Triple-negative breast cancer (TNBC) is a highly diverse group of cancers, and subtyping is necessary to better identify molecular-based therapies. In this study, we analyzed gene expression (GE) profiles from 21 breast cancer data sets and identified 587 TNBC cases. Cluster analysis identified 6 TNBC subtypes displaying unique GE and ontologies, including 2 basal-like (BL1 and BL2), an immunomodulatory (IM), a mesenchymal (M), a mesenchymal stem-like (MSL), and a luminal androgen receptor (LAR) subtype. Further, GE analysis allowed us to identify TNBC cell line models representative of these subtypes. Predicted "driver" signaling pathways were pharmacologically targeted in these cell line models as proof of concept that analysis of distinct GE signatures can inform therapy selection. BL1 and BL2 subtypes had higher expression of cell cycle and DNA damage response genes, and representative cell lines preferentially responded to cisplatin. M and MSL subtypes were enriched in GE for epithelial-mesenchymal transition, and growth factor pathways and cell models responded to NVP-BEZ235 (a PI3K/mTOR inhibitor) and dasatinib (an abl/src inhibitor). The LAR subtype includes patients with decreased relapse-free survival and was characterized by androgen receptor (AR) signaling. LAR cell lines were uniquely sensitive to bicalutamide (an AR antagonist). These data may be useful in biomarker selection, drug discovery, and clinical trial design that will enable alignment of TNBC patients to appropriate targeted therapies.

Purpose-The objective of this study was to assess changes in the water apparent diffusion coefficient (ADC) and in pharmacokinetic parameters obtained from the fast-exchange regime (FXR) modeling of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) during neoadjuvant chemotherapy in breast cancer.Materials and Methods-Eleven patients with locally advanced breast cancer underwent MRI examination prior to and after chemotherapy but prior to surgery. A 1.5-T scanner was used to obtain T 1 , ADC and DCE-MRI data. DCE-MRI data were analyzed by the FXR model returning estimates of K trans (volume transfer constant), ν e (extravascular extracellular volume fraction) and τs i (average intracellular water lifetime). Histogram and correlation analyses assessed parameter changes posttreatment.Results-Significant ( P <.05) changes or trends towards significance ( P <.10) were seen in all parameters except τ i , although there was qualitative reduction in τ i values post-treatment. In particular, there was reduction ( P <.035) in voxels with K trans values in the range 0.2-0.5 min -1 and a decrease ( P <.05) in voxels with ADC values in the range 0.99×10 -3 to 1.35×10 -3 mm 2 /s. ADC and ν e were negatively correlated (r = -.60, P <.02). Parameters sensitive to water distribution and geometry (T 1 , ν e ,τs i and ADC) correlated with a multivariable linear regression model. Conclusion-The analysis presented here is sensitive to longitudinal changes in breast tumor status; K trans and ADC are most sensitive to these changes. Relationships between parameters provide information on water distribution and geometry in the tumor environment.

While there is 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 oncological 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 pathological 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 (RECIST; 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.

clinicaltrials.gov Identifier: NCT00655876.

Objectives To determine if combined measurements from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) and diffusion weighted MRI (DW-MRI), obtained before and after the first cycle of neoadjuvant chemotherapy (NAC), are superior to single parameter measurements for predicting pathological complete response (pCR) in breast cancer patients. Materials and Methods Patients with Stage II/III breast cancer were enrolled in an IRB-approved study in which 3T DCE- and DW-MRI data were acquired before (n = 37) and after one cycle (n = 33) of NAC. Estimates of Ktrans, ve, vp, and kep (= Ktrans/ve) were generated from the DCE-MRI data using the Extended Tofts-Kety (ETK) model. The apparent diffusion coefficient (ADC) was estimated from the DW-MRI data. The derived parameter kep/ADC was compared to single parameter measurements for its ability to predict pCR after the first cycle of NAC. Results kep/ADC after the first cycle of NAC discriminated patients who went on to achieve a pCR (P < 0.001), and achieved a sensitivity, specificity, positive predictive value, and area under the receiver operator curve (AUC) of 0.92, 0.75, 0.69, and 0.86, respectively. These values were superior to the single parameters kep (AUC = 0.77) and ADC (AUC = 0.81). The AUCs between kep/ADC and kep were significantly different based on the bootstrapped 95% CIs (0.0062, 0.20), while the AUCs between kep/ADC and ADC trended towards significance (−0.12, 0.24). Conclusions A combined analysis of DCE-MRI and DW-MRI parameters was superior to single-parameter measurements for predicting pCR after the first cycle of NAC.

There is currently a paucity of reliable techniques for predicting the response of breast tumors to neoadjuvant chemotherapy. The standard approach is to monitor gross changes in tumor size as measured by physical exam and/or conventional imaging, but these methods generally do not show whether a tumor is responding until the patient has received many treatment cycles. One promising approach to address this clinical need is to integrate quantitative in vivo imaging data into biomathematical models of tumor growth in order to predict eventual response based on early measurements during therapy. In this work, we illustrate a novel biomechanical mathematical modeling approach in which contrast enhanced and diffusion weighted magnetic resonance imaging data acquired before and after the first cycle of neoadjuvant therapy are used to calibrate a patient-specific response model which subsequently is used to predict patient outcome at the conclusion of therapy. We present a modification of the reaction-diffusion tumor growth model whereby mechanical coupling to the surrounding tissue stiffness is incorporated via restricted cell diffusion. We use simulations and experimental data to illustrate how incorporating tissue mechanical properties leads to qualitatively and quantitatively different tumor growth patterns than when such properties are ignored. We apply the approach to patient data in a preliminary dataset of eight patients exhibiting a varying degree of responsiveness to neoadjuvant therapy, and we show that the mechanically coupled reaction-diffusion tumor growth model, when projected forward, more accurately predicts residual tumor burden at the conclusion of therapy than the non-mechanically coupled model. The mechanically coupled model predictions exhibit a significant correlation with data observations (PCC = 0.84, p < 0.01), and show a statistically significant >4 fold reduction in model/data error (p = 0.02) as compared to the non-mechanically coupled model.

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 taxanebased 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.

Purpose The purpose of this pilot study is to determine 1) if early changes in both semi-quantitative and quantitative DCE-MRI parameters, observed after the first cycle of neoadjuvant chemotherapy in breast cancer patients, show significant difference between responders and non-responders, and 2) if these parameters can be used as a prognostic indicator of the eventual response. Methods Twenty-eight patients were examined using DCE-MRI pre-, post-one cycle, and just prior to surgery. The semi-quantitative parameters included longest dimension, tumor volume, initial area under the curve (iAUC), and signal enhancement ratio (SER) related parameters, while quantitative parameters included Ktrans, ve, kep, vp, and τi estimated using the standard Tofts-Kety (TK), extended Tofts-Kety (ETK), and fast exchange regime (FXR) models. Results Our preliminary results indicated that the SER washout volume and kep were significantly different between pathologic complete responders from non-responders (P < 0.05) after a single cycle of chemotherapy. Receiver operator characteristic (ROC) analysis showed that the AUC of the SER washout volume was 0.75, and the AUCs of kep estimated by three models were 0.78, 0.76, and 0.73, respectively. Conclusion In summary, the SER washout volume and kep appear to predict breast cancer response after one cycle of neoadjuvant chemotherapy. This observation should be confirmed with additional prospective studies.