Prediction Of Breast Cancer Response Research Articles

Abstract 3043: Quantitative MRI during neoadjuvant therapy for predicting breast cancer response in the community setting

Abstract Purpose: Quantification of accurate and early response to neoadjuvant therapy (NAT) provides the opportunity to replace an ineffective treatment with an alternative regimen, thereby potentially avoiding ineffective systemic therapy. Quantitative magnetic resonance imaging (MRI) has been shown to predict breast cancer response to treatment early during the course of NAT within academic medical centers. Integrating quantitative imaging techniques into community-based medical practices has the potential to reach a larger percentage of breast cancer patients, and allow more community center participation in clinical trials that require quantitative imaging. This study evaluated the reproducibility and accuracy of quantitative dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DW-MRI) in the community setting and the ability to implement these techniques to predict the eventual response of breast tumors to NAT. Experiment Procedure: MRI was performed at two community imaging centers and one academic research facility using a 3T Siemens Skyra scanners equipped with a breast coil. To assess reproducibility across sites, normal subjects (N=3) were scanned at three imaging centers. Quantitative T1 (required for pharmacokinetic modeling of DCE-MRI) and apparent diffusion coefficient (ADC) maps were calculated of normal fibroglandular breast tissue. Accuracy was tested through evaluation of T1 and DW-MRI in phantoms. Women undergoing NAT for breast cancer (N=10) were scanned with DCE-MRI and DW-MRI at baseline (prior to beginning therapy) and three longitudinal time points during the course of NAT to evaluate early prediction of response to therapy. Quantitative measures of ADC (evaluating cellularity from DW-MRI) and Ktrans (evaluating vascular perfusion and permeability from DCE-MRI) were calculated for the segmented tumor volume. Imaging was compared to pathology reports at the conclusion of NAT. Results: Reproducibility scans of normal breast fibroglandular tissue yielded an average difference of 8.4% and 7.0% in T1 and ADC measurements, respectively, across sites. Phantom studies revealed accurate measurements of T1 mapping and ADC, with reproducibility measurements showing a difference of 2.8% and 1.6%, respectively. Patients achieving a pathological complete response (pCR) revealed a 13.8% ± 19.0% increase in the mean ADC values of the tumor from t1 (baseline, prior to beginning NAT) and t2 (following one round of NAT) and a 15.4% ± 40.9% decrease in mean Ktrans. Conversely, patients that did not achieve non-pCR had little change in ADC (-0.9% ± 12.6% change between t1 and t2) and a 15.3% ± 42.8% increase in Ktrans. Conclusions: Quantitative MRI has been shown to be accurate and reproducible across community medical centers. Furthermore, the preliminary results discussed above parallel those previously reported in the academic research setting. Citation Format: Anna Sorace, Jack Virostko, Chengyue Wu, Angela M. Jarrett, Stephanie L. Barnes, Debra Patt, Boone Goodgame, Sarah Avery, Thomas E. Yankeelov. Quantitative MRI during neoadjuvant therapy for predicting breast cancer response in the community setting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3043.

Identifying an early treatment window for predicting breast cancer response to neoadjuvant chemotherapy using immunohistopathology and hemoglobin parameters

BackgroundBreast cancer pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) varies with tumor subtype. The purpose of this study was to identify an early treatment window for predicting pCR based on tumor subtype, pretreatment total hemoglobin (tHb) level, and early changes in tHb following NAC.MethodsTwenty-two patients (mean age 56 years, range 34–74 years) were assessed using a near-infrared imager coupled with an Ultrasound system prior to treatment, 7 days after the first treatment, at the end of each of the first three cycles, and before their definitive surgery. Pathologic responses were dichotomized by the Miller-Payne system. Tumor vascularity was assessed from tHb; vascularity changes during NAC were assessed from a percentage tHb normalized to the pretreatment level (%tHb). After training the logistic prediction models using the previous study data, we assessed the early treatment window for predicting pathological response according to their tumor subtype (human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), triple-negative (TN)) based on tHb, and %tHb measured at different cycles and evaluated by the area under the receiver operating characteristic (ROC) curve (AUC).ResultsIn the new study cohort, maximum pretreatment tHb and %tHb changes after cycles 1, 2, and 3 were significantly higher in responder Miller-Payne 4–5 tumors (n = 13) than non-or partial responder Miller-Payne 1–3 tumors (n = 9). However, no significance was found at day 7. The AUC of the predictive power of pretreatment tHb in the cohort was 0.75, which was similar to the performance of the HER2 subtype as a single predictor (AUC of 0.78). A greater predictive power of pretreatment tHb was found within each subtype, with AUCs of 0.88, 0.69, and 0.72, in the HER2, ER, and TN subtypes, respectively. Using pretreatment tHb and cycle 1 %tHb, AUC reached 0.96, 0.91, and 0.90 in HER2, ER, and TN subtypes, respectively, and 0.95 regardless of subtype. Additional cycle 2 %tHb measurements moderately improved prediction for the HER2 subtype but did not improve prediction for the ER and TN subtypes.ConclusionsBy combining tumor subtypes with tHb, we predicted the pCR of breast cancer to NAC before treatment. Prediction accuracy can be significantly improved by incorporating cycle 1 and 2 %tHb for the HER2 subtype and cycle 1 %tHb for the ER and TN subtypes.Trial registrationClinicalTrials.gov, NCT02092636. Registered in March 2014.

A PRM approach for early prediction of breast cancer response to chemotherapy based on registered MR images.

This study aims to provide and optimize a performing algorithm for predicting the breast cancer response rate to the first round of chemotherapy using Magnetic Resonance Imaging (MRI). This provides an early recognition of breast tumor reaction to chemotherapy by using the Parametric Response Map (PRM) method. PRM may predict the breast cancer response to chemotherapy by analyzing voxel-by-voxel temporal intra-tumor changes during one round of chemotherapy. Indeed, the tumor recognizes intra-tumor changes concerning its vascularity, which is an important criterion in the present study. This method is mainly based on spatial image affine registration between the breast tumor MRI volumes, acquired before and after the first cycle of chemotherapy, and region growing segmentation of the tumor volume. To evaluate our method, we used a retrospective study of 40 patients provided by a collaborating institute. PRM allows a color map to be created with the percentages of positive, negative and stable breast tumor response during the first round of chemotherapy, identifying each region with its response rate. We assessed the accuracy of the proposed method using technical and medical validation methods. The technical validation was based on landmarks-based registration and fully manual segmentation. The medical evaluation was based on the accuracy calculation of the standard reference of anatomic pathology. The p-values and the Area Under the Curve (AUC) of the Receiver Operating Characteristics were calculated to evaluate the proposed PRM method. We performed and evaluated the proposed PRM method to study and analyze the behavior of a tumor during the first round of chemotherapy, based on the intra-tumor changes of MR breast tumor images. The AUC obtained for the PRM method is considered as relevant in the early prediction of breast tumor response.

Abstract P4-02-08: Repeatability and reproducibility of quantitative breast MRI in community imaging centers: Preliminary results

Abstract Introduction: The primary purpose of this study is to evaluate the repeatability and reproducibility of quantitative breast MRI across community imaging centers with the ultimate goal of using these techniques to predict breast cancer response early in the course of neoadjuvant therapy (NAT). Dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted MRI (DW-MRI), and magnetization transfer MRI (MT-MRI) performed early in the course of breast NAT has the potential to predict eventual response prior to changes in tumor size. This enables tailoring of treatment plans and the opportunity to substitute ineffective therapies with alternative approaches. We present preliminary results on the reproducibility and repeatability of T1, apparent diffusion coefficient (ADC), and magnetization transfer ratio (MTR) measurements in normal breast fibroglandular tissue (FGT) in the community setting. Experimental Design: MRI was performed at two community imaging centers and one academic research facility using 3T Siemens Skyra scanners equipped with 8- or 16-channel breast coils. To assess repeatability of the imaging techniques, normal subjects (N=10, ages 22-62) were scanned twice, separated by subject repositioning. To assess reproducibility across sites, normal subjects (N=3) were scanned at three imaging centers. To assess quantitative T1 measurements, subjects were scanned using a spoiled gradient echo (SPGE) sequence and variable flip angles (2, 4, 6, …, 20) with TR/TE = 7.9/2.71 ms and corrected for B1inhomogeneity. To assess the ADC, subjects were scanned using an echo-planar monopolar spin echo sequence with the following parameters: TR/TE = 3000/52 ms and b-values = 0, 200, 800 s/mm2. MT-MRI was acquired using two gradient echo sequences with TR/TE = 48.0/6.40 ms, one with the inclusion of a 1500 Hz off-resonance saturation pulse. FGT was segmented using k-means clustering. Women undergoing NAT for breast cancer are being recruited and scanned with DCE-MRI, DW-MRI, and MT-MRI at baseline (prior to beginning therapy) and three early time points during the course of NAT to evaluate early prediction of response to therapy. Results: Reproducibility scans of normal breast FGT yielded an average difference of 8.4% in T1 measurement, 7.0% in ADC measurement, and 12.7% in MTR measurement between sites. Repeatability scans of the same subject's FGT showed an average percent difference of 6.7% in T1 measurement, 4.5% in ADC measurement, and 11.6% in MTR measurement between the two scans. A multi-site trial performing quantitative DCE-MRI, DW-MRI, and MT-MRI in patients undergoing breast NAT in the community setting (N=16 at the time of submission) has been ongoing to predict response to NAT using quantitative MRI. Conclusion: Quantitative DCE-, DW-, and MT-MRI of the breast is both repeatable and reproducible across MRI scanners in community imaging centers. A quantitative breast MRI protocol can be deployed at community imaging centers for breast cancer patients. These results and ongoing work highlight the feasibility of future clinical dissemination of quantitative MRI for predicting early response to NAT, therefore expanding these novel techniques to a widespread patient population. We acknowledge the support of CPRIT RR160005. Citation Format: Sorace AG, Virostko J, Wu C, Jarrett AM, Barnes SL, Luci J, Patt DA, Goodgame B, Avery S, Yankeelov TE. Repeatability and reproducibility of quantitative breast MRI in community imaging centers: Preliminary results [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-02-08.

Predicting Treatment Response of Breast Cancer to Neoadjuvant Chemotherapy Using Ultrasound-Guided Diffuse Optical Tomography

PURPOSE: To prospectively investigate ultrasound-guided diffuse optical tomography (US-guided DOT) in predicting breast cancer response to neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS: Eighty-eight breast cancer patients, with a total of 93 lesions, were included in our study. Pre– and post–last chemotherapy, size and total hemoglobin concentration (THC) of each lesion were measured by conventional US and US-guided DOT 1 day before biopsy (time point t0, THC THC0, SIZE S0) and 1 to 2 days before surgery (time point tL, THCL, SL). The relative changes in THC and SIZE of lesions after the first and last NAC cycles were considered as the variables ΔTHC and ΔSIZE. Receiver operating characteristic curve was performed to calculate ΔTHC and ΔSIZE cutoff values to evaluate pathologic response of 93 breast cancers to NAC, which were then prospectively used to predicate response of 61 breast cancers to NAC. RESULTS: The cutoff values of ΔTHC and ΔSIZE for evaluation of breast cancers NAC treatment response were 23.9% and 42.6%. At ΔTHC 23.9%, the predicted treatment response in 61 breast lesions for the time points t1 to t3 was calculated by area under the curve (AUC), which were AUC1 0.534 (P=.6668), AUC2 0.604 (P=.1893), and AUC3 0.674(P =. 0.027), respectively; for ΔSIZE 42.6%, at time points t1 to t3, AUC1 0.505 (P=.9121), AUC2 0.645 (P=.0115), and AUC3 0.719 (P=.0018). CONCLUSION: US-guided DOT ΔTHC 23.9% and US ΔSIZE 42.6% can be used for the response evaluation and earlier prediction of the pathological response after three rounds of chemotherapy.

Predicting breast cancer response to neoadjuvant chemotherapy using pretreatment diffuse optical spectroscopic texture analysis

Background:Diffuse optical spectroscopy (DOS) has been demonstrated capable of monitoring response to neoadjuvant chemotherapy (NAC) in locally advanced breast cancer (LABC) patients. In this study, we evaluate texture features of pretreatment DOS functional maps for predicting LABC response to NAC.Methods:Locally advanced breast cancer patients (n=37) underwent DOS breast imaging before starting NAC. Breast tissue parametric maps were constructed and texture analyses were performed based on grey-level co-occurrence matrices for feature extraction. Ground truth labels as responders (R) or non-responders (NR) were assigned to patients based on Miller–Payne pathological response criteria. The capability of DOS textural features computed on volumetric tumour data before the start of treatment (i.e., ‘pretreatment’) to predict patient responses to NAC was evaluated using a leave-one-out validation scheme at subject level. Data were analysed using a logistic regression, naive Bayes, and k-nearest neighbour classifiers.Results:Data indicated that textural characteristics of pretreatment DOS parametric maps can differentiate between treatment response outcomes. The HbO2 homogeneity resulted in the highest accuracy among univariate parameters in predicting response to chemotherapy: sensitivity (%Sn) and specificity (%Sp) were 86.5% and 89.0%, respectively, and accuracy was 87.8%. The highest predictors using multivariate (binary) combination features were the Hb-contrast+HbO2-homogeneity, which resulted in a %Sn/%Sp=78.0/81.0% and an accuracy of 79.5%.Conclusions:This study demonstrated that the pretreatment DOS texture features can predict breast cancer response to NAC and potentially guide treatments.

DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response.

This study investigates the effectiveness of hundreds of texture features extracted from voxel-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps for early prediction of breast cancer response to neoadjuvant chemotherapy (NAC). In total, 38 patients with breast cancer underwent DCE-MRI before (baseline) and after the first of the 6–8 NAC cycles. Quantitative pharmacokinetic (PK) parameters and semiquantitative metrics were estimated from DCE-MRI time-course data. The residual cancer burden (RCB) index value was computed based on pathological analysis of surgical specimens after NAC completion. In total, 1043 texture features were extracted from each of the 13 parametric maps of quantitative PK or semiquantitative metric, and their capabilities for early prediction of RCB were examined by correlating feature changes between the 2 MRI studies with RCB. There were 1069 pairs of feature–map combinations that showed effectiveness for response prediction with 4 correlation coefficients >0.7. The 3-dimensional gray-level cooccurrence matrix was the most effective feature extraction method for therapy response prediction, and, in general, the statistical features describing texture heterogeneity were the most effective features. Quantitative PK parameters, particularly those estimated with the shutter-speed model, were more likely to generate effective features for prediction response compared with the semiquantitative metrics. The best feature–map pair could predict pathologic complete response with 100% sensitivity and 100% specificity using our cohort. In conclusion, breast tumor heterogeneity in microvasculature as measured by texture features of voxel-based DCE-MRI parametric maps could be a useful biomarker for early prediction of NAC response.

Key Survival Factor, Mcl-1, Correlates with Sensitivity to Combined Bcl-2/Bcl-xL Blockade.

An estimated 40,000 deaths will be attributed to breast cancer in 2016, underscoring the need for improved therapies. Evading cell death is a major hallmark of cancer, driving tumor progression and therapeutic resistance. To evade apoptosis, cancers use antiapoptotic Bcl-2 proteins to bind to and neutralize apoptotic activators, such as Bim. Investigation of antiapoptotic Bcl-2 family members in clinical breast cancer datasets revealed greater expression and more frequent gene amplification of MCL1 as compared with BCL2 or BCL2L1 (Bcl-xL) across three major molecular breast cancer subtypes, Luminal (A and B), HER2-enriched, and Basal-like. While Mcl-1 protein expression was elevated in estrogen receptor α (ERα)-positive and ERα-negative tumors as compared with normal breast, Mcl-1 staining was higher in ERα+ tumors. Targeted Mcl-1 blockade using RNAi increased caspase-mediated cell death in ERα+ breast cancer cells, resulting in sustained growth inhibition. In contrast, combined blockade of Bcl-2 and Bcl-xL only transiently induced apoptosis, as cells rapidly acclimated through Mcl-1 upregulation and enhanced Mcl-1 activity, as measured in situ using Mcl-1/Bim proximity ligation assays. Importantly, MCL1 gene expression levels correlated inversely with sensitivity to pharmacologic Bcl-2/Bcl-xL inhibition in luminal breast cancer cells, whereas no relationship was seen between the gene expression of BCL2 or BCL2L1 and sensitivity to Bcl-2/Bcl-xL inhibition. These results demonstrate that breast cancers rapidly deploy Mcl-1 to promote cell survival, particularly when challenged with blockade of other Bcl-2 family members, warranting the continued development of Mcl-1-selective inhibitors for targeted tumor cell killing.Implications: Mcl-1 levels predict breast cancer response to inhibitors targeting other Bcl-2 family members, and demonstrate the key role played by Mcl-1 in resistance to this drug class. Mol Cancer Res; 15(3); 259-68. ©2016 AACR.

A Comparison of Near-Infrared Diffuse Optical Imaging and 18F-FDG PET/CT for the Early Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy.

In recent years, neoadjuvant chemotherapy (NAC) before surgery has become the standard of care for patients who present with advanced breast cancers or multiple lesions, in order to control distant metastases as well as reduce breast tumor size before surgery ([1][1]). The remaining medical oncology

Abstract P4-03-06: Near-infrared diffuse optical imaging for early prediction to neoadjuvant chemotherapy in patients with primary breast cancer

Abstract Background: Diffuse optical spectroscopic imaging (DOSI) can be exploited as a marker of tumor blood volume quantified by tissue hemoglobin (tHb) concentration. In DOSI, frequent measurement is possible for breast cancer patients because of its non-invasiveness. The tHb concentration determined by DOSI is expected to be a new biomarker for prediction of breast cancer response to neoadjuvant chemotherapy (NAC). Purpose: Our objective is to determine whether early change of tumor tHb concentration predicts pathological complete response (pCR) to NAC in patients with operable breast cancer. Methods: In a prospective study, one hundred patients with primary breast cancer were enrolled for primary objective analysis. The regimens of NAC were according to the standard of care. Patients underwent sequential scans using DOSI at baseline, after 1st course and 2nd course of chemotherapy. The mean value of tHb (tHbmean) concentration of the targeted lesion was measured and the percentage change in tHbmean (ΔtHbmean) concentration was calculated. Receiver operating curve analysis demonstrated diagnostic performance of DOSI for predicting a pCR. Results: In interim analysis, it was regarded as a good outcome that area under the curve (AUC) for ΔtHbmean after 1nd course was 0.797 (SE 0.104, 95%CI 0.633-0.911), and after 2st course was 0.867 (SE 0.06, 95%CI 0.715-0.956). Conclusion: DOSI could predict accurately a pCR to neoadjuvant chemotherapy in patients with primary breast cancer. Citation Format: Ogura H, Yoshizawa N, Ueda S, Hosokawa Y, Matsunuma R, Tochikubo J, Nasu H, Shigekawa T, Takeuchi H, Osaki A, Saeki T, Yoshimoto K, Ohmae E, Suzuki T, Ueda Y, Yamashita Y, Sakahara H. Near-infrared diffuse optical imaging for early prediction to neoadjuvant chemotherapy in patients with primary breast cancer. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P4-03-06.

Early Prediction and Evaluation of Breast Cancer Response to Neoadjuvant Chemotherapy Using Quantitative DCE-MRI

The purpose is to compare quantitative dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) metrics with imaging tumor size for early prediction of breast cancer response to neoadjuvant chemotherapy (NACT) and evaluation of residual cancer burden (RCB). Twenty-eight patients with 29 primary breast tumors underwent DCE-MRI exams before, after one cycle of, at midpoint of, and after NACT. MRI tumor size in the longest diameter (LD) was measured according to the RECIST (Response Evaluation Criteria In Solid Tumors) guidelines. Pharmacokinetic analyses of DCE-MRI data were performed with the standard Tofts and Shutter-Speed models (TM and SSM). After one NACT cycle the percent changes of DCE-MRI parameters Ktrans (contrast agent plasma/interstitium transfer rate constant), ve (extravascular and extracellular volume fraction), kep (intravasation rate constant), and SSM-unique τi (mean intracellular water lifetime) are good to excellent early predictors of pathologic complete response (pCR) vs. non-pCR, with univariate logistic regression C statistics value in the range of 0.804 to 0.967. ve values after one cycle and at NACT midpoint are also good predictors of response, with C ranging 0.845 to 0.897. However, RECIST LD changes are poor predictors with C = 0.609 and 0.673, respectively. Post-NACT Ktrans, τi, and RECIST LD show statistically significant (P < .05) correlations with RCB. The performances of TM and SSM analyses for early prediction of response and RCB evaluation are comparable. In conclusion, quantitative DCE-MRI parameters are superior to imaging tumor size for early prediction of therapy response. Both TM and SSM analyses are effective for therapy response evaluation. However, the τi parameter derived only with SSM analysis allows the unique opportunity to potentially quantify therapy-induced changes in tumor energetic metabolism.

The role of oestrogen and progesterone receptors in breast cancer - immunohistochemical evaluation of oestrogen and progesterone receptor expression in invasive breast cancer in women.

Aim of the studyExpression of oestrogen and progesterone receptors is a very powerful and useful predictor. Because the response rate to hormonal treatment in breast cancer is associated with the presence of oestrogen and progesterone receptors, assessment of the receptor expression profile allows for prediction of breast cancer response to hormonal treatment. The aim of this study was to assess whether the expression of receptors for oestrogen (ER) and progesterone (PR) in the tumour tissue of patients with invasive breast cancer correlated with tumour histological type, histological grade of malignancy, tumour size, and lymph node status.Material and methodsMaterials consisted of histological preparations derived from patients treated for invasive breast cancer. Evaluations were conducted with histopathological and immunohistochemical methods using suitable antibodies.ResultsAmong 231 cases of breast cancer 18 invasive lobular carcinomas (ILC) and 213 invasive ductal carcinomas (IDC) were diagnosed. Taking the histological type of tumour into account, oestrogen receptor-positive reaction was observed in 74.2% of IDC and 77.8% of ILC, and the positive response to PR was observed in 67.1% of IDC and 61.1% of ILC. Considering the histological grade, ER- in the largest percentage (72%) was observed in second-grade (G2) invasive carcinomas. Similarly, PR expression (75%) was found in the largest percentage in second-grade (G2) carcinomas. Based on our own studies and data from literature, it appears that the ER (+) status is an indicator of good prognosis, because it points to a less aggressive cancer, in which overall survival and disease-free time is longer in comparison with ER (–) tumours.ConclusionsDetermination of ER status may, therefore, have significant clinical value and is widely used in routine pathological diagnostics.

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. Twenty-eight patients were examined using DCE-MRI pre-, post-one cycle, and just prior to surgery. The semiquantitative parameters included longest dimension, tumor volume, initial area under the curve, and signal enhancement ratio related parameters, while quantitative parameters included K(trans), v(e), k(ep), v(p), and τ(i) estimated using the standard Tofts-Kety, extended Tofts-Kety, and fast exchange regime models. Our preliminary results indicated that the signal enhancement ratio washout volume and k(ep) were significantly different between pathologic complete responders from nonresponders (P < 0.05) after a single cycle of chemotherapy. Receiver operator characteristic analysis showed that the AUC of the signal enhancement ratio washout volume was 0.75, and the AUCs of k(ep) estimated by three models were 0.78, 0.76, and 0.73, respectively. In summary, the signal enhancement ratio washout volume and k(ep) appear to predict breast cancer response after one cycle of neoadjuvant chemotherapy. This observation should be confirmed with additional prospective studies.

MR Imaging for the Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy

MR Imaging for the Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy

OT2-05-02: ACRIN 6691 Monitoring and Predicting Breast Cancer Neoadjuvant Chemotherapy Response Using Diffuse Optical Spectroscopic Imaging (DOSI).

Abstract Background Imaging technologies monitoring and predicting breast cancer response to neoadjuvant chemotherapy (NAC) are of increasing interest. The utility of conventional imaging approaches varies and identifies the need for alternate functional imaging strategies. The use of model-based photon migration methods to quantitatively separate light absorption from scattering in multiply-scattering tissues is a type of near-infrared spectroscopy (NIRS) broadly referred to as diffuse optical spectroscopy (DOS) [Bevilacqua, et al. Applied Optics, 2000; Jakubowski, et al., J of Applied Optics, 2009]. DOSI is a promising experimental technology that allows patients undergoing NAC to be followed with a “no significant risk” device meeting Food and Drug Administration criteria for exempt status. The current design is a mobile device which offers increased accessibility and is relatively simple to perform and interpret, as compared to mammography, magnetic resonance imaging, and positron emission tomography. Due to its size and portability, DOSI is a low barrier-to-access technology, creating new opportunities for patients to receive personalized treatment and for physicians to gain new insight into response mechanisms. The long-term goal is to provide oncologists with a relatively simple, risk-free bedside tool that can be used to help inform medical decisions on chemotherapy regimen, duration, and timing of surgery, thereby maximizing therapeutic response and minimizing unnecessary toxicity. Trial design: In this phase I/II prospective single arm study, patients will receive SOC NAC at five (5) NCI Network for Translational Research in Optical Imaging (NTROI) clinical sites with identical DOSI instruments and procedures. Patients will receive four DOSI exams: at baseline before chemotherapy, at early therapy 5–10 days after NAC initiation, at mid therapy, and at post therapy prior to surgery. The protocol will evaluate a harmonized DOSI technology platform that has been standardized for NAC monitoring. Eligibility: Women who have been diagnosed with breast cancer, have had confirmation by pre-treatment biopsy, and are scheduled to receive NAC followed by surgery are eligible for this trial. Specific aims: The primary aim of this clinical trial is to determine whether the baseline to mid-therapy changes in the DOSI measurement of the quantitative tumor tissue optical index can predict final pathologic complete response in patients with breast cancer undergoing NAC. The secondary aims investigate the correlation between additional DOSI quantitative measurements of tumor biochemical composition obtained at other timepoints, the full range of pathologic response (i.e. complete, partial, and non-response), and any corresponding imaging measurements. Statistical methods: Logistic regression models will be used to study the relationships between pathological complete response and percent change in tissue optical index tumor to normal ratio at different imaging time points. Study size: A total of sixty (60) patients will be enrolled in this imaging study. Currently, one patient has accrued. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr OT2-05-02.

βIII-Tubulin is required for interphase microtubule dynamics in untransformed human mammary epithelial cells

Numerous works have questioned the pertinence of using βII- and/or βIII-tubulin expression as markers of prognosis and/or prediction of breast cancer response to chemotherapy containing microtubule-targeting agents. The rationale of such studies was essentially based on microtubule dynamics analysis using purified tubulin in vitro and cancer cell lines. Nonetheless, the significance of βII- and βIII-tubulin expression in the control of microtubule dynamics in normal mammary epithelium has never been addressed. Here we investigate the expression and the consequences of βII- and/or βIII-tubulin depletion in interphase microtubule dynamics in non-tumor human mammary epithelial cells. We find that both isoforms contribute to the tubulin isotype composition in primary and immortalized human mammary epithelial cells. Moreover, while βII-tubulin depletion has limited effects on interphase microtubule behavior, βIII-tubulin depletion causes a strong exclusion of microtubules from lamella and a severe suppression of dynamic instability. These results demonstrate that, while βII-tubulin is dispensable, βIII-tubulin is required for interphase microtubule dynamics in untransformed mammary epithelial cells. This strongly suggests that βIII-tubulin is an essential regulator of interphase microtubule functions in normal breast epithelium cells.

ACRIN 6691 monitoring and predicting breast cancer neoadjuvant chemotherapy response using diffuse optical spectroscopic imaging (DOSI).

TPS249 Background: Imaging technologies monitoring and predicting breast cancer response to neoadjuvant chemotherapy (NAC) are of increasing interest. The utility of conventional imaging approaches varies and identifies the need for alternate functional imaging strategies. DOSI is a promising experimental technology that allows patients undergoing NAC to be followed with a “no significant risk” device meeting Food and Drug Administration criteria for exempt status. The current design is a mobile device which offers increased accessibility. DOSI is relatively simple to perform and interpret, as compared to mammography, magnetic resonance imaging, and positron emission tomography. Due to its size and portability, DOSI is a low barrier-to-access technology, creating new opportunities for patients to receive personalized treatment and for physicians to gain new insight into response mechanisms. Our long-term goal is to provide oncologists with a relatively simple, risk-free bedside tool that can be used to help inform medical decisions on chemotherapy regimen, duration, and timing of surgery, thereby maximizing therapeutic response and minimizing unnecessary toxicity. Methods: In this phase I/II multicenter study, women who have been diagnosed with breast cancer, have had confirmation by pre-treatment biopsy, and are scheduled to receive NAC followed by surgery are eligible for this trial. Approximately 60 patients receiving NAC will participate at five clinical sites. This protocol will evaluate a harmonized DOSI technology platform that has been standardized for NAC monitoring. The primary aim of this clinical trial is to determine whether the baseline to mid-therapy changes in the DOSI measurement of the quantitative tumor tissue optical index can predict final pathologic complete response in patients with breast cancer undergoing NAC. The secondary aims investigate the correlation between additional DOSI quantitative measurements of tumor biochemical composition obtained at other timepoints, the full range of pathologic response (i.e. complete, partial, and non-response), and any corresponding imaging measurements.

Contrast-enhanced ultrasound of axillary lymph node for prediction of breast cancer response to neoadjuvant chemotherapy

Objective To explore the predictive value of response to neoadjuvant chemotherapy(NAC)in local advanced breast cancer with contrast-enhanced ultrasound(CEUS)of axillary lymph node.Methods CEUS of metastatic axillary lymph nodes in 58 patients stacng Ⅱ-Ⅲ breast cancer was performed before and after NAC treatment. The enhancement patterns and parameters of time-intensity curve were assessed and compared with the pathology.Results The clinic response evaluation were drug-effective in 35 cases and no change in 23 ones.There Were no significant differences in enhancement patterns between no-change and drugeffective groups.Lymph node cortex arriving time was longer in drug-effective cases than that in no-change ones after NAC,whereas it showed no significant differences before NAC.Statistical significant difierence in enhancement duration(ED)was found between the two groups before NAC,which decreased markedly in drug-effective case8 after NAC.Histopatholngic response could be predicted with a sensitivity of 77% and a specificity of 90% by standardized ED below 275 seconds after NAC.No significant difference was found in time to peak(TP),peak intensity(PI)between the two groups.Conclusion The perfusion pattern of axillary lymph node CEUS after NAC Was insufficient to predict curative effect.But the lymph node cortex arriving time and enhancement duration may be of value in the prediction of clinical response to chemotherapy. Key words: Breast neoplasms; Drug thempy; Neoplasms metastasis; Lymphgraphy

A gene expression signature that predicts the therapeutic response of the basal-like breast cancer to neoadjuvant chemotherapy

Several gene expression profiles have been reported to predict breast cancer response to neoadjuvant chemotherapy. These studies often consider breast cancer as a homogeneous entity, although higher rates of pathologic complete response (pCR) are known to occur within the basal-like subclass. We postulated that profiles with higher predictive accuracy could be derived from a subset analysis of basal-like tumors in isolation. Using a previously described "intrinsic" signature to differentiate breast tumor subclasses, we identified 50 basal-like tumors from two independent clinical trials associated with gene expression profile data. 24 tumor data sets were derived from a 119-patient neoadjuvant trial at our institution and an additional 26 tumor data sets were identified from a published data set (Hess et al. J Clin Oncol 24:4236-4244, 2006). The combined 50 basal-like tumors were partitioned to form a 37 sample training set with 13 sequestered for validation. Clinical surveillance occurred for a mean of 26 months. We identified a 23-gene profile which predicted pCR in basal-like breast cancers with 92% predictive accuracy in the sequestered validation data set. Furthermore, distinct cluster of patients with high rates of cancer recurrence was observed based on cluster analysis with the 23-gene signature. Disease-free survival analysis of these three clusters revealed significantly reduced survival in the patients of this high recurrence cluster. We identified a 23-gene signature which predicts response of basal-like breast cancer to neoadjuvant chemotherapy as well as disease-free survival. This signature is independent of tissue collection method and chemotherapeutic regimen.

Dynamic Contrast-Enhanced MRI for Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy: Initial Results

The aim of this study was to establish changes in contrast-enhanced MRI of breast cancer during neoadjuvant chemotherapy that are indicative of pathology outcome. In 54 patients with breast cancer, dynamic contrast-enhanced MRI was performed before chemotherapy and after two chemotherapy cycles. Imaging was correlated with final histopathology. Multivariate analysis with cross-validation was performed on MRI features describing kinetics and morphology of contrast uptake in the early and late phases of enhancement. Receiver operating characteristic (ROC) analysis was used to develop a guideline that switches patients at high risk for incomplete remission to a different chemotherapy regimen while maintaining first-line therapy in 95% of patients who are not at risk (i.e., high specificity). Change in largest diameter of late enhancement during chemotherapy was the single most predictive MRI characteristic for tumor response in multivariate analysis (A(z) [area under the ROC curve] = 0.73, p < 0.00001). Insufficient (< 25%) decrease in largest diameter of late enhancement during chemotherapy was most indicative of residual tumor at final pathology. Using this criterion, the fraction of unfavorable responders indicated by MRI was 41% (22/54). Approximately half (44%, 14/32) of the patients who showed favorable response at MRI achieved complete remission at pathology. Conversely, 95% (21/22) of patients who showed unfavorable response at MRI had residual tumor at pathology. Reduction of less than 25% in largest diameter of late enhancement during neoadjuvant chemotherapy shows the potential to predict residual tumor after therapy with high specificity.