DCE‐ and DW‐MRI as early imaging biomarkers of treatment response in a preclinical model of triple negative breast cancer

This work evaluates quantitative dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) and diffusion‐weighted MRI (DW‐MRI) parameters as early biomarkers of response in a preclinical model of triple negative breast cancer (TNBC). The standard Tofts' model of DCE‐MRI returns estimates of the volume transfer constant (Ktrans) and the extravascular extracellular volume fraction (ve). DW‐MRI returns estimates of the apparent diffusion coefficient (ADC). Mice (n = 38) were injected subcutaneously with MDA‐MB‐231. Tumors were grown to approximately 275 mm3 and sorted into the following groups: saline controls, low‐dose Abraxane (15 mg/kg) and high‐dose Abraxane (25 mg/kg). Animals were imaged at days zero, one and three. On day three, tumors were extracted for immunohistochemistry. The positive percentage change in ADC on day one was significantly higher in both treatment groups relative to the control group (p < 0.05). In addition, the positive percentage change in Ktrans was significantly higher than controls (p < 0.05) on day one for the high‐dose group and on days one and three for the low‐dose group. The percentage change in tumor volume was significantly different between the high‐dose and control groups on day three (p = 0.006). Histology confirmed differences at day three through reduced numbers of proliferating cells (Ki67 staining) in the high‐dose group (p = 0.03) and low‐dose group (p = 0.052) compared with the control group. Co‐immunofluorescent staining of vascular maturity [using von Willebrand Factor (vWF) and α‐smooth muscle actin (α‐SMA)] indicated significantly higher vascular maturation in the low‐dose group compared with the controls on day three (p = 0.03), and trending towards significance in the high‐dose group compared with controls on day three (p = 0.052). These results from quantitative imaging with histological validation indicate that ADC and Ktrans have the potential to serve as early biomarkers of treatment response in murine studies of TNBC.

[1]  Thomas E Yankeelov,et al.  Assessing reproducibility of diffusion-weighted magnetic resonance imaging studies in a murine model of HER2+ breast cancer. , 2014, Magnetic resonance imaging.

[2]  P. Schiff,et al.  Promotion of microtubule assembly in vitro by taxol , 1979, Nature.

[3]  D. Yardley nab-Paclitaxel mechanisms of action and delivery. , 2013, Journal of controlled release : official journal of the Controlled Release Society.

[4]  J C Gore,et al.  Analysis and correction of motion artifacts in diffusion weighted imaging , 1994, Magnetic resonance in medicine.

[5]  R. Jain Normalization of Tumor Vasculature: An Emerging Concept in Antiangiogenic Therapy , 2005, Science.

[6]  J. Pietenpol,et al.  Identification and use of biomarkers in treatment strategies for triple‐negative breast cancer subtypes , 2014, The Journal of pathology.

[7]  T. Beasley,et al.  Early therapy assessment of combined anti‐DR5 antibody and carboplatin in triple‐negative breast cancer xenografts in mice using diffusion‐weighted imaging and 1H MR spectroscopy , 2014, Journal of magnetic resonance imaging : JMRI.

[8]  Thomas E. Yankeelov,et al.  Practical Dynamic Contrast Enhanced MRI in Small Animal Models of Cancer: Data Acquisition, Data Analysis, and Interpretation , 2012, Pharmaceutics.

[9]  Lei Xu,et al.  Normalization of the vasculature for treatment of cancer and other diseases. , 2011, Physiological reviews.

[10]  Thierry Metens,et al.  Quantitative DCE-MRI for prediction of pathological complete response following neoadjuvant treatment for locally advanced breast cancer: the impact of breast cancer subtypes on the diagnostic accuracy , 2016, European Radiology.

[11]  D. Collins,et al.  Vascular characterisation of triple negative breast carcinomas using dynamic MRI , 2011, European Radiology.

[12]  Jie Li,et al.  DW-MRI ADC values can predict treatment response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy , 2011, Medical Oncology.

[13]  S. Gordon,et al.  F4/80, a monoclonal antibody directed specifically against the mouse macrophage , 1981, European journal of immunology.

[14]  Hassan Bagher-Ebadian,et al.  Dynamic contrast enhanced MRI parameters and tumor cellularity in a rat model of cerebral glioma at 7 T , 2014, Magnetic resonance in medicine.

[15]  Modeling the Effect of Intra-Voxel Diffusion of Contrast Agent on the Quantitative Analysis of Dynamic Contrast Enhanced Magnetic Resonance Imaging , 2014, PloS one.

[16]  T. Uematsu,et al.  Triple-negative breast cancer: correlation between MR imaging and pathologic findings. , 2009, Radiology.

[17]  Thomas E Yankeelov,et al.  Correlation of tumor characteristics derived from DCE‐MRI and DW‐MRI with histology in murine models of breast cancer , 2015, NMR in biomedicine.

[18]  A. Jackson,et al.  Candidate Biomarkers of Extravascular Extracellular Space: A Direct Comparison of Apparent Diffusion Coefficient and Dynamic Contrast-Enhanced MR Imaging—Derived Measurement of the Volume of the Extravascular Extracellular Space in Glioblastoma Multiforme , 2010, American Journal of Neuroradiology.

[19]  R. Danesi,et al.  The pharmacological bases of the antiangiogenic activity of paclitaxel , 2013, Angiogenesis.

[20]  Xiaoyuan Chen,et al.  DCE-MRI-Derived Parameters in Evaluating Abraxane-Induced Early Vascular Response and the Effectiveness of Its Synergistic Interaction with Cisplatin , 2016, PloS one.

[21]  L. Bonomo,et al.  Role of the Apparent Diffusion Coefficient in the Prediction of Response to Neoadjuvant Chemotherapy in Patients With Locally Advanced Breast Cancer. , 2015, Clinical breast cancer.

[22]  R. Cress,et al.  Descriptive analysis of estrogen receptor (ER)‐negative, progesterone receptor (PR)‐negative, and HER2‐negative invasive breast cancer, the so‐called triple‐negative phenotype , 2007, Cancer.

[23]  M. Seshadri,et al.  Tumor Vascular Maturation and Improved Drug Delivery Induced by Methylselenocysteine Leads to Therapeutic Synergy with Anticancer Drugs , 2008, Clinical Cancer Research.

[24]  Rakesh K. Jain,et al.  Role of vascular density and normalization in response to neoadjuvant bevacizumab and chemotherapy in breast cancer patients , 2015, Proceedings of the National Academy of Sciences.

[25]  P. Choyke,et al.  Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. , 2009, Neoplasia.

[26]  Thomas E Yankeelov,et al.  Comparisons of the efficacy of a Jak1/2 inhibitor (AZD1480) with a VEGF signaling inhibitor (cediranib) and sham treatments in mouse tumors using DCE-MRI, DW-MRI, and histology. , 2012, Neoplasia.

[27]  D. Le Bihan,et al.  Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. , 1988, Radiology.

[28]  L. Schwartz,et al.  New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). , 2009, European journal of cancer.

[29]  M. Piccart,et al.  Neoadjuvant therapy for breast cancer. , 2015, Annual review of medicine.

[30]  P. Schiff,et al.  Taxol stabilizes microtubules in mouse fibroblast cells. , 1980, Proceedings of the National Academy of Sciences of the United States of America.

[31]  K. Hess,et al.  Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[32]  David J Collins,et al.  Technology Insight: water diffusion MRI—a potential new biomarker of response to cancer therapy , 2008, Nature Clinical Practice Oncology.

[33]  Huang Jun,et al.  Descriptive analysis of estrogen receptor (ER)-negative, progesterone receptor (PR)-negative, and HER2-negative invasive breast cancer, the so-called triple-negative phenotype , 2014 .

[34]  J. Henkin,et al.  Paclitaxel at ultra low concentrations inhibits angiogenesis without affecting cellular microtubule assembly , 2003, Anti-cancer drugs.

[35]  W. Rooney,et al.  Determination of the MRI contrast agent concentration time course in vivo following bolus injection: Effect of equilibrium transcytolemmal water exchange , 2000, Magnetic resonance in medicine.

[36]  Steven Staelens,et al.  99mTc-(CO)3 His-Annexin A5 Micro-SPECT Demonstrates Increased Cell Death by Irinotecan During the Vascular Normalization Window Caused by Bevacizumab , 2011, The Journal of Nuclear Medicine.

[37]  A. Elias Triple-Negative Breast Cancer: A Short Review , 2010, American journal of clinical oncology.

[38]  A. Forero-Torres,et al.  How do I Treat “Triple-Negative” Disease , 2011, Current treatment options in oncology.

[39]  T. Nagaoka,et al.  Neoadjuvant chemotherapy in breast cancer: prediction of pathologic response with PET/CT and dynamic contrast-enhanced MR imaging--prospective assessment. , 2012, Radiology.

[40]  W. Yang,et al.  Multimodality imaging of triple receptor-negative tumors with mammography, ultrasound, and MRI. , 2010, AJR. American journal of roentgenology.

[41]  C. Painter,et al.  Comparison of dynamic contrast-enhanced MR, ultrasound and optical imaging modalities to evaluate the antiangiogenic effect of PF-03084014 and sunitinib , 2014, Cancer medicine.

[42]  Eun-Kyung Kim,et al.  Triple-negative invasive breast cancer on dynamic contrast-enhanced and diffusion-weighted MR imaging: comparison with other breast cancer subtypes , 2012, European Radiology.

[43]  Paul Ellis,et al.  Dissecting the heterogeneity of triple-negative breast cancer. , 2012, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[44]  T. Yankeelov,et al.  Evaluating treatment response using DW-MRI and DCE-MRI in trastuzumab responsive and resistant HER2-overexpressing human breast cancer xenografts , 2014, Translational oncology.

[45]  S. Rodenhuis,et al.  Magnetic resonance imaging response monitoring of breast cancer during neoadjuvant chemotherapy: relevance of breast cancer subtype. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[46]  Ning-Yu An,et al.  Differentiation of clinically benign and malignant breast lesions using diffusion‐weighted imaging , 2002, Journal of magnetic resonance imaging : JMRI.

[47]  C. Perou,et al.  The Triple Negative Paradox: Primary Tumor Chemosensitivity of Breast Cancer Subtypes , 2007, Clinical Cancer Research.

[48]  G. Viale,et al.  The microtubule-affecting drug paclitaxel has antiangiogenic activity. , 1996, Clinical cancer research : an official journal of the American Association for Cancer Research.

[49]  M. Knopp,et al.  Estimating kinetic parameters from dynamic contrast‐enhanced t1‐weighted MRI of a diffusable tracer: Standardized quantities and symbols , 1999, Journal of magnetic resonance imaging : JMRI.

[50]  J. Gore,et al.  A quantitative comparison of the influence of individual versus population‐derived vascular input functions on dynamic contrast enhanced‐MRI in small animals , 2012, Magnetic resonance in medicine.

[51]  Andreas Makris,et al.  Early Changes in Functional Dynamic Magnetic Resonance Imaging Predict for Pathologic Response to Neoadjuvant Chemotherapy in Primary Breast Cancer , 2008, Clinical Cancer Research.

[52]  Gianluca Franceschini,et al.  Effect of breast cancer phenotype on diagnostic performance of MRI in the prediction to response to neoadjuvant treatment. , 2014, European journal of radiology.

[53]  T. Chenevert,et al.  Anisotropic diffusion in human white matter: demonstration with MR techniques in vivo. , 1990, Radiology.

[54]  Isabelle Thomassin,et al.  Diffusion-weighted MRI in pretreatment prediction of response to neoadjuvant chemotherapy in patients with breast cancer , 2013, European Radiology.

[55]  N. Neamati,et al.  18F-FPPRGD2 and 18F-FDG PET of Response to Abraxane Therapy , 2011, The Journal of Nuclear Medicine.

[56]  R. Ponzone,et al.  Correlations between diffusion-weighted imaging and breast cancer biomarkers , 2012, European Radiology.

[57]  W. Moon,et al.  Correlation of perfusion parameters on dynamic contrast‐enhanced MRI with prognostic factors and subtypes of breast cancers , 2012, Journal of magnetic resonance imaging : JMRI.

[58]  M. Flister,et al.  Nab-paclitaxel efficacy in the orthotopic model of human breast cancer is significantly enhanced by concurrent anti-vascular endothelial growth factor A therapy. , 2008, Neoplasia.

[59]  Wei Huang,et al.  Early Prediction and Evaluation of Breast Cancer Response to Neoadjuvant Chemotherapy Using Quantitative DCE-MRI1 , 2016, Translational oncology.

[60]  P. Morris,et al.  Biological subtypes of breast cancer: current concepts and implications for recurrence patterns. , 2013, The quarterly journal of nuclear medicine and molecular imaging : official publication of the Italian Association of Nuclear Medicine (AIMN) [and] the International Association of Radiopharmacology (IAR), [and] Section of the Society of....

[61]  B. Naume,et al.  Neoadjuvant chemotherapy in breast cancer-response evaluation and prediction of response to treatment using dynamic contrast-enhanced and diffusion-weighted MR imaging , 2010, European Radiology.

[62]  L. Esserman,et al.  Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy--results from ACRIN 6657/I-SPY TRIAL. , 2012, Radiology.

[63]  Hee Jung Shin,et al.  Prediction of pathologic response to neoadjuvant chemotherapy in patients with breast cancer using diffusion‐weighted imaging and MRS , 2012, NMR in biomedicine.

[64]  U. Sharma,et al.  Longitudinal study of the assessment by MRI and diffusion‐weighted imaging of tumor response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy , 2009, NMR in biomedicine.

[65]  J. E. Tanner,et al.  Spin diffusion measurements : spin echoes in the presence of a time-dependent field gradient , 1965 .

[66]  E. Pasquier,et al.  Antiangiogenic activity of paclitaxel is associated with its cytostatic effect, mediated by the initiation but not completion of a mitochondrial apoptotic signaling pathway. , 2004, Molecular cancer therapeutics.

[67]  Thomas E Yankeelov,et al.  Multiparametric Magnetic Resonance Imaging for Predicting Pathological Response After the First Cycle of Neoadjuvant Chemotherapy in Breast Cancer , 2015, Investigative radiology.

[68]  Andreas Makris,et al.  Use of dynamic contrast-enhanced MR imaging to predict survival in patients with primary breast cancer undergoing neoadjuvant chemotherapy. , 2011, Radiology.

[69]  Shangang Liu,et al.  Diffusion‐weighted imaging in assessing pathological response of tumor in breast cancer subtype to neoadjuvant chemotherapy , 2015, Journal of magnetic resonance imaging : JMRI.

[70]  T. Yankeelov,et al.  Trastuzumab improves tumor perfusion and vascular delivery of cytotoxic therapy in a murine model of HER2+ breast cancer: preliminary results , 2016, Breast Cancer Research and Treatment.

[71]  Thomas E. Yankeelov,et al.  On the relationship between the apparent diffusion coefficient and extravascular extracellular volume fraction in human breast cancer. , 2011, Magnetic resonance imaging.