvβ3 is highly located on the surface of tumor cells (61, 102, 109, 110). In the other solid tumors, namely, glioma, lung cancer, differentiated thyroid cancer, and breast cancer (56, 102, 108–112), αvβ3 expression is located on the neovasculature and on the tumor cells at varying degrees. For example, a significant correlation between αvβ3 expression and radiotracer uptake was reported by a study employing ¹⁸F-Galacto-RGD PET in melanoma and sarcoma patients (56). Specially, in ccRCC cases, the ¹⁸F-FB-mini-PEG-E[c(RGDyK)]2 (denoted as 18F-FPRGD2) PET signal correlated with integrin αvβ3 expression on tumor cells, and immunohistochemistry results confirmed high expression of αvβ3 on ccRCC cells. In contrast, in papillary RCC (pRCC) cases, the ¹⁸F-FPRGD2 PET signal correlated with the integrin αvβ3 level on tumor vessels (67). These results demonstrate that RGD-based tracer uptake can reflect integrin αvβ3 expression in renal tumors, but represents angiogenesis only when tumor cells do not express integrin αvβ3. In other words, RGD-based PET may be regarded as a surrogate marker of angiogenesis for the visualization and noninvasive quantitation of integrin expression when αvβ3 expression is predominantly confined to the tumor vasculature as in HNSCC (107). Overall, the association between RGD SUVs and TBR and the corresponding αvβ3 expression level demonstrated the value of noninvasive techniques for appropriately diagnosing tumor lesions, although a positive correlation was not always clear (56, 61, 107, 113–116). The heterogeneous distribution pattern among different lesions described above may explain this phenomenon. Next, we provide a review of reported clinical investigations related to the application of RGD-based tracers for diagnosis, definition of tumor subvolume, and therapeutic response prediction according to specific tumor types ( Table 1 ).

Table 1.

Preliminary case-by-case diagnostic value of RGD-containing peptides and FDG.

Tumor type	Purpose	Preferred agents	Supporting evidence	Opposing evidence	Reference
GBM	HGG vs. LGG	RGD	68Ga-PRGD2: TBR 0.80–2.00 vs. 3.55–11.00, sensitivity 100% (8/8), specificity 100% (4/4)		(114)
			18F-FDG: TBR 0.57–1.38 vs. 1.11–4.77; sensitivity 88% (7/8), specificity 75% (3/4)
	meningioma vs. HGG	RGD	68Ga-NOTA-PRGD2: SUVmax 4.23 ± 2.48 vs. 1.57 ± 0.33, p = 0.0047		(115)
			18F-FDG: SUVmax 5.38 ± 2.37 vs. 12.64 ± 1.91, p = 0.0537
HNSCC	diagnosis of lesions	RGD and FDG	68Ga-NODAGA-RGD vs. 18F-FDG: both detected 100% of primary lesions, lymph nodes, distant metastases		(87)
Thyroid cancer	diagnosis of radioiodine-refractory thyroid cancer	RGD	68Ga-DOTA-RGD2: sensitivity 82.3%, specificity 100%, accuracy 86.4%, PPV 100%, NPV 62.5%	68Ga-DOTA-RGD2: detect 123 lesions. 63.6% patients; 10.6% thyroid bed lesions; 82.9% nodal lesions; 6.5% skeletal lesions	(92)
			18F-FDG: sensitivity 82.3%, specificity 50%, accuracy 75%, PPV 84.8%, NPV 45.4%	18F-FDG: detect 144 lesions, 75.0% patients; 8.3% thyroid bed lesions; 86.1% nodal lesions; 5.5% skeletal lesions
	radioiodine-refractory lesions vs. benign lesions	none	18F-Alfatide: SUVmean 2.5 ± 0.9 vs. 2.8 ± 0.9, p = 0.576		(91)
			18F-FDG: SUVmean 4.5 ± 1.6 vs. 3.3 ± 1.2, p = 0.133
	radioiodine-refractory lesions vs. iodine-avid lesions	none	18F-Alfatide: SUVmean p >0.05
			18F-FDG: SUVmean p >0.05
	lesions diameter >1.5 cm vs. <1.5 cm in differentiated thyroid cancer	RGD	18F-Alfatide: SUVmean 3.1 ± 0.7 vs. 2.4 ± 0.8, p <0.05
			18F-FDG: SUVmean p >0.05
	lymph nodes diameter >1 cm vs. <1 cm in differentiated thyroid cancer	none	18F-Alfatide: SUVmean p = 0.06; 18F-FDG: SUVmean p = 0.41
Lung cancer	malignancies vs. benign ones	undefined	68Ga-NOTA-PRGD2: sensitivity 83.8%, specificity 91.3%, accuracy 85.7% (SUVmean = 1.3); sensitivity 80.9%, specificity 82.6%, accuracy 81.3% (SUVmax = 2.0)	18F-FDG: sensitivity 86.8%, specificity 69.6%, accuracy 82.4% (SUVmean = 2.0); sensitivity 85.3%, specificity 69.6%, accuracy 81.3% (SUVmax = 3.0)	(100)
	metastatic vs. non-metastatic lymph nodes	RGD	68Ga-NOTA-PRGD2: SUVmax 1.93 ± 1.03 vs. 0.75 ± 0.75, p <0.05; PPV 90.0%, NPV 93.8%		(100)
			18F-FDG: SUVmax 3.91 ± 2.37 vs. 2.30 ± 2.31, p = 0.48; PPV 30.2%, NPV 90.5%
			18F-FDG: sensitivity 75%, specificity 66.7%
	lung cancer vs. tuberculosis	RGD	18F-Alfatide II: SUVmax 4.08 ± 1.51 vs. 2.63 ± 1.34, p = 0.0078		(117)
			18F-FDG: SUVmax 12.04 ± 4.67 vs. 7.53 ± 2.88
	NSCLC vs. SCLC	RGD	68Ga-DOTA-RGD2: SUVmax 3.83 ± 1.00 vs. 2.05 ± 0.74, p <0.0001; TNR 1.94 ± 0.70 vs. 0.94 ± 0.24, p = 0.0099		(111)
			18F-FDG: SUVmax 10.39 ± 4.03 vs. 7.84 ± 2.25, p = 0.08;TNR 5.03 ± 2.52 vs. 3.62 ± 0.93, p = 0.10
	diagnosis of neuroendocrine tumor	RGD	68Ga-NOTA-3P-TATE-RGD vs. 18F-FDG: 24.12 ± 22.39 vs. 3.75 ± 2.81, p = 0.0241		(52)
ESCC	pathological stages I–II vs. III–IV	RGD and FDG	18F-Alfatide: 2.41 ± 1.06 vs. 3.41 ± 0.68, p = 0.03
			18F-FDG: 4.02 ± 1.83 vs. 7.24 ± 3.70, p = 0.002
	positive vs. negative lymph nodes	RGD and FDG	18F-Alfatide: 3.57 ± 0.58 vs. 2.16 ± 0.80, p = 0.001
			18F-FDG: 7.41 ± 3.35 vs. 3.2 ± 0.44, p < 0.001		(118)
	well vs. moderate vs. poor differentiation	RGD	18F-Alfatide: 2.44 ± 0.83 vs. 2.91 ± 0.62 vs. 3.56 ± 0.98, p = 0.049
			18F-FDG: 3.26 ± 0.36 vs. 4.49 ± 1.67 vs. 7.05 ± 4.02, p = 0.053
Breast cancer	diagnosis of breast and metastatic lesions	RGD	18F-FPPRGD2: sensitivity, all lesions 95.7%, breast lesions 100%, axillary lymph nodes 75.0%, bone 100%, other metastases 100%; specificity, all lesions 100%, breast lesions 100%, axillary lymph nodes 100%, bone 100%, other metastases 100%	18F-RGD-K5 detected only 122 lesions (77.7%) among 157 lesions observed on 18F-FDG PET	(98)
			18F-FDG: sensitivity, all lesions 87.0%, breast lesions 83.3%, axillary lymph nodes 75.0%, bone 87.5%, other metastases 100%; specificity, all lesions 57.1%, breast lesions 100%, axillary lymph nodes 0%, bone 100%, other metastases 0%		(63)
	breast cancer vs. benign breast lesion	undefined	18F-Alfatide II: sensitivity 97.6% (SUVmax = 1.6); 18F-FDG: sensitivity 78.6% (SUVmax = 3.68)	18F-Alfatide II: specificity 54.5%, Youden index 52.1%, AUC 0.738; 18F-FDG: specificity 81.8%, Youden index 60.4%, AUC 0.838	(88)
	different HR status	RGD	68Ga-BBN-RGD: ER(+) vs. ER (–), p = 0.0083 18F-Alfatide II: Luminal A/Luminal B vs. Triple negative, p = 0.032/0.026; 68Ga-NOTA-RGD: ER/PR(+),HER2(−) or ER/PR(+),HER2(+) vs. ER/PR(−),HER2(−), p <0.05	18F-FDG: ER/PR(+),HER2(−) or ER/PR(+),HER2(+) vs. ER/PR(−),HER2(−), p <0.05	(116)
	different HER2 status	FDG	18F-FDG: HER-2(+) vs. Luminal A/Luminal B, p = 0.001/0.000 68Ga-BBN-RGD: HER-2(+) vs. HER-2(−), p = 0.6589	68Ga-NOTA-RGD: HER2(+) vs. HER2(−), 2.90 ± 0.75 vs. 2.42 ± 0.59, p = 0.04 for SUVmax; 1.95 ± 0.53 vs. 1.60 ± 0.38, p = 0.04 for SUVmean	(89)
HCC or liver metastasis	diagnosis	FDG	68Ga-DOTA RGD: 0 patients; 18F-FDG: detect liver metastases in 9 patients (16%)		(119)
LARC	diagnosis	FDG	18F-FPRGD2: 18 (51%) pelvic lymph nodes; 18F-FDG: 35 pelvic lymph nodes		(66)
Metastatic RCC	diagnosis	undefined	18F-FPPRGD2: 55/60 lesions; 18F-FDG: 52/60 lesions		(97)
Cervical cancer and ovarian cancer	diagnosis	undefined	18F-FPPRGD2: 48/52 lesions; 18F-FDG: 50/52 lesions		(90)
Brain metastasis	diagnosis	RGD	18F-Alfatide II: detected 100% of lesions, TNR 18.9 ± 14.1; 18F-FDG: 50% of lesions, TNR 1.5 ± 0.5		(47)
Bone metastasis	diagnosis	undefined	18F-Alfatide II: 100% of osteolytic metastases, 70% of osteoblastic metastases, 100% of mixed bone metastases, 98% of bone marrow metastases, 92% of all metastases		(120)
			18F-FDG: 90% of osteolytic metastases, 53% of osteoblastic metastases, 90% of mixed bone metastases, 77% of bone marrow metastases, 77% of all metastases

GBM, glioblastoma multiforme; HGG, high-grade glioma; LGG, low-grade glioma; TBR, tumor-to-blood ratio; SUV_max, maximum standard uptake values; HNSCC, squamous cell carcinoma of head and neck; PPV, positive predictive value; NPV, negative predictive value; NSCLC, non-small cell lung cancer; SCLC, small cell lung cancer; TNR, tumor-to-normal ratio; ESCC, esophageal squamous cell carcinoma; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2; HCC, hepatocellular carcinoma; LARC, locally advanced rectal cancer; RCC, Renal cell cancer.

Glioblastoma

Tumor tracer uptake was heterogeneously restricted to the periphery of the tumor in glioma patients, with no activity in the necrotic center of the lesions and normal brain tissue. The correlation between tracer accumulation and immunohistochemical staining intensity for αvβ3 expression in 21 tissue samples was weak but significant (r = 0.463, p = 0.034), which provided evidence that ¹⁸F-Galacto-RGD PET is a promising tool for noninvasively monitoring αvβ3 integrin expression in patients with malignant gliomas (113). However, the characteristics revealed in this study mainly related to diagnosed glioblastoma multiforme (GBM) and recurrent GBM after external beam radiation or chemotherapy, which may reduce the reliability of the results. Li et al. further explored patients with newly diagnosed GBM of different grades in a prospective ⁶⁸Ga-NOTA-PRGD2 (denoted as ⁶⁸Ga-PRGD2) PET/CT imaging study. The results from 12 GBM patients also showed significant accumulation in the tumors, but not in the white matter or cortical gray matter, with the exception of the choroid plexus. The maximum TBR (TBR_max) of ⁶⁸Ga-PRGD2 PET/CT was superior for differentiating high-grade glioma (HGG) from low-grade glioma (LGG) compared with ¹⁸F-FDG PET/CT (sensitivity, 100% vs. 88%; specificity, 100% vs. 75%). This clinical case-by-case evaluation provides a sufficient basis for applying RGD-based imaging to determine the boundary and stage of glioma (114). Data obtained by Liu et al. also supported this phenomenon of HGG exhibiting a more intense ¹⁸F-Alfatide SUV_max, mean SUV (SUV_mean), and tumor-to-normal ratio (TNR) than LGG (121). These results could be explained by the higher αvβ3 expression level in HGG compared with LGG (122). Overall, this research demonstrates that RGD-based tracer imaging might be superior to ¹⁸F-FDG PET/CT for GBM imaging, as the tracer molecules only specifically accumulated in the tumors, while not accumulating in the normal brain tissue as characterized by high ¹⁸F-FDG uptake.

For distinguishing meningioma and HGG lesions, RGD also provided considerable added value to ¹⁸F-FDG PET/CT imaging. Li et al. showed that higher SUV_max (4.23 ± 2.48) of ⁶⁸Ga-PRGD2 was observed in the uncommon meningiomas than in the HGG lesions (1.57 ± 0.33, p = 0.0047). The uptake ratios of ⁶⁸Ga-PRGD2 over ¹⁸F-FDG normalized as lg100 ∗ SUV_max (RGD/FDG) were significantly higher in the uncommon meningiomas than those in HGG (1.87 ± 1.36 vs. 1.04 ± 0.87, p = 0.0001) with a defined cutoff value of 1.58. However, no significant differences were detected by ¹⁸F-FDG PET between the rare subtypes of meningioma and HGG (115).

Head and Neck Squamous Cell Carcinoma (HNSCC)

Given the predominant αvβ3 expression on the neovasculature in HNSCC, angiogenesis maybe the optimal surrogate variable in such cases (56, 110). Eleven patients with HNSCC were examined by ¹⁸F-Galacto-RGD static emissions scanning, it successfully identified 83.3% (10/12) of tumor-bearing cases and 33.3% (2/6) of pathological metastatic lymph nodes (107). When comparing with ¹⁸F-FDG uptake, Durante et al. demonstrated that all primary tumors, lymph nodes and distant metastases could be seen using both tracers, with higher tumor uptake was observed in ¹⁸F-FDG than in ⁶⁸Ga-NODAGA-c(RGDyK) (⁶⁸Ga-NODAGA-RGD) imaging. Notably, the SUV_mean for RGD showed a positive correlation with that for ¹⁸F-FDG (Spearman’s r = 0.89, p = 0.0068) (87). The feasibility of ⁶⁸Ga-DOTA-E-[c(RGDfK)]2 (⁶⁸Ga-DOTA-RGD2) PET/CT for visualizing angiogenesis was also validated in patients with oral squamous cell carcinoma with SUV_max ranging from 4.0 to 12.7 (77).

Thyroid Cancer

In radioiodine refractoryIn differentiated thyroid cancer (RAIR-DTC) patients, ¹⁸F-FDG PET/CT showed higher SUV_max compared to RGD PET/CT whether in primary tumor lesions or lymph node metastasis (91, 92). These two tracers showed similar sensitivities of 82.3% in detecting lesions, but ⁶⁸Ga-DOTA-RGD2 PET/CT had a higher specificity and accuracy of 100 and 86.4% compared to 50 and 75% for ¹⁸F-FDG PET/CT (92). No significant differences were observed between RAIR lesions and benign lesions by ¹⁸F-Alfatide and ¹⁸F-FDG imaging. Notably, ¹⁸F-Alfatide uptake was significantly higher in lesions with a diameter larger than 1.5 cm than lesions smaller than 1.5 cm (91). These results supported feasibility of RGD PET/CT not only on the diagnosis of RAIR-DTC but also on the evaluation of treatment response of ¹⁷⁷Lu-DOTA-RGD in these patients, especially in cases deemed negative/suspicious on ¹⁸F-FDG PET/CT (123).

Lung Cancer

In lung cancer, RGD has been widely explored in tumor diagnosis and differential diagnosis. ¹⁸F-Alfatide PET imaging successfully identified all tumors with desirable image contrast, indicating a lower variance in tumor SUV_mean (2.90 ± 0.10) and high tumor-to-background ratio [tumor-to-muscle ratio (TMR), 5.87 ± 2.02; TBR, 2.71 ± 0.92] in 9 lung cancer patients ⁵⁸. Later, Gao et al. and Zhou et al. further explored the value of this imaging method in the diagnosis of lymph nodes. Their data showed that the sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) were 100, 44.44, 80.77, 77.27, and 100, respectively, for the diagnosis of suspected lung cancer and 100.0, 94.9, 95.4, 69.0, and 100%, respectively, for confirming malignant lymph node metastasis. Significantly higher ¹⁸F-Alfatide RGD uptake was observed either in differentiating malignant tumor lesions (malignant lesions vs. hamartomas, 5.37 ± 2.17 vs. 1.60 ± 0.11, p <0.001; TBR, 4.13 ± 0.91 vs. 1.56 ± 0.24, p <0.001) or positive lymph node metastases. However, no significant differences were observed between malignant lesions and inflammatory lesions or inflammatory pseudotumors (p >0.05) (124, 125).

In another study using ⁶⁸Ga-NOTA-PRGD2 PET/CT, benign and malignant tumors and lymph nodes were successfully identified in 91 patients with suspected lung lesions. Both the SUV_mean and SUV_max of primary malignancies were higher than those of benign lesions, and the SUV_max of metastatic lymph nodes was higher than that of non-metastatic nodes. In addition, comparative results of ⁶⁸Ga-NOTA-PRGD2 and ¹⁸F-FDG PET/CT were reported. For ⁶⁸Ga-NOTA-PRGD2, with a cut-off SUV_mean of 1.3, malignant lesions could be distinguished from benign lesions with a sensitivity, specificity, and accuracy of 83.8% (57/68), 91.3% (21/23), and 85.7% (78/91), respectively. With a SUV_max threshold of 2.0, the corresponding sensitivity, specificity and accuracy were 80.9% (55/68), 82.6% (19/23), and 81.3% (74/91), respectively. By comparison, for ¹⁸F-FDG PET/CT, with a cut-off SUV_mean of 2.0, the sensitivity, specificity, and accuracy were 86.8% (59/68), 69.6% (16/23), and 82.4% (75/91), respectively, and with the SUV_max cut-off of 3.0, the sensitivity, specificity, and accuracy of ¹⁸F-FDG PET/CT for the diagnosis of lung cancer were 85.3% (58/68), 69.6% (16/23), and 81.3% (74/91), respectively. However, for the assessment of lymph node metastasis, the PPV and NPV of ⁶⁸Ga-NOTA-PRGD2 were 90.0% (27/30) and 93.8% (121/129), respectively, compared with 30.2% (29/96) and 90.5% (57/63), respectively for ¹⁸F-FDG. From these results, ⁶⁸Ga-NOTA-PRGD2 PET/CT offered similar sensitivity and higher specificity for detecting lung lesions compared with ¹⁸F-FDG PET/CT, but remarkable improvement for the assessment of lymph nodes metastasis with a higher specificity (100, 126).

Consistent results were also obtained for the ability of RGD-based radiotracers to differentiate tuberculosis and lung cancer, with studies reporting that tracer uptake values for ¹⁸F-Alfatide II was significantly higher in malignant lesions than in tuberculosis, which indicated that RGD-based PET/CT might differentiate lung cancer from tuberculosis (117). The ⁶⁸Ga-NOTA-RGD SUV_max allowed for significant delineation of a solitary pulmonary nodule from non-malignant lung tissue (3.7 ± 0.7 vs. 0.4–0.9, p <0.001) (60). Consistent with the different αvβ3 expression patterns in NSCLC and small cell lung cancer (SCLC), NSCLC samples showed positive αvβ3 expression in both tumor cells and the vasculature epithelium, whereas SCLC exhibited inconspicuous αvβ3 expression. Tracer accumulation of ⁶⁸Ga-DOTA-RGD2 was higher in NSCLC (ranging from 5.7 to 1.2) than in SCLC (3.3 to 1.1) for both primary lesions and metastatic lymph nodes. In contrast, synchronous ¹⁸F-FDG PET/CT examination did not show the differences between NSCLC and SCLC groups (111). ¹⁸F-Alfatide imaging also showed statistically higher peak SUV (SUV_peak), SUV_mean and total lesion angiogenesis (TLA) for squamous cell carcinoma than for adenocarcinoma (127). Given the low levels of αvβ3 expression in SCLC, ⁶⁸Ga-NOTA-3P-TATE-RGD was developed to overcome the insufficiencies of TATE imaging in NSCLC and RGD imaging in SCLC. The TBR with ⁶⁸Ga-NOTA-3P-TATE-RGD was significantly higher than that with ⁶⁸Ga-NOTA-RGD (6.06 ± 6.09 vs. 2.65 ± 1.19, p = 0.0344) in SCLC, or that with ¹⁸F-FDG in neuroendocrine tumors (2.91 ± 1.71, p = 0.0234) (52). It is important to note that lung cancer and inflammatory lesions or inflammatory pseudotumors cannot be distinguished with RGD imaging, nor with FDG imaging (124). Based on these data, RGD-related imaging may be an appropriate strategy for αvβ3-associated therapy and imaging, which showed advantages on distinguishing between NSCLC and SCLC.

Esophageal Cancer

In a case report for a moderately differentiated carcinoma of the gastro-esophageal junction, ⁶⁸Ga-NODAGA-RGD tracer uptake was clearly localized in non-FDG-avid perilesional structures, demonstrating that angiogenesis imaging might be a crucial tool for early disease identification and localization, metastasis evaluation, and efficacy prediction (128). Dong et al. prospectively enrolled 46 patients with newly diagnosed esophageal squamous cell carcinoma (ESCC) (n = 21 for ¹⁸F-Alfatide PET/CT and n = 25 for ¹⁸F-FDG PET/CT). The lymph nodes showed different accumulation patterns for separate pathological stages and metastatic status both on ¹⁸F-Alfatide PET/CT (stages I–II vs. III–IV, p = 0.03; positive vs. negative, p = 0.003) and ¹⁸F-FDG PET/CT (stages I–II vs. III–IV, p = 0.001. positive vs. negative, p <0.001). These results suggest that tracer uptake characteristics were consistent between ¹⁸F-Alfatide and ¹⁸F-FDG PET/CT imaging, and thus, the ¹⁸F-Alfatide PET SUV in lymph nodes may provide complementary molecular information for risk stratification of ESCC patients (118).

Breast Cancer

The RGD accumulation was thought to represent the sum of uptake in neovasculature and tumor cells according to the immunohistochemical staining in IDC patients (108). ¹⁸F-AH111585 tracer uptake either homogeneously distributed in tumors or appearing within the tumor rim in metastatic breast cancer patients, which was consistent with the general principles of angiogenesis localization within the outer zones of breast tumors, where viable tumors exist (129–132). Vatsa et al. reported the results of ⁶⁸Ga-DOTA-RGD2 PET/CT in patients with locally advanced breast carcinoma (LABC) and found both ⁶⁸Ga-DOTA-RGD2 and ¹⁸F-FDG were similarly able to diagnose primary and metastatic lymph nodes, and RGD-based imaging showed additional value for distinguishing nonspecific ¹⁸F-FDG uptake due to an infectious or inflammatory etiology (133). The sensitivity and specificity of ¹⁸F-FPPRGD2 PET for identifying either breast lesions or metastatic lesions were higher than those for ¹⁸F-FDG PET/CT (95.7% vs. 87.0%, and 100% vs. 57.1%) (63). The relative uptake values of glucose metabolism and angiogenesis imaging were also investigated. In newly diagnosed or recurrent breast cancer, all SUV_max for ¹⁸F-FPPRGD2 were lower than those for ¹⁸F-FDG, although the differences were not statistically significant (63). Again, the ¹⁸F-Alfatide II SUV_max was also lower than the ¹⁸F-FDG SUV_max (3.77 ± 1.78 vs. 7.37 ± 4.48) in breast cancer lesions in the study of Wu et al., and the difference was significant (p <0.05). However, in HER2 (–) and ER(+) breast cancer lesions, ¹⁸F-Alfatide II uptake was higher than ¹⁸F-FDG uptake (88). It is worthy noting that RGD PET may neglect the distant metastasis in liver; for example, Kumar et al. found that ⁶⁸Ga-DOTA-RGD was not retained in liver metastases in 9 patients (16%) that were discovered with ¹⁸F-FDG imaging (119, 133). The high background activity of liver regions may contribute to this phenomenon (60).

The relative uptake values of RGD and FDG varied with the pathological or molecular phenotypes of breast cancer. Higher uptake values were observed in malignant compared with benign breast lesions on both ¹⁸F-Alfatide II and ¹⁸F-FDG imaging (all p <0.05). However, ¹⁸F-Alfatide II imaging showed a higher sensitivity (97.6% vs. 78.6%), lower specificity (54.5% vs. 81.8%), lower Youden index (52.1% vs. 60.4%), and lower area under the curve (0.738 vs. 0.838) on SUV_max analysis. These results indicate that ¹⁸F-Alfatide II has comparable diagnostic value to ¹⁸F-FDG, but may not be superior for identification of breast cancer (88). Concerning the molecular phenotypes in breast cancer patients, the SUV_max from ⁶⁸Ga-NOTA-RGD-BBN (⁶⁸Ga-BBN-RGD) PET was lower in ER(−) primary lesions than in ER(+) primary lesions (p <0.01). Additionally, ¹⁸F-Alfatide II uptake in the triple-negative subtype was significantly lower than that those in the luminal A and luminal B subtypes. However, no significant differences in uptake of either ¹⁸F-Alfatide II or ⁶⁸Ga-BBN-RGD were observed between HER2(+) and HER2(−) subtypes in either patients with suspected breast cancer on screening mammography or patients who underwent breast cancer radical mastectomy (p = 0.6589) (88, 116). In patients with large or locally advanced IDC, the RGD SUV_max and SUV_mean differed significantly between the HER2(+) and HER2(−) groups (2.90 ± 0.75 vs. 2.42 ± 0.59, p = 0.04 for SUV_max; 1.95 ± 0.53 vs. 1.60 ± 0.38, p = 0.04 for SUV_mean) and showed significantly higher values in the ER/PR(+), HER2(−) and ER/PR(+), HER2(+) subgroups versus the ER/PR(−), HER2(−) subgroup in large or locally advanced IDC. ¹⁸F-FDG uptake was higher in HER2(+) breast cancer lesions than in the other 3 subtypes (triple-negative subtype, luminal A and luminal B subtypes) (88, 89). Both RGD-based tracers offer great performance for identifying breast cancer, but not superior to ¹⁸F-FDG. However, RGD-based tracers may be superior to ¹⁸F-FDG in detecting breast cancer that is strongly ER(+) and HER2(−). This implies that differing HER2 status, even if the hormonal receptor status is the same, can cause a difference in FDG uptake. At the same time, different hormonal receptor status can cause a difference in RGD uptake, even if the HER2 status is the same.

Hepatocellular Carcinoma

As described above for the biodistribution of RGD-based tracers in the liver, two out of nine patients showed lower tracer accumulation in the HCC lesion as compared to the remaining liver parenchyma (72, 73). A similar lack of RGD-based tracer uptake was reported in liver metastases in breast cancer patients (60, 119). These findings were unexpected based on results demonstrating that 77% of investigated HCC specimens were highly vascularized with integrin αvβ3 expression compared with only 22% of normal liver tissue showing detectable αvβ3 expression (134), supporting the assumption that RGD uptake should be relatively higher in HCC than in normal liver tissue. The contradictory hypothesis that high RGD uptakes in HCC and later observations of hypointense tumors in the liver needs to be further clarified. The relatively clear background for ¹⁸F-FDG imaging in the liver appears to result from elevated glucose-6-phosphatase levels in liver cells. Overall, the research to date indicates the unsuitability and associated challenges of RGD-based imaging for HCC tumors or diseases with frequent liver metastases.

Rectal Carcinoma

A study comparing ¹⁸F-FPRGD2 and ¹⁸F-FDG imaging in LARC patients reported that all primary LARC lesions with obvious ¹⁸F-FDG accumulation (SUV_max, 16.5 ± 8) could also be detected by ¹⁸F-FPRGD2 uptake (SUV_max, 5.4 ± 1.5), even though ¹⁸F-FPRGD2 uptake was lower than that of ¹⁸F-FDG (p <0.001). However, only 18 (51%) pelvic lymph nodes showed ¹⁸F-FPRGD2 uptake among 35 pelvic lymph nodes detected by ¹⁸F-FDG imaging. A moderate positive correlation was observed between ¹⁸F-FPRGD2 and FDG uptake (SUV_max, Pearson’s r = 0.49, p = 0.0026; SUV_mean, Pearson’s r = 0.54, p = 0.0007) (66).

Renal Cancer

In a study of RGD-based imaging for renal cancer by Mena et al., all primary or metastatic renal tumors with a target tumor lesion larger than 2.0 cm could be detected by ¹⁸F-AH111585 PET/CT correctly, based on increased tracer retention with an average SUV_80%max of 6.4 ± 2.0, TBR of 1.5 ± 0.4 and TMR of 8.5 ± 3.4. ¹⁸F-AH111585 imaging also identified higher uptake values in chromophobe RCCs than in non-chromophobe RCCs (average SUV_80%max, 8.2 ± 1.8 vs. 5.4 ± 1.4, p = 0.020; average TNR, 1.5 ± 0.4 vs. 0.9 ± 0.2, p = 0.029), and, similarly, αvβ3 integrin expression showed a strong correlation with the SUV_80%max in chromophobe and nonchromophobe RCC (61). In another study involving patients with metastatic RCC, the ¹⁸F-FPPRGD2 SUV_max was lower than that on ¹⁸F-FDG PET/CT (4.4 ± 2.9 vs. 7.8 ± 5.6, p <0.001), and the detection rates of metastatic RCC lesions were comparable between ¹⁸F-FPPRGD2 PET/CT (55/60) and ¹⁸F-FDG PET/CT (52/60) (97).

In the study by Withofs et al., ¹⁸F-FPRGD2 PET/CT allowed estimation of both intra- and interindividual variability of integrin αvβ3 expression successfully, the correlation between ¹⁸F-FPRGD2 uptake and αvβ3 expression varied among different cases. In whole tumor samples, ¹⁸F-FPRGD2 uptake correlated significantly with αvβ3 expression. In the subgroup analysis of ccRCC, the ¹⁸F-FPRGD2 PET signal correlated with integrin αvβ3 expression on tumor cells, whereas in the pRCC group, the signal correlated with integrin αvβ3 expression on blood vessels. Immunohistochemical examination on tumor cells showed that integrin αvβ3 expression was significantly higher on ccRCC than on pRCC (3.6 ± 2 vs. 2.14 ± 1.8, p = 0.0099). However, for ¹⁸F-FPRGD2 uptake as a reflection of integrin αvβ3 staining on entire tumors, the mean SUV_max did not different between ccRCC (4.1 ± 1.2) and pRCC (3.3 ± 0.7) (67). These results indicated that integrin αvβ3 may highly expressed on tumor cells in ccRCC and new blood vessels (endothelial cells) in pRCC. ¹⁸F-FPRGD2 PET/CT can reliably depict αvβ3 expression in renal tumors but is only representative of angiogenesis when tumor cells do not significantly express integrin αvβ3.

Prostate Cancer

Neoplastic cells of human prostate cancer are characterized by the overexpression of both integrin αvβ3 and gastrin-releasing peptide receptor (GRPR) (135). ⁶⁸Ga-BBN-RGD targets both αvβ3 and GRPR and thus was tested for its ability to improve the detection rate of primary prostate cancer. ⁶⁸Ga-BBN-RGD PET/CT could successfully revealed 3 of 4 (75%) lesions and all 14 metastasized lymph nodes. It could also detect 20 bone lesions in seven patients, among which 11 showed a positive signal on ^99mTc-MDP bone scans and only 5 of these lesions were detectable with MRI (74). In a recently published clinical study in prostate cancer patients using ¹⁸F-Galacto-RGD PET, 58 of 74 (78.4%) bone lesions and 2 of 5 metastatic lymph nodes were identified. Thus, RGD imaging was feasible for achieving good visualization and detection of bone metastases due to low background activity in the surrounding normal bone tissue (136).

Importantly, prostate-specific membrane antigen (PSMA), a well-known target in prostate cancer, is not expressed in non-prostate tumors nor in healthy vasculature, but is expressed in the endothelial cells of tumor-associated neovasculature (137). PSMA expression appears to be prognostic biomarker in HNSCC, osteosarcoma, colorectal cancer, adenocarcinoma of the pancreas, lung cancer and others (138). In prostate cancer patients, the correlation of ¹⁸F-Galacto-RGD uptake and PSMA expression was also investigated. For all patients including one outlier or not with a very high PSMA value of 1,935 ng/ml, a significant and inverse correlation was identical between PSMA expression and ¹⁸F-Galacto-RGD SUV_mean. Thus, RGD PET may serve as a new method for monitoring the status of PSMA.

Cervical Cancer and Ovarian Cancer

In a study including 52 lesions in five patients with cervical cancer and ovarian cancer, ¹⁸F-FPPRGD2 imaging identified 48 lesions from 50 lesions on ¹⁸F-FDG imaging. Tracer uptake was significantly lower than ¹⁸F-FDG uptake in the lesions in terms of SUV_max (3.7 ± 1.3 vs. 6.0 ± 1.8, p <0.001), SUV_mean (2.6 ± 0.7 vs. 4.2 ± 1.3, p <0.001), TBR based on SUV_max (2.4 ± 1.0 vs. 2.6 ± 1.0, p <0.04), and TBR based on SUV_mean (1.9 ± 0.6 vs. 2.4 ± 1.0, p <0.003). Lesion accumulation of ¹⁸F-FPPRGD2 was mildly correlated with ¹⁸F-FDG uptake (SUV_max r = 0.39, p <0.005; SUV_mean r = 0.29, p <0.04) (90).

Brain or Bone Metastasis

Almost all RGD-containing imaging agents showed no or very low tracer retention in normal brain tissue, as reported by preliminary studies and case reports, indicating that RGD-based radiotracer does not penetrate the BBB but can localize in tumor tissue through the disruption of the BBB (43, 61). In a study of RGD-based imaging of brain metastasis, ¹⁸F-Alfatide II PET could visualize all 20 brain lesions successfully, while only 10 lesions were visualized by ¹⁸F-FDG imaging, and 13 by CT. Similar to uptake at other tumor sites, ¹⁸F-FDG showed an overall higher SUV_max (10.0 ± 5.7) than ¹⁸F-Alfatide II (1.8 ± 1.1) in brain tumor lesions. However, ¹⁸F-Alfatide II showed a much better TNR (18.9 ± 14.1) than ¹⁸F-FDG (1.5 ± 0.5) in these brain lesions (47). A possible explanation is that the high physiologic glucose uptake in the surrounding normal brain tissues further enhanced RGD uptake, and low ¹⁸F-Alfatide II PET uptake in normal tissue may allow clear diagnosis and evaluation of tumor metastases in the brain (139). Another possible explanation for these results is that angiogenesis, the target of ¹⁸F-Alfatide II, is one of the key pathophysiologic processes in brain metastases (47, 140).

In a study of RGD-based imaging of bone metastasis in breast cancer patients, 18 lesions (33%) were detected with both ⁶⁸Ga-DOTA-RGD and ¹⁸F-FDG imaging in (119). In another larger prospective study comparing ¹⁸F-Alfatide II and ¹⁸F-FDG PET, bone metastases were divided into four groups: osteolytic, osteoblastic, mixed and bone marrow based on their characteristics (141). In 11 patients with a total of 126 bone metastasis lesions, ¹⁸F-Alfatide II PET showed a higher positive detection rate than ¹⁸F-FDG PET (92% vs. 77%). In the subgroup analysis, ¹⁸F-Alfatide II PET was comparable to ¹⁸F-FDG PET/CT for the detection of osteolytic (100% vs. 90%) and mixed bone metastases (100% vs. 90%), but superior to ¹⁸F-FDG PET/CT for detecting osteoblastic (70% vs. 53%) and bone marrow lesions (98% vs. 77%). The sensitivity of ¹⁸F-Alfatide II PET/CT for osteolytic, mixed, and bone marrow lesions was nearly 100% (120). The higher positive detection rate of ¹⁸F-Alfatide II PET for bone marrow metastatic lesions may be due to either subclonal selection of integrin αvβ3-expressing tumor cell populations or upregulation of integrin αvβ3 in the bone microenvironment during the early phase of bone metastasis (142). Interestingly, RGD-containing tracer uptake was not lower than that of FDG in bone metastatic sites (4.27 ± 2.42 vs. 4.18 ± 2.58, p >0.05), which may be related to abundant integrin αvβ3 expression on the metastatic tumor cells, endothelial cells, and osteoclasts, whereas glycolysis is increased only within metastatic tumor cells and not in endothelial cells or osteoclasts (143, 144).

Both types of tracers showed significantly lower accumulation in osteoblastic lesions than that in osteolytic and mixed lesions (p <0.001 for all comparisons). The SUV_max of ¹⁸F-FDG in osteoblastic lesions was also significantly lower than that in bone marrow metastases (p <0.05). However, the ¹⁸F-Alfatide II SUV_max did not differ between osteoblastic lesions and bone marrow metastases. Another case report with ⁶⁸Ga-DOTA-RGD2 imaging, it clearly depicted a left-sided frontal tumor without ¹⁸F-FDG PET/CT uptake. Considering the limitations of ¹⁸F-FDG PET in a setting of low glycolytic activity, ⁶⁸Ga-DOTA-RGD2 PET/CT may be a clinically useful imaging modality for early detection of recurrent osteosarcoma (145).

Definition of Tumor Subvolumes Using RGD-Containing Peptides as Radiotracers

For radiation treatment planning, imaging quality is vitally important for biological target volume definition. Beer et al. fused images from ¹⁸F-Galacto-RGD PET and MRI and/or multislice CT (MSCT) scanning in HNSCC patients. The mean gross tumor volume (GTV) was 26.4 ± 20.2 cm³, and when a SUV threshold >3.0 was applied, the mean tumor subvolume was only 3.4 ± 3.4 cm³, accounting for 13.1 ± 11.7% of the GTV (107). These results confirm the imaging quality and heterogeneous subvolume achieved with RGD-containing peptide tracers, which allows for adequate manual fusion with MRI or MSCT using anatomic landmarks. As displayed in a HNSCC patient population by Durante et al., ⁶⁸Ga-NODAGA-RGD and ¹⁸F-FDG PET/CT individually led to inconsistent tumor subvolume delineation. The tracer avid tumor volume (TATV) calculated from RGD imaging tended to be larger than the metabolic tumor volume (MTV) calculated from ¹⁸F-FDG imaging when a 42% SUV_max fixed threshold similarly to MTV delineation was used (p = 0.085) (87). That difference in tumor delineation is assumed to be attributable to the difference in tracer targeting between FDG and RGD, as mostly homogeneous FDG uptake is significantly correlated with glucose metabolism and heterogeneous RGD uptake is associated with αvβ3 on angiogenesis. In addition, the physical properties of radionuclides fluorine-18 and gallium-68 also should be considered. At the same time, the 42% SUV_max of RGD was much lower than that of ¹⁸F-FDG, which may also have contributed to the calculation of a larger volume for the same tumor. In LARC cases examined with ¹⁸F-FPRGD2 imaging, the baseline integrin tumor volume (ITV) 70% (ITV_70%, delineated using a threshold of 70% of the SUV_max) was moderately correlated with the MTV_40% (delineated using a threshold of 40% of the SUV_max) and total lesion glycolysis (calculated as SUV_mean × MTV_40%) (66).

However, such results have not been reproduced in Lewis lung carcinoma (LLC) tumor-bearing C57BL/6 mice. The GTV determined from ¹⁸F-Alfatide PET was smaller and much closer to the pathological volume than that from ¹⁸F-FDG PET although a similar 40% of the SUV_max threshold was used (146). Several points are worth discussing regarding this difference. First, the expression pattern of αvβ3 varies among cancer types; it is mainly expressed on the neovasculature of HNSCC but on both the neovasculature and tumor cells in lung cancer (107, 111, 147). Secondly, different radionuclides were used in the two studies with ¹⁸F-Alfatide and ⁶⁸Ga-NODAGA-RGD. Additionally, differences in metabolism between animals and humans may also influence the results. Importantly, it is unscientific to define the cutoff value for tumor subvolume delineation in reference to the established threshold for FDG. Instead, threshold optimization for estimating tumor volume based on a spatial comparison of αvβ3 expression on whole-tumor histological slices needs to be performed.

Assessment of Therapeutic Response and Adverse Events Using RGD-Containing Peptides as Radiotracers

Because RGD is a marker of αvβ3 expression on angiogenesis which is of paramount importance for tumor oxygenation, RGD-containing PET tracers have been considered as a potentially valuable tool for monitoring treatment response and identifying patients less likely to respond to therapy. Several clinical pilot studies have explored their value in assessing treatment efficacy and adverse events, with encouraging results for both antiangiogenic therapy and chemoradiotherapy. Knowledge of the precision of repeated imaging parameters is a prerequisite for serial response measurements and, therefore, response to treatment. In the study by Sharma et al., solid tumor patients underwent two ¹⁸F-AH111585 imaging sessions within an interval of 10 days before commencement of therapy. No significant change in the SUV_peak was observed between the two scans, and the 95% reference range of spontaneous fluctuations was 35–39% (148). Measurements of ¹⁸F-FPPRGD2 uptake in normal organs also showed no significant changes between pre-, 1-week post- and 6-week post-therapy scans (149). Thus, with their protocol, the serial reproducibility of RGD-based tracer uptake supports further investigation of these novel tracers as biomarkers of treatment response ( Table 2 ).

Table 2.

Assessment of therapeutic response and adverse events using RGD-containing peptides as radiotracers.

Therapeutic regimen	Parameters	Prognosis	Threshold	Tracer agents	Tumor type	Reference
Single antiangiogenic therapy	higher baseline lesion SUVmean	better response and PFS	SUVmean = 3.82	18F-Alfatide	locally advanced and metastatic malignancies	(150, 151)
	lower SUVmax in normal organs	less adverse events	SUVmax-liver = 4.57; SUVmax-spleen = 6.77; SUVmax-cardia = 2.10
Pazopanib followed by combination therapy with paclitaxel	lower baseline lesion SUVmean	prolonged PFS	–	18F-AH111585	platinum-resistant/refractory ovarian cancer	(152)
	higher decreases of lesion SUVmean	better response	–
Concurrent chemoradiotherapy	lower baseline and 3-week tumor SUVmax and TNR	better response	baseline SUVmax = 1.57; 3-week SUVmax = 1.35; 3-week TNR = 19.3	18F-Alfatide	GBM	(153)
Concurrent chemoradiotherapy	lower baseline tumor SUVmax, SUVpeak, TNR, TBR, TMR	better response	SUVmax = 5.65; SUVpeak = 4.46; TNR = 7.11; TBR = 5.41; TMR = 11.75	18F-Alfatide	advanced NSCLC	(154)
Concurrent chemoradiotherapy	lower 3-month tumor SUVmean	better response	–	18F-RDG-K5	locally advanced HNSCC	(99)
Concurrent long-course chemoradiotherapy	higher baseline SUVmax and SUVmean	better TRG	RGD SUVmax = 5.6; SUVmean = 4.5 FDG SUVmax = 17; SUVmean = 10.4	18F-FPRGD2 and 18F-FDG	locally advanced rectal cancer	(66)
	higher baseline SUVmax	better PET response	–

PFS, progression free survival; SUV_mean, mean standard uptake values; SUV_max, maximum standard uptake values; SUV_peak, peak standard uptake values; TNR, tumor-to normal ratio; TBR, tumor-to-blood ratio; TMR, tumor-to-muscle ratio; TRG, tumour regression grade; GBM, glioblastoma multiform; NSCLC, non-small cell lung cancer; HNSCC, squamous cell carcinoma of head and neck.

Significant differences in RGD-based tracer uptake before and after antiangiogenic therapy have been reported in several studies (90, 97, 149). In GBM patients receiving bevacizumab-containing therapy, decreases of less than 15% in the SUV_max and angiogenic volumes from ¹⁸F-FPPRGD2 imaging at 1 week predicted recurrence and poor prognosis, while decreases of more than 50% predicted good outcomes to treatment (149). In another study including ovarian and cervical cancer, patients who experienced clinical disease progression, complete response, and partial response showed decreases in the lesional ¹⁸F-FPPRGD2 SUV_mean of 1.6, 25.2, and 7.9%, respectively, with similar decreases in the ¹⁸F-FDG SUV_mean of 9.4, 71.8, and 76.4%, respectively (90). The above studies mainly evaluated the changes in tracer uptake with the application of different antiangiogenic combination therapies, which may confound our understanding of RGD-based imaging for monitoring the response. Li et al. successfully investigated the relationships of ¹⁸F-Alfatide with the response to single antiangiogenic therapy in solid malignancies, higher pretreatment SUV_peak and SUV_mean were seen in responding tumors compared with non-responding tumors (4.98 ± 2.34 vs. 3.59 ± 1.44, p = 0.048; 3.71 ± 1.15 vs. 2.95 ± 0.49, p = 0.036). Cases with an ¹⁸F-Alfatide SUV_mean above 3.82 showed longer progression-free survival (PFS) than those with a SUV_mean less than 3.82. These results provided clinical evidence of the effectiveness of RGD-based PET at baseline for predicting the response to antiangiogenic therapy, suggesting this approach could potentially replace indicators based on change ratios in most previous studies (150). In addition, they also reported the predictive value of RGD-based imaging for antiangiogenic adverse events for the first time. Patients with low maximum ¹⁸F-Alfatide SUVs in the liver, spleen, and cardiac tissue were more likely to experience fatigue, hypertension, and nausea during Apatinib treatment (151). Thus, higher ¹⁸F-Alfatide uptake in specific normal organs predicted less adverse events, whereas higher uptake in tumor lesions predicted a better response to therapy. Higher accumulation in normal organs may indicate more regular, functional vasculature and sufficient blood flow, and higher accumulation within tumors may indicate greater expression of αvβ3 integrins (155, 156).

Results suggesting RGD-based tracer uptake at baseline can predict the response to antiangiogenic therapy have also been obtained in patients with platinum-resistant/refractory ovarian cancer (152). Of interest, a negative trend was observed between the baseline SUV_60,mean and PFS. There are several possible explanations for this contradictory observation. First, unlike the study by Li et al., the patients in this study received antiangiogenic combination therapy. It is well established that the addition of chemotherapy also enables a reduction in tumor size, which may account for the lack of association. Secondly, the PET response was assessed after 1 week, while the RECIST 1.1 response was evaluated after 3 months. Finally, preclinical work suggests that normalization of vessels occurs within hours and lasts 7–10 days, it is possible that performing the second PET scan at 7 days may have missed the neovascularization window (157). Overall, further research is needed to explore the potential of RGD-based tracers as biomarkers for response to antiangiogenic and antiangiogenic combination therapies.

The predictive value of RGD-based imaging for the response to chemoradiotherapy was also studied. Twenty-five newly diagnosed GBM patients who were ready for concurrent chemoradiotherapy (CCRT) 3–5 weeks after surgical resection were prospectively enrolled in a study by Zhang et al. The baseline ¹⁸F-Alfatide SUV_max, the 3-week SUV_max, and the TNR were found to be predictive of sensitivity to CCRT. The 3-week SUV_max (AUC = 0.846) was shown to be the best predictive parameter, with a 3-week SUV_max higher than 1.35 predicting worse efficacy (153). Further, Luan et al. explored the similar potential value of ¹⁸F-Alfatide PET/CT in locally advanced NSCLC. From their analysis, the pretreatment SUV_max, SUV_peak, TNR, TBR, and TMR were all significant parameters, with higher SUVs in non-responders than responders, and the TNR was shown to be an independent predictor (nonresponders vs. responders, 8.31 ± 0.61 vs. 6.53 ± 0.78, p <0.001) to CCRT (154). Chen et al. prospectively investigated the potential usefulness of RGD-K5 PET in HNSCC, patients whose primary tumors had lower accumulation of both tracers at 3 months achieved response to CCRT successfully (99). For LARC patients treated with CCRT, baseline ¹⁸F-FPRGD2 SUV_max and SUV_mean were negatively correlated with the tumor regression grade (TRG). The baseline SUV_max of both RGD and FDG imaging were significantly higher in TRG 0 cases than in the other patients. A cut-off value of 5.6 for RGD SUV_max identified TRG 0 status with 100% sensitivity and 66.7% specificity (AUC = 0.84) (66)..

From the research described above, it is interesting to note that RGD-based tracer uptake follows different trends according to differing treatment modalities. For antiangiogenic therapy, the higher uptake values may indicate a high density of effective target receptors and a better response to apatinib therapy. For CCRT, higher uptake values may represent high malignancy and severe hypoxia, which could contribute to increased chemoradiation resistance in tumors (158–160).

Conclusion and Other Perspectives

A variety of radiolabeled, RGD-based PET imaging agents have been developed and extensively investigated in terms of their value for tumor detection and diagnosis, patient stratification, and monitoring of treatment response. According to promising results, these PET imaging peptides were transferred successfully from preclinical animal studies to clinical human trials. Different RGD-based PET imaging can be used in patients with glioma, lung cancer, esophageal cancer, breast cancer, renal carcinoma and other tumors to differentiate benign and malignant lesions and also pathological types and stages, and also can further distinguish molecular subtypes of breast cancer, delineate tumor volume, and predict the efficacy of radiochemotherapy and antiangiogenic therapy. The results regarding their diagnostic and predictive value are mostly derived from preliminary studies, and continued research is necessary to guarantee their development for clinical application.

In addition, RGD-based imaging was useful as a noninvasive surrogate parameter of cardiovascular disease. Normal angiogenesis under pathological conditions in cardiovascular diseases such as carotid atherosclerosis, myocardial infarction or reperfusion were also characterized by the overexpression of αvβ3 (3, 4, 161). In atherosclerotic lesions, highly expressed αvβ3 were found in both macrophages and activated endothelial cells (105, 162). The uptakes of ⁶⁸Ga-DOTA-RGD and ¹⁸F-GalactoRGD PET increased in the atherosclerotic plaques in mice (95, 163). Clinically, RGD uptakes were heterogeneously distributed and significantly correlated with avβ3 integrin staining score in human atherosclerotic carotid plaques (164, 165). For ischemia tissue repair after myocardial infarction, αvβ3 is one of the necessary integrins involved in the formation of new capillaries (25, 166). RGD-based imaging of myocardial infarction observed consistent findings from studies of rats, specific RGD tracer accumulation uptake increased in vivo corresponding to infarcted and peri-infarct myocardial regions, and decreased after reperfusion (167–170). Tracer uptakes were expected to be used to monitor the process of myocardial repair (171–173). Makowski et al. confirmed the feasibility of ¹⁸F-Galacto-RGD to monitor vascular remodeling during evolving MI in human studies; tracer uptake was significantly correlated with infarct size and impairment of myocardial blood flow (174). Thus, RGD-based imaging of αvβ3 integrin expression could represent a novel promising imaging approach in determining the angiogenesis of carotid atherosclerosis and myocardial repair, which is potentially affecting the plaque vulnerability and left ventricular remodeling.

The RGD peptides also served as the new target for tumor treatment. RGD peptides could significantly reduce the functional vascular density, inhibit the adhesion and induce apoptosis of tumor cells according to inhibiting the interactions between integrins and their ligands (175–177). The RGD modified gene carriers and target drugs also showed special interest in delivery (178, 179). RGD peptides for imaging or tumor treatment all need to be further investigations for clinical translation.

It is important to note that contradictory reports about the correlation between RGD tracer uptake and αvβ3 expression level exist and that the ultimate meaning of αvβ3 expression in the context of angiogenesis is very complex and requires further characterization. The highly abnormal and dysfunctional vasculature created tumor hypoxic environment and impaired the ability of immune effector cells to penetrate solid tumors. In turn, stimulation of immune cell could also help normalize tumor vessels (180). Other studies have demonstrated the expression of integrin αvβ3 on activated macrophages by different methods (162, 181, 182). Therefore, RGD-based imaging targeting tumor neovascularization may offer a strategy to monitor the tumor microenvironment status and therapeutic response during immune checkpoint blockade or combination treatment. Moreover, the development of the clinically available applications of RGD-based tracers has mainly focus on radionuclide-labeled tracers, approaches to develop molecularly targeted nanoparticles for molecular imaging should be explored. Additionally, the simultaneous acquisition of functional parameters by MRI and PET may help compensate for the shortcomings of these different approaches. A thorough and complete assessment of different aspects of angiogenesis observed via multimodal imaging and biomomics characteristics should be implemented as part of the concept of personalized functional medicine.

Author Contributions

SY proposed the conception and edited the manuscript. LL participated and drafted the manuscript. XC and JY participated and edited the manuscript. All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Funding

This study was supported in part by the National Natural Science Foundation of China (grant nos. NSFC81872475 and NSFC82073345) and the Jinan Clinical Medicine Science and Technology Innovation Plan (202019060) to SY, the Academic Promotion Program of Shandong First Medical University (grant No. 2019ZL002) to JY, the National University of Singapore Start-up Grant (NUHSRO/2020/133/Startup/08), the NUS School of Medicine Nanomedicine Translational Research Programme (NUHSRO/2021/034/TRP/09/Nanomedicine), and the NUS School of Medicine Kickstart Initiative (NUHSRO/2021/044/Kickstart/09/LOA) to XC.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2022.837952/full#supplementary-material