Sage Journals: Discover world-class research

Abstract

BACKGROUND:

Endostar (rh-endostatin) is a new recombinant human endostatin, which could inhibit cell proliferation, angiogenesis, and tumor growth.

OBJECTIVE:

To explore anti-angiogenesis short-term efficacy combined with neoadjuvant chemotherapy for stage IIIA (N2) non-small cell lung cancer (NSCLC), and identify the potential predictive factors.

METHODS:

We pathologically examined 26 patients diagnosed with stage IIIA (N2) NSCLC who received NP chemotherapy alone or combined with Endostar, respectively.

RESULTS:

Our results indicated that total clinical benefit rate (CBR) 87.5% and 64% ( $p=$ 0.76), respectively. The clinical benefit (CB) patients in the treatment group showed significant changes in endothelial progenitor cells (EPC), vascular endothelial growth factor (VEGF), blood flow (BF), permeability surface (PMS), and microvascular density (MVD) before and after treatment. Compared with CB patients in the control group, changes in EPC and MVD (only) before and after treatment were significant. The variation of EPC, PMS, and MVD before and after treatment in the treatment group showed positive correlation with tumor regression rate (TRR) and the variation of MVD, whereas those of EPC and PMS demonstrated positive correlations with variation of MVD before and after treatment.

CONCLUSION:

Our findings suggested that PMS and EPC may be used as a predictive factor for the short-term efficacy of the combined therapy in NSCLC.

Keywords

RH-endostatin non-small cell lung cancer predictor circulating endothelial progenitor cells perfusion imaging

1. Introduction

Lung cancer is the leading cause of cancer-related human deaths and this cancer comprises less than 15% of the overall 5-year survival rate for cancers worldwide [1]. Non-small cell lung cancer (NSCLC) – which includes adenocarcinoma, squamous cell, and large cell carcinomas – comprises approximately 85% of lung cancers [2]. Poor prognosis is expected with mediastinal lymph node-positive stage IIIA NSCLC, confirmed either preoperatively or by postoperative pathology [3].

With advances in the treatment of tumors, neoadjuvant therapy of locally advanced NSCLC has developed from single treatment to multidisciplinary treatment. As chemotherapy provides limited choice and shows significant toxicity, side effects, and low efficacy for the treatment of NSCLC, evidence has suggested that the combination of anti-tumor angiogenesis therapy (which showed low toxicity with targeted treatment) and the combined treatment with anti-angiogenesis and neoadjuvant chemotherapy might provide a promising clinical therapeutic strategy for patients with lung cancer. However, there are still no molecular indicators or predictors that can accurately predict the efficacy of this treatment.

Endostatin, a 20-kDa C-terminal cleavage product of collagen XVIII, was originally identified by O’Reilly et al. as a tumor-derived, highly active, and endothelial-specific angiogenic inhibitor [4]. Endostatin is a specific inhibitor of endothelial proliferation and angiogenesis, and recombinant endostatin has been shown to inhibit the growth of a wide variety of tumors in vivo and in vitro, with no toxic side effects [5, 6]. Systemic administration of non-refolded precipitated protein expressed in Escherichia coli caused growth regression of Lewis lung carcinoma, T241 fibrosarcoma, B16 melanoma, and EOMA cells in a xenograft model, and no drug resistance was noted in these tumor cell lines [4, 7]. A recent study also indicated that endostatin can specific inhibit the migration of human umbilical vein endothelial cells (HUVECs) in response to vascular endothelial growth factor (VEGF), which result in the suppression of tumor growth [8]. In addition, more evidence shows that endostatin can interact with integrin on the surface of human umbilical vein endothelial cells, and, when used in a soluble form, endostatin functions as an antagonist of integrins to inhibit endothelial cell function [9, 10]. Moreover, several studies showed that endostatin can induce apoptotic effects in endothelial cells through the Bcl-2 pathway [11, 12, 13]. Until now, as one of the most broad spectrum and least toxic angiogenesis inhibitors, endostatin were reported to inhibit 65 different tumor types and modifies 12% of the human genome to downregulate pathological angiogenesis without side effects [14], thus suggesting it could be used in the combination of effective synergy in the control of tumor angiogenesis and growth.

Endostar is a novel recombinant human endostatin expressed and purified in Escherichia coli with an additional nine-amino acid sequence and forming another His-tag structure [15, 16]. To determine the short-term efficacy of Endostar combined with neoadjuvant chemotherapy for stage IIIA (N2) NSCLC, we compared its efficacy combined with NP (vinorelbine and cisplatin) chemotherapy versus the efficacy of NP chemotherapy alone. We used imaging, cell factors, morphological results, and other factors to identify the effective predictors for the short-term efficacy of the combined clinical therapy in NSCLC.

2. Materials and methods

2.1 Patients

Thirty patients with IIIA (N2) NSCLC hospitalized between April 2011 and December 2013 were selected and divided into a treatment group (20 patients) and a control group (10 patients). We used a random number table as the randomized method. The selected patients met the following inclusion criteria: (1) pathologically confirmed as NSCLC (histological confirmation); (2) evaluated as stage IIIA (N2) based on auxiliary examination; (3) imaging, laboratory examination, and other examination suggested the tumor could be completely resected; (4) received no chemotherapy or anti-angiogenesis treatment; (5) lesion could be measured; (6) signed the informed consent form.

The exclusion criteria were: pregnant women, women in lactation, or potentially pregnant women; serious infection; serious heart disease; uncontrollable nervous or mental disorders; serious diabetes mellitus; significant sign of hemorrhage; history of other tumors in past 5 years. The study was approved by the Clinical Research Ethics Committee of Tianjin Medical University Cancer Institute and Hospital. All of the patients signed the informed consent form after giving informed consent.

Twenty-six patients (16 in the treatment group and 10 in the control group) received a total of 52 cycles of treatment, and none experiencing any adverse events or died. Among those patients who were suitable for efficacy evaluation, differences in age, sex, and tumor characteristics were not statistically significant ( $p>$ 0.05) [17].

2.2 Treatments

This study adopted a randomized, controlled, and open clinical experiment design. The treatment group received NP combined with Endostar chemotherapy, whereas the control group received only NP.

Medications: Endostar 7.5 mg/m ${}^{2}$ (1.2 $\times$ 10 ${}^{5}$ U/m ${}^{2}$ ) was added to 500 ml saline to form a uniform intravenous (IV) drip administered for 3–4 hours.

Continuous IV administration from day 1 to day 14: vinorelbine 25 mg/m ${}^{2}$ for IV drip from day 1 and day 8; cisplatin (DDP) 75 mg/m ${}^{2}$ for IV divided into day 1 to day 3; dose of chemotherapy could be adjusted to integer, within 5%, 21 days for one cycle. Patients of the treatment group and the control group both completed two cycles of treatment as planned. The efficacy was evaluated after two cycles of treatment.

Evaluation of the patients’ indicators

Main indicators for the efficacy evaluation included: response rate (RR), clinical benefit rate (CBR), and tumor regression rate (TRR). Efficacy was evaluated using the Response Evaluation Criteria Solid Tumors version 1.1 (RECIST1.1) guideline.

RR was defined as percent of patients with complete response (CR) or partial response (PR) in all the patients evaluable for efficacy; CBR was defined as the percent of patients with CR, PR, or stable disease (SD); TRR was defined as the proportion of reduction in the longest dimension of the target lesion.

2.3 CT perfusion imaging

A 64-slice CT scanner (Discovery CT750; General Electric Healthcare [GE], Fairfield, CT) was used at 100 kV and 100 mA. The center of the tumor received dynamic scanning. Patients received an antecubital intravenous injection of 50 ml iohexol contrast agent at 5 ml/second (s) before the scanning. The scanning began 5 s after the injection: one exposure each second for 60 s with the thickness of the scanning slice being 5 mm.

After the scanning, GE’s ADW4.0 workstation Perfusion software was used for data processing. The thoracic aorta was set as the input artery and brachiocephalic vein as the output vein. Images were processed and reconstructed via computer. The following parameters of the perfusion were measured: capillary permeability surfaces (PMS), mean transit time (MTT), blood volume (BV), and blood flow (BF). CT scanning and data measurement were conducted before and after the treatment.

2.4 Flow cytometry (FCM) analysis

After peripheral blood was taken from patients before and after the treatment, FCM analysis (BD FACSAria ${}^{\text{TM}}$ II; BD Biosciences, San Jose, CA) was performed to measure the number of activated circulating vascular endothelial cells (aCECs) and endothelial progenitor cells (EPC) subpopulations.

According to the manufacturers’ protocols, peripheral blood mononuclear cells (PBMC) were isolated from whole blood using Ficoll-Paque ${}^{\text{TM}}$ PLUS (GE Healthcare, Fairfield, CT) gradient centrifugation. The aCECs were defined as CD146 ${}^{+}$ /CD105 ${}^{+}$ /CD45 ${}^{-/\text{dim}}$ whereas EPC were defined as CD34 ${}^{+}$ /CD133 ${}^{+}$ / VEGFR2 ${}^{+}$ ; 2 $\times$ 10 ${}^{6}$ mononuclear cells were blocked with FcR blocking reagent for 15 min at 4 ${}^{\circ}$ C.

PBMC (100 $\mu$ L) was added in the isotype control tube and incubated for 30 min in darkness with 10 $\mu$ L each of the following basic combinations of fluorescein isothiocyanate (FITC), phycoerythrin (PE), and phycoerythrin cyanine 5 (PE-Cy5) IgG1 isotype control mouse antibodies.

The same procedure was performed in a test tube incubated for 30 min in the dark with 10 $\mu$ L each of CD45PE-Cy5 (Beckman Coulter, Los Angeles, CA); CD146-PE (Beckman Coulter, Los Angeles, CA); CD105-FITC (Chemicon International, Inc., Temecula, CA); CD34PE-Cy5 (Abcam, Cambridge, UK); CD133-FITC (eBioscience, San Diego, CA); and VEGFR2-PE (Cell Signaling Technology, Danvers, MA) antibodies, respectively.

The samples were incubated with these desired antibodies at 4 ${}^{\circ}$ C in the dark for 30 min. Following incubation, cells were washed twice with PBS, resuspended in FACS flow solution, and analyzed immediately on a BD FACSAria ${}^{\text{TM}}$ cell sorter.

2.5 ELISA assay

According to the protocol for serum preparation after collection of the patients’ whole blood, the blood was allowed to clot by leaving it undisturbed at room temperature; 15–30 min later, the clot was removed by centrifuging at 1,000–2,000 g for 10 min. Following centrifugation, the liquid component was immediately transferred into a fresh tube. The serum VEGF concentration was measured by a human VEGF ELISA kit according to the manufacturer’s protocol (Boster Biotechnology, Wuhan, Hubei Province, China).

Table 1
Changes of predictors before and after the chemotherapy treatments

Factor	Treatment group ( $n=$ 16)				t value	$p$ value	Control group ( $n=$ 8)				t value	$p$ value
	Before the treatment		After the treatment				Before the treatment		After the treatment
aCECs	786.44	$\pm$ 1203.09	524.56	$\pm$ 823.82	0.923	0.371	170.50	$\pm$ 221.23	652.50	$\pm$ 1376.50	$-$ 1.031	0.337
EPC	818.62	$\pm$ 1202.34	369.19	$\pm$ 525.71	1.885	0.079	339.88	$\pm$ 262.92	833.62	$\pm$ 801.39	$-$ 1.906	0.098
VEGF	56.36	$\pm$ 56.71	40.00	$\pm$ 39.65	2.669	0.017	38.70	$\pm$ 14.01	31.08	$\pm$ 12.81	3.504	0.010
BF	44.80	$\pm$ 11.64	36.87	$\pm$ 7.99	2.979	0.009	38.22	$\pm$ 20.31	34.72	$\pm$ 23.76	1.472	0.184
BV	14.08	$\pm$ 6.57	12.55	$\pm$ 6.03	1.666	0.116	15.67	$\pm$ 4.31	16.05	$\pm$ 6.46	$-$ 0.229	0.826
PMS	21.39	$\pm$ 8.14	17.91	$\pm$ 7.73	2.964	0.010	18.95	$\pm$ 7.03	15.76	$\pm$ 6.30	2.309	0.054
MTT	12.91	$\pm$ 3.95	11.69	$\pm$ 4.26	1.330	0.203	14.20	$\pm$ 4.76	13.67	$\pm$ 4.44	1.003	0.349
MVD	52.18	$\pm$ 21.15	23.76	$\pm$ 19.35	5.740	0.000	57.38	$\pm$ 18.94	46.38	$\pm$ 21.35	$-$ 3.166	0.016

Figure 1.

In the treatment group, the percentages of PR, SD, and PD after two cycles of neoadjuvant chemotherapy combined with anti-angiogenesis therapy are 50%, 37%, and 13%, respectively. In the control group, the percentages of PR, SD, and PD after two cycles of neoadjuvant chemotherapy are 40%, 20%, and 40%, respectively.

2.6 Immunohistochemistry and micro vascular density (MVD) count

Fresh pathological specimens were fixed in formalin, and then embedded in paraffin. The sections were incubated and stained with anti-CD34 antibody (Golden Bridge Company, Beijing, China). After the specimens were dehydrated and mounted, the areas with most significant CD34 reaction (hot spots) were reviewed and scored under a light microscope, and the number of MVD in 10 random spots was counted at $\times$ 200 amplifications. The average of obtained numbers was taken as the MVD count.

2.7 Statistical analysis

All data were processed with SPSS v19.0 software (SPSS, Inc., Chicago, IL). Variables of normal distribution were represented with mean $\pm$ standard deviation. All tests were two-sided. T test and Z test were used to compare parameters of the two groups; Spearman parameter was used to analyze the correlation between the predictors, MVD, and TRR; and paired-sample T test was used to analyze predictors before and after the treatment. A p-value $<$ 0.05 was considered statistically significantly.

3. Results

3.1 Efficacy evaluation of Endostar combined with NP chemotherapy

To evaluate the efficacy of Endostar combined with NP chemotherapy, we first compared the efficacy between the treatment group and the control group. Comparing the efficacies in the treatment group and the control group, respectively, total RR were 50% and 40% ( $p=$ 1.00), total CBR were 87.5% and 64% ( $p=$ 0.76), and TRR were 19.7% and 7.1% ( $p=$ 0.036). In addition, the downstaging rates of the treatment group and control group were 12.5% and 0%, respectively (Fig. 1).

Notably, we did not find CR in any of the patients in either the treatment group or the control group. In the control group, the disease in two patients progressed to stage IV because of metastasis. Therefore, these results suggested that Endostar combined with NP exhibits better efficacy for patients than NP chemotherapy only.

3.2 Parameter analysis before and after the treatments

For the patients with NSCLC, to evaluate the changes of potential predictors in the anti-angiogenesis combined with neoadjuvant chemotherapy, we examined the levels of aCECs; EPC; serum VEGF; and CT perfusion parameters (BF, BV, PMS, and MTT); as well as some other parameters including MVD before and after the treatments (the NP chemotherapy alone versus anti-angiogenesis combined with neoadjuvant chemotherapy).

Our results indicated that anti-angiogenesis combined with neoadjuvant chemotherapy resulted in downregulation of the levels of serum VEGF, BF, PMS, and MVD, whereas reductions of the levels of VEGF and MVD only display statistical difference in the traditional NP chemotherapy for the patients with NSCLC (Table 1).

Table 2
Changes of predictors for patients with clinical benefit in the treatment group

Factor	Before the treatment		After the treatment		t value	$p$ value
aCECs	684.64	$\pm$ 1213.27	350.57	$\pm$ 599.08	1.043	0.316
EPC	932.64	$\pm$ 1247.38	372.93	$\pm$ 548.33	2.178	0.048
VEGF	49.77	$\pm$ 48.62	33.71	$\pm$ 29.87	2.369	0.034
BF	45.17	$\pm$ 11.79	36.12	$\pm$ 7.92	3.122	0.008
BV	15.08	$\pm$ 6.41	13.44	$\pm$ 5.88	1.576	0.139
PMS	20.34	$\pm$ 7.60	16.63	$\pm$ 7.39	3.211	0.007
MTT	13.22	$\pm$ 4.15	12.18	$\pm$ 4.27	1.009	0.332
MVD	50.21	$\pm$ 20.81	25.83	$\pm$ 19.85	5.822	0.000

Table 3

Changes of predictors for patients with clinical benefit in the treatment and the control group

Factor	Treatment group ( $n=$ 14)		Control group ( $n=$ 6)		t value	$p$ value
$\Delta$ aCECs	334.07	$\pm$ 1198.81	$-$ 657.17	$\pm$ 1514.66	1.570	0.143
$\Delta$ EPC	559.71	$\pm$ 961.53	$-$ 406.17	$\pm$ 763.27	2.179	0.043
$\Delta$ VEGF	16.05	$\pm$ 25.35	8.83	$\pm$ 6.00	0.680	0.505
$\Delta$ BF	9.05	$\pm$ 10.84	5.64	$\pm$ 5.89	0.718	0.482
$\Delta$ BV	1.64	$\pm$ 3.90	1.48	$\pm$ 2.00	0.097	0.924
$\Delta$ PMS	3.72	$\pm$ 4.33	3.73	$\pm$ 4.40	$-$ 0.006	0.996
$\Delta$ MTT	1.04	$\pm$ 3.85	1.30	$\pm$ 2.59	$-$ 0.153	0.880
$\Delta$ MVD	24.39	$\pm$ 15.67	8.67	$\pm$ 10.44	2.235	0.038

$\Delta$ aCECs $=$ aCECs before the treatment–aCECs after the treatment; other factors followed the same formula.

These results suggested that these potential indicators might be involved in the evaluation of the treatment of anti-angiogenesis combined with neoadjuvant chemotherapy.

3.3 A comparison of indicators of patients who had clinical benefit (CB) in the treatments

To further investigate the changes of the indicators of patients with CB in these two treatments, we next analyzed the levels of some indicators, including aCECs, EPC, VEGF, BF, BV, PMS, MTT, and MVD.

We found that the levels of EPC, VEGF, BF, PMS, and MVD were downregulated in the patients enjoying CB after anti-angiogenesis combined with neoadjuvant chemotherapy ( $p<$ 0.05; Table 2). In contrast, in the control group, only the levels of EPC and MVD showed statistical difference ( $p<$ 0.05) before and after the treatment (Table 3).

Table 4
Correlations between variation of predictors TRR and variation of MVD

Factor	Correlation with TRR				Correlation with MVD
	Treatment group		Control group		Treatment group		Control group
	( $n=$ 14)		( $n=$ 6)		( $n=$ 14)		( $n=$ 6)
	$r$ value	$p$ value	$r$ value	$p$ value	$r$ value	$p$ value	R value	$P$ value
aCECs	0.275	0.342	0.808	0.052	0.198	0.497	0.068	0.898
EPC	0.672	0.009	0.486	0.328	0.502	0.049	0.087	0.870
VEGF	0.364	0.201	0.087	0.869	0.294	0.307	0.030	0.954
BF	0.267	0.356	0.700	0.122	0.495	0.072	0.136	0.797
BV	0.461	0.097	0.294	0.571	0.337	0.238	0.721	0.106
PMS	0.650	0.012	0.306	0.555	0.604	0.022	0.169	0.748
MTT	0.475	0.086	0.272	0.602	0.497	0.071	0.295	0.570
MVD	0.832	0.000	0.280	0.591	–	–	–	–

3.4 The correlation between the variation of predictors and TRR/MVD in anti-angiogenesis combined with neoadjuvant chemotherapy

Next we analyzed the correlation between the variation of these predictors and TRR in the treatment group; our results revealed that the variation of EPC and PMS showed positive correlation with TRR ( $p<$ 0.05) in the treatment group, and significant positive correlation was found between the variation of MVD and TRR ( $p<$ 0.01) before and after the combination treatment. In contrast, the control group showed undetectable correlation between any predicative factor and the variation of TRR ( $p>$ 0.05; Table 4).

Since the variation of MVD displays significant positive correlation with TRR, we therefore determined the correlation between the variation of other predictors and MVD level. Similarly, the variation of EPC and PMS also showed positive correlation with the variation of MVD ( $p<$ 0.05), whereas the control group showed no statistical correlation between the variation of other predictors and MVD ( $p>$ 0.05) (Table 4).

These results suggested that PMS and EPC may be used as a convenient and effective predictive factor for the short-term efficacy of the combination treatment of anti-angiogenesis and neoadjuvant chemotherapy.

4. Discussion

Many clinical studies indicated that the efficacy of neoadjuvant chemotherapy for stage IIIA (N2) NSCLC before the surgery is not significant and the effect is still controversial [18, 19]. A recent study also demonstrated that anti-angiogenesis drugs combined with chemotherapy can improve CBR of patients with NSCLC, and that the combination treatment can prolong the total survival time as well as the disease-free survival time [20].

Because anti-angiogenesis therapy has been found to be involved in many pathways, independent anti-angiogenesis therapy does not exhibit effective efficacy and short-term improvement in imaging with morphological changes later than functional changes. Therefore, there are still no specific predictors for evaluating the short-term efficacy of anti-angiogenesis combined chemotherapy.

Since the most common method for quantitative analysis of tumor angiogenesis is the determination of the level of MVD with immunohistochemistry staining of tissue sections combined with vascular endothelial staining, we noted that among the patients with CB the level of MVD in the treatment group decreased more significantly than that of the control group using only chemotherapy thus suggesting that changes of MVD level can mainly reflect the efficacy of anti-angiogenesis therapy.

Moreover, the positive correlation between the variation of MVD and TRR ( $p<$ 0.01) suggested that MVD might be helpful to predict the efficacy of anti-angiogenesis treatment (Table 4). However, the undistinguishable MVD staining in the dead or collapsed endothelial cells of the tumor tissues usually results in inaccurate evaluation of the function and perfusion of blood vessels in the tumor, which might be the limitation of the application of MVD as a predictor independently. Besides, the morphological changes of the blood vessels in tumor tissues must be confirmed with repeated pathological examinations. Poor patient compliance, limited MVD material, and uneven microvascular distribution of the tumor tissues make it difficult to obtain a reliable evaluation.

Mononuclear cells developed from bone marrow can differentiate into mature endothelial cells CD34 ${}^{+}$ / VEGFR2 ${}^{+}$ , which were originally separated from circulating endothelial progenitor cells (EPC) by Asahara using MACS ${}^{\@setsize{\scriptsize}{8pt}{\viipt}{\@viipt}{\textregistered}}$ Technology [21].

A variety of cells and chemokines can promote EPC in stem cell nests to enter the cell proliferation cycle and then move into the peripheral blood circulation, or to mobilize EPC into new blood vessels to participate in the physiological or pathological process of angiogenesis.

A lack or inhibition of angiogenesis can suppress tumor growth; this might be helpful to predict the efficacy of tumor therapy. Schuch et al. found that anti-angiogenic drugs can inhibit the number of circulating EPC induced by VEGF and promote tumor regression [22]. In addition, the treatment with VEGFR2 inhibitor, which can specific inhibit membrane antigen VEGFR2 on the surface of EPC, will lead to a dose-dependent reduction in the number of EPC to exert its anti-tumor activity [23].

Rectal cancer and lung cancer clinical studies also showed that the EPC number and tumor vascular density were significantly decreased in patients who received anti-angiogenesis therapy [24]. To verify the influence of anti-angiogenesis therapy on EPC, we examined variation of EPC before and after the combination treatment to analyze the correlation between EPC and TRR/MVD (Table 4). Compared with the control group, the treatment group showed significantly different results before and after the combination treatment ( $p<$ 0.05). In terms of the correlation, the variation of EPC showed significant positive correlation with TRR and MVD ( $r=$ 0.672, $p<$ 0.01; $r=$ 0.502, $p<$ 0.05), thus suggesting that in view of either imaging changes or morphological changes of blood vessels, as a noninvasive indicator, the number of EPC in peripheral blood could be an effective predictor for the short-term efficacy of anti-angiogenesis combined chemotherapy for the patients with NSCLC.

aCECs, mostly derived from endothelial progenitor cells (EPC) in bone marrow and activated by tumor secreted-VEGF, can be mobilized to surrounding areas of the tumor, develop into new blood vessels, and stay on the inner surfaces of these new blood vessels. Several studies indicated that dynamic changes of aCECs in patients with advanced tumors are positively correlated with tumor progression and treatment efficacy, thus suggesting that it might a useful biological marker for predicting the efficacy of chemotherapy [25, 26, 27]. Additionally, patients with CB showed significant reduction in the level of CEC, whereas patients with clinical progress showed the increment of the level of CEC [28]. However, our results demonstrated that the reduction of aCECs in the patients with CB of the treatment group did not display any statistical difference before and after either combination treatment of anti-angiogenesis and chemotherapy (or chemotherapy only) (Tables 2 and 3), which is not consistent with the previous reports [25, 26, 27, 28]. We surmise that the conflicting results obtained by these studies might be associated with different test methods (which caused the difference in measured CECs subsets), since the treatments increased CECs mainly in the stage of apoptosis.

In our study, Endostar may reduce fluid static pressure among tumor tissues – causing dilated, disordered, and distorted blood vessels to contract, thus reducing the area of tumor vascular bed and prompting activated neovascular endothelial cells to enter the blood circulation and increase CECs. When Endostar was applied via a long cycle, CECs would increase temporarily (in less than two cycles) and then decrease. Furthermore, chemotherapy may possibly increase aCECs through vascular normalization in a similar way.

VEGF – the strongest growth factor contributing to angiogenesis in tumor tissues – was produced and secreted by tumor cells, spliced, and expressed under the influence of mRNA, and then stored by platelets and monocytes. Endostatin can specifically influence vascular endothelial cells by blocking VEGFR and competitive binding downstream targets proliferating from vascular endothelial cells [7, 29, 30].

In this study, we expected that the reduction of VEGF level was mainly caused by the effective chemotherapy-induced inhibition of tumor growth, which can reduce tumor burden and subsequently downregulate tumor autocrine.

Our results also demonstrated that (in the patients with CB) regardless of whether patients received anti-angiogenesis therapy or not, VEGF values decreased significantly ( $p<$ 0.05), and either the treatment group or the control group showed an undetectable difference in the reduction of VEGF level, thus suggesting that the reduction of VEGF level mainly resulted from chemotherapy treatment.

By observing perfusion changes in tumor tissues, CT perfusion imaging can be helpful for evaluating early treatment efficacy, particularly quantitatively evaluating microvascular physiology and tissue metabolism in tumor tissues. Our results showed that patients with CB in the treatment group exhibited significant changes in BF and PMS ( $p<$ 0.05), but not in BV and MTT (Table 2), which is consistent with a previous clinical study [31].

As we know, BF mainly represents the blood flow passing blood vessels of selected regions over a unit of time, and is influenced by blood volume, venous and lymphatic circulation, and oxygen consumption. PMS refers to the one-way transmission rate of a contrast agent entering the intercellular spaces from capillary endothelium, and reflects the integrity of the vascular endothelium and permeability of vessel walls. Therefore, the BF and PMS indicators might reflect the main targets of anti-angiogenesis therapy, and the variation of either BF or PMS should be the effective indicator for the short-term efficacy of the combined clinical therapy in NSCLC. Furthermore, PMS, a specific marker of vascular permeability, is subject to little interference of other factors and has a significant positive correlation with both TRR and MVD [32], direct morphological indicators of angiogenesis ( $r=$ 0.65, $p<$ 0.05; $r=$ 0.60, $p<$ 0.05) (Table 4), thus suggesting that PMS might be a potential non-invasive predictor for the efficacy evaluation of early anti-angiogenic therapy.

Additional evidence has shown that anti- angiogenesis therapy will be a promising anti-tumor clinical therapeutic strategy. Indeed, the application of anti-angiogenesis combined with chemotherapy in the treatment of IIIA (N2) NSCLC has been found to improve the clinical benefit rate, realize clinical downstaging, and increase the resection rate [16, 20].

After the analysis of the morphological and functional imaging of vascular endothelial cells, as well as the examination of the levels of VEGF and other regulators, our findings demonstrated that PMS and EPC may be used as a convenient and effective predictor for the short-term efficacy of the combined therapy.

Footnotes

Acknowledgments

This work was supported by the National Key Clinical Specialist Construction Programs of China and The National Key Research and Development Program of China (2016YFC0905501) and by grants from the National Nature Science Foundation of China (81470137) and the Tianjin Science and Technology Major Project, P.R. China (12ZCDZSY15400).

Conflict of interest

All authors declared that there were no conflicts of interest in this study.

References

Torre

L.A.

Bray

Siegel

R.L.

Ferlay

Lortet-Tieulent

and Jemal

, Global cancer statistics, 2012, CA Cancer J Clin 65 (2015), 87–108.

Travis

W.D.

, Pathology of lung cancer, Clin Chest Med 32 (2011), 669–692.

Togashi

Saito

Shinohara

Wakabayashi

and Asami

, Long-term prognosis after surgery for pathological stage III lung cancer, Kyobu Geka 58 (2005), 938–941; discussion 942-3.

O’Reilly

M.S.

Boehm

Shing

Fukai

Vasios

Lane

W.S.

Flynn

Birkhead

J.R.

Olsen

B.R.

and Folkman

, Endostatin: an endogenous inhibitor of angiogenesis and tumor growth, Cell 88 (1997), 277–285.

Dhanabal

Ramchandran

Volk

Stillman

I.E.

Lombardo

Iruela-Arispe

M.L.

Simons

and Sukhatme

V.P.

, Endostatin: yeast production, mutants, and antitumor effect in renal cell carcinoma, Cancer Res 59 (1999), 189–197.

Dhanabal

Volk

Ramchandran

Simons

and Sukhatme

V.P.

, Cloning, expression, and in vitro activity of human endostatin, Biochem Biophys Res Commun 258 (1999), 345–352.

Boehm

Folkman

Browder

and O’Reilly

M.S.

, Antiangiogenic therapy of experimental cancer does not induce acquired drug resistance, Nature 390 (1997), 404–407.

Yamaguchi

Anand-Apte

Lee

Sasaki

Fukai

Shapiro

Que

Lowik

Timpl

and Olsen

B.R.

, Endostatin inhibits VEGF-induced endothelial cell migration and tumor growth independently of zinc binding, EMBO J 18 (1999), 4414–4423.

Rehn

Veikkola

Kukk-Valdre

Nakamura

Ilmonen

Lombardo

Pihlajaniemi

Alitalo

and Vuori

, Interaction of endostatin with integrins implicated in angiogenesis, Proc Natl Acad Sci U S A 98 (2001), 1024–1029.

10.

Sudhakar

Sugimoto

Yang

Lively

Zeisberg

and Kalluri

, Human tumstatin and human endostatin exhibit distinct antiangiogenic activities mediated by alpha v beta 3 and alpha 5 beta 1 integrins, Proc Natl Acad Sci U S A 100 (2003), 4766–4771.

11.

Dhanabal

Ramchandran

Waterman

M.J.

Knebelmann

Segal

and Sukhatme

V.P.

, Endostatin induces endothelial cell apoptosis, J Biol Chem 274 (1999), 11721–11726.

12.

Dixelius

Larsson

Sasaki

Holmqvist

Engstrom

Timpl

Welsh

and Claesson-Welsh

, Endostatin-induced tyrosine kinase signaling through the Shb adaptor protein regulates endothelial cell apoptosis, Blood 95 (2000), 3403–3411.

13.

Ling

Gao

Chen

Wang

Yang

and Guo

, Endostar induces apoptotic effects in HUVECs through activation of caspase-3 and decrease of Bcl-2, Anticancer Res 29 (2009), 411–417.

14.

Folkman

, Antiangiogenesis in cancer therapy – endostatin and its mechanisms of action, Exp Cell Res 312 (2006), 594–607.

15.

Jia

Liu

Cao

Zhao

Ren

Wang

and Li

, Recombinant human endostatin, Endostar, enhances the effects of chemo-radiotherapy in a mouse cervical cancer xenograft model, Eur J Gynaecol Oncol 32 (2011), 316–324.

16.

Wen

Q.L.

Meng

M.B.

Yang

L.L.

Jia

Zhou

and Lu

, Endostar, a recombined humanized endostatin, enhances the radioresponse for human nasopharyngeal carcinoma and human lung adenocarcinoma xenografts in mice, Cancer Sci 100 (2009), 1510–1519.

17.

Zhao

You

Gong

Zhang

Wang

Zhao

Zhang

and Wang

, Combining antiangiogenic therapy with neoadjuvant chemotherapy increases treatment efficacy in stage IIIA (N2) non-small cell lung cancer without increasing adverse effects, Oncotarget 7 (2016), 62619–62626.

18.

Albain

K.S.

Swann

R.S.

Rusch

V.W.

Turrisi

A.T.

Shepherd

F.A.

Smith

Chen

Livingston

R.B.

Feins

R.H.

Gandara

D.R.

Fry

W.A.

Darling

Johnson

D.H.

Green

M.R.

Miller

R.C.

Ley

Sause

W.T.

and Cox

J.D.

, Radiotherapy plus chemotherapy with or without surgical resection for stage III non-small-cell lung cancer: a phase III randomised controlled trial, Lancet 374 (2009), 379–386.

19.

van Meerbeeck

J.P.

Kramer

G.W.

Van Schil

P.E.

Legrand

Smit

E.F.

Schramel

Tjan-Heijnen

V.C.

Biesma

Debruyne

van Zandwijk

Splinter

T.A.

Giaccone

, R. European Organisation for and G. Treatment of Cancer-Lung Cancer, Randomized controlled trial of resection versus radiotherapy after induction chemotherapy in stage IIIA-N2 non-small-cell lung cancer, J Natl Cancer Inst 99 (2007), 442–450.

20.

Wang

L.J.

C.X.

Cao

and Chen

Q.Y.

, Endostar combined with chemotherapy compared with chemotherapy alone in the treatment of nonsmall lung carcinoma: A meta-analysis based on Chinese patients, Indian J Cancer 51(Supple 3) (2014), e106–9.

21.

Asahara

Murohara

Sullivan

Silver

van der Zee

Witzenbichler

Schatteman

and Isner

J.M.

, Isolation of putative progenitor endothelial cells for angiogenesis, Science 275 (1997), 964–967.

22.

Schuch

Heymach

J.V.

Nomi

Machluf

Force

Atala

Eder

J.P.

, Jr. Folkman

and Soker

, Endostatin inhibits the vascular endothelial growth factor-induced mobilization of endothelial progenitor cells, Cancer Res 63 (2003), 8345–8350.

23.

Shaked

Bertolini

Man

Rogers

M.S.

Cervi

Foutz

Rawn

Voskas

Dumont

D.J.

Ben-David

Lawler

Henkin

Huber

Hicklin

D.J.

D’Amato

R.J.

and Kerbel

R.S.

, Genetic heterogeneity of the vasculogenic phenotype parallels angiogenesis; Implications for cellular surrogate marker analysis of antiangiogenesis, Cancer Cell 7 (2005), 101–111.

24.

Willett

C.G.

Boucher

di Tomaso

Duda

D.G.

Munn

L.L.

Tong

R.T.

Chung

D.C.

Sahani

D.V.

Kalva

S.P.

Kozin

S.V.

Mino

Cohen

K.S.

Scadden

D.T.

Hartford

A.C.

Fischman

A.J.

Clark

J.W.

Ryan

D.P.

Zhu

A.X.

Blaszkowsky

L.S.

Chen

H.X.

Shellito

P.C.

Lauwers

G.Y.

and Jain

R.K.

, Direct evidence that the VEGF-specific antibody bevacizumab has antivascular effects in human rectal cancer, Nat Med 10 (2004), 145–147.

25.

Mancuso

Burlini

Pruneri

Goldhirsch

Martinelli

and Bertolini

, Resting and activated endothelial cells are increased in the peripheral blood of cancer patients, Blood 97 (2001), 3658–3661.

26.

Beerepoot

L.V.

Mehra

Vermaat

J.S.

Zonnenberg

B.A.

Gebbink

M.F.

and Voest

E.E.

, Increased levels of viable circulating endothelial cells are an indicator of progressive disease in cancer patients, Ann Oncol 15 (2004), 139–145.

27.

Zhang

Vakil

Braunstein

Smith

E.L.

Maroney

Chen

Dai

Berenson

J.R.

Hussain

M.M.

Klueppelberg

Norin

A.J.

Akman

H.O.

Ozcelik

and Batuman

O.A.

, Circulating endothelial progenitor cells in multiple myeloma: implications and significance, Blood 105 (2005), 3286–3294.

28.

Chu

T.Q.

Ding

Garfield

D.H.

A.Q.

Pei

W.D.

and Han

B.H.

, Can determination of circulating endothelial cells and serum caspase-cleaved CK18 predict for response and survival in patients with advanced non-small-cell lung cancer receiving endostatin and paclitaxel-carboplatin chemotherapy? a retrospective study, J Thorac Oncol 7 (2012), 1781–1789.

29.

Huang

Wong

M.K.

Zhao

Zhu

Wang

K.Z.

Huang

Gorelik

and Li

, Soluble recombinant endostatin purified from Escherichia coli: antiangiogenic activity and antitumor effect, Cancer Res 61 (2001), 478–481.

30.

Zhou

Chen

and Luo

, Acid-induced unfolding mechanism of recombinant human endostatin, Biochemistry 43 (2004), 2550–2557.

31.

Zhang

F.L.

Gao

E.Y.

Shu

R.B.

Wang

Zhang

Sun

Tang

Jiang

B.Q.

Chen

S.Q.

and Cui

F.B.

, Human Recombinant Endostatin Combined with Cisplatin Based Doublets in Treating Patients with Advanced NSCLC and Evaluation by CT Perfusion Imaging, Asian Pac J Cancer Prev 16 (2015), 6765–6768.

32.

Carr

J.P.

Buckley

D.L.

Tessier

and Parker

G.J.

, What levels of precision are achievable for quantification of perfusion and capillary permeability surface area product using ASL? Magn Reson Med 58 (2007), 281–289.

Predictors for the efficacy of Endostar combined with neoadjuvant chemotherapy for stage IIIA (N2) NSCLC

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Materials and methods

2.1 Patients

2.2 Treatments

Evaluation of the patients’ indicators

2.3 CT perfusion imaging

2.4 Flow cytometry (FCM) analysis

2.5 ELISA assay

Table 1 Changes of predictors before and after the chemotherapy treatments

2.7 Statistical analysis

3. Results

3.1 Efficacy evaluation of Endostar combined with NP chemotherapy

3.2 Parameter analysis before and after the treatments

Table 2 Changes of predictors for patients with clinical benefit in the treatment group

Table 4 Correlations between variation of predictors TRR and variation of MVD

4. Discussion

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
Changes of predictors before and after the chemotherapy treatments

Table 2
Changes of predictors for patients with clinical benefit in the treatment group

Table 4
Correlations between variation of predictors TRR and variation of MVD