Biomarkers of evasive resistance predict disease progression in cancer patients treated with antiangiogenic therapies

Abstract

Numerous antiangiogenic agents are approved for the treatment of oncological diseases. However, almost all patients develop evasive resistance mechanisms against antiangiogenic therapies. Currently no predictive biomarker for therapy resistance or response has been established. Therefore, the aim of our study was to identify biomarkers predicting the development of therapy resistance in patients with hepatocellular cancer (n = 11), renal cell cancer (n = 7) and non-small cell lung cancer (n = 2). Thereby we measured levels of angiogenic growth factors, tumor perfusion, circulating endothelial cells (CEC), circulating endothelial progenitor cells (CEP) and tumor endothelial markers (TEM) in patients during the course of therapy with antiangiogenic agents, and correlated them with the time to antiangiogenic progression (aTTP). Importantly, at disease progression, we observed an increase of proangiogenic factors, upregulation of CEC/CEP levels and downregulation of TEMs, such as Robo4 and endothelial cell-specific chemotaxis regulator (ECSCR), reflecting the formation of torturous tumor vessels. Increased TEM expression levels tended to correlate with prolonged aTTP (ECSCR high = 275 days vs. ECSCR low = 92.5 days; p = 0.07 and for Robo4 high = 387 days vs. Robo4 low = 90.0 days; p = 0.08). This indicates that loss of vascular stabilization factors aggravates the development of antiangiogenic resistance. Thus, our observations confirm that CEP/CEC populations, proangiogenic cytokines and TEMs contribute to evasive resistance in antiangiogenic treated patients. Higher TEM expression during disease progression may have clinical and pathophysiological implications, however, validation of our results is warranted for further biomarker development.

Keywords: Robo4; angiogenesis; antiangiogenic therapies; placental growth factor (PlGF); vascular endothelial growth factor (VEGF).

Figure 1. Study synopsis showing the planned investigations at each clinical visit (baseline, follow-up and disease progression)

Abbreviations: peripheral blood mononuclear cells (PBMC), dynamic contrast enhanced magnetic response imaging (DCE-MRI).

Figure 2. Cytokine analyses comparing baseline investigations with disease progression

Paired serum samples were analyzed from patients at baseline and disease progression. X-axis depicts the time points of measurements baseline versus disease progression. Y-axis depicts the measured cytokine in picograms per milliliter (pg/ml). *p ≤ 0.05. Abbreviations: vascular endothelial growth factor-A (VEGF), placental growth factor (PlGF), platelet derived growth factor (PDGF), hepatocyte growth factor (HGF), soluble vascular endothelial growth factor receptor2 (sVEGFR2), dickkopf3 (DKK3), monokine induced by gamma interferon (MIG), intracellular adhesion molecule1 (ICAM).

Figure 3. Measurement of different circulating cell populations at baseline and disease progression (A–D) by multicolor FACS analysis

X-axis depicts the time points of measurements at baseline versus disease progression. Y-axis depicts the measured cell populations cells/microliter. *p ≤ 0.05; ***p ≤ 0.001. Abbreviations: circulating endothelial cells (CEC), circulating endothelial progenitor cells (CEP), vascular endothelial growth factor receptor2 (VEGFR2).

Figure 4. Expression levels of TEMs at baseline and disease progression by qPCR. Analyses are shown for Robo4 (A), ECSCR (B) and Clec14 (C) by qPCR

X-axis depicts the time points of measurements at baseline versus disease progression. Y-axis depicts relative gene expression of Robo4, ECSCR and Clec14 normalized to GAPDH. *p ≤ 0.05; ***p ≤ 0.001. Abbreviations: endothelial cell-specific chemotaxis regulator (ECSCR), tumor endothelial marker (TEM), glyceraldehyde 3-phosphate dehydrogenase (GAPDH).

Figure 5. Representative examples of DCE-MRI of patients benefitting from antiangiogenic therapies (baseline assessment)

(A) On the left side contrast media uptake curves and on the right side corresponding MRI images of the tumor at baseline are shown (the circle and arrows indicate the tumor). (B) Two regions of interest (ROI1 and 2) of an extensive primary RCC during antiangiogenic therapy are shown (upper picture). ROI1 shows contrast media uptake (type3 curve, normalization during sunitinib therapy) while ROI2 reflects necrosis in the tumor with almost no perfusion/permeability (lower picture). Abbreviations: dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), renal cell cancer (RCC), non-small cell lung cancer (NSCLC), region of interest (ROI).

Figure 6. Representative example of DCE-MRI in a NSCLC patient during bevacizumab maintenance therapy at baseline and disease progression

In the upper row representative MRI images of the tumor at baseline and progression are shown (the circles indicate the tumor). Below the corresponding curves and changes in contrast media uptake behavior are depicted. The upper curve at baseline represents a type 3 curve, which correlates with an increased tumor microcirculation (permeability/perfusion) induced by the antiangiogenic agent (vessel normalization). At disease progression the contrast uptake curve was changed to a type 1 curve, which corresponds to lower permeability/perfusion. Abbreviations: dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), non-small cell lung cancer (NSCLC), region of interest (ROI).

Figure 7. Kaplan-Meier curves showing the time to aTTP and PFS

(A) and (B) show aTTP and PFS according to each tumor type. (A) aTTP (128 days [range 34–312 days] in HCC patients [n = 10], 394 days [range 35–743 days] in RCC patients [n = 7] and 144 days [range 76–212 days] in NSCLC patients [n = 2]); (B) PFS (241 days [range 69–462 days] in HCC patients [n = 10], 493 days [range 56–1599 days] in RCC patients [n = 7] and 161 days [range 109–212 days] for the NSCLC patients [n = 2]). (C) and (D) show the correlation of a high Robo4 (C) and ECSCR (D) expression at disease progression (PD) with a prolonged aPTT (Robo4 high = 387 days aPTT vs Robo4 low = 90.0 days, p = 0.08; ECSCR high = 275 days aPTT vs ECSCR low = 92.5 days, p = 0.07). (E) and (F) depict linear regression analysis showing a significant correlation of low DKK3 (E) and high PlGF (F) levels at disease progression with a prolonged aPTT (DKK3 levels at disease progression: r² = 0.21, p = 0.05; PlGF levels at disease progression: r² = 0.30, p = 0.02). (A–C): X-axis depicts the time of PFS and aPTT (days). (D–E): X-axis depicts the cytokine levels of DKK3 and PlGF at disease progression. (A–F): Y-axis depicts the percentage of patients showing a disease progression. *p ≤ 0.05; ***p ≤ 0.001. Abbreviations: time to antiangiogenic progression (aTTP), progression free survival (PFS), dickkopf3 (DKK3), placental growth factor (PlGF), endothelial cell-specific chemotaxis regulator (ECSCR), hepatocellular cancer (HCC), renal cell cancer (RCC), non-small cell lung cancer (NSCLC), progressive disease (PD).