Anuradha Bapsi Chakravarthy

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.

Phase I trial of vorinostat added to chemoradiation with capecitabine in pancreatic cancer

BACKGROUND AND PURPOSEnThis single institution phase I trial determined the maximum tolerated dose (MTD) of concurrent vorinostat and capecitabine with radiation in non-metastatic pancreatic cancer.nnnMATERIAL AND METHODSnTwenty-one patients received escalating doses of vorinostat (100-400mg daily) during radiation. Capecitabine was given 1000mg q12 on the days of radiation. Radiation consisted of 30Gy in 10 fractions. Vorinostat dose escalation followed the standard 3+3 design. No dose escalation beyond 400mg vorinostat was planned. Diffusion-weighted (DW)-MRI pre- and post-treatment was used to evaluate in vivo tumor cellularity.nnnRESULTSnThe MTD of vorinostat was 400mg. Dose limiting toxicities occurred in one patient each at dose levels 100mg, 300mg, and 400mg: 2 gastrointestinal toxicities and one thrombocytopenia. The most common adverse events were lymphopenia (76%) and nausea (14%). The apparent diffusion coefficient (ADC) increased in most tumors. Nineteen (90%) patients had stable disease, and two (10%) had progressive disease at time of surgery. Eleven patients underwent surgical exploration with four R0 resections and one R1 resection. Median overall survival was 1.1years (95% confidence interval 0.78-1.35).nnnCONCLUSIONSnThe combination of vorinostat 400mg daily M-F and capecitabine 1000mg q12 M-F with radiation (30Gy in 10 fractions) was well tolerated with encouraging median overall survival.

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 (pu2009=u20090.002), 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 (pu2009=u20090.005).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

53BP1 expression is a modifier of the prognostic value of lymph node ratio and CA 19–9 in pancreatic adenocarcinoma

Background53BP1 binds to the tumor suppressor p53 and has a key role in DNA damage response and repair. Low 53BP1 expression has been associated with decreased survival in breast cancer and has been shown to interact with several prognostic factors in non-small cell lung cancer. The role of 53BP1 in pancreatic ductal adenocarcinoma (PDAC) has yet to be determined. We aimed to investigate whether 53BP1 levels interact with established prognostic factors in PDAC.Methods106 patients for whom there was tissue available at time of surgical resection for PDAC were included. A tissue microarray was constructed using surgical specimens, stained with antibodies to 53BP1, and scored for expression intensity. Univariate and multivariate statistical analyses were performed to investigate the association between 53BP1 and patient survival with known prognostic factors for survival.ResultsThe association of 53BP1 with several established prognostic factors was examined, including stage, tumor grade, surgical margin, peripancreatic extension, lymph node ratio (LNR), and CA 19–9. We found that 53BP1 modified the effects of known prognostic variables including LNR and CA 19–9 on survival outcomes. When 53BP1 intensity was low, increased LNR was associated with decreased OS (HR 4.84, 95% CI (2.26, 10.37), p<0.001) and high CA19-9 was associated with decreased OS (HR 1.72, 95% CI (1.18, 2.51), p=0.005). When 53BP1 intensity was high, LNR and CA19-9 were no longer associated with OS (p=0.958 and p=0.606, respectively).ConclusionsIn this study, 53BP1, a key player in DNA damage response and repair, was found to modify the prognostic value of two established prognostic factors, LNR and CA 19–9, suggesting 53BP1 may alter tumor behavior and ultimately impact how we interpret the value of other prognostic factors.

Association between cytosolic expression of BRCA1 and metastatic risk in breast cancer

Background:Although BRCA1 has been extensively studied for its role as a tumour-suppressor protein, the role of BRCA1 subcellular localisation in oncogenesis and tumour progression has remained unclear. This study explores the impact of BRCA1 mislocalisation on clinical outcomes in breast cancer.Methods:Tissue microarrays assembled from a cohort of patients with all stages of breast cancer were analysed for BRCA1 localisation and correlated with patient survival. Tissue microarrays of patients who had breast cancer that had metastasised to the lung were assembled from an independent cohort of patients. These were analysed for BRCA1 subcellular expression. In vitro studies using cultured human breast cancer cells were conducted to examine the effect of cytosolic BRCA1 on cell migration and efficiency of invasion.Results:An inverse association was found between cytosolic BRCA1 expression and metastasis-free survival in patients aged >40 years. Further analysis of BRCA1 subcellular expression in a cohort of breast cancer patients with metastatic disease revealed that the cytosolic BRCA1 content of breast tumours that had metastasised to the lung was 36.0% (95% CI=(31.7%, 40.3%), which was markedly higher than what is reported in the literature (8.2–14.8%). Intriguingly, these lung metastases and their corresponding primary breast tumours demonstrated similarly high cytosolic BRCA1 distributions in both paired and unpaired analyses. Finally, in vitro studies using human breast cancer cells demonstrated that genetically induced BRCA1 cytosolic sequestration (achieved using the cytosol-sequestering BRCA1 5382insC mutation) increased cell invasion efficiency.Conclusions:Results from this study suggest a model where BRCA1 cytosolic mislocalisation promotes breast cancer metastasis, making it a potential biomarker of metastatic disease.

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

PURPOSEnTo 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).nnnMETHODSnWe 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.nnnRESULTSnUsing 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%.nnnCONCLUSIONnWe 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.

Abstract P4-01-03: quantitative DCE-MRI to predict the response of primary breast cancer to neoadjuvant therapy

The goal of this study was to determine if quantitative changes in dynamic contrast enhanced MRI (DCE-MRI) following a single cycle of chemotherapy can be used to separate pathologic responders (i.e., no residual tumor in the breast at surgery) from pathologic non-responders. 28 patients with Stage II/III breast cancer were enrolled in an IRB-approved clinical trial where breast MRI scans were acquired before ( t 1 ) and after one cycle of therapy ( t 2 ). Imaging was performed on a 3.0T MR scanner (Philips Healthcare, The Netherlands) and employed a 3D spoiled gradient echo sequence with a spatial resolution of 6.6 mm 3 and a temporal resolution of 16 seconds collected at 25 time points before and after the intravenous injection of 0.1 mmol/kg of gadopentetate dimeglumine (Magnevist, Wayne, NJ). At surgery, 12 patients were responders while 16 patients were non-responders. Both semi-quantitative and quantitative analyses were used to summarize the DCE-MRI data. The semi-quantitative parameters were the signal enhancement rate (SER [1]) and tumor volume (TV). Three pharmacokinetic models, the Tofts-Kety (TK), the Extended Tofts-Kety (ETK), and the fast exchange regime (FXR), were used to estimate the following quantitative parameters: the volume transfer constant ( K trans ), efflux rate constant ( k ep ), vascular volume ( v p ), and the extravascular extracellular volume fraction ( v e ) [2]. Each parameter was summarized in two ways: 1) the change in mean from t 1 to t 2 , and 2) the mean at t 2 . Receiver operating characteristic (ROC) analysis was then performed to determine the ability of each parameter to predict treatment response. The table displays the areas under the ROC curves (AUC) for each parameter. For the early change in parameters, the AUC for TV and SER were 0.48 and 0.66, respectively. The best AUC of the quantitative parameters was 0.73 from k ep estimated by the ETK model. The sensitivities/specificities for TV, SER, and k ep for predicting pathologic response were 88%/33%, 64%/79%, and 56%/92%, respectively. For the mean parameter values at t 2 , the AUCs of TV, SER, and k ep were 0.50, 0.56, and 0.79, with sensitivities/specificities for predicting pathologic response of 63%/50%, 93%/21%, and 81%/75%, respectively. Our results can be interpreted in light of the ACRIN 6657/I-SPY trial [3] which found that change in TV and SER at an early time point were the most predictive of response with AUCs of 0.72 and 0.71, respectively. Our preliminary results, especially our AUC of 0.79 for k ep at t 2 , suggest that a more quantitative analysis of higher temporal resolution DCE-MRI data may achieve comparable or even superior results. Our ongoing efforts involve combining multiple parameters in a multivariate analysis with apparent diffusion coefficient data from diffusion weighted MRI. [1] Arasu et al. Acad Radiol 2011;18:716–721. [2] Yankeelov & Gore. CMIR 2009;3:91–107. [3] Hylton et al. Radiology. 2012;263:663–672. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P4-01-03.