Pharmacokinetic parameters from 3-Tesla DCE-MRI as surrogate biomarkers of antitumor effects of...

Pharmacokinetic parameters from 3-Tesla DCE-MRI assurrogate biomarkers of antitumor effects of bevacizumabplus FOLFIRI in colorectal cancer with liver metastasis

Yoshinori Hirashima1, Yasuhide Yamada2, Ukihide Tateishi3, Ken Kato2, Mototaka Miyake4, Yosuke Horita2,

Kohei Akiyoshi2, Atsuo Takashima2, Natsuko Okita2, Daisuke Takahari2, Takako Nakajima2, Tetsuya Hamaguchi2,

Yasuhiro Shimada2 and Kuniaki Shirao1

1 Department of Medical Oncology Oita University, Faculty of Medicine, 1-1 Idaigaoka, Hasama-machi, Yufu, Oita, Japan2 Gastrointestinal Oncology Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, Japan3 Department of Radiology, Yokohama City University School of Medicine, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, Japan4 Diagnostic Radiology Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, Japan

Bevacizumab (BV) is an antivascular endothelial growth factor antibody. When administered with other chemotherapeutic

drugs, BV-combined regimens prolong survival of colorectal cancer patients. We conducted a phase II trial to confirm the

pharmacokinetic parameters from 3-Tesla dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as surrogate

biomarkers of BV 1 FOLFIRI regimen efficacy in colorectal cancer with liver metastases. DCE-MRI was performed before

treatment, on the seventh day after first treatment and every 8 weeks thereafter using a 3-Tesla MRI system. DCE-MRI

parameters-area under the contrast concentration versus time curve at 90 and 180 s (AUC90 and AUC180, respectively) after

contrast injection, and volume transfer constant of contrast agents (Ktrans and Kep) were calculated from liver metastases.

Fifty-eight liver metastases were analyzed. Univariate analysis revealed that a decrease in Ktrans ratios (DKtrans), Kep ratios

(DKep), AUC90 ratios (DAUC90) and AUC180 ratios (DAUC180) correlated with higher response (all p < 0.0001) and longer

time to progression (TTP) (DKtrans: p 5 0.001; DKep: p 5 0.004; DAUC90: p 5 0.006; DAUC180: p < 0.0001). Multivariate

analysis showed that DAUC180 was correlated with higher response (p 5 0.009), and DKtrans and DAUC180 were correlated

with longer TTP (DKtrans: p 5 0.001; DAUC180: p 5 0.024). DKtrans and DAUC180 are pharmacodynamic biomarkers of the

blood perfusion of BV 1 FOLFIRI. Our data suggest that DKtrans and DKep can predict response to chemotherapy at 1 week.

Changes in 3-Tesla DCE-MRI parameters confirmed the potential of these biomarkers of blood perfusion as surrogate

predictors of response and TTP.

Tumor cell growth requires an increased supply of bloodthrough the process of angiogenesis, which provides oxygenand nutrients, and vascular endothelial growth factor (VEGF)and its receptor have been identified as playing key roles inangiogenesis.1 Bevacizumab (BV), the first VEGF antibodydrug, inhibits the formation of new blood vessels to tumorsand normalizes tumor vasculature.2 Combination chemother-apy with BV has been investigated for efficacy in the treat-ment of various cancers. In colorectal cancer, since Hurwitzet al. demonstrated the benefits of IFL þ BV in 2003,BV-combined chemotherapy has become the standardchemotherapeutic approach.3 Several candidate predictivebiomarkers have been proposed for BV, similarly to K-rasmutation for cetuximab,4,5 but such predictive biomarkershave remained elusive.

Recently, the development of quantitative predictive bio-markers has led to increased use of imaging in the evaluationof tumor angiogenesis. Dynamic contrast enhanced-magneticresonance imaging (DCE-MRI) is a noninvasive imagingtechnique that can assess tumor perfusion, microvascular ves-sel wall permeability and extravascular-extracellular volume

Key words: biomarker, colorectal cancer, 3-Tesla DCE-MRI, tumor

angiogenesis

Abbreviations: AUC90: the area under the time-intensity curve over

the first 90 seconds after injection of contrast material; AUC90

ratios: DAUC90; AUC180: the area under the time-intensity curve

over the first 180 seconds after injection of contrast material;

AUC180 ratios: DAUC180; BV: Bevacizumab; DCE-MRI: dynamic

contrast-enhanced magnetic resonance imaging; Kep: reverse reflux

rate constant of contrast material between the extracellular space

and plasma; Kep ratios: DKep; Ktrans: forward volume transfer

constant of contrast material between the extracellular space and

plasma; Ktrans ratios: DKtrans; SNR: signal-to-noise ratio; TTP: time

to progression; VEGF: vascular endothelial growth factor

DOI: 10.1002/ijc.26282

History: Received 19 Apr 2011; Accepted 20 Jun 2011; Online 21 Jul

2011

Correspondence to: Yasuhide Yamada, MD, PhD,5-1-1 Tsukiji,

Chuo-ku, Tokyo 104-0045, Japan, E-mail: [email protected]

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Int. J. Cancer: 130, 2359–2365 (2012) VC 2011 UICC

International Journal of Cancer

IJC

fraction.6,7 The results of DCE-MRI are usually interpretedquantitatively using pharmacokinetic modeling, via parame-ters such as the forward volume transfer constant of contrastagent (Ktrans), the reverse reflux rate constant between extrac-ellular space and plasma (Kep), and the area under the con-trast concentration versus time curve at 90 and 180 s aftercontrast injection (AUC90 and AUC180, respectively).8–10

In oncology, DCE-MRI parameters have been incorporatedas biomarkers of drug efficacy in clinical trials of angiogenesisinhibitors because of correlations between tumor grade, micro-vessel density and VEGF expression.11,12 If DCE-MRI changesmeasured during treatment correlate with disease outcomes,DCE-MRI could be developed as a predictive biomarker forangiogenic activity in tumors. Some studies have suggestedthat changes in pharmacokinetic parameters can act as phar-macodynamic biomarkers and may predict clinical benefitsusing conventional 1- or 1.5-Tesla DCE-MRI.13–24 However,very few studies have shown any correlations between parame-ter changes during treatment and treatment effects.

There are several methods that can improve sensitivity andspecificity of predicting tumor angiogenesis using DCE-MRI.One method is to perform MRI at a higher field strength, whichimproves the signal-to-noise ratio (SNR). 3-Tesla DCE-MRIproduces an approximately 2-fold higher SNR when comparedwith conventional DCE-MRI.25 This allows higher resolutionT1- and T2-weighted images and faster dynamic images to beobtained with an SNR comparable to 1.5-Tesla DCE-MRI.Thus, clinical trials that evaluate angiogenesis should move to-ward the use of 3-Tesla DCE-MRI with quality assurance.

Here, we report the results of a phase II trial to confirmthe pharmacokinetic parameters obtained using 3-Tesla DCE-MRI as predictive biomarkers of blood perfusion by BV þFOLFIRI in colorectal cancer with liver metastases. We dem-onstrate that changes in DCE-MRI parameters are usefulpharmacodynamic biomarkers for BV regimens and that pa-rameter changes can predict response and prognosis.

Material and MethodsPatients

Patients with liver metastases of histologically proven colo-rectal carcinoma and who had started second line BVþFOL-FIRI (UMIN-CTR, number 000001060: AVASIRI study) werestudied prospectively. Inclusion criteria were as follows:measurable liver disease with at least one lesion �10 mmand suitable for DCE-MRI; Eastern Cooperative OncologyGroup performance status of 0 to 2; age 20–75 years; no pre-vious exposure to BV or CPT-11; adequate organ and bonemarrow function; and written informed consent. The proto-col and informed consent were approved by National CancerCenter Hospital Institutional Review Board.

Treatment and evaluations

Patients received the FOLFIRI regimen consisting of CPT-11(150 mg m�2 IV over 90 min), l-LV (200 mg m�2 IV over 2

h) and 5-FU (400 mg m�2 IV bolus), followed by 5-FU(2,400 mg m�2 IV over a 46-h infusion), and this wasrepeated every 2 weeks. BV was administered as a 30-min in-travenous infusion at a biweekly dose of 10 mg kg�1 beforethe FOLFIRI regimen. Patients were evaluated for tumorresponse by standard computed tomography (CT) scans per-formed every 8 weeks using response evaluation criteria insolid tumors.26

DCE-MRI methodology

DCE-MRI was performed before the first treatment, on theseventh day after the first treatment and every 8 weeks there-after until the 12th treatment. All DCE-MRI scans wereacquired using a 3-Tesla whole-body magnet equipped withdedicated receive-only six-channel dual coil (Magnetom Trio,A Tim System; Siemens, Erlangen, Germany). All patientsunderwent a transverse fat saturated T2-weighted turbo spinecho examination with a 35- to 40-cm field of view, 4000/76.0[repetition time (ms)/echo time (ms)], 120� flip angle (FA),section thickness of 5.0 mm, and matrix of 144 � 320 pixels.DCE-MRI images were obtained with a three-dimensional-vol-umetric interpolated breath-hold examination (VIBE)sequence in the transverse plane using 4.45/2.46, 13� FA, sec-tion thickness of 5.0 mm, and matrix of 132 � 320 pixels.Low-molecular-weight gadolinium-chelate 0.1 mmol kg�1

(Magnevist; Bayer-Schering, Berlin, Germany) was injectedintravenously at 2.0 ml s�1 followed by a 20-ml normal salineflush using an automatic injector (Sonic shot GX; NemotoKyorindo, Tokyo, Japan). Under the same breath-hold condi-tions, continuous 10-s data acquisition was performed before,during, and after administration of contrast medium for a totalof 3 min 20 s, with 11–12 repeated data sets.

Quantitative parameters were measured for a region-of-in-terest (ROI) defined manually by the radiologist (M.M.) inthe tumor and aorta separately. The profile of the tumor wasconstant throughout the image sequence, which encompassedover 95% of the tumor area. Errors related to ROI placementwere minimized by evaluating anatomic images and record-ing the exact sites of ROIs. We applied a two-compartmentpharmacokinetic model to the MRI enhancement timecourse. Dynamic gadolinium concentration curves were gen-erated using the signal enhancement ratio and the baselinetumor T1 relaxation time. Time courses of gadolinium trans-fer into the tumor interstitium were subjected to nonlinearleast squares fit according to the two-compartment pharma-cokinetic model.27 The following equation relating tumorgadolinium concentration Ct (t) to arterial plasma gadoliniumconcentration Cp (t) was used for analyses:

dCðtÞ=dt ¼ K transCpðtÞ � KepCðtÞ

where Ktrans is the transfer constant and Kep is the rate con-stant. The AUC was calculated as the area under the time-in-tensity curve over the first 90 s and 180 s after bolus admin-istration. Curve fitting was performed using an in-house

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2360 DCE-MRI pharmacodynamic study of bevacizumab


software package produced by two of the authors (TomoakiNagaoka and Ukihide Tateishi).

Statistical analysis

The primary end points of this study were to identify anychanges in the parameters obtained from 3-Tesla DCE-MRIat baseline versus those during treatment, and to confirmthese pharmacokinetic parameters as predictive biomarkers.Decreases in Ktrans ratio (DKtrans), Kep ratio (DKep), AUC90ratio (DAUC90) and AUC180 ratio (DAUC180) from base-line to after the start of treatment were tested. After data col-lection, exploratory analyses of associations between DKtrans,DKep, DAUC90, DAUC180 and therapeutic effects were con-ducted. When baseline levels and changes in DCE-MRI pa-rameters were dichotomized for log-rank tests, median valueswere selected as cut-off points. For all patients, the initialprogression date from the start of FOLFIRI þ BV was usedto calculate time to progression (TTP). Statistical analysiswas performed with the SPSS version 16 software program(SPSS, Chicago, IL). Pearson’s correlations were used toassess Ktrans, Kep, AUC90 and AUC180, and a chi-squaredtest was used to assess the relationships between each param-

eter and therapeutic effects. Parameters and TTP were deter-mined by the Kaplan-Meier method, and were analyzed bylog-rank test. Multivariate analysis was performed using aCox proportional hazard model.

ResultsPatient characteristics and treatment effects

Between February 2008 and March 2009, data on 17 consec-utive patients with metastases of liver (17), lymph node (6),peritoneum (1) and lung (6) were collected prospectively atthe National Cancer Center Hospital (Table 1). All patientsreceived BV þ FOLFIRI, and all liver metastases were eval-uated by 3-Tesla DCE-MRI. Median follow-up time was 14.3months (range: 3.0–21.5 months), the overall response ratewas 47%, and the median TTP was 12.4 months.

Changes in DCE-MRI parameters and clinical outcome

Complete DCE-MRI data were available for all patients.There were strong correlations between Ktrans and Kep (r ¼0.78) and between AUC90 and AUC180 (r ¼ 0.88), but notbetween Ktrans or Kep and AUC. Figure 1 shows a compari-son of dynamic gadolinium concentration versus time curvesfor a responding patient. Reductions in Ktrans, Kep, AUC90and AUC180 during treatment are shown in Table 2. MedianDKtrans, DKep, DAUC90 and DAUC180 were 46.8/min, 48.1/min, 18.8 mM s�1 and 16.4 mM s�1, respectively. Univariateanalysis revealed that each parameter was correlated with tu-mor shrinkage (all p < 0.0001; Table 3), and multivariateanalysis showed that DAUC180 was correlated with higher

Table 1. Patient Characteristics

Characteristics Number of Cases (N 5 17)

Gender

Male 14

Female 3

Age (y)

Median 60

Range 38–73

ECOG1 performance status

0 9

1 8

Primary tumor

Colon 8

Rectum 9

Metastatic sites2

Liver 17

Lymph node 6

Peritoneum 1

Lung 6

Previous chemotherapy

5-FU based regimen3 6

FOLFOX 11

Therapeutic effect

Response rate 47%

Time to progression 12.4 mo

1ECOG—European Clinical Oncology Group. 2Some patients hadmultiple sites. 3As adjuvant chemotherapy.

Figure 1. Comparison of dynamic gadolinium concentration versus

time curves for a responding patient: Before treatment (squares), 7

days after the start of treatment (triangles), after 4 weeks

(crosses), after 8 weeks (diamonds) and after 12 weeks (circles).

[Color figure can be viewed in the online issue, which is available

at wileyonlinelibrary.com.]

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Hirashima et al. 2361


response (p ¼ 0.009; Table 4). In particular, DKtrans andDKep showed a significant difference at 7 days after the startof treatment (DKtrans: p ¼ 0.009; DKep: p ¼ 0.043), and maybe useful as biomarkers for the early diagnostics of tumorshrinkage.

To clarify the relevance of marker positivity in predictingdisease outcome, DKtrans, DKep, DAUC90 and DAUC180were found to be correlated with TTP by log-rank test. Uni-variate analysis revealed that DKtrans, DKep, DAUC90 andDAUC180 were correlated with longer TTP (DKtrans: p ¼0.001; DKep: p ¼ 0.004; DAUC90: p ¼ 0.006; DAUC180: p <

0.0001; Fig. 2). Moreover, multivariate analysis showed thatDKtrans and DAUC180 were correlated with longer TTP(DKtrans: p ¼ 0.001; DAUC180: p ¼ 0.024); thus, DKtrans andDAUC180 are pharmacodynamic biomarkers of BV regimens,and changes in these parameters may predict greaterresponse and TTP (Table 4).

DiscussionThis prospective study demonstrated that 3-Tesla DCE-MRIparameters are predictive biomarkers for angiogenic activityin tumors. All 58 liver metastases were suitable for pharma-cokinetic analysis, and we were able to demonstrate tumorvascular normalization. We also demonstrated that each pa-rameter was correlated with higher response and TTP; ourresults suggest that both quantitative and semi-quantitativeDCE-MRI parameters, DKtrans and DAUC180, are predictivebiomarkers for BV þ FOLFIRI in metastatic colorectal cancerwith liver metastasis. Changes in 3-Tesla DCE-MRI parame-ters thus appear to be useful biomarkers of blood perfusion

and microvascular vessel wall permeability, acting as surro-gate predictors of clinical outcomes.

Several biomarkers have been identified following theclinical introduction of molecular target therapy. In colo-rectal cancer, it has been clarified that cetuximab is noteffective in patients with the KRAS gene mutation.4,5 How-ever, there has been little evidence regarding the efficacy ofbiomarkers in BV regimens, which are standard chemothera-peutic approaches for colorectal cancer. The use of bio-markers has the potential to maximize clinical efficacy byidentifying the patients most likely to benefit from anti-VEGF therapy.

Recently, the development of quantitative biomarkers hasled to increased use of imaging in the evaluation of tumorangiogenesis. Characterization of tumor vasculature by DCE-MRI involves the acquisition of a series of T1-weightedimages before, during and after injection of a small moleculecontrast agent. The change in signal reflects the exchange ofcontrast agent between the vascular and extravascular-extrac-ellular spaces. This exchange depends on blood flow, vesselpermeability surface area, fractional blood volume, fractionalvolume of extravascular-extracellular space, and blood con-centration of contrast agent as a function of time. Thus,DCE-MRI can noninvasively assess tumor microvasculatureand quantitatively measure kinetic parameters related to per-fusion and permeability.

DCE-MRI parameters have been incorporated as bio-markers of drug efficacy in clinical trials of angiogenesisinhibitors, and some studies have suggested that pharmacoki-netic parameters may be predictive of clinical benefit. Jarna-gin et al. demonstrated that DKtrans, DKep and baseline AUCwere correlated with better disease-specific survival inpatients who received hepatic arterial infusion with floxuri-dine and dexamethasone in primary liver cancer.22 DCE-MRIparameters also reflected significantly lower tumor blood per-fusion after VEGF antibody drug administration. Baar et al.demonstrated a greater decrease in AUC with docetaxel þBV when compared with docetaxel alone in breast cancer.21

Hahn et al. found that baseline Ktrans is a prognostic or pre-dictive biomarker for sorafenib in metastatic renal carci-noma.19 DCE-MRI is therefore a promising approach tomonitor predictive biomarkers in oncology, but most studieshave been performed with 1.5-Tesla DCE-MRI, and did not

Table 2. Changes in DCE-MRI Parameters after Chemotherapy

OverallSeventh day afterfirst treatment

Parameter Mean 6 SD Median Mean 6 SD Median

DKtrans (1 min�1) 32.9 6 8.6 46.8 13.8 6 7.3 21.2

DKep (1 min�1) 39.3 6 6.9 48.1 18.5 6 6.2 26

DAUC90 (mM s�1) 19 6 3.1 18.8 7.9 6 1.8 10.3

DAUC180 (mM s�1) 17.5 6 3.6 16.4 6.9 6 1.7 6.6

Table 3. Relationship between DCE-MRI parameters and therapeuticeffects

Tumor shrinkage Time to progression

Seventh dayafter firsttreatment Overall

Seventh dayafter firsttreatment Overall

Parameter p-value1 p-value2

DKtrans 0.009 <0.0001 0.173 0.001

DKep 0.043 <0.0001 0.177 0.004

DAUC90 0.118 <0.0001 0.408 0.006

DAUC180 0.071 <0.0001 0.024 <0.0001

1Chi-squared test. 2Log-rank test.

Table 4. Impact of MRI parameters on therapeutic effects(multivariate analysis)

Parameter H.R. 95% C.I. p-value

Tumor shrinkage

DAUC180 4.5 1.5–13.9 0.009

Time to progression

DAUC180 4.63 1.9–11.6 0.001

DKtrans 2.83 1.1–7.0 0.024

Cox proportional hazard model.

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show statistically significant differences in parameter changesduring treatment.

Our study using 3-Tesla DCE-MRI showed that thequantitative and semi-quantitative DCE-MRI parametersDKtrans and DAUC180 are pharmacodynamic and predic-tive biomarkers of BV regimens in metastatic colorectalcancer. Furthermore, our data suggest that DKtrans andDKep can predict response to chemotherapy within a weekof the start of therapy. We calculated whether we pre-dicted antitumor effects based on changes in tumor sizeafter 1 week, but could not identify a significant difference(p ¼ 0.8444: data not shown). Our data suggest thatchanges in these parameters are useful early predictors oftumor shrinkage and TTP, and apparently reflect the anti-tumor effects of treatment. Although our study was limitedin size, further clinical trials should investigate the correla-tions between these imaging data, treatment efficacy anddisease outcome.

What are the advantages and disadvantages of 3-TeslaDCE-MRI when compared with 1.5- or 1.0-Tesla DCE-MRIfor evaluation of tumor angiogenesis? The most characteristicfeatures of 3-Tesla DCE-MRI are the increased potency ofmagnetic susceptibility and chemical shift, in addition to thehigher SNR. The accuracy of DCE-MRI relies on the abilityto model the pharmacokinetics of a contrast agent based onsignal intensity changes in sequential MR images. Signal in-tensity changes can be rapid immediately after contrast injec-tion, and the high SNR is therefore important for evaluation.However, high SNR is increasing susceptibility to air artifacts,3-Tesla DCE-MRI is considered unsuitable of evaluation oftumors adjacent to air or gas. 3-Tesla DCE-MRI is useful foran evaluation of the angiogenesis in liver tumors becauseincreasing potency of magnetic susceptibility and chemicalshift can improve spatial resolution in the liver. Some authorshave recommended that clinical trials move toward the useof 3-Tesla instruments with quality assurance,28 and several

Figure 2. Kaplan-Meier estimates for time to progression (TTP) in patients with parameter changes on DCE-MRI. (a) DKtrans (cut-off point

defined as median value for DKtrans ¼ 46.8). (b) DKep (cut-off point defined as median value for DKep ¼ 48.1). (c) DAUC90 (cut-off point

defined as median value for DAUC90 ¼ 18.8). (d) DAUC180 (cut-off point defined as median value for DAUC180 ¼ 16.4).

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recent studies have conducted clinical trials using 3-TeslaDCE-MRI.29–31

In conclusion, this prospective trial shows that the DCE-MRI parameters DKtrans and DAUC180 are pharmacody-namic and predictive biomarkers for BV regimens in meta-static colorectal cancer. It is difficult to evaluate the subtlechanges occurring during BV treatment using the currentmethod of measuring tumor diameter. 3-Tesla DCE-MRI isadequate for evaluating angiogenesis and allows the use ofpromising candidate biomarkers for assessing antiangiogenic

treatment and it may be used in a proof of concept study forthe development of antiangiogenic agents. Further develop-ments should be needed confirming the methods of DCE-MRI acquisition and analysis and evaluating 3-Tesla DCE-MRI as surrogate biomarker in the large populations.

AcknowledgementsThe authors would like to thank Ms. Hideko Morita and Makiko Shinogi fortheir help in collecting and organizing clinical samples.

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