Purpose: There is an unmet need for noninvasive markers to measure the biological effects of targeted agents, particularly those inhibiting the vascular endothelial growth factor (VEGF) receptor (VEGFR) pathway, and identify patients most likely to benefit from treatment. In this study, we investigated potential blood-based biomarkers for SU11248 (sunitinib malate), a multitargeted tyrosine kinase inhibitor, in patients with metastatic imatinib-refractory gastrointestinal stromal tumors.

Experimental Design: Patients (n = 73) enrolled in a phase I/II trial received SU11248 daily for 14 or 28 days followed by 14 days without treatment per cycle. Clinical benefit was defined as progression-free survival of >6 months. We assessed plasma markers, including VEGF and soluble VEGFR-2 (sVEGFR-2), and two cellular populations bearing VEGF receptors: monocytes and, in a subset of patients, mature circulating endothelial cells (CEC).

Results: Compared to patients with progressive disease, patients with clinical benefit had significantly greater increases in CECs (0.52 versus −−0.01 CEC/μL/d, P = 0.03) and smaller decreases in monocyte levels (47% versus 60%, P = 0.007) during cycle 1. VEGF increased by 2.2-fold and sVEGFR-2 decreased 25% during the first 2 weeks of treatment. Neither plasma marker correlated with clinical outcome although a modest inverse correlation was observed between sVEGFR-2 changes and plasma drug levels. Monocytes, VEGF, and sVEGFR-2 all rebounded towards baseline off treatment.

Conclusions: Monocytes, VEGF, and sVEGFR-2 were consistently modulated by treatment, suggesting that they may serve as pharmacodynamic markers for SU11248. Changes in CECs and monocytes, but not the plasma markers, differed between the patients with clinical benefit and those with progressive disease. These end points merit further investigation in future trials to determine their utility as markers of SU11248 activity and clinical benefit in gastrointestinal stromal tumors and other tumor types.

Targeted agents such as inhibitors of the vascular endothelial growth factor (VEGF) pathway have shown evidence of clinical activity in a variety of malignancies including colorectal, lung, and renal cell cancer (13). Despite this activity, it has been difficult to assess the biological effects of these agents as they seem to be primarily cytostatic when used as monotherapy. To optimize the clinical testing of these agents, there is an unmet need for validated biomarkers to measure their biological effects, determine their optimal dose level, identify patients most likely to benefit from a given agent, and monitor responses to treatment (4, 5).

One approach for assessing the biological activity of targeted agents is to evaluate drug-induced effects on tumor cells or the vasculature (6, 7). However, because this approach is invasive, it is not practical for routine clinical use. The development of blood-based or other noninvasive biomarkers would represent a significant advance.

Circulating endothelial cells (CEC) have emerged as a potentially useful biomarker for several reasons (813). Increased levels of CECs have been observed in cancer patients (14, 15), they are known to be mobilized in response to VEGF (1618) and express VEGF receptor (VEGFR)-2 (19, 20). At least two distinct populations of CECs have been identified: bone marrow–derived circulating endothelial progenitors (CEP), which may contribute to tumor neovascularization (21), and mature CECs, which are thought to be shed from established vessels (22, 23). Recently, we have shown that in murine models, VEGF pathway inhibitors can have differential effects on mature CECs and CEPs, and that inhibition of tumor angiogenesis is accompanied by an increase in mature CECs (11). Consistent with this model, a recent study suggests that an increase in apoptotic CECs, thought to be shed from the tumor vasculature, is associated with improved outcome in breast cancer patients receiving metronomic chemotherapy (13).

Levels of VEGF and soluble VEGFR-1 have been investigated in serum, plasma, or urine in clinical trials with mixed results (6, 24, 25). Recently, a soluble form of VEGFR-2 (sVEGFR-2) has been identified and was found to be elevated in cancer patients (2629).

In this study of patients with imatinib-resistant metastatic gastrointestinal stromal tumor (GIST), we sought to identify potential biomarkers for sunitinib malate (SU11248, Sutent; Pfizer, Inc., New York, NY), a multitargeted receptor tyrosine kinase inhibitor of VEGFR-1, VEGFR-2, VEGFR-3, platelet-derived growth factor receptor, KIT, and FLT-3 (30). Substantial clinical activity was observed in this study, with 54% of patients experiencing clinical benefit defined as objective response or progression-free survival for >6 months and partial tumor responses in 13% (31, 32). We examined baseline levels (prior to SU11248 treatment) and posttreatment changes in plasma VEGF and sVEGFR-2 levels, as well as two cellular markers, CECs and monocytes, which are known to express VEGFRs (33, 34). We show that all of these markers change after SU11248 administration and that for two of them—CECs, and monocytes—these changes correlated with clinical outcome.

Patients and study design. Patients enrolled in an Institutional Review Board–approved protocol for a phase I/II study of SU11248 for metastatic or unresectable GIST. All patients had objective evidence of disease progression prior to enrollment and were resistant, refractory, or intolerant to imatinib mesylate. Patients were ≥18 years old and signed an informed consent. Two main schedules of drug administration were used: 4 weeks of treatment followed by a 2-week rest period (4/2 schedule) or 2 weeks of treatment followed by a 2-week rest period (2/2 schedule). Patients received doses of 25, 50, or 75 mg of SU11248 per day orally. All patients for whom biomarker data is presented in this study were treated with the 50 mg dose with the exception of the monocyte analysis. For this analysis (n = 73 total), 64 patients received 50 mg, 5 patients received 25, and 4 patients received the 75 mg dose. Clinical benefit (CB) was defined as progression-free survival ≥6 months, and progressive disease (PD) defined as progression-free survival <6 months. Samples were analyzed blinded to clinical outcome. Blood was also obtained from healthy volunteers who consented to an Institutional Review Board–approved protocol.

Plasma markers and pharmacokinetic assessments. Plasma VEGF and sVEGFR-2 levels were quantified by ELISA kits (R&D Systems, Minneapolis, MN; ref. 26). The assays were run under Good Laboratory Practice conditions at Alta Analytical Laboratory (San Diego, CA) and the performance specifications of each ELISA were validated (35). Pharmacokinetic analyses of SU11248 and its major active metabolite, SU12662, were done by means of a validated and sensitive liquid chromatography–mass spectrometry–mass spectrometry method as previously described (36). The lower limits of detection for the assay were 0.099 ng/mL for SU11248 and 0.088 ng/mL for SU12662.

Antibodies for flow cytometry. Anti–human VEGFR-1-APC and VEGFR-2PE were purchased from R&D Systems. CD14-PE, CD31-FITC, and CD45-PerCp were purchased from BD Biosciences (San Jose CA). CD133-APC and CD14-FITC were purchased from Miltenyi Biotec (Auburn, CA). P1H12-PE (CD146-PE) was purchased from Chemicon (Temecula, CA).

VEGF binding and receptor expression in peripheral blood mononuclear cells. Blood was collected in EDTA tubes and peripheral blood mononuclear cells (PBMC) were isolated by density gradient (Histopaque 1077; Sigma, St. Louis, MO). Cells were incubated with biotinylated rhVEGF followed by Avidin-FITC as a secondary antibody (Fluorokine Kit, R&D Systems) and additional antibodies as indicated. For VEGFR-1 and VEGFR-2 staining, red cell lysis was done using FACSLyse solution (BD Biosciences). Flow cytometry was done using a FACSCalibur (BD Biosciences) and data analyzed using FlowJo (Tree Star, CA).

Measurement of CECs. Blood was collected and PBMCs were isolated by Ficoll gradient as described above. Freezing medium (40% RPMI 1640, 10% DMSO, 50% plasma) was added to the cell pellet and samples underwent a controlled freeze using an isopropanol bath in a −80°C freezer. Samples were then stored in liquid nitrogen until the day of analysis. This method yielded >85% viable cells after thawing (data not shown). For each patient, all samples (baseline and subsequent follow-ups) were thawed and analyzed at the same day to minimize interassay variability. CECs were enumerated using four-color flow cytometry as described previously (15) with the modifications described below. Briefly, cells were washed with PBS with 1% albumin and incubated with a panel of four antibodies to establish markers including CD45, CD31, CD146, and CD133 (13, 15, 19, 37). CECs were defined as negative for hematopoietic marker CD45, positive for endothelial markers CD31 and CD146, and negative for the progenitor marker CD133; CEPs had the same phenotype but were CD133+. Human umbilical vein endothelial cells (Cambrex, East Rutherford, NJ) were used as positive control for CD146 staining and WERI cells (American Type Culture Collection, Manassas, VA) as positive controls for CD133. The percentage of stained cells was determined comparing with appropriate isotype controls. The volume of blood analyzed was determined using the lymphocyte and monocyte numbers obtained from the patient's differential blood count.

Statistical analysis. Descriptive statistics were used to characterize cell counts and percentage changes. For all comparisons, except those involving CECs, values were approximately normal in distribution and are presented as means with SEs. For these comparisons, Student's t tests were used for testing the significance. For counts that were not normally distributed (CECs), medians and interquartile ranges are provided. The Wilcoxon signed rank test was used to explore changes over time. The Mann-Whitney test (Wilcoxon rank sum test) was used to test for differences in the percent change between patients experiencing CB and those who did not, and between treated patients and normal controls. P = 0.05 was considered statistically significant. All tests were two-sided.

Plasma levels of VEGF and sVEGFR-2 change in patients with GIST during treatment with SU11248. We first investigated plasma VEGF and sVEGFR-2 as biomarkers for SU11248. At baseline, the mean plasma levels of VEGF and sVEGFR-2 were 104 pg/mL (range, 23-355) and 8,350 pg/mL (range, 5,822-14,245), respectively. There was no significant difference between the CB and PD groups at baseline for either marker, although a trend was observed for VEGF (86.8 pg/mL for CB versus 120.2 pg/mL for PD groups, P = 0.3).

We then investigated changes in plasma markers during treatment with SU11248. The drug was administered once daily on two schedules, 2 weeks on treatment followed by 2 weeks of rest (2/2 schedule) or 4 weeks on treatment followed by 2 weeks of rest (4/2 schedule). This permitted us to assess changes in biomarkers both during the first 2 weeks of treatment in the two groups combined, during which time patients in both groups received identical treatment. After the first 2 weeks of cycle 1, VEGF levels increased significantly by 2.2-fold from baseline (P = 0.008). sVEGFR-2, in contrast with VEGF, decreased significantly to 75% of baseline over the first 2 weeks of treatment (P < 0.001). The increase in VEGF, and the decrease in sVEGFR-2, did not differ between the CB and PD groups, but after 2 weeks of treatment, the decrease in sVEGFR-2 showed a modest inverse linear correlation with trough plasma drug levels of SU11248 and its major metabolite SU12662 (R2 = 0.33; Fig. 1C).

Fig. 1.

Reciprocal changes in VEGF and sVEGFR-2 during treatment with SU11248 and during the 2-wk rest period. Plasma levels of VEGF and sVEGFR-2 were measured at the beginning of each cycle and at the end of each treatment period over four cycles (n = 53). Data shown is for patients receiving the 4/2 schedule and is presented as ratio to baseline levels. A, plasma VEGF increased during the 4-wk treatment period to a mean of 2.5 times the baseline level, and then decreased to 1.37 times of the baseline at the end of the 2-wk rest period. B, sVEGFR-2 levels decreased during 4 wks of treatment to a mean level of 65% of baseline, then rebounded during the 2-wk rest period. C, the decrease in sVEGFR-2 at cycle 1, day 14 (both 2/2 and 4/2 groups combined) showed a modest inverse correlation with trough plasma drug levels of SU11248 and its major metabolite SU12662 (R2 = 0.33).

Fig. 1.

Reciprocal changes in VEGF and sVEGFR-2 during treatment with SU11248 and during the 2-wk rest period. Plasma levels of VEGF and sVEGFR-2 were measured at the beginning of each cycle and at the end of each treatment period over four cycles (n = 53). Data shown is for patients receiving the 4/2 schedule and is presented as ratio to baseline levels. A, plasma VEGF increased during the 4-wk treatment period to a mean of 2.5 times the baseline level, and then decreased to 1.37 times of the baseline at the end of the 2-wk rest period. B, sVEGFR-2 levels decreased during 4 wks of treatment to a mean level of 65% of baseline, then rebounded during the 2-wk rest period. C, the decrease in sVEGFR-2 at cycle 1, day 14 (both 2/2 and 4/2 groups combined) showed a modest inverse correlation with trough plasma drug levels of SU11248 and its major metabolite SU12662 (R2 = 0.33).

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Overall, VEGF and sVEGFR-2 continued to change in a reciprocal manner and the changes during treatment reversed directions during the rest period for both groups. This pattern is illustrated for the 4/2 group in Fig. 1A and B. These changes were consistently observed through four cycles of treatment. A similar pattern was observed for the 2/2 group (data not shown).

Screening of PBMCs expressing VEGFRs. We next investigated potential blood-based cellular markers for SU11248 activity. Given the pan-VEGFR inhibition by SU11248, we started by screening peripheral blood from normal volunteers to identify VEGF-binding cells that might be useful as biomarkers. As seen in Fig. 2A, VEGF binding was predominantly observed in mononuclear cells positive for the monocyte marker CD14; out of the total CD14+ population, 65.3% showed VEGF binding, as compared with only 5.3% of CD14− cells. These VEGF-binding cells were also identified as monocytes based on their forward and side scatter characteristics (data not shown). The majority of CD14+ cells expressed VEGFR-1 (Fig. 2B) and <1% of these cells were VEGFR-2+ (Fig. 2C). Only 5% of CD14− cells expressed VEGFR-1 (Fig. 2D). Therefore, the majority of VEGF binding to PBMCs was observed on CD14+ monocytes, which express VEGFR-1 but not VEGFR-2. Limited VEGFR-1 staining was also observed on neutrophils (data not shown). These results suggest that CD14+ monocytes are the major VEGF-binding population in peripheral blood, consistent with their known expression of VEGFR-1. We also assessed VEGFR-1 and VEGFR-2 expression in CD45−/P1H12+ CECs. VEGFR-1 and VEGFR-2 staining was detected in 47% and 50% of CECs, respectively, with 19% staining for both antigens (Fig. 2E). Given their low frequency compared with monocytes (see below), it is unlikely that CECs contributed significantly to total VEGF binding in peripheral blood. We then investigated changes in CECs and monocytes in patients treated with SU11248.

Fig. 2.

Screening PBMCs for VEGF binding and receptor expression. Histograms indicated fluorescent staining on X-axis and relative frequency on Y-axis. VEGF binds primarily to CD14+ cells (A). CD14+ PBMCs express VEGFR-1 (B) but not VEGFR-2 (C). VEGFR-1 staining is present in a minority of CD14− PBMC (D); isotype controls (gray). VEGFR-1 and VEGFR-2 staining on CECs (E).

Fig. 2.

Screening PBMCs for VEGF binding and receptor expression. Histograms indicated fluorescent staining on X-axis and relative frequency on Y-axis. VEGF binds primarily to CD14+ cells (A). CD14+ PBMCs express VEGFR-1 (B) but not VEGFR-2 (C). VEGFR-1 staining is present in a minority of CD14− PBMC (D); isotype controls (gray). VEGFR-1 and VEGFR-2 staining on CECs (E).

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Monocyte counts decrease in patients with GIST during treatment with SU11248. We investigated the changes in the number of monocytes and other peripheral blood cell types in 73 patients treated for 2 weeks with SU11248 (both 2/2 and 4/2 schedule; Fig. 3A) during cycle 1. Total WBC count dropped from a mean of 8,320 to 6,430 cells/μL (20.4%, P < 0.001; Student's t test). Monocytes underwent the largest proportionate decrease, from a mean of 770 to 350 cells/μL (53.7%, P < 0.001). Neutrophils were among other cell types that decreased during this period, albeit to a lesser extent (20.3%). After a period of 2 weeks rest, only monocytes increased significantly, from a mean of 340 to 520 cells/μL (95.6%, P < 0.001). This pattern continued during subsequent cycles, as shown for the 32 patients treated in the 4/2 group in Fig. 3B. A similar pattern was observed in the 2/2 group (data not shown). Lymphocytes, in contrast, did not change significantly over time. Baseline monocyte levels did not differ between the CB and PD groups, but after the first 2 weeks of treatment in cycle 1 patients with CB had a significantly smaller decrease in monocytes compared with the PD group (60.4% versus 47.4%, P = 0.007; Fig. 3D). There were no significant differences in the changes observed in patients receiving 25 mg (n = 5), 50 mg (n = 64) or 75 mg (n = 4), although there was a limited power to detect differences given the small sample number of patients in the 25 and 75 mg groups.

Fig. 3.

Changes in monocyte counts after 2 wks of treatment and 2 wks of rest. A, WBC and differential blood count were assessed at baseline and after 2 wks of treatment (cycle 1) of treatment with SU11248. Monocyte counts decreased by 53.7% (P < 0.001). The lymphocyte population did not significantly change. B, monocyte counts decreased during treatment, then rebounded after 2 wks of rest and continued this pattern of change during subsequent cycles (4/2 group). C, monocyte counts in the PD group decreased by 60.4% after the first 2 wks of treatment, whereas the counts in the CB group decreased only by 47.4% (P = 0.007). *, P = 0.005; **, P = 0.002; ***, P < 0.001.

Fig. 3.

Changes in monocyte counts after 2 wks of treatment and 2 wks of rest. A, WBC and differential blood count were assessed at baseline and after 2 wks of treatment (cycle 1) of treatment with SU11248. Monocyte counts decreased by 53.7% (P < 0.001). The lymphocyte population did not significantly change. B, monocyte counts decreased during treatment, then rebounded after 2 wks of rest and continued this pattern of change during subsequent cycles (4/2 group). C, monocyte counts in the PD group decreased by 60.4% after the first 2 wks of treatment, whereas the counts in the CB group decreased only by 47.4% (P = 0.007). *, P = 0.005; **, P = 0.002; ***, P < 0.001.

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CECs in patients with GIST treated with SU11248. CECs were assessed by four-color flow cytometry using a panel of established antibodies as previously described (15), with modifications detailed in Patients and Methods (Fig. 4). CEC analyses were included in only a subset of the patients in the study (n = 16) treated exclusively with the 50 mg dose using the 4/2 schedule because the sample preparation and analysis was done only at one study site (Dana-Farber Cancer Institute) and was introduced after enrollment in this cohort had started.

Fig. 4.

Detection of CECs using four-color flow cytometry. PBMCs were stained with CD45, CD31, CD146, and CD133 with human umbilical vein endothelial cells added as a positive control. A, forward and side scatter plot with gate for viable PBMCs. B, viable cells assessed for CD45-PerCp and CD31-FITC staining. C, CD45−/CD31+ cells, which were then assessed for P1H12-PE and CD133-APC staining (black rectangle). Mature CECs defined as CD45−/CD31+/P1H12+/CD133− (purple rectangle).

Fig. 4.

Detection of CECs using four-color flow cytometry. PBMCs were stained with CD45, CD31, CD146, and CD133 with human umbilical vein endothelial cells added as a positive control. A, forward and side scatter plot with gate for viable PBMCs. B, viable cells assessed for CD45-PerCp and CD31-FITC staining. C, CD45−/CD31+ cells, which were then assessed for P1H12-PE and CD133-APC staining (black rectangle). Mature CECs defined as CD45−/CD31+/P1H12+/CD133− (purple rectangle).

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In normal controls (n = 15), the median level of mature CECs was 0.54 CEC/μL (interquartile range, 0.37-0.69). For patients with GIST, median baseline levels of CECs were ∼2-fold higher [1.09 (0.67-3.54) CEC/μL; P = 0.01; Fig. 5]. Within the 16 GIST patients, 7 showed CB, whereas 9 had PD. At baseline, there was no significant difference in CEC levels between these two groups (0.93 versus 1.21 CECs/μL; P = 0.37, respectively).

Fig. 5.

Baseline levels of CECs in patients with GIST. A, CECs were quantified by four-color flow cytometry. Higher levels of mature CECs were observed in patients with GIST at baseline, with a median level of 1.09 (0.67-3.54) cells/μL compared with 0.54 (0.37-0.69) cells/μL for normal controls (P = 0.01). Interquartile range () and the range of the data (•).

Fig. 5.

Baseline levels of CECs in patients with GIST. A, CECs were quantified by four-color flow cytometry. Higher levels of mature CECs were observed in patients with GIST at baseline, with a median level of 1.09 (0.67-3.54) cells/μL compared with 0.54 (0.37-0.69) cells/μL for normal controls (P = 0.01). Interquartile range () and the range of the data (•).

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Next, we assessed changes in CECs during SU11248 treatment (Fig. 6A). Overall, the median CEC number increased by 3.5-fold during the first cycle of therapy. All seven patients with CB had an increase in their CEC numbers between baseline and a second sample taken 6 to 20 days after the initiation of SU11248 to a median value of 3.97 (3.8-52.4) CEC/μL. In contrast, only three out of nine patients with PD displayed an increase in their CEC numbers, with the group median value decreasing to 0.45 (0.02-12.57) CEC/μL at first follow-up. Thus, there was a statistically significant difference in the rate of change in CECs per day between patients with CB and PD [0.52 versus −0.01 CEC/μL/d (0.3 to 8.1 versus −0.1 to 0.3, respectively); P = 0.03]. In the majority of patients assessed, CECs returned to near baseline levels by the end of the first treatment cycle, as illustrated for two patients in Fig. 6B.

Fig. 6.

Changes in CECs during treatment with SU11248. A, change in CECs in patients with CB versus PD. All seven patients with CB had an increase in their CEC numbers between baseline and a second sample taken 6 to 20 d after the initiation of SU11248 to a median value of 3.97 (3.80-52.46) CEC/μL and only three out of nine patients with PD had an increase in their CEC numbers to a median value of 0.45 (0.02-12.57) CEC/μL. Bottom, circled data on a different scale. The rate of change per day in CECs was significantly different between patients with CB and PD: 0.52 (0.30-8.10) versus −−0.01 (−−0.1 to 0.3) cells/μL/d (P = 0.03). B, changes in CECs over time. Pattern of change in CECs in two representative patients with CB treated with SU11248. After an initial increase during the first cycle of treatment, CEC levels returned to near-baseline levels.

Fig. 6.

Changes in CECs during treatment with SU11248. A, change in CECs in patients with CB versus PD. All seven patients with CB had an increase in their CEC numbers between baseline and a second sample taken 6 to 20 d after the initiation of SU11248 to a median value of 3.97 (3.80-52.46) CEC/μL and only three out of nine patients with PD had an increase in their CEC numbers to a median value of 0.45 (0.02-12.57) CEC/μL. Bottom, circled data on a different scale. The rate of change per day in CECs was significantly different between patients with CB and PD: 0.52 (0.30-8.10) versus −−0.01 (−−0.1 to 0.3) cells/μL/d (P = 0.03). B, changes in CECs over time. Pattern of change in CECs in two representative patients with CB treated with SU11248. After an initial increase during the first cycle of treatment, CEC levels returned to near-baseline levels.

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A major obstacle in the clinical development of targeted agents, particularly those inhibiting the VEGF pathway, is the lack of noninvasive biomarkers for identifying patients most likely to respond to a given therapy or monitor response during treatment. As a step towards addressing these needs, we investigated plasma and peripheral blood cellular biomarkers for SU11248, a multitargeted tyrosine kinase inhibitor with activity against VEGFR-1, VEGFR-2, and VEGFR-3, and other receptors. This work focused on biomarkers from the VEGF pathway.

We observed that plasma VEGF levels increased by ∼2.2-fold on average after the first 2 weeks of treatment, then decreased to near baseline levels after the 2-week rest period and repeated a similar pattern during subsequent cycles. In previous reports, the levels of plasma, serum, or urine VEGF have been evaluated before and during treatment with mixed results (6, 24, 38, 39). Interestingly, in preclinical models, an increase in VEGF was observed after treatment of either tumor-bearing or normal mice with a VEGFR-2 blocking monoclonal antibody, but not with small molecule VEGFR-2 inhibitors (5), implying that VEGF blockade at the level of the ligand versus the receptor can elicit different responses. Possible mechanisms for the changes observed in this study include the induction of hypoxia (in normal and/or tumor tissues), increased release of VEGF from existing stores (i.e., platelets), or alterations in VEGF clearance from blood. These results highlight the complexities in VEGF regulation and suggest that plasma VEGF may serve as a pharmacodynamic marker of antiangiogenic therapy but does not seem to be a predictive marker of CB in patients with GIST.

sVEGFR-2 was previously found to be elevated in patients with certain types of cancer (26, 28, 29). We investigated whether sVEGFR-2 may serve as a marker of activity of SU11248. In general, sVEGFR-2 changed in the opposite direction to VEGF, decreasing on average to 75% of baseline value during the first 2 weeks of treatment, and rising to near-baseline after the rest period. The source of sVEGFR-2 remains unclear, but the striking inverse correlation with VEGF levels suggests a coregulation or link between the two. Changes in sVEGFR-2 were inversely associated with trough plasma drug concentrations, although not strictly correlated. The pattern and magnitude of changes of sVEGFR-2 were similar between patients with CB and PD, suggesting that sVEGFR-2 may be useful as a pharmacodynamic marker of drug exposure but not CB in patients with GIST. Preliminary results from a phase I study of the pan-VEGFR inhibitor AZD2171 (40), and recently published results using SU11248 in a phase I study (41) and phase II study in patients with RCC (42), suggest that sVEGFR-2 may be a useful pharmacodynamic marker for other drugs targeting VEGFRs, and furthermore, have utility as a biomarker across other types of malignancies as well.

Next, we investigated cellular markers in the peripheral blood. As a starting point, we screened PBMCs for VEGF binding and identified monocytes as the major population (Fig. 2). As previously reported, monocytes were found to have VEGFR-1 but not VEGFR-2 immunoreactivity (43). The presence of VEGFR-1 and VEGFR-2 staining was also observed on CECs, although only a minority were doubly positive for both receptors (Fig. 2E). Because these two populations express VEGFRs, we hypothesized that they may serve as biomarkers of SU11248 activity.

To test this hypothesis, monocytes were evaluated in all patients, whereas CECs were evaluated in a subset of patients enrolled in a phase I/II trial of SU11248 for metastatic, imatinib-resistant GIST. Patients were treated with an intermittent dosing schedule, which provided an opportunity to observe changes during both treatment and rest periods. Monocytes underwent the largest proportional decrease of any PBMC population after the first 2 weeks of treatment (Fig. 3A). Furthermore, monocytes subsequently underwent the largest proportional increase during the 2-week rest period, and this pattern continued throughout the treatment duration. By contrast, lymphocytes and other PBMC populations did not change to a similar extent during this time period, suggesting that the decrease in monocytes was unlikely to be due solely to a generalized bone marrow effect. There was, however, a 14% rate of grade 3 or 4 neutropenia observed overall, suggesting that the drug did have at least some effect on other PBMC populations over time (31).

There are several potential explanations for the SU11248-induced decrease in monocytes and, to a lesser extent, neutrophils. VEGFR-1 is known to be involved in monocyte migration (34) and has been implicated in hematopoietic reconstitution after chemotherapy, particularly in the recovery of the myeloid lineage which include both monocytes and neutrophils (44). Other targets of SU11248 that might play a role include c-KIT, FLT-3, platelet-derived growth factor receptor-α and -β, which are all known to be expressed on monocytes or granulocytic/monocytic precursors (34, 43, 4549). Consistent with this possibility, impaired growth of monocyte/macrophage colonies was observed after treatment with imatinib, an inhibitor of c-KIT and PDGR (45). Other studies have shown that Flt-3 has a stimulatory effect on hematopoietic stem cells and a specific proliferative/differentiative action on granulocytic/monocytic precursors when combined with KIT-ligand and macrophage-specific colony-stimulating factor (46).

In this study, the patients with PD had a larger decrease in their monocyte counts after the initial 2 weeks of treatment with SU11248 compared with patients with CB. This observation is, at first glance, counterintuitive. It might be expected that a reduction in monocyte levels would be associated with a higher degree of target inhibition and greater clinical efficacy. The mechanism underlying this phenomenon is unknown. One possible explanation is that for patients with CB, there may be greater SU11248 binding and uptake by the tumor because of the presence of increased levels of target receptors (i.e., VEGFR and KIT), leaving less unbound drug circulating in the plasma. The observation merits further investigation.

We also investigated changes in CECs in a subset of 16 patients at baseline and during treatment with SU11248. Consistent with earlier reports from patients with other malignancies, we found that at baseline, CECs were elevated in patients with GIST compared with normal controls (15). The median levels observed for normal controls (0.55 CEC/μL), was similar to previously reported levels measured by flow cytometry (50). There was no significant difference in CECs at baseline between the two outcome groups, although given the sample size, there was a limited power to detect modest differences. CEPs were present at significantly lower levels than mature CECs in all patients and were undetectable in some patients. No consistent pattern of change in CEPs was observed (data not shown). Future studies may require a higher number of events (i.e., >500,000 PBMCs per analysis) to obtain reliable CEP data.

Interestingly, an increase in CECs was observed in seven of seven patients with CB during the first cycle as compared with four of nine patients with PD (Fig. 6A). The changes in CECs could be observed after a week of treatment but it seemed that such changes may be transient in at least some cases. This finding, and other recent studies, suggest that more detailed kinetic studies should be conducted to determine the optimal time for CEC measurement (12, 51). This is particularly relevant given the recent observations that changes in CECs may be observed within hours of treatment (52), whereas in other studies, changes have been observed a month or more after starting treatment (13).

Given that CECs were only assessed in a subset of patients retrospectively, it should be regarded as an exploratory rather than definitive analysis. Nevertheless, it does suggest that CECs merit further investigation in larger, preferably randomized studies. The findings are consistent with our previous study in which antiangiogenic treatment caused an increase in mature CECs, but not bone marrow–derived CEPs, in murine models (11). As an increase in mature CECs was seen in tumor-bearing mice, but non–tumor-bearing mice receiving the same treatment, we proposed a model in which these mature CECs were derived at least in part from the shedding of damaged tumor endothelium. In this study, CB was associated with an increase in mature CECs. Interestingly, CB was also associated with increased tumor endothelial apoptosis in a separate analysis of 20 paired tumor biopsies from the same clinical trial. Patients with CB experienced an 8-fold induction in tumor endothelial apoptosis after 11 to 14 days of treatment, which was significantly greater than the 1.8-fold induction observed in patients with PD (53). Taken together, these observations suggest that CB is associated with an induction of tumor endothelial apoptosis and increase in mature CECs over the first 2 weeks of treatment, supporting the possibility that shed tumor endothelia are a source of mature CECs. This hypothesis is also consistent with a recent report that an increase in apoptotic CECs was associated with benefit in patients with breast cancer treated with metronomic chemotherapy (13). Furthermore, in a recent phase I study of the vascular targeting agent ZD6126, an increase in CECs was seen within the first day of treatment, which was thought to reflect damage to tumor endothelium. There are, however, other potential explanations for the observed changes. For example, increased VEGF levels during treatment may have prolonged the survival of CECs in the circulation (23, 54). It is worth noting, however, that VEGF levels rose similarly in the CB and PD groups, suggesting that VEGF levels did not solely account for the CEC changes observed in this trial. Additional studies are needed to further characterize the sources of these mature CECs.

In summary, we identified an approach that involves screening PBMCs for relevant molecular targets that may lead to the identification of novel markers for SU11248 and other targeted agents. Furthermore, we found that two plasma markers, VEGF and sVEGFR-2, as well as two cellular markers, CECs and monocytes, were modulated during treatment with SU11248 in patients with imatinib-resistant GIST. Changes in the cellular markers correlated with clinical outcome and sVEGFR-2 changes correlated inversely with plasma drug concentrations. These markers merit further investigation in larger studies to determine whether they will be useful for identifying patients likely to benefit from treatment with, and monitoring response to, SU11248 and other targeted agents in patients with GIST or other tumor types.

Grant support: NIH grant P50 CA101942-02 and P20 CA090578, and by Pfizer Global Research and Development, La Jolla, CA. J.V. Heymach is a Damon Runyon-Lilly Clinical Investigator supported in part by the Damon Runyon Cancer Research Foundation (CI 24-04) and the American Society for Clinical Oncology Career Development Award.

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

Note: Presented in part at the Annual Meeting of the American Society of Clinical Oncology, 2005.

1
Hurwitz H, Fehrenbacher L, Novotny W, et al. Bevacizumab plus irinotecan, fluorouracil, and leucovorin for metastatic colorectal cancer.
N Engl J Med
2004
;
350
:
2335
–42.
2
Johnson DH, Fehrenbacher L, Novotny WF, et al. Randomized phase II trial comparing bevacizumab plus carboplatin and paclitaxel with carboplatin and paclitaxel alone in previously untreated locally advanced or metastatic non-small-cell lung cancer.
J Clin Oncol
2004
;
22
:
2184
–91.
3
Yang JC, Haworth L, Sherry RM, et al. A randomized trial of bevacizumab, an anti-vascular endothelial growth factor antibody, for metastatic renal cancer.
N Engl J Med
2003
;
349
:
427
–34.
4
Davis DW, McConkey DJ, Abbruzzese JL, Herbst RS. Surrogate markers in antiangiogenesis clinical trials.
Br J Cancer
2003
;
89
:
8
–14.
5
Bocci G, Man S, Green SK, et al. Increased plasma vascular endothelial growth factor (VEGF) as a surrogate marker for optimal therapeutic dosing of VEGF receptor-2 monoclonal antibodies.
Cancer Res
2004
;
64
:
6616
–25.
6
Heymach JV, Desai J, Manola J, et al. Phase II study of the antiangiogenic agent SU5416 in patients with advanced soft tissue sarcomas.
Clin Cancer Res
2004
;
10
:
5732
–40.
7
Davis DW, Shen Y, Mullani NA, et al. Quantitative analysis of biomarkers defines an optimal biological dose for recombinant human endostatin in primary human tumors.
Clin Cancer Res
2004
;
10
:
33
–42.
8
Monestiroli S, Mancuso P, Burlini A, et al. Kinetics and viability of circulating endothelial cells as surrogate angiogenesis marker in an animal model of human lymphoma.
Cancer Res
2001
;
61
:
4341
–4.
9
Shaked Y, Bertolini F, Man S, et al. Genetic heterogeneity of the vasculogenic phenotype parallels angiogenesis; implications for cellular surrogate marker analysis of antiangiogenesis.
Cancer Cell
2005
;
7
:
101
–11.
10
Rafii S, Lyden D, Benezra R, Hattori K, Heissig B. Vascular and haematopoietic stem cells: novel targets for anti-angiogenesis therapy?
Nat Rev Cancer
2002
;
2
:
826
–35.
11
Beaudry P, Force J, Naumov GN, et al. Differential effects of vascular endothelial growth factor receptor-2 inhibitor ZD6474 on circulating endothelial progenitors and mature circulating endothelial cells: implications for use as a surrogate marker of antiangiogenic activity.
Clin Cancer Res
2005
;
11
:
3514
–22.
12
Willett CG, Boucher Y, di Tomaso E, et al. Direct evidence that the VEGF-specific antibody bevacizumab has antivascular effects in human rectal cancer.
Nat Med
2004
;
10
:
145
–7.
13
Mancuso P, Colleoni M, Calleri A, et al. Circulating endothelial-cell kinetics and viability predict survival in breast cancer patients receiving metronomic chemotherapy.
Blood
2006
;
108
:
452
–9.
14
Beerepoot LV, Mehra N, Vermaat JS, Zonnenberg BA, Gebbink MF, Voest EE. Increased levels of viable circulating endothelial cells are an indicator of progressive disease in cancer patients.
Ann Oncol
2004
;
15
:
139
–45.
15
Mancuso P, Burlini A, Pruneri G, Goldhirsch A, Martinelli G, Bertolini F. Resting and activated endothelial cells are increased in the peripheral blood of cancer patients.
Blood
2001
;
97
:
3658
–61.
16
Kalka C, Tehrani H, Laudenberg B, et al. VEGF gene transfer mobilizes endothelial progenitor cells in patients with inoperable coronary disease.
Ann Thorac Surg
2000
;
70
:
829
–34.
17
Hattori K, Dias S, Heissig B, et al. Vascular endothelial growth factor and angiopoietin-1 stimulate postnatal hematopoiesis by recruitment of vasculogenic and hematopoietic stem cells.
J Exp Med
2001
;
193
:
1005
–14.
18
Asahara T, Takahashi T, Masuda H, et al. VEGF contributes to postnatal neovascularization by mobilizing bone marrow-derived endothelial progenitor cells.
Embo J
1999
;
18
:
3964
–72.
19
Peichev M, Naiyer AJ, Pereira D, et al. Expression of VEGFR-2 and AC133 by circulating human CD34(+) cells identifies a population of functional endothelial precursors.
Blood
2000
;
95
:
952
–8.
20
Reyes M, Dudek A, Jahagirdar B, Koodie L, Marker PH, Verfaillie CM. Origin of endothelial progenitors in human postnatal bone marrow.
J Clin Invest
2002
;
109
:
337
–46.
21
Peters BA, Diaz LA, Polyak K, et al. Contribution of bone marrow-derived endothelial cells to human tumor vasculature.
Nat Med
2005
;
11
:
261
–2.
22
Lin Y, Weisdorf DJ, Solovey A, Hebbel RP. Origins of circulating endothelial cells and endothelial outgrowth from blood.
J Clin Invest
2000
;
105
:
71
–7.
23
Solovey A, Gui L, Ramakrishnan S, Steinberg MH, Hebbel RP. Sickle cell anemia as a possible state of enhanced anti-apoptotic tone: survival effect of vascular endothelial growth factor on circulating and unanchored endothelial cells.
Blood
1999
;
93
:
3824
–30.
24
Stopeck A, Sheldon M, Vahedian M, Cropp G, Gosalia R, Hannah A. Results of a phase I dose-escalating study of the antiangiogenic agent, SU5416, in patients with advanced malignancies. PG-2798-805.
Clin Cancer Res
2002
;
8
:
2798
–805.
25
Drevs J, Hofmann I, Hugenschmidt H, et al. Effects of PTK787/ZK 222584, a specific inhibitor of vascular endothelial growth factor receptor tyrosine kinases, on primary tumor, metastasis, vessel density, and blood flow in a murine renal cell carcinoma model.
Cancer Res
2000
;
60
:
4819
–24.
26
Ebos JM, Bocci G, Man S, et al. A naturally occurring soluble form of vascular endothelial growth factor receptor 2 detected in mouse and human plasma.
Mol Cancer Res
2004
;
2
:
315
–26.
27
Gora-Tybor J, Blonski JZ, Robak T. Circulating vascular endothelial growth factor (VEGF) and its soluble receptors in patients with chronic lymphocytic leukemia.
Eur Cytokine Netw
2005
;
16
:
41
–6.
28
Wierzbowska A, Robak T, Wrzesien-Kus A, Krawczynska A, Lech-Maranda E, Urbanska-Rys H. Circulating VEGF and its soluble receptors sVEGFR-1 and sVEGFR-2 in patients with acute leukemia.
Eur Cytokine Netw
2003
;
14
:
149
–53.
29
Verstovsek S, Lunin S, Kantarjian H, et al. Clinical relevance of VEGF receptors 1 and 2 in patients with chronic myelogenous leukemia.
Leuk Res
2003
;
27
:
661
–9.
30
Mendel DB, Laird AD, Xin X, et al. In vivo antitumor activity of SU11248, a novel tyrosine kinase inhibitor targeting vascular endothelial growth factor and platelet-derived growth factor receptors: determination of a pharmacokinetic/pharmacodynamic relationship.
Clin Cancer Res
2003
;
9
:
327
–37.
31
Maki RG, Fletcher JA, Heinrich MC, et al. Results from a continuation trial of SU11248 in patients (pts) with imatinib (IMEur Cytokine Netw)-resistant gastrointestinal stromal tumor (GIST) [2005 ASCO Annual Meeting Proceedings].
J Clin Oncol
2005
;
23
:
9011
.
32
Demetri GD, George S, Heinrich MC, et al. Clinical activity and tolerability of the multi-targeted tyrosine kinase inhibitor SU11248 in patients (pts) with metastatic gastrointestinal stromal tumor (GIST) refractory to imatinib mesylate [abstract 3273]. Proc Am Soc Clin Oncol 2003;22.
33
Clauss M, Weich H, Breier G, et al. The vascular endothelial growth factor receptor Flt-1 mediates biological activities. Implications for a functional role of placenta growth factor in monocyte activation and chemotaxis.
J Biol Chem
1996
;
271
:
17629
–34.
34
Barleon B, Sozzani S, Zhou D, Weich HA, Mantovani A, Marme D. Migration of human monocytes in response to vascular endothelial growth factor (VEGF) is mediated via the VEGF receptor flt-1.
Blood
1996
;
87
:
3336
–43.
35
DeSilva B, Smith W, Weiner R, et al. Recommendations for the bioanalytical method validation of ligand-binding assays to support pharmacokinetic assessments of macromolecules.
Pharm Res
2003
;
20
:
1885
–900.
36
Fiedler W, Serve H, Dohner H, et al. A phase 1 study of SU11248 in the treatment of patients with refractory or resistant acute myeloid leukemia (AML) or not amenable to conventional therapy for the disease.
Blood
2005
;
105
:
986
–93.
37
Solovey A, Lin Y, Browne P, Choong S, Wayner E, Hebbel RP. Circulating activated endothelial cells in sickle cell anemia.
N Engl J Med
1997
;
337
:
1584
–90.
38
Drevs J, Zirrgiebel U, Schmidt-Gersbach CI, et al. Soluble markers for the assessment of biological activity with PTK787/ZK 222584 (PTK/ZK), a vascular endothelial growth factor receptor (VEGFR) tyrosine kinase inhibitor in patients with advanced colorectal cancer from two phase I trials.
Ann Oncol
2005
;
16
:
558
–65.
39
Braybrooke JP, O'Byrne KJ, Propper DJ, et al. A phase II study of razoxane, an antiangiogenic topoisomerase II inhibitor, in renal cell cancer with assessment of potential surrogate markers of angiogenesis.
Clin Cancer Res
2000
;
6
:
4697
–704.
40
Drevs J, Medinger M, Mross K, et al. Phase I clinical evaluation of AZD2171, a highly potent VEGF receptor tyrosine kinase inhibitor, in patients with advanced tumors [2005 ASCO Annual Meeting Proceedings].
J Clin Oncol
2005
;
23
:
3002
.
41
Faivre S, Delbaldo C, Vera K, et al. Safety, pharmacokinetic, and antitumor activity of SU11248, a novel oral multitarget tyrosine kinase inhibitor, in patients with cancer.
J Clin Oncol
2006
;
24
:
25
–35.
42
Motzer RJ, Michaelson MD, Redman BG, et al. Activity of SU11248, a multitargeted inhibitor of vascular endothelial growth factor receptor and platelet-derived growth factor receptor, in patients with metastatic renal cell carcinoma.
J Clin Oncol
2006
;
24
:
16
–24.
43
Sawano A, Iwai S, Sakurai Y, et al. Flt-1, vascular endothelial growth factor receptor 1, is a novel cell surface marker for the lineage of monocyte-macrophages in humans.
Blood
2001
;
97
:
785
–91.
44
Hattori K, Heissig B, Wu Y, et al. Placental growth factor reconstitutes hematopoiesis by recruiting VEGFR1(+) stem cells from bone-marrow microenvironment.
Nat Med
2002
;
8
:
841
–9.
45
Dewar AL, Domaschenz RM, Doherty KV, Hughes TP, Lyons AB. Imatinib inhibits the in vitro development of the monocyte/macrophage lineage from normal human bone marrow progenitors.
Leukemia
2003
;
17
:
1713
–21.
46
Gabbianelli M, Pelosi E, Montesoro E, et al. Multi-level effects of flt3 ligand on human hematopoiesis: expansion of putative stem cells and proliferation of granulomonocytic progenitors/monocytic precursors.
Blood
1995
;
86
:
1661
–70.
47
Inaba T, Shimano H, Gotoda T, et al. Expression of platelet-derived growth factor β receptor on human monocyte-derived macrophages and effects of platelet-derived growth factor BB dimer on the cellular function.
J Biol Chem
1993
;
268
:
24353
–60.
48
Krettek A, Ostergren-Lunden G, Fager G, Rosmond C, Bondjers G, Lustig F. Expression of PDGF receptors and ligand-induced migration of partially differentiated human monocyte-derived macrophages. Influence of IFN-γ and TGF-β.
Atherosclerosis
2001
;
156
:
267
–75.
49
Selvaraj SK, Giri RK, Perelman N, Johnson C, Malik P, Kalra VK. Mechanism of monocyte activation and expression of proinflammatory cytochemokines by placenta growth factor.
Blood
2003
;
102
:
1515
–24.
50
Khan SS, Solomon MA, McCoy JP, Jr. Detection of circulating endothelial cells and endothelial progenitor cells by flow cytometry.
Cytometry B Clin Cytom
2005
;
64B
:
1
–8.
51
Duda DG, Cohen KS, Di Tomaso E, Scadden DT, Willett CG, Jain RK. Differential circulation kinetics during antiangiogenic therapy of four distinct blood cell populations expressing endothelial markers [2006 ASCO Annual Meeting Proceedings Part I].
J Clin Oncol
2006
;
24
:
3038
.
52
Beerepoot LV, Radema SA, Witteveen EO, et al. Phase I clinical evaluation of weekly administration of the novel vascular-targeting agent, ZD6126, in patients with solid tumors.
J Clin Oncol
2006
;
24
:
1491
–8.
53
Davis D, McConkey D, Heymach J, et al. Pharmacodynamic analysis of target receptor tyrosine kinase activity and apoptosis in GIST tumors responding to therapy with SU11248 [2005 ASCO Annual Meeting Proceedings].
J Clin Oncol
2005
;
23
:
3006
.
54
Schuch G, Heymach JV, Nomi M, et al. Endostatin inhibits the vascular endothelial growth factor-induced mobilization of endothelial progenitor cells.
Cancer Res
2003
;
63
:
8345
–50.