The partner's insecure attachment, depression and psychological well-being as predictors of diurnal cortisol patterns for breast cancer survivors and their spouses

The purpose of this study was to explore whether stress from individual's and partner's depression, anxiety, sleep disturbances, insecure attachment and meaning in life were predictors of diurnal cortisol patterns in breast cancer survivors and their spouses. Thirty-four couple dyads participated in this eight-month follow-up study. The breast cancer survivors and their spouses completed the Medical Outcomes Study Sleep scale, the Beck Depression Inventory-II, the State Trait Anxiety Inventory, the Experiences in Close Relationships-Revised scale and the Meaning in Life Questionnaire, and they collected salivary cortisol at home at the time of awakening, 30 and 45 min after waking and at 1200 h, 1700 h and 2100 h. Diurnal cortisol slopes of survivors and spouses are positively correlated. But the factors associated with diurnal cortisol patterns are different between survivors and spouses. For survivors, neither survivor individuals' nor spouses' psychosocial factors were the predictors of survivors' diurnal cortisol patterns. For spouses, the survivors' higher anxious attachment style was the main predictor of spouses' flatter diurnal cortisol patterns. In conclusion, for spouses, psychophysiological stress responses are mainly influenced by breast cancer survivors' insecure attachment. Future couple supportive care interventions can address survivors' attachment styles in close relationships in order to improve neuroendocrine functions for both breast cancer survivors and their spouses.

A spatiotemporally defined in vitro microenvironment for controllable signal delivery and drug screening

An in vitro model of mimicking tumor microenvironments to study in vivo-like cancer migration and screening of inhibitors is demonstrated.

Computer-aided multiview tumor detection for automated whole breast ultrasound

Automated whole breast ultrasound (ABUS) has become a popular screening tool in recent years. To reduce the review time and misdetection from ABUS images by physicians, a computer-aided detection (CADe) system for ABUS images based on a multiview method is proposed in this study. A total of 58 pathology-proven lesions from 41 patients were used to evaluate the performance of the system. In the proposed CADe system, the fuzzy c-mean clustering method was applied to detect tumor candidates from these ABUS images. Subsequently, the tumor likelihoods of these candidates could be estimated by a logistic linear regression model based on the intensity, morphology, location, and size features in the transverse, longitudinal, and coronal views. Finally, the multiview tumor likelihoods of the tumor candidates could be obtained from the estimated tumor likelihoods of the three views, and the tumor candidates with high multiview tumor likelihoods were regarded as the detected tumors in the proposed system. The sensitivities of the multiview tumor detection for selecting 5, 10, 20, and 30 tumor candidates with the largest multiview tumor likelihoods were 79.31%, 86.21%, 96.55%, and 98.28%, respectively.

Quantitative ultrasound analysis for classification of BI-RADS category 3 breast masses

The accuracy of an ultrasound (US) computer-aided diagnosis (CAD) system was evaluated for the classification of BI-RADS category 3, probably benign masses. The US database used in this study contained 69 breast masses (21 malignant and 48 benign masses) that at blinded retrospective interpretation were assigned to BI-RADS category 3 by at least one of five radiologists. For computer-aided analysis, multiple morphology (shape, orientation, margin, lesions boundary, and posterior acoustic features) and texture (echo patterns) features based on BI-RADS lexicon were implemented, and the binary logistic regression model was used for classification. The receiver operating characteristic curve analysis was used for statistical analysis. The area under the curve (Az) of morphology, texture, and combined features were 0.90, 0.75, and 0.95, respectively. The combined features achieved the best performance and were significantly better than using texture features only (0.95 vs. 0.75, p value = 0.0163). The cut-off point at the sensitivity of 86 % (18/21), 95 % (20/21), and 100 % (21/21) achieved the specificity of 90 % (43/48), 73 % (35/48), and 33 % (16/48), respectively. In conclusion, the proposed CAD system has the potential to be used in upgrading malignant masses misclassified as BI-RADS category 3 on US by the radiologists.

Computer-aided diagnosis of mass-like lesion in breast MRI: differential analysis of the 3-D morphology between benign and malignant tumors

This study aimed to evaluate the value of using 3-D breast MRI morphologic features to differentiate benign and malignant breast lesions. The 3-D morphological features extracted from breast MRI were used to analyze the malignant likelihood of tumor from ninety-five solid breast masses (44 benign and 51 malignant) of 82 patients. Each mass-like lesion was examined with regards to three categories of morphologic features, including texture-based gray-level co-occurrence matrix (GLCM) feature, shape, and ellipsoid fitting features. For obtaining a robust combination of features from different categories, the biserial correlation coefficient (|r(pb)|)≧0.4 was used as the feature selection criterion. Receiver operating characteristic (ROC) curve was used to evaluate performance and Student's t-test to verify the classification accuracy. The combination of the selected 3-D morphological features, including conventional compactness, radius, spiculation, surface ratio, volume covering ratio, number of inside angular regions, sum of number of inside and outside angular regions, showed an accuracy of 88.42% (84/95), sensitivity of 88.24% (45/51), and specificity of 88.64% (39/44), respectively. The AZ value was 0.8926 for these seven combined morphological features. In conclusion, 3-D MR morphological features specified by GLCM, tumor shape and ellipsoid fitting were useful for differentiating benign and malignant breast masses.

Pattern of rash, diarrhea, and hepatic toxicities secondary to lapatinib and their association with age and response to neoadjuvant therapy: analysis from the NeoALTTO trial

PURPOSE

We investigated the pattern of rash, diarrhea, and hepatic adverse events (AEs) secondary to lapatinib and their association with age and pathologic complete response (pCR) in the Neoadjuvant Lapatinib and/or Trastuzumab Treatment Optimisation (NeoALLTO) phase III trial.

PATIENTS AND METHODS

Patients with HER2-positive early breast cancer were randomly assigned to receive lapatinib (Arm A), trastuzumab (Arm B), or their combination (Arm C) for 6 weeks followed by the addition of paclitaxel for 12 weeks before surgery. We investigated the frequency and time to developing each AE according to age (≤ 50 v > 50 years) and their association with pCR in a logistic regression model adjusted for age, hormone receptors, tumor size, nodal status, planned breast surgery, completion of lapatinib administration, and treatment arm.

RESULTS

Only patients randomly assigned to arms A and C were eligible (n = 306). Younger patients (≤ 50 years) experienced significantly more rash compared with older patients (74.4% v 47.9%; P < .0001). Diarrhea and hepatic AEs were observed in 78.8% and 41.2% of patients, respectively, with no differences in rate or severity or time of onset according to age. Early rash (ie, before starting paclitaxel) was independently associated with a higher chance of pCR, mainly in patients older than 50 years (odds ratio [OR] = 3.76; 95% CI, 1.69 to 8.34) but not in those ≤ 50 years (OR = 0.92; 95% CI, 0.45 to 1.88; P for interaction = .01). No significant association was observed between pCR and diarrhea or hepatic AEs.

CONCLUSION

Our results indicate that the frequency and clinical relevance of lapatinib-related rash is largely dependent on patient age.

Rapid breast density analysis of partial volumes of automated breast ultrasound images

Rapid volume density analysis (RVDA) for automated breast ultrasound (ABUS) has been proposed as a more efficient method for estimating breast density. In the current experiment, ABUS images were obtained for 67 breasts from 40 patients. For each case, three rectangular volumes of interest (VOIs) were extracted, including the VOIs located at the 6 and 12 o'clock positions relative to the nipple in the anterior to posterior pass and the lateral position relative to the nipple in the lateral pass. The centers of these VOIs were defined to align with the center of nipple, and the depths reached the retromammary fat boundary. The fuzzy c-means classifier was applied to differentiate the fibroglandular and fat tissues to estimate the density. The classification results of the three VOIs were averaged to obtain the breast density. The density correlations between the RVDA and the ABUS methods were 0.98 and 0.96 using Pearson's correlation and linear regression coefficients, respectively. The average computation times for RVDA and ABUS were 4.2 and 17.8 seconds, respectively, using an Intel Core2 2.66 GHz computer with 3.25 GB memory. In conclusion, the RVDA method offers a quantitative and efficient breast density estimation for ABUS.

Computer-aided tumor detection based on multi-scale blob detection algorithm in automated breast ultrasound images

Automated whole breast ultrasound (ABUS) is an emerging screening tool for detecting breast abnormalities. In this study, a computer-aided detection (CADe) system based on multi-scale blob detection was developed for analyzing ABUS images. The performance of the proposed CADe system was tested using a database composed of 136 breast lesions (58 benign lesions and 78 malignant lesions) and 37 normal cases. After speckle noise reduction, Hessian analysis with multi-scale blob detection was applied for the detection of tumors. This method detected every tumor, but some nontumors were also detected. The tumor like lihoods for the remaining candidates were estimated using a logistic regression model based on blobness, internal echo, and morphology features. The tumor candidates with tumor likelihoods higher than a specific threshold (0.4) were considered tumors. By using the combination of blobness, internal echo, and morphology features with 10-fold cross-validation, the proposed CAD system showed sensitivities of 100%, 90%, and 70% with false positives per pass of 17.4, 8.8, and 2.7, respectively. Our results suggest that CADe systems based on multi-scale blob detection can be used to detect breast tumors in ABUS images.

Classification of breast tumors using elastographic and B-mode features: comparison of automatic selection of representative slice and physician-selected slice of images

Inter-observer variability and image quality are two key factors that can affect the diagnostic performance of elastography and B-mode ultrasound for breast tumor characterization. The purpose of this study is to use an image quantification method that automatically chooses a representative slice and then segments the tumor contour to evaluate the diagnostic features for tumor characterization. First, the representative slice is selected based on either the stiffness inside the tumor (the signal-to-noise ratio on the elastogram [SNRe]) or the contrast between the tumor and the surrounding normal tissue (the contrast-to-noise ratio on the elastogram [CNRe]). Next, the level set method is used to segment the tumor contour. Finally, the B-mode and elastographic features related to the segmented tumor are extracted for tumor characterization. The performance of the representative slice selected using the proposed methods is compared to that of the physician-selected slice in 151 biopsy-proven lesions (89 benign and 62 malignant). The diagnostic accuracies using elastographic features are 82.1% (124/151) for the slice with the maximum CNRe value, 82.1% (124/151) for the slice with the maximum SNRe value and 82.8% (125/151) for the physician-selected slice, whereas the diagnostic accuracies using B-mode features are 80.8% (122/151) for the slice with the maximum CNRe value, 87.4% (132/151) for the slice with the maximum SNRe value and 84.1% (127/151) for the physician-selected slice. When using both the B-mode and elastographic features to characterize the tumor, the accuracy of diagnosis is 86.1% (130/151) for the slice with the maximum CNRe value, 90.1% (136/151) for the slice with the maximum SNRe value and 89.4% (135/151) for the physician-selected slice. Our results show that the representative slice selected by SNRe and CNRe could be used to reduce the observer variability and to increase the diagnostic performance by the B-mode and elastographic features.

Computer-aided diagnosis of breast masses using quantified BI-RADS findings

The information from radiologists was utilized in the proposed computer-aided diagnosis (CAD) for breast tumor classification. The ultrasound (US) database used in this study contained 166 benign and 78 malignant masses. For each mass, six quantitative feature sets were used to describe the radiologists' grading of six Breast Imaging Reporting and Data System (BI-RADS) categories including shape, orientation, margins, lesion boundary, echo pattern, and posterior acoustic features on breast US. The descriptive abilities were between 76% and 82% and the predicted descriptors were then used for tumor classification. Using receiver operating characteristic curve for evaluation, the area under curve (AUC) of the proposed CAD was slightly better than that of a conventional CAD based on the combination of all quantitative features (0.96 vs. 0.93, p=0.18). The partial AUC over 90% sensitivity of the proposed CAD was significantly better than that of the conventional CAD (0.90 vs. 0.76, p<0.05). In conclusion, the computer-aided analysis with qualitative information from radiologists showed a promising result for breast tumor classification.

Common genetic determinants of breast-cancer risk in East Asian women: a collaborative study of 23 637 breast cancer cases and 25 579 controls

In a consortium including 23 637 breast cancer patients and 25 579 controls of East Asian ancestry, we investigated 70 single-nucleotide polymorphisms (SNPs) in 67 independent breast cancer susceptibility loci recently identified by genome-wide association studies (GWASs) conducted primarily in European-ancestry populations. SNPs in 31 loci showed an association with breast cancer risk at P < 0.05 in a direction consistent with that reported previously. Twenty-one of them remained statistically significant after adjusting for multiple comparisons with the Bonferroni-corrected significance level of <0.0015. Eight of the 70 SNPs showed a significantly different association with breast cancer risk by estrogen receptor (ER) status at P < 0.05. With the exception of rs2046210 at 6q25.1, the seven other SNPs showed a stronger association with ER-positive than ER-negative cancer. This study replicated all five genetic risk variants initially identified in Asians and provided evidence for associations of breast cancer risk in the East Asian population with nearly half of the genetic risk variants initially reported in GWASs conducted in European descendants. Taken together, these common genetic risk variants explain ~10% of excess familial risk of breast cancer in Asian populations.

A longitudinal study of cortisol responses, sleep problems, and psychological well-being as the predictors of changes in depressive symptoms among breast cancer survivors

OBJECTIVE

This study examined whether the changes in sleep problems, attachment styles, meaning in life, and salivary cortisol over the course of 14 months were the predictors of changes in depressive symptoms in women with breast cancer at post-treatment stage.

METHODS

The study included 76 participants who completed active breast cancer treatment with longitudinal data collected at five points, including baseline assessment (T0) and the four follow-ups after baseline: T1 (in the 2nd month), T2 (in the 5th month), T3 (in the 8th month), and T4 (in the 14th month). The self-reported questionnaires included the Medical Outcomes Study Sleep (MOS-Sleep) scale; the Beck Depression Inventory-II (BDI-II); the Experiences in Close Relationships-Revised (ECR-R) scale for measuring anxiety and avoidance dimensions of attachment style; and the Meaning in Life Questionnaire (MLQ), consisting of the MLQ-Presence scale and the MLQ-Search scale. The participants collected their salivary cortisol at home at six time points: upon waking, 30 and 45 min after waking, and at 1200 h, 1700 h, and 2100 h.

RESULTS

Higher scores on for anxiety-related attachment style and the sleep problems index at baseline were associated with more severe initial depressive symptoms after the age, BMI, cancer, and treatment variables were controlled. The presence of meaning in life at baseline was negatively correlated with initial depressive symptoms. Moreover, the decreases in the presence of meaning in life over the course of 14 months predicted more severe depressive symptoms. In addition, the persistent increases of cortisol level at 2100 h across 14-month follow-ups predicted worsening depressive symptoms.

CONCLUSIONS

Lacking presence of meaning in life as a predictor for severe depressive symptoms demonstrates that breast cancer survivors who lack psychological well-being are more likely to be depressed. The persistent elevation of cortisol levels at night also indicates breast cancer survivors to be at high risk of depression.

BCAS2 is essential for Drosophila viability and functions in pre-mRNA splicing

Here, we show that dBCAS2 (CG4980, human Breast Carcinoma Amplified Sequence 2 ortholog) is essential for the viability of Drosophila melanogaster. We find that ubiquitous or tissue-specific depletion of dBCAS2 leads to larval lethality, wing deformities, impaired splicing, and apoptosis. More importantly, overexpression of hBCAS2 rescues these defects. Furthermore, the C-terminal coiled-coil domain of hBCAS2 binds directly to CDC5L and recruits hPrp19/PLRG1 to form a core complex for splicing in mammalian cells and can partially restore wing damage induced by knocking down dBCAS2 in flies. In summary, Drosophila and human BCAS2 share a similar function in RNA splicing, which affects cell viability.

Clinical significance of ESR1 gene copy number changes in breast cancer as measured by fluorescence in situ hybridisation

AIMS

The ESR1 gene encodes for oestrogen receptor (ER) α, which plays a crucial role in mammary carcinogenesis and clinical outcome in patients with breast cancer. However, the clinical significance of the ESR1 gene copy number change for breast cancer has not been clarified.

METHODS

ESR1 gene copy number was determined by fluorescence in situ hybridisation (FISH) on tissue sections. A minimum of 20 tumour cells were counted per section, and a FISH ratio of ESR1 gene to CEP6 ≥ 2.0 was considered ESR1 amplification. A ratio >1.2 but <2.0 was considered ESR1 gain. The ESR1 copy number was further measured by quantitative real-time PCR (Q-PCR) with ASXL2 as a reference.

RESULTS

FISH revealed ESR1 amplification in six cases (4.0%) and ESR1 gain in 13 cases (8.7%) from a total of 150 cases. ESR1 gain and amplification were more common in older patients (p<0.001), and correlated well with ER protein expression (p=0.03) measured by immunohistochemistry, and ESR1 copy number (p<0.001) measured by Q-PCR. Furthermore, the multivariate analysis revealed that ESR1 amplification was associated with a shorter disease-free survival (HR=5.56, p=0.03) and a shorter overall survival (HR=5.11, p=0.04).

CONCLUSIONS

In general, the frequency of ESR1 amplification in breast cancer is low when measured by FISH in large sections. ESR1 gain and amplification in breast cancer may be associated with older age and poorer outcomes.

Preoperative systemic therapy in locoregional management of early breast cancer: highlights from the Kyoto Breast Cancer Consensus Conference

Data reviewed at the Kyoto Breast Cancer Consensus Conference (KBCCC) showed that preoperative systemic therapy (PST) could optimize surgery through the utilization of information relating to pre- and post-PST tumor stage, therapeutic sensitivity, and treatment-induced changes in the biological characteristics of the tumor. As such, it was noted that the biological characteristics of the tumor, such as hormone receptors, human epidermal growth factor receptor-2, histological grade, cell proliferative activity, mainly defined by the Ki67 labeling index, and the tumor's multi-gene signature, should be considered in the planning of both systemic and local therapy. Furthermore, the timing of axillary sentinel lymph node diagnosis (i.e., before or after the PST) was also noted to be critical in that it may influence the likelihood of axillary preservation, even in node positive cases. In addition, axillary diagnosis with ultrasound and concomitant fine needle aspiration cytology or core needle biopsy (CNB) was reported to contribute to the construction of a treatment algorithm for patient-specific or individualized axillary surgery. Following PST, planning for breast surgery should therefore be based on tumor subtype, tumor volume and extent, therapeutic response to PST, and patient preference. Nomograms for predicting nodal status and drug sensitivity were also recognized as a tool to support decision-making in the selection of surgical treatment. Overall, review of data at the KBCCC showed that PST increases the likelihood of patients receiving localized surgery and individualized treatment regimens.

Vascular morphology and tortuosity analysis of breast tumor inside and outside contour by 3-D power Doppler ultrasound

This study aimed to evaluate morphologic and tortuous features of vessels inside and outside the tumor region on three-dimensional power Doppler ultrasonography (PDUS) in 113 breast mass lesions, including 60 benign and 53 malignant tumors. Compared with benign lesions, malignant breast lesions had significantly larger values of vascular morphologic and tortuous features and larger tumor sizes. The receiver operating characteristic curve analysis and Student's t-test were used to estimate the performance of a proposed classification system using 13 vascular features and tumor size selected by the neural network. Accuracy, sensitivity, specificity, positive predictive value, negative predictive value and the A(Z) value of the diagnosis performance based on 14 features were 89.38% (101/113), 84.91% (45/53), 93.33% (56/60), 91.84% (45/49), 87.50% (56/64) and 0.9188, respectively. The three-dimensional PDUS morphologic and tortuous characteristics of blood vessels inside and outside breast mass lesions can be effectively used to classify benign and malignant tumors.

Whole breast lesion detection using naive bayes classifier for portable ultrasound

In recent years, portable PC-based ultrasound (US) imaging systems developed by some companies can provide an integrated computer environment for computer-aided diagnosis and detection applications. In this article, an automatic whole breast lesion detection system based on the naive Bayes classifier using the PC-based US system Terason t3000 (Terason Ultrasound, Burlington, MA, USA) with a hand-held probe is proposed. To easily retrieve the US images for any regions of the breast, a clock-based storing system is proposed to record the scanned US images. A computer-aided detection (CAD) system is also developed to save the physicians' time for a huge volume of scanned US images. The pixel classification of the US is based on the naive Bayes classifier for the proposed lesion detection system. The pixels of the US are classified into two types: lesions or normal tissues. The connected component labeling is applied to find the suspected lesions in the image. Consequently, the labeled two-dimensional suspected regions are separated into two clusters and further checked by two-phase lesion selection criteria for the determination of the real lesion, while reducing the false-positive rate. The free-response operative characteristics (FROC) curve is used to evaluate the detection performance of the proposed system. According to the experimental results of 31 cases with 33 lesions, the proposed system yields a 93.4% (31/33) sensitivity at 4.22 false positives (FPs) per hundred slices. Moreover, the speed for the proposed detection scheme achieves 12.3 frames per second (fps) with an Intel Dual-Core Quad 3 GHz processor and can be also effectively and efficiently used for other screening systems.

Enumeration and viability of rare cells in a microfluidic disk via positive selection approach

Recent studies have shown that specific rare cells in the blood can serve as an indicator of cancer prognosis, among other purposes. This article demonstrates the concept of separating and detecting rare cells from peripheral blood mononuclear cells via an economical microfluidic disk with a model system. MCF7, labeled with magnetic beads, was used to simulate circulating tumor cells as a target. Jurkat clone E6-1 was used to simulate leukocytes or other cells abundant in human blood. A tailored multistage magnet maximized the magnetic field to ensure optimal trapping efficiency. Results indicate that the yield of detected MCF7 was consistent at approximately 80% when fewer than hundreds of MCF7 cells were mixed in greater than 1 million Jurkat cells. The 80% yield also held for 10 MCF7 in 100 million Jurkat (rarity of 10(7)). Compared with the results from autoMACS, the performance was at least 20% higher and was more independent of the number of Jurkat. The viability of the enriched cells was approximately 90 ± 20%, showing that this method caused little damage to trapped cells. The microfluidic disk should be applicable for separation and detection of various rare cells, such as circulating tumor cells and circulating endothelial cells in human blood.

Computer-aided diagnosis based on speckle patterns in ultrasound images

For breast ultrasound, the scatterer number density from backscattered echo was demonstrated in previous research to be a useful feature for tumor characterization. To take advantage of the scatterer number density in B-mode images, spatial compound imaging was obtained, and the statistical properties of speckle patterns were analyzed in this study for use in distinguishing between benign and malignant lesions. A total of 137 breast masses (95 benign cases and 42 malignant cases) were used in the proposed computer-aided diagnosis (CAD) system. For each mass, the average number of speckle pixels in a region of interest (ROI) was calculated to use the concept of scatterer number density. In addition, the first-order and second-order statistics of the speckle pixels were quantified to obtain the distributions of the pixel values and the spatial relations among the pixels. The performance of the speckle features extracted from each ROI was compared with the performance of the segmentation features extracted from each segmented tumor. As a result, the proposed CAD system using the speckle features achieved an accuracy of 89.1% (122/137); a sensitivity of 81.0% (34/42); and a specificity of 92.6% (88/95). All of the differences between the speckle features and the segmentation features are not statistically significant (p > 0.05). In a receiver operating characteristic (ROC) curve analysis, the Az value, area under ROC curve, of the speckle features was significantly better than the Az value of the segmentation features (0.93 vs. 0.86, p = 0.0359). The performance of this approach supports the notion that the speckle patterns induced by the scatterers in tissues can provide information for classifying tumors. The proposed speckle features, which were extracted readily from drawing an ROI without any preprocessing, also provide a more efficient classification approach than tumor segmentation.

LUX-breast 2: Phase II, open-label study of oral afatinib in HER2-overexpressing metastatic breast cancer (MBC) patients (pts) who progressed on prior trastuzumab (T) and/or lapatanib (L)

TPS651

Background: Management of HER2-overexpressing MBC has improved over the past decade. However, pts still develop resistance to currently available HER2-targeted therapies and novel effective treatments are increasingly required as dual targeted combinations are given in early treatment lines already. Current therapies focus on targeting HER2 and do not inhibit all relevant ErbB Family dimers. Afatinib is an oral, irreversible ErbB Family Blocker that inhibits signaling through activated EGFR (ErbB1), HER2 (ErbB2) and ErbB4 receptors and transphosphorylation of ErbB3. Preclinical studies have demonstrated efficacy in T-sensitive and T-resistant human BC xenograft models dependent on ErbB signaling. Efficacy of afatinib in a T-resistant SUM 190 xenograft model has been shown to be increased by addition of IV vinorelbine (V). Afatinib monotherapy has shown promising clinical benefit in 46% of HER2-overexpressing MBC pts who progressed on prior T, with 10% of pts achieving PR. Methods: This open-label Phase II trial (NCT01271725) investigates efficacy and safety of afatinib alone (40 mg/d) followed by afatinib ‘beyond progression’ plus chemotherapy in 120 pts with HER2-overexpressing MBC, who progressed on prior neoadjuvant and/or adjuvant T and/or L. Pts who progress on afatinib monotherapy receive afatinib plus either weekly paclitaxel (P) 80 mg/m2 or V 25 mg/m2. Eligible pts have confirmed HER2-overexpressing BC, stage IV disease measurable by RECIST 1.1, progressed on T and/or L therapy in either neoadjuvant and/or adjuvant setting, are eligible for retreatment with P or V and should not have been pretreated with P (≤12 months) or V, respectively. Exclusion criteria: inadequate cardiac, renal, hepatic and hematological function, pre-existing gastrointestinal dysfunction, rapidly progressing visceral disease, ILD and active brain metastases. The primary endpoint is objective response (OR) and secondary endpoints include best overall response, duration of OR and PFS; safety will be assessed separately for afatinib mono- and combination therapy. Patient enrollment began in May 2011 in ~35 sites and 5 countries.

LUX-breast 1: Randomized, phase III trial of afatinib and vinorelbine versus trastuzumab and vinorelbine in patients with HER2-overexpressing metastatic breast cancer (MBC) failing one prior trastuzumab treatment

TPS649

Background: Afatinib is an ErbB Family Blocker that irreversibly blocks signaling from all relevant ErbB Family dimers. Afatinib is being developed in EGFR (ErbB1)-driven (NSCLC/HNSCC) and HER2 (ErbB2)-driven (breast) malignancies. In trastuzumab-resistant, HER2-positive (SUM190) xenografts, afatinib showed antitumor activity which was superior to lapatinib and increased by addition of IV vinorelbine. Afatinib monotherapy also demonstrated clinical activity (progression-free survival [PFS] = 15.1 wk; objective response [OR] = 10%) in an open-label, single-arm, Phase II trial in patients with HER2-positive MBC after progression on trastuzumab. Methods: LUX-Breast 1 (NCT01125566) is a Phase III, open-label, multicenter trial evaluating the efficacy and safety of afatinib + vinorelbine vs. trastuzumab + vinorelbine in patients with HER2-overexpressing MBC who progressed on, or after one prior trastuzumab-based treatment regimen. Patients are randomized 2:1 to afatinib (40 mg/d oral) + vinorelbine (IV 25 mg/m2/wk) or trastuzumab (IV 2 mg/kg/wk after 4 mg/kg loading dose) + vinorelbine (IV 25 mg/m2/wk). Patients receive continuous treatment in the absence of disease progression or adverse events. Key eligibility criteria include histologically-confirmed HER2-positive BC, stage IV disease; no prior treatment with vinorelbine or HER2-targeted treatment other than trastuzumab; progression on one prior trastuzumab based regimen in either the adjuvant (or <12 months after trastuzumab completion) or first-line (or <6 months after trastuzumab completion) setting; prior anthracycline and/or taxane chemotherapy; ECOG score 0 or 1. The primary endpoint is PFS and secondary endpoints include OR, overall survival and safety. Serum and tissue biomarkers will be assessed on archival tissue. HER-receptor and HER-ligand reprogramming, putative resistance markers and EGFR response signature will be explored in fresh tissue biopsies. Enrollment began in June 2010 and is ongoing, targeting >240 sites with a recruitment target of 780 patients.