Jaime Farley

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.

Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer.

ObjectivesThe purpose of this study was to determine whether multiparametric magnetic resonance imaging (MRI) using dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DWI), obtained before and after the first cycle of neoadjuvant chemotherapy (NAC), is superior to single-parameter measurements for predicting pathologic complete response (pCR) in patients with breast cancer. Materials and MethodsPatients with stage II/III breast cancer were enrolled in an institutional review board–approved study in which 3-T DCE-MRI and DWI data were acquired before (n = 42) and after 1 cycle (n = 36) of NAC. Estimates of the volume transfer rate (Ktrans), extravascular extracellular volume fraction (ve), blood plasma volume fraction (vp), and the efflux rate constant (kep = Ktrans/ve) were generated from the DCE-MRI data using the Extended Tofts-Kety model. The apparent diffusion coefficient (ADC) was estimated from the DWI data. The derived parameter kep/ADC was compared with single-parameter measurements for its ability to predict pCR after the first cycle of NAC. ResultsThe 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, 0.78, 0.69, and 0.88, respectively. These values were superior to the single parameters kep (AUC, 0.76) and ADC (AUC, 0.82). The AUCs between kep/ADC and kep were significantly different on the basis of the bootstrapped 95% confidence intervals (0.018–0.23), whereas the AUCs between kep/ADC and ADC trended toward significance (−0.11 to 0.24). ConclusionsThe multiparametric analysis of DCE-MRI and DWI was superior to the single-parameter measurements for predicting pCR after the first cycle of NAC.

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).

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.

Motion correction in diffusion-weighted MRI of the breast at 3T

To provide a quantitative assessment of motion and distortion correction of diffusion‐weighted images (DWIs) of the breast and to evaluate the effects of registration on the mean apparent diffusion coefficient (mADC).

An algorithm for longitudinal registration of PET/CT images acquired during neoadjuvant chemotherapy in breast cancer: Preliminary results

BackgroundBy providing estimates of tumor glucose metabolism, 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) can potentially characterize the response of breast tumors to treatment. To assess therapy response, serial measurements of FDG-PET parameters (derived from static and/or dynamic images) can be obtained at different time points during the course of treatment. However, most studies track the changes in average parameter values obtained from the whole tumor, thereby discarding all spatial information manifested in tumor heterogeneity. Here, we propose a method whereby serially acquired FDG-PET breast data sets can be spatially co-registered to enable the spatial comparison of parameter maps at the voxel level.MethodsThe goal is to optimally register normal tissues while simultaneously preventing tumor distortion. In order to accomplish this, we constructed a PET support device to enable PET/CT imaging of the breasts of ten patients in the prone position and applied a mutual information-based rigid body registration followed by a non-rigid registration. The non-rigid registration algorithm extended the adaptive bases algorithm (ABA) by incorporating a tumor volume-preserving constraint, which computed the Jacobian determinant over the tumor regions as outlined on the PET/CT images, into the cost function. We tested this approach on ten breast cancer patients undergoing neoadjuvant chemotherapy.ResultsBy both qualitative and quantitative evaluation, our constrained algorithm yielded significantly less tumor distortion than the unconstrained algorithm: considering the tumor volume determined from standard uptake value maps, the post-registration median tumor volume changes, and the 25th and 75th quantiles were 3.42% (0%, 13.39%) and 16.93% (9.21%, 49.93%) for the constrained and unconstrained algorithms, respectively (p = 0.002), while the bending energy (a measure of the smoothness of the deformation) was 0.0015 (0.0005, 0.012) and 0.017 (0.005, 0.044), respectively (p = 0.005).ConclusionThe results indicate that the constrained ABA algorithm can accurately align prone breast FDG-PET images acquired at different time points while keeping the tumor from being substantially compressed or distorted.Trial registrationNCT00474604

Early DCE-MRI changes after longitudinal registration may predict breast cancer response to neoadjuvant chemotherapy

To monitor tumor response to neoadjuvant chemotherapy, investigators have begun to employ quantitative physiological parameters available from dynamic contrast enhanced MRI (DCE-MRI). However, most studies track the changes in these parameters obtained from the tumor region of interest (ROI) or histograms, thereby discarding all spatial information on tumor heterogeneity. In this study, we applied a nonrigid registration to longitudinal DCE-MRI data and performed a voxel-by-voxel analysis to examine the ability of early changes in parameters at the voxel level to separate pathologic complete responders (pCR) from non-responders (NR). Twenty-two patients were examined using DCE-MRI pre-, post one cycle, and at the conclusion of all neoadjuvant chemotherapy. The fast exchange regime model (FXR) was applied to both the original and registered DCE-MRI data to estimate tumor-related parameters. The results indicate that compared with the ROI analysis, the voxel-based analysis after longitudinal registration may improve the ability of DCE-MRI to separate complete responders from non-responders after one cycle of therapy when using the FXR model (p = 0.02).

Clinical and pathologic characteristics of patients with PI3K-mutant breast cancers.

8 Background: Mutations in PIK3CA are the most common somatic alterations in breast cancer and represent a potentially useful therapeutic target. As more PI3K pathway inhibitors enter the clinical arena, it is important to understand the characteristics of patients harboring mutations. This study seeks to identify the clinico/pathological characteristics of PI3K mutant breast cancers in patients evaluated at Vanderbilt University Medical Center. METHODS Molecular profiling (SNaPShot) was used to detect mutations in three genes in the PI3K pathway (PIK3CA, PTEN, AKT1). Electronic medical records of breast cancer patients whose tumors underwent testing from June 2010 to January 2013 were reviewed. PI3K mutation rates, histological tumor grade, receptor status (ER/PR/HER2), and recurrence-free survival were tabulated. RESULTS Three hundred evaluable tests were identified, with PI3K mutations detected in 83/300 (28%). Patients with PI3K mutations were more likely to be ER/PR positive (73% vs. 48%; p<0.001) and less likely to be HER2 positive (6.0% vs. 20.7%, p=0.0022). Only 6/83 patients (7.2%) with triple negative cancers harbored PI3K mutations. 32% of patients with PI3K mutations participated in clinical trials, versus 25% without. CONCLUSIONS Tumors with PI3K mutations were more likely to be ER/PR positive, of intermediate grade, and associated with longer recurrence-free survival. Patients with PI3K mutations were more likely to participate in clinical trials. These data and the potential eligibility of patients harboring mutations for clinical trials support the prognostic and clinical utility of SNaPShot testing for all breast cancer patients at a tertiary care center. [Table: see text].

Abstract PD6-3: Recurrent ESR1 fusion transcripts are associated with endocrine resistance in estrogen receptor positive, HER2 negative breast cancer

Breast cancer proliferation measured by Ki67 immunohistochemistry after short-term antiestrogen therapy has been shown to correlate with disease-free survival. This suggests the use of biomarkers of the early effects of endocrine therapy on ER+ tumors will identify resistant cancers. Thus, we hypothesized that profiling operable ER+ tumors after short term treatment with an aromatase inhibitor would discover actionable molecular alterations causally associated with resistance to estrogen deprivation. We performed whole exome sequencing, RNA-Seq and quantitative immunofluorescence (QIF) of ER, PR, HER2, and Ki67 in biopsies from 130 patients with an operable ER+/HER2– breast cancer that had received letrozole for 10-21 days prior to surgery. Tumors were categorized by the natural log of 2-week post-letrozole Ki67 as sensitive, intermediate, or resistant. We sequenced RNA from 50 frozen tumors and performed fusion transcript analysis using 4 programmatic algorithms (dRanger, TopHat, DeFuse, Chimera Scan), resulting in 304 candidate gene fusions in 44 tumors. Primers with universal sequencing tags were designed against 3’ and 5’ sites of breakpoints mapping to RefSeq exon coding regions (n=187); fusion sequences were amplified by qRT-PCR from tumor and breast cancer cell line RNA. Single or multiple distinct product bands were visualized by gel electrophoresis in 96 tumor samples and Sanger-sequenced. Results were mapped to the human RNA reference transcriptome using BLAST. Overall, 9% of putative fusion transcripts (n=27 from 16 unique tumors) were validated by mapping to the open reading frames of predicted 3’ and 5’ genes. Fusion transcripts called by more than one program were more likely to validate (13 of 24 redundant versus 14 of 269 unique; p Using the 2-week Ki67 to stratify for response to treatment, the validated ESR1 fusions were present only in tumors that maintained high (≥7.4%) to intermediate (>2.7%) Ki67 labeling indices upon estrogen deprivation with letrozole (p=0.01). PR expression was lower (p=0.003) and ER expression higher (p=0.05) in ESR1 fusion+ tumors compared to fusion negative tumors. RNA extracted from 14 additional tumors were screened for ESR1 fusions by qRT-PCR and the ESR1:CCDC170 fusion was validated in 1 of 8 resistant/intermediate and 0 of 6 sensitive tumors. In summary, biomarkers of early response to antiestrogens are needed in order to identify ER+ cancers that are treatment resistant. In a prospective trial of operable ER+/HER2− breast tumors, we discovered recurrent intrachromosomal ESR1 fusion transcripts associated with intrinsic resistance to estrogen deprivation with letrozole. Additional work investigating the genomic basis and function of the fusion transcripts is underway. Citation Format: Jennifer M Giltnane, Justin M Balko, Thomas L Stricker, Christian Young, M Valeria Estrada, Nikhil Wagle, Eliezer van Allen, X Jasmine Mu, Violeta Sanchez, Jaime Farley, Kerry Fitzgerald, Armin Graber, Joseph A Pinto, Franco Doimi, Henry Gomez, Monica Rizzo, Thomas B Julian, Vandana Abramson, Ingrid Mayer, Mark Kelley, Ashwini Yenamandra, Ferrin C Wheeler, Melinda Sanders, Levi Garraway, Ingrid Meszoely, Carlos L Arteaga. Recurrent ESR1 fusion transcripts are associated with endocrine resistance in estrogen receptor positive, HER2 negative breast cancer [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr PD6-3.

WE‐E‐17A‐08: Prediction of Response to Neoadjuvant Chemotherapy Using a Mechanically Coupled Reaction‐Diffusion Model

PURPOSE To develop a clinically-relevant patient-specific modeling framework for oncology that is amenable to readily available clinical imaging data and yet retains the most salient features of response prediction. We use a mechanically coupled mathematical model of tumor growth that is initialized and constrained by MRI data early in the course of therapy, to guide the determination of model parameters and predict the response of breast cancers to neoadjuvant chemotherapy (NAC). METHODS We adopt a patient-scale spatiotemporal tumor growth modeling framework and apply patient-specific predictive modeling, constrained by quantitative imaging data, to a group of 26 patients exhibiting a varying degree of response to NAC. Dynamic contrast enhanced MRI, diffusion weighted MRI, and anatomical T1 -weighted MRI volumes were acquired prior to beginning NAC, after one cycle of NAC, and at the conclusion of NAC. Tumor response is parameterized using data from before and after the first cycle of therapy, and the model is driven forward in time to predict tumor burden at the conclusion of therapy. Model reconstructed parameters and predictions are retrospectively assessed for prognostic value in predicting patients that eventually respond or do not respond to NAC. RESULTS Using our mechanics-coupled modeling approach, we are able to discriminate, after the first cycle of therapy, breast cancer patients that would eventually achieve a complete pathological response and those who would not, with an area under the receiver operator characteristic curve of 0.81, sensitivity of 90%, and specificity of 56%. CONCLUSION We show the potential for model-predictions at the conclusion of therapy for use as a prognostic indicator of response to therapy. This work provides considerable promise for predictive modeling centered on integrating quantitative in vivo imaging data with biomechanical models of tumor growth. National Institutes of Health NCI 1U01CA142565, NCI U01CA174706, NCI R25CA092043, NCI 1P50 098131, NCI P30CA68485, NCI R01CA138599, NINDS R01NS049251. The Vanderbilt initiative in Surgery and Engineering Pilot Award Program and the Whitaker Foundation.