A. Bapsi Chakravarthy

Identification of markers of taxane sensitivity using proteomic and genomic analyses of breast tumors from patients receiving neoadjuvant paclitaxel and radiation.

Purpose: To identify molecular markers of pathologic response to neoadjuvant paclitaxel/radiation treatment, protein and gene expression profiling were done on pretreatment biopsies. Experimental Design: Patients with high-risk, operable breast cancer were treated with three cycles of paclitaxel followed by concurrent paclitaxel/radiation. Tumor tissue from pretreatment biopsies was obtained from 19 of the 38 patients enrolled in the study. Protein and gene expression profiling were done on serial sections of the biopsies from patients that achieved a pathologic complete response (pCR) and compared to those with residual disease, non-pCR (NR). Results: Proteomic and validation immunohistochemical analyses revealed that α-defensins (DEFA) were overexpressed in tumors from patients with a pCR. Gene expression analysis revealed that MAP2, a microtubule-associated protein, had significantly higher levels of expression in patients achieving a pCR. Elevation of MAP2 in breast cancer cell lines led to increased paclitaxel sensitivity. Furthermore, expression of genes that are associated with the basal-like, triple-negative phenotype were enriched in tumors from patients with a pCR. Analysis of a larger panel of tumors from patients receiving presurgical taxane-based treatment showed that DEFA and MAP2 expression as well as histologic features of inflammation were all statistically associated with response to therapy at the time of surgery. Conclusion: We show the utility of molecular profiling of pretreatment biopsies to discover markers of response. Our results suggest the potential use of immune signaling molecules such as DEFA as well as MAP2, a microtubule-associated protein, as tumor markers that associate with response to neoadjuvant taxane–based therapy. Clin Cancer Res; 16(2); 681–90

DCE-MRI analysis methods for predicting the response of breast cancer to neoadjuvant chemotherapy: Pilot study findings

The purpose of this pilot study is to determine (1) if early changes in both semiquantitative and quantitative DCE‐MRI parameters, observed after the first cycle of neoadjuvant chemotherapy in breast cancer patients, show significant difference between responders and nonresponders and (2) if these parameters can be used as a prognostic indicator of the eventual response.

A novel AIF tracking method and comparison of DCE-MRI parameters using individual and population-based AIFs in human breast cancer.

Quantitative analysis of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data requires the accurate determination of the arterial input function (AIF). A novel method for obtaining the AIF is presented here and pharmacokinetic parameters derived from individual and population-based AIFs are then compared. A Philips 3.0 T Achieva MR scanner was used to obtain 20 DCE-MRI data sets from ten breast cancer patients prior to and after one cycle of chemotherapy. Using a semi-automated method to estimate the AIF from the axillary artery, we obtain the AIF for each patient, AIF(ind), and compute a population-averaged AIF, AIF(pop). The extended standard model is used to estimate the physiological parameters using the two types of AIFs. The mean concordance correlation coefficient (CCC) for the AIFs segmented manually and by the proposed AIF tracking approach is 0.96, indicating accurate and automatic tracking of an AIF in DCE-MRI data of the breast is possible. Regarding the kinetic parameters, the CCC values for K(trans), v(p) and v(e) as estimated by AIF(ind) and AIF(pop) are 0.65, 0.74 and 0.31, respectively, based on the region of interest analysis. The average CCC values for the voxel-by-voxel analysis are 0.76, 0.84 and 0.68 for K(trans), v(p) and v(e), respectively. This work indicates that K(trans) and v(p) show good agreement between AIF(pop) and AIF(ind) while there is a weak agreement on v(e).

A nonrigid registration algorithm for longitudinal breast MR images and the analysis of breast tumor response

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can estimate parameters relating to blood flow and tissue volume fractions and therefore may be used to characterize the response of breast tumors to treatment. To assess treatment response, values of these DCE-MRI parameters are observed at different time points during the course of treatment. We propose a method whereby DCE-MRI data sets obtained in separate imaging sessions can be co-registered to a common image space, thereby retaining spatial information so that serial DCE-MRI parameter maps can be compared on a voxel-by-voxel basis. In performing inter-session breast registration, one must account for patient repositioning and breast deformation, as well as changes in tumor shape and volume relative to other imaging sessions. One challenge is to optimally register the normal tissues while simultaneously preventing tumor distortion. We accomplish this by extending the adaptive bases algorithm through adding a tumor-volume preserving constraint in the cost function. We also propose a novel method to generate the simulated breast magnetic resonance (MR) images, which can be used to evaluate the proposed registration algorithm quantitatively. The proposed nonrigid registration algorithm is applied to both simulated and real longitudinal 3D high resolution MR images and the obtained transformations are then applied to lower resolution physiological parameter maps obtained via DCE-MRI. The registration results demonstrate the proposed algorithm can successfully register breast MR images acquired at different time points and allow for analysis of the registered parameter maps.

Neoadjuvant Concurrent Paclitaxel and Radiation in Stage II/III Breast Cancer

Purpose: The aim of this study was to determine the safety and pathologic response rates following neoadjuvant paclitaxel and radiation in patients with stage II/III breast cancer and to evaluate the use of sequential biopsies to allow an in vivo assessment of biological markers as potential predictive markers of response to this regimen. Patients and Methods: Patients with high-risk, operable breast cancer were treated with three cycles of paclitaxel 175 mg/m2 every 3 weeks, followed by twice-weekly paclitaxel 30 mg/m2 and concurrent radiation. Core biopsies were obtained at baseline and 24 to 72 hours after the first cycle of paclitaxel. After completing neoadjuvant treatment, patients underwent definitive surgery. The primary end point was pathologic complete response, which is defined as the absence of any invasive cancer at surgery. Potential markers of therapeutic response were evaluated including markers of proliferation, apoptosis, p21, HER2, estrogen receptor, and progesterone receptor status. Results: Of the 38 patients enrolled, 13 (34%) had a pathologic complete response. There was no significant difference in baseline Ki-67 between responders (35%) and nonresponders (28%; P = 0.45). There was also no significant change in Ki-67 following paclitaxel administration. Despite this lack of immunohistologic change in proliferative activity, baseline mitotic index was higher for patients with pathologic complete response over nonresponders (27 versus 10, P = 0.003). Moreover, the increase in mitotic index following paclitaxel administration was associated with pathologic complete response. Conclusions: Neoadjuvant paclitaxel/radiation is effective and well tolerated. Tumor proliferation at baseline and response to chemotherapy as measured by mitotic activity may serve as an important indicator of pathologic response to neoadjuvant paclitaxel/radiation.

p53-Dependent BRCA1 Nuclear Export Controls Cellular Susceptibility to DNA Damage

Subcellular localization regulates BRCA1 function, and BRCA1 is exported to the cytoplasm following DNA damage in a p53-dependent manner. Because more than 50% of solid tumors harbor p53 mutations, it is possible that genetically wild-type (wt) BRCA1 is functionally abnormal through compromised nuclear-cytoplasmic shuttling in sporadic breast cancer patients with dysfunctional p53. In this study, we have investigated the mechanisms of p53-dependent BRCA1 subcellular distribution and DNA damage-induced nuclear export, as well as the impact on the resulting cytotoxic response to therapy in human breast cancer. We first show that p53 mediates BRCA1 nuclear export via protein-protein binding, rather than by modulation of its transcription. Furthermore, it is the C-terminal (BRCT) region of BRCA1 that is critical for its interaction with p53, and p53 may promote BRCA1 nuclear export by interrupting the association of BRCA1 with BARD1. In sporadic breast cancer specimens, dysfunctional p53 strongly correlates with nuclear retention of sequence-verified wt BRCA1. This p53-dependent BRCA1 shuttling determines cellular susceptibility to DNA damage as augmentation of cytosolic BRCA1 significantly enhances cancer cell susceptibility to ionizing radiation. Taken together, our data suggest that p53 dysfunction compromises nuclear export of wt BRCA1 as a mechanism to increase cellular resistance to DNA damage in sporadic breast cancer. We propose that targeting nuclear BRCA1 to the cytoplasm may offer a unique strategy to sensitize p53-deficient sporadic breast cancers to DNA damage-based therapy.

Early assessment of breast cancer response to neoadjuvant chemotherapy by semi-quantitative analysis of high-temporal resolution DCE-MRI: Preliminary results

PURPOSE To evaluate whether semi-quantitative analysis of high temporal resolution dynamic contrast-enhanced MRI (DCE-MRI) acquired early in treatment can predict the response of locally advanced breast cancer (LABC) to neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS As part of an IRB-approved prospective study, 21 patients with LABC provided informed consent and underwent high temporal resolution 3T DCE-MRI before and after 1cycle of NAC. Using measurements performed by two radiologists, the following parameters were extracted for lesions at both examinations: lesion size (short and long axes, in both early and late phases of enhancement), radiologists subjective assessment of lesion enhancement, and percentages of voxels within the lesion demonstrating progressive, plateau, or washout kinetics. The latter data were calculated using two filters, one selecting for voxels enhancing ≥50% over baseline and one for voxels enhancing ≥100% over baseline. Pretreatment imaging parameters and parameter changes following cycle 1 of NAC were evaluated for their ability to discriminate patients with an eventual pathological complete response (pCR). RESULTS All 21 patients completed NAC followed by surgery, with 9 patients achieving a pCR. No pretreatment imaging parameters were predictive of pCR. However, change after cycle 1 of NAC in percentage of voxels demonstrating washout kinetics with a 100% enhancement filter discriminated patients with an eventual pCR with an area under the receiver operating characteristic curve (AUC) of 0.77. Changes in other parameters, including lesion size, did not predict pCR. CONCLUSION Semi-quantitative analysis of high temporal resolution DCE-MRI in patients with LABC can discriminate patients with an eventual pCR after one cycle of NAC.

Statistical comparison of dynamic contrast-enhanced MRI pharmacokinetic models in human breast cancer.

By fitting dynamic contrast‐enhanced MRI data to an appropriate pharmacokinetic model, quantitative physiological parameters can be estimated. In this study, we compare four different models by applying four statistical measures to assess their ability to describe dynamic contrast‐enhanced MRI data obtained in 28 human breast cancer patient sets: the chi‐square test (χ2), Durbin–Watson statistic, Akaike information criterion, and Bayesian information criterion. The pharmacokinetic models include the fast exchange limit model with (FXL_vp) and without (FXL) a plasma component, and the fast and slow exchange regime models (FXR and SXR, respectively). The results show that the FXL_vp and FXR models yielded the smallest χ2 in 45.64 and 47.53% of the voxels, respectively; they also had the smallest number of voxels showing serial correlation with 0.71 and 2.33%, respectively. The Akaike information criterion indicated that the FXL_vp and FXR models were preferred in 42.84 and 46.59% of the voxels, respectively. The Bayesian information criterion also indicated the FXL_vp and FXR models were preferred in 39.39 and 45.25% of the voxels, respectively. Thus, these four metrics indicate that the FXL_vp and the FXR models provide the most complete statistical description of dynamic contrast‐enhanced MRI time courses for the patients selected in this study. Magn Reson Med, 2012.

Temporal sampling requirements for reference region modeling of DCE-MRI data in human breast cancer

To assess the temporal sampling requirements needed for quantitative analysis of dynamic contrast‐enhanced MRI (DCE‐MRI) data with a reference region (RR) model in human breast cancer.

Predicting the Response of Breast Cancer to Neoadjuvant Therapy Using a Mechanically Coupled Reaction–Diffusion Model

Although there are considerable data on the use of mathematical modeling to describe tumor growth and response to therapy, previous approaches are often not of the form that can be easily applied to clinical data to generate testable predictions in individual patients. Thus, there is a clear need to develop and apply clinically relevant oncologic models that are amenable to available patient data and yet retain the most salient features of response prediction. In this study we show how a biomechanical model of tumor growth can be initialized and constrained by serial patient-specific magnetic resonance imaging data, obtained at two time points early in the course of therapy (before initiation and following one cycle of therapy), to predict the response for individual patients with breast cancer undergoing neoadjuvant therapy. Using our mechanics coupled modeling approach, we are able to predict, after the first cycle of therapy, breast cancer patients that would eventually achieve a complete pathologic response and those who would not, with receiver operating characteristic area under the curve (AUC) of 0.87, sensitivity of 92%, and specificity of 84%. Our approach significantly outperformed the AUCs achieved by standard (i.e., not mechanically coupled) reaction-diffusion predictive modeling (0.75), simple analysis of the tumor cellularity estimated from imaging data (0.73), and the Response Evaluation Criteria in Solid Tumors (0.71). Thus, we show the potential for mathematical model prediction for use as a prognostic indicator of response to therapy. The work indicates the considerable promise of image-driven biophysical modeling for predictive frameworks within therapeutic applications.