Breast Cancer Patient Datasets Research Articles

Long-Term Disease-Free Survival of Non-Metastatic Breast Cancer Patients in Iran: A Survival Model with Competing Risks Taking Cure Fraction and Frailty into Account

Introduction:Survival modeling is a very important tool to detect risk factors and provide a basis for health care planning. However, cancer data may have properties leading to distorted results with routine methods. Therefore, this study aimed to cover specific factors (competing risk, cure fraction and heterogeneity) with a real dataset of Iranian breast cancer patients using a competing risk-cure-frailty model.Materials and methods:For this historical cohort study, information for 550 Iranian breast cancer patients who underwent surgery for tumor removal from 2001 to 2007 and were followed up to March 2017, was analyzed using R 3.2 software.Results:In contrast to T-stage and N-stage, hormone receptor status did not have any significant effect on the cure fraction (long-term disease-free survival). However, T-stage, N-stage and hormone receptor status all had a significant effect on short-term disease-free survival so that the hazard of loco-regional relapse or distant metastasis in cases positive for a hormone receptor was only 0.3 times that for their negative hormone receptor counterparts. The likelihood of locoregional relapse in the first quartile of follow up was nearly twice that of other quartiles. The least cumulative incidence of time to locoregional relapse was for cases with a positive hormone receptor, low N stage and low T stage. The effect of frailty term was significant in this study and a model with frailty appeared more appropriate than a model without, based on the Akaike information criterion (AIC); values for the frailty model and one without the frailty parameter were 1370.39 and 1381.46, respectively.Conclusions:The data from this study indicate ae necessity to consider competing risk, cure fraction and heterogeneity in survival modeling. The competing risk-cure-frailty model can cover complex situations with survival data.

High Intratumoral Stromal Content Defines Reactive Breast Cancer as a Low-risk Breast Cancer Subtype.

The current study evaluated associative effects of breast cancer cells with the tumor microenvironment and its influence on tumor behavior. Formalin-fixed, paraffin-embedded tissue and matched protein lysates were evaluated from two independent breast cancer patient datasets (TCGA and MD Anderson). Reverse-phase protein arrays (RPPA) were utilized to create a proteomics signature to define breast tumor subtypes. Expression patterns of cell lines and normal breast tissues were utilized to determine markers that were differentially expressed in stroma and cancer cells. Protein localization and stromal contents were evaluated for matched cases by imaging. A subtype of breast cancers designated "Reactive," previously identified by RPPA that was not predicted by mRNA profiling, was extensively characterized. These tumors were primarily estrogen receptor (ER)-positive/human EGF receptor (HER)2-negative, low-risk cancers as determined by enrichment of low-grade nuclei, lobular or tubular histopathology, and the luminal A subtype by PAM50. Reactive breast cancers contained high numbers of stromal cells and the highest extracellular matrix content typically without infiltration of immune cells. For ER-positive/HER2-negative cancers, the Reactive classification predicted favorable clinical outcomes in the TCGA cohort (HR, 0.36; P < 0.05). A protein stromal signature in breast cancers is associated with a highly differentiated phenotype. The stromal compartment content and proteins are an extended phenotype not predicted by mRNA expression that could be utilized to subclassify ER-positive/HER2-negative breast cancers. Clin Cancer Res; 22(20); 5068-78. ©2016 AACR.

A special case of reduced rank models for identification and modelling of time varying effects in survival analysis.

Flexible survival models are in need when modelling data from long term follow-up studies. In many cases, the assumption of proportionality imposed by a Cox model will not be valid. Instead, a model that can identify time varying effects of fixed covariates can be used. Although there are several approaches that deal with this problem, it is not always straightforward how to choose which covariates should be modelled having time varying effects and which not. At the same time, it is up to the researcher to define appropriate time functions that describe the dynamic pattern of the effects. In this work, we suggest a model that can deal with both fixed and time varying effects and uses simple hypotheses tests to distinguish which covariates do have dynamic effects. The model is an extension of the parsimonious reduced rank model of rank 1. As such, the number of parameters is kept low, and thus, a flexible set of time functions, such as b-splines, can be used. The basic theory is illustrated along with an efficient fitting algorithm. The proposed method is applied to a dataset of breast cancer patients and compared with a multivariate fractional polynomials approach for modelling time-varying effects. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract P3-12-04: The genomically adjusted radiation dose (GAD) and its association with distant metastases in breast cancer: A feasible approach to precision medicine in radiation oncology

Abstract Background: Clinical validation studies in over 2,200 patients across 8 different disease sites, including breast cancer, have shown the radiosensitivity index (RSI), a gene expression signature, predicts outcomes in patients treated with radiation. We hypothesize that an approach to personalize radiation dose could be developed by integrating RSI into the linear quadratic model of dose and fractionation. Methods: Utilizing the linear quadratic model and RSI, we derived an expression for the genomically adjusted dose (GAD) to model radiation dose effect for individual patients. A higher GAD implies a higher predicted radiation therapy effect. GAD was evaluated as a predictor of clinical outcome in two independent datasets of breast cancer patients treated with surgery and radiation. The association between GAD and distant-metastasis free survival (DMFS) and relapse-free survival (RFS) using univariate (UVA) and multivariate (MVA) Cox proportional hazard models was assessed. Clinical and array-based gene expression were obtained from two independent, previously described cohorts from the Karolinska Institutet and Erasmus University Medical Center. Results: Full radiation treatment details were available for 263 patients in the Erasmus dataset, median follow-up 60 months. GAD-low patients (&amp;lt;75% GAD distribution) were found to have decreased DMFS when compared to GAD-high patients (≥25% GAD distribution) (Hazard Ratio (HR) = 2.31 (95% CI 1.25, 4.25), p=0.006). On MVA, GAD was an independent predictor of DMFS for the whole cohort (HR= 2.11 (1.13, 3.94), p=0.02). When the analysis was restricted to the ER positive cohort, GAD was an independent predictor of outcome both as a continuous (HR=0.977, (0.955, 1.0), p = 0.049) and as a dichotomous variable (HR = 3.42, (1.53, 7.67), p=0.003). These results were independently confirmed in the second Karolinska dataset. The 5 year RFS was 95% for GAD-high patients and 76% in GAD-low patients (p=0.027) and GAD was a significant predictor on MVA for RFS (HR =7.42, (1.41, 137.6), p=0.014). In the Karolinska cohort, we estimate a significant proportion of GAD-low patients (59%) would achieve GAD-high with dose escalation up to 70 Gy. Conclusions: In this study, we develop and validate GAD, a novel and patient-specific measure of radiation dose effect. Importantly, GAD is a clinically actionable metric by adjusting radiation dose. We propose that GAD based radiation dosing is a feasible approach to precision medicine in breast radiation oncology. Citation Format: Ahmed KA, Scott JG, Diaz RJ, Fulp WJ, Torres-Roca JF. The genomically adjusted radiation dose (GAD) and its association with distant metastases in breast cancer: A feasible approach to precision medicine in radiation oncology. [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 P3-12-04.

In-depth characterization of breast cancer tumor-promoting cell transcriptome by RNA sequencing and microarrays.

Numerous studies have reported the existence of tumor-promoting cells (TPC) with self-renewal potential and a relevant role in drug resistance. However, pathways and modifications involved in the maintenance of such tumor subpopulations are still only partially understood. Sequencing-based approaches offer the opportunity for a detailed study of TPC including their transcriptome modulation. Using microarrays and RNA sequencing approaches, we compared the transcriptional profiles of parental MCF7 breast cancer cells with MCF7-derived TPC (i.e. MCFS). Data were explored using different bioinformatic approaches, and major findings were experimentally validated. The different analytical pipelines (Lifescope and Cufflinks based) yielded similar although not identical results. RNA sequencing data partially overlapped microarray results and displayed a higher dynamic range, although overall the two approaches concordantly predicted pathway modifications. Several biological functions were altered in TPC, ranging from production of inflammatory cytokines (i.e., IL-8 and MCP-1) to proliferation and response to steroid hormones. More than 300 non-coding RNAs were defined as differentially expressed, and 2,471 potential splicing events were identified. A consensus signature of genes up-regulated in TPC was derived and was found to be significantly associated with insensitivity to fulvestrant in a public breast cancer patient dataset. Overall, we obtained a detailed portrait of the transcriptome of a breast cancer TPC line, highlighted the role of non-coding RNAs and differential splicing, and identified a gene signature with a potential as a context-specific biomarker in patients receiving endocrine treatment.

Joint modelling of longitudinal and survival data: incorporating delayed entry and an assessment of model misspecification.

A now common goal in medical research is to investigate the inter‐relationships between a repeatedly measured biomarker, measured with error, and the time to an event of interest. This form of question can be tackled with a joint longitudinal‐survival model, with the most common approach combining a longitudinal mixed effects model with a proportional hazards survival model, where the models are linked through shared random effects. In this article, we look at incorporating delayed entry (left truncation), which has received relatively little attention. The extension to delayed entry requires a second set of numerical integration, beyond that required in a standard joint model. We therefore implement two sets of fully adaptive Gauss–Hermite quadrature with nested Gauss–Kronrod quadrature (to allow time‐dependent association structures), conducted simultaneously, to evaluate the likelihood. We evaluate fully adaptive quadrature compared with previously proposed non‐adaptive quadrature through a simulation study, showing substantial improvements, both in terms of minimising bias and reducing computation time. We further investigate, through simulation, the consequences of misspecifying the longitudinal trajectory and its impact on estimates of association. Our scenarios showed the current value association structure to be very robust, compared with the rate of change that we found to be highly sensitive showing that assuming a simpler trend when the truth is more complex can lead to substantial bias. With emphasis on flexible parametric approaches, we generalise previous models by proposing the use of polynomials or splines to capture the longitudinal trend and restricted cubic splines to model the baseline log hazard function. The methods are illustrated on a dataset of breast cancer patients, modelling mammographic density jointly with survival, where we show how to incorporate density measurements prior to the at‐risk period, to make use of all the available information. User‐friendly Stata software is provided. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

TIP60-miR-22 axis as a prognostic marker of breast cancer progression.

MicroRNAs (miRNAs) are 22- to 24-nucleotide, small, non-coding RNAs that bind to the 3'UTR of target genes to control gene expression. Consequently, their dysregulation contributes to many diseases, including diabetes and cancer. miR-22 is up-regulated in numerous metastatic cancers and recent studies have suggested a role for miR-22 in promoting stemness and metastasis. TIP60 is a lysine acetyl-transferase reported to be down-regulated in cancer but the molecular mechanism of this reduction is still unclear. In this study, we identify TIP60 as a target of miR-22. We show a negative correlation in the expression of TIP60 and miR-22 in breast cancer patients, and show that low levels of TIP60 and high levels of miR-22 are associated with poor overall survival. Furthermore, pathway analysis using high miR-22/low TIP60 and low miR-22/high TIP60 breast cancer patient datasets suggests association of TIP60/miR-22 with epithelial-mesenchymal transition (EMT), a key alteration in progression of cancer cells. We show that blocking endogenous miR-22 can restore TIP60 levels, which in turn decreases the migration and invasion capacity of metastatic breast cancer cell line. These results provide mechanistic insight into TIP60 regulation and evidence for the utility of the combination of TIP60 and miR-22 as prognostic indicator of breast cancer progression.

Death-associated protein kinase 1 promotes growth of p53-mutant cancers.

Estrogen receptor-negative (ER-negative) breast cancers are extremely aggressive and associated with poor prognosis. In particular, effective treatment strategies are limited for patients diagnosed with triple receptor-negative breast cancer (TNBC), which also carries the worst prognosis of all forms of breast cancer; therefore, extensive studies have focused on the identification of molecularly targeted therapies for this tumor subtype. Here, we sought to identify molecular targets that are capable of suppressing tumorigenesis in TNBCs. Specifically, we found that death-associated protein kinase 1 (DAPK1) is essential for growth of p53-mutant cancers, which account for over 80% of TNBCs. Depletion or inhibition of DAPK1 suppressed growth of p53-mutant but not p53-WT breast cancer cells. Moreover, DAPK1 inhibition limited growth of other p53-mutant cancers, including pancreatic and ovarian cancers. DAPK1 mediated the disruption of the TSC1/TSC2 complex, resulting in activation of the mTOR pathway. Our studies demonstrated that high DAPK1 expression causes increased cancer cell growth and enhanced signaling through the mTOR/S6K pathway; evaluation of multiple breast cancer patient data sets revealed that high DAPK1 expression associates with worse outcomes in individuals with p53-mutant cancers. Together, our data support targeting DAPK1 as a potential therapeutic strategy for p53-mutant cancers.

Luminal progenitor and fetal mammary stem cell expression features predict breast tumor response to neoadjuvant chemotherapy

Mammary gland morphology and physiology are supported by an underlying cellular differentiation hierarchy. Molecular features associated with particular cell types along this hierarchy may contribute to the biological and clinical heterogeneity observed in human breast carcinomas. Investigating the normal cellular developmental phenotypes in breast tumors may provide new prognostic paradigms, identify new targetable pathways, and explain breast cancer subtype etiology. We used transcriptomic profiles coming from fluorescence-activated cell sorted (FACS) normal mammary epithelial cell types from several independent human and murine studies. Using a meta-analysis approach, we derived consensus gene signatures for both species and used these to relate tumors to normal mammary epithelial cell phenotypes. We then compiled a dataset of breast cancer patients treated with neoadjuvant anthracycline and taxane chemotherapy regimens to determine if normal cellular traits predict the likelihood of a pathological complete response (pCR) in a multivariate logistic regression analysis with clinical markers and genomic features such as cell proliferation. Most human and murine tumor subtypes shared some, but not all, features with a specific FACS-purified normal cell type; thus for most tumors a potential distinct cell type of ‘origin’ could be assigned. We found that both human luminal progenitor and mouse fetal mammary stem cell features predicted pCR sensitivity across all breast cancer patients even after controlling for intrinsic subtype, proliferation, and clinical variables. This work identifies new clinically relevant gene signatures and highlights the value of a developmental biology perspective for uncovering relationships between tumor subtypes and their potential normal cellular counterparts.Electronic supplementary materialThe online version of this article (doi:10.1007/s10549-014-3262-6) contains supplementary material, which is available to authorized users.

283P - Mad2L1 Overexpression Leads to Early Metastasis in Breast Cancer

ABSTRACT Aim: Mitotic arrest deficient 2 (MAD2) is an essential spindle assembly checkpoint (SAC) protein. It prevents the onset of anaphase until all chromosomes are properly aligned at the metaphase plate. Its overexpression leads to short survival and chromosomal instability in lung cancer but its significance in breast cancer has not been investigated yet. Methods: We obtained 7 previously published Affymetrix microarray datasets of breast cancer patients. Probes 203362_s and 1554768_a were mapped on MAD2L1 (MAD2 gene) transcripts and chosen for further analysis. We constructed multivariate Cox proportional hazard survival models of gene expression with distant metastasis-free survival (DMFS). The resulting hazard ratios from each dataset were combined in a meta-analysis (random effects model). Results: In total, 2,538 patients were included. High expression of MAD2L1 was associated with shorter DMFS (HR = 1.17, 95% CI 1.03-3.67, p = 0.011) after multivariate adjustment for tumor node, size and grade. Conclusions: This is the first report showing that overexpression of MAD2L1 leads to early metastasis in breast cancer. Since MAD2 is a major gatekeeper of the mitotic mechanism, understanding MAD2L1 pathophysiology could guide the development of new targeted therapies in breast cancer. Disclosure: All authors have declared no conflicts of interest.

Unraveling the Hidden Heterogeneities of Breast Cancer Based on Functional miRNA Cluster

It has become increasingly clear that the current taxonomy of clinical phenotypes is mixed with molecular heterogeneity, which potentially affects the treatment effect for involved patients. Defining the hidden molecular-distinct diseases using modern large-scale genomic approaches is therefore useful for refining clinical practice and improving intervention strategies. Given that microRNA expression profiling has provided a powerful way to dissect hidden genetic heterogeneity for complex diseases, the aim of the study was to develop a bioinformatics approach that identifies microRNA features leading to the hidden subtyping of complex clinical phenotypes. The basic strategy of the proposed method was to identify optimal miRNA clusters by iteratively partitioning the sample and feature space using the two-ways super-paramagnetic clustering technique. We evaluated the obtained optimal miRNA cluster by determining the consistency of co-expression and the chromosome location among the within-cluster microRNAs, and concluded that the optimal miRNA cluster could lead to a natural partition of disease samples. We applied the proposed method to a publicly available microarray dataset of breast cancer patients that have notoriously heterogeneous phenotypes. We obtained a feature subset of 13 microRNAs that could classify the 71 breast cancer patients into five subtypes with significantly different five-year overall survival rates (45%, 82.4%, 70.6%, 100% and 60% respectively; p = 0.008). By building a multivariate Cox proportional-hazards prediction model for the feature subset, we identified has-miR-146b as one of the most significant predictor (p = 0.045; hazard ratios = 0.39). The proposed algorithm is a promising computational strategy for dissecting hidden genetic heterogeneity for complex diseases, and will be of value for improving cancer diagnosis and treatment.

Abstract P5-04-06: Loss of the E3 ubiquitin ligase HACE1 plays a critical role in transformation of mammary cells and clinical progression of human breast cancer via accumulation of active Rac1

Abstract Invasive ductal breast cancer arises when the immediate precursor, ductal carcinoma in situ (DCIS), breaches the basement membrane of the ductal lumen and invades into the surrounding tissue. Although this transition has been implicated as a crucial step in the progression of breast cancer, the specific alterations responsible for this transition have yet to be identified. HER2/neu, a member of the epidermal growth family receptor (EGFR) family, is frequently overexpressed in non-invasive lesions (50-60%) but is significantly less common in invasive breast cancer (20-30%). This suggests that while HER2/neu may be partially transformative, additional alterations are required for mammary epithelial cells to achieve full malignant transformation. To identify novel genes capable of cooperating with HER2, we performed an insertional mutagenesis screen for genes whose alteration induces anchorage-independent growth on primary mouse mammary epithelial cells isolated from MMTV-neu transgenic mice. We identified HECT domain and ankyrin repeat containing E3 ubiquitin-protein ligase 1 (HACE1) as a putative breast tumor suppressor gene whose loss contributes to the transformative process. Loss of HACE1 expression is commonly seen in publicly available breast cancer patient data sets as well as in established breast cancer cell lines supporting the role of HACE1 as a breast cancer tumor suppressor gene. Knockdown of HACE1 in HER2 overexpressing human mammary epithelial cells (HMECs) enhanced colony formation over HER2 overexpressing HMEC controls cells. Moreover, knockdown of HACE1 alone was sufficient to allow anchorage-independent growth in soft agar in the HMECs while the overexpression of HACE1 in breast cancer cell lines diminishes clonogenic capacity in soft agar. We confirmed recent studies that have shown that HACE1 is capable of tagging the Rho GTPase Rac1 for ubiquitin-mediated proteasomal degradation. In mammary epithelial cells, the loss of HACE1 leads to enhanced levels of active Rac1 resulting in increased clonogenicity, migration and invasion. Importantly, we show that targeting Rac1 can rescue the effects of HACE1 loss in mammary epithelial cells. Our results establish HACE1 as a breast cancer tumor suppressor gene by attenuating active Rac1 signaling. Our work supports the role of Rac1 as a critical signaling node in breast cancer and that loss of HACE1 leads to enhanced Rac1 signaling resulting in driving cancer progression. Furthermore, our results suggest the role of HACE1 loss as a biomarker for tumor progression and may identify patients vulnerable to Rac1 or Rac effector targeted therapies. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P5-04-06.

MAPC-GibbsOS: an integrated approach for robust identification of gene regulatory networks

BackgroundIdentification of cooperative gene regulatory network is an important topic for biological study especially in cancer research. Traditional approaches suffer from large noise in gene expression data and false positive connections in motif binding data; they also fail to identify the modularized structure of gene regulatory network. Methods that are capable of revealing underlying modularized structure and robust to noise and false positives are needed to be developed.ResultsWe proposed and developed an integrated approach to identify gene regulatory networks, which consists of a novel clustering method (namely motif-guided affinity propagation clustering (mAPC)) and a sampling based method (called Gibbs sampler based on outlier sum statistic (GibbsOS)). mAPC is used in the first step to obtain co-regulated gene modules by clustering genes with a similarity measurement taking into account both gene expression data and binding motif information. This clustering method can reduce the noise effect from microarray data to obtain modularized gene clusters. However, due to many false positives in motif binding data, some genes not regulated by certain transcription factors (TFs) will be falsely clustered with true target genes. To overcome this problem, GibbsOS is applied in the second step to refine each cluster for the identification of true target genes. In order to evaluate the performance of the proposed method, we generated simulation data under different signal-to-noise ratios and false positive ratios to test the method. The experimental results show an improved accuracy in terms of clustering and transcription factor identification. Moreover, an improved performance is demonstrated in target gene identification as compared with GibbsOS. Finally, we applied the proposed method to two breast cancer patient datasets to identify cooperative transcriptional regulatory networks associated with recurrence of breast cancer, as supported by their functional annotations.ConclusionsWe have developed a two-step approach for gene regulatory network identification, featuring an integrated method to identify modularized regulatory structures and refine their target genes subsequently. Simulation studies have shown the robustness of the method against noise in gene expression data and false positives in motif binding data. The proposed method has been applied to two breast cancer gene expression datasets to infer the hidden regulation mechanisms. The experimental results demonstrate the efficacy of the method in identifying key regulatory networks related to the progression and recurrence of breast cancer.

On an ensemble algorithm for clustering cancer patient data.

BackgroundThe TNM staging system is based on three anatomic prognostic factors: Tumor, Lymph Node and Metastasis. However, cancer is no longer considered an anatomic disease. Therefore, the TNM should be expanded to accommodate new prognostic factors in order to increase the accuracy of estimating cancer patient outcome. The ensemble algorithm for clustering cancer data (EACCD) by Chen et al. reflects an effort to expand the TNM without changing its basic definitions. Though results on using EACCD have been reported, there has been no study on the analysis of the algorithm. In this report, we examine various aspects of EACCD using a large breast cancer patient dataset. We compared the output of EACCD with the corresponding survival curves, investigated the effect of different settings in EACCD, and compared EACCD with alternative clustering approaches.ResultsUsing the basic T and N definitions, EACCD generated a dendrogram that shows a graphic relationship among the survival curves of the breast cancer patients. The dendrograms from EACCD are robust for large values of m (the number of runs in the learning step). When m is large, the dendrograms depend on the linkage functions.The statistical tests, however, employed in the learning step have minimal effect on the dendrogram for large m. In addition, if omitting the step for learning dissimilarity in EACCD, the resulting approaches can have a degraded performance. Furthermore, clustering only based on prognostic factors could generate misleading dendrograms, and direct use of partitioning techniques could lead to misleading assignments to clusters.ConclusionsWhen only the Partitioning Around Medoids (PAM) algorithm is involved in the step of learning dissimilarity, large values of m are required to obtain robust dendrograms, and for a large m EACCD can effectively cluster cancer patient data.

LOXL2 induces aberrant acinar morphogenesis via ErbB2 signaling

IntroductionLysyl oxidase-like 2 (LOXL2) is a matrix-remodeling enzyme that has been shown to play a key role in invasion and metastasis of breast carcinoma cells. However, very little is known about its role in normal tissue homeostasis. Here, we investigated the effects of LOXL2 expression in normal mammary epithelial cells to gain insight into how LOXL2 mediates cancer progression.MethodsLOXL2 was expressed in MCF10A normal human mammary epithelial cells. The 3D acinar morphogenesis of these cells was assessed, as well as the ability of the cells to form branching structures on extracellular matrix (ECM)-coated surfaces. Transwell-invasion assays were used to assess the invasive properties of the cells. Clinically relevant inhibitors of ErbB2, lapatinib and Herceptin (traztuzumab), were used to investigate the role of ErbB2 signaling in this model. A retrospective study on a previously published breast cancer patient dataset was carried out by using Disease Specific Genomic Analysis (DSGA) to investigate the correlation of LOXL2 mRNA expression level with metastasis and survival of ErbB2-positive breast cancer patients.ResultsFluorescence staining of the acini revealed increased proliferation, decreased apoptosis, and disrupted polarity, leading to abnormal lumen formation in response to LOXL2 expression in MCF10A cells. When plated onto ECM, the LOXL2-expressing cells formed branching structures and displayed increased invasion. We noted that LOXL2 induced ErbB2 activation through reactive oxygen species (ROS) production, and ErbB2 inhibition by using Herceptin or lapatinib abrogated the effects of LOXL2 on MCF10A cells. Finally, we found LOXL2 expression to be correlated with decreased overall survival and metastasis-free survival in breast cancer patients with ErbB2-positive tumors.ConclusionsThese findings suggest that LOXL2 expression in normal epithelial cells can induce abnormal changes that resemble oncogenic transformation and cancer progression, and that these effects are driven by LOXL2-mediated activation of ErbB2. LOXL2 may also be a beneficial marker for breast cancer patients that could benefit most from anti-ErbB2 therapy.

Abstract LB-205: The calcium activated chloride channel ANO1 promotes breast cancer progression by activating EGFR- and CAMK-signaling.

Abstract Genomic alterations are the underlining cause of many human cancers: Amplified and overexpressed genes can drive neoplastic transformation and become essential survival factors for cancer cells. Thus, they represent promising targets for anti-cancer therapies and their identification and validation is of paramount importance. In a search for novel survival factors contributing to breast cancer oncogenesis, we performed genomic fine mapping of the 11q13 amplicon, one of the most frequently amplified chromosomal regions in human neoplasia, in a large dataset of breast cancer patients. Remarkably, we found ANO1, a calcium-activated chloride channel, to be located within the summit of the most frequently and highly amplified genomic region. Furthermore, amplification of ANO1 correlates with its overexpression and elevated protein levels and is a negative predictor for overall survival in breast cancer patients. Next, we examined how ANO1 contributes to cancer cell survival and proliferation. We found that inhibition of ANO1 expression or function reduced cancer cell viability and colony formation, and triggered apoptosis in 11q13 amplified breast cancer, HNSCC and ESCC. Furthermore, expression of ANO1 in non-transformed mammary cells increased cell viability and established a novel addiction to ANO1 biochemical activity. We next applied inducible shRNAs against ANO1 in vivo to assess the effect of ANO1 inhibition on the maintenance of established tumors. We found in four different 11q13-amplified tumor models that knockdown of ANO1 reduced tumor growth, indicating an important role for ANO1 not only in oncogenesis, but also in tumor maintenance of 11q13 amplified cancers. To explore the mechanism by which inhibition of ANO1 contributes to cancer cell viability and tumor growth, we performed antibody arrays to measure the activity of major oncogenic signaling pathways after knockdown of ANO1. Upon depletion of ANO1, activation of EGFR and several survival signaling pathways (AKT-, SRC- and ERK1/2 pathways) were reduced in different models of human cancer. Subsequent experiments showed that ANO1 modulates both EGFR- and Ca2+/calmodulin-dependent protein kinase (CAMK) signaling in breast cancer and HNSCC cells. Consistently, activation of EGFR- and CAMK correlated with the expression of ANO1 in human primary breast tumor samples. Lastly, we found that only the simultaneous stimulation of EGFR- and CAMK-signaling pathways rescued the effect of the ANO1-inhibition. In summary, our study establishes ANO1 as a key tumor-promoting factor in 11q13-amplified breast and other malignancies. Our results highlight the importance of chloride channels in cancer and provide the first detailed mechanistic insight into the activity of ANO1 in tumorigenesis. Most importantly, our findings open up new opportunities for therapeutic intervention in several prevalent cancers. Citation Format: Adrian Britschgi, Anke Bill, Heike Brinkhaus, Christopher Rothwell, Ieuan Clay, Stephan Duss, Michael Rebhan, Pichai Raman, Chantale Guy, Kristie Wetzel, Elizabeth George, M. Oana Popa, Sarah Lilley, Hedaythul Choudhury, Martin Gosling, Louis Wang, Stephanie Fitzgerald, Jason Borawski, Jonathan Baffoe, Mark Labow, L. Alex Gaither, Mohamed Bentires-Alj. The calcium activated chloride channel ANO1 promotes breast cancer progression by activating EGFR- and CAMK-signaling. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr LB-205. doi:10.1158/1538-7445.AM2013-LB-205

Characterizing the clinical relevance of epithelial mesenchymal transition (EMT) in human tumors.

10507 Background: We propose that the sonic hedge hog pathway (SHH) induces EMT in vitro, resulting in gene expression changes that are clinically relevant. Methods: EMT was induced in lung, breast, and prostate cancer cells in vitro through co-culture with myofibroblasts, a rich source of the SHH ligand. Real-time PCR (QRT-PCR) and immunoflorescence were used to confirm EMT, and invasiveness of the phenotype was assessed with matrigel assays. Microarray analysis was performed on RNA extracted from EMT induced cells and controls and bayesian regression methods were used to generate an EMT signature. Clinically annotated gene expression data from patients with lung (n = 128), breast (n = 286) and prostate (n = 79) cancer was used to ascertain the clinical importance of EMT, using survival as a relevant outcome. Results: QRT-PCR confirmed upregulation of EMT related genes, demonstrating a several fold increase in the expression of Snail, N- cadherin, Gli 2 and SHH genes, respectively (p < 0.0001). Immunoflorescence confirmed the necessary Cadherin switch and the EMT induced cells demonstrated increased invasion (p = 0.001). Cyclopamine inhibited increased expression of Snail, N-cadherin, SHH, and Gli2 (p <0.0001) confirming that SHH was a principal mediator of EMT in human tumors. Using EMT induced cells and control cells, a genomic signature for EMT in adenocarcinoma was characterized, comprised of 200 genes. Upregulated pathways include keratan sulfate biosynthesis and extracellular matrix interaction. Importantly, the EMT signature predicts for 5-yr metastasis-free survival in a large dataset of node negative breast cancer patients (p = 0.004). Similar statistically significant survival differences exist in prostate (p = 0.03) and lung cancer (p = 0.04). In addition, chemosensitivity for over 40 lung cancer cell lines was assessed, with median EC50 values significantly higher in cell lines with a high probability of EMT. Conclusions: Activation of the SHH pathway induces EMT in human cancers and genome-wide changes occurring during EMT can be used to predict clinical outcomes. The SHH pathway represents a unique therapeutic target for suppression of distant metastasis and modulating chemotherapeutic resistance. No significant financial relationships to disclose.

Clinical Relevance of Early Disseminated Breast Cancer Cells Depends on Their Cytokeratin Expression Pattern.

Abstract Background: The factors determining the clinical relevance of disseminated tumor cells (DTC) in breast cancer patients are largely unknown. Here we compared the specificity and clinical performance of two anti-cytokeratin(CK) antibodies frequently used for DTC detection, namely A45-B/B3 (A45) and AE1/AE3 (AE). Additionally we explored the CK gene expression patterns in primary breast tumors.Methods: Specificities of antibodies A45 and AE for selected CKs were assessed by 2-DE western blot analysis. Using these antibodies bone marrow aspirates from 391 breast cancer patients (M0, pT1-3, pN0-3) were screened for the presence of DTC. To obtain prognostic information, patients were followed up over a median of 83 months for time to relapse and 99 months for time to death. Two breast cancer patient datasets (Oslo (n=123), and van de Vijver et al. (NEJM 2002; n=295)) were analysed for CK primary tumor gene expression patterns and grouped by hormone receptor (HR) status of the primary tumors. T-tests and hierarchial clustering analyses were applied.Results: AE detected CK5, CK7, CK8, and CK19, whereas A45 recognized CK7 and CK18. In total, 24 of 391 patients (6.1%) were DTC-positive for A45, and 41 (10.5%) for AE. Although concordance between the two antibodies was 84.4%, overlap among positive cases was only 3.2%. DTC-positivity with AE and A45 was more frequent in patients of higher nodal status (p=0.019 and p=0.036, respectively). Nearly all patients with A45-positive DTC had hormone receptor-positive tumors (23/24), while detection of AE-positive DTC was more frequent among hormone receptor negative patients (p=0.006). Clear differences in primary tumor CK expression patterns between HR+ and HR- tumors were detected in both datasets. Compared to HR- tumors, CKs up-regulated in HR+ tumors were CK8 and 18, in addition CK19 in two datasets, whereas e.g. CK5, CK6, CK7, CK14, CK15, CK16, and CK17 were significantly down-regulated.Survival analyses of all patients revealed shorter distant disease-free survival (p=0.039) for patients with A45-positive DTC, whereas the prognostic relevance of AE-positive DTC was restricted to node positive patients.Conclusion: The clinical utility of immunocytochemical DTC detection depends on the anti-CK antibody used, which may reflect the complex CK composition of DTC. Tumor CK expression patterns and their correlation to antibody-sprecific DTC detection will further be explored by A45- and AE- staining of corresponding patient samples. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 3021.

348 Adjuvant therapy in melanoma

Sentinel lymph node (SLN) biopsy is the current prognostic tool for clinically node-negative breast cancer patients. If the SLN reveals macrometastasis, axillary lymph node dissection (ALND) is recommended. However, the use of ALND in patients with micrometastasis is debated. The objective of this study was to assess the utilization of ALND in the treatment of micrometastatic breast cancer.An IRB approved, retrospective study of a pooled dataset of breast cancer patients with micrometastatic disease on SLN biopsy was performed. Patients diagnosed from 1999-2016 were identified via query of a single-institution National Comprehensive Cancer Network (NCCN) breast cancer database as well as a prospective tumor board.A total of 91 patients were diagnosed with micrometastatic nodal disease. The median age at diagnosis was 56 y (range: 31-85); median follow-up time was 47 mo (range: 0-203 mo). 42/91(46.2%) patients had ALND of which 37/42 (88.1%) were a second operation; 3/42(7.1%) patients had additional positive nodes found at ALND. 44/91 (48.4%) patients received radiation. 7/91 (7.7%) patients had a recurrence, 5/7 local, including one axillary (2.1%; patient declined ALND).Given that the risk of lymphedema after ALND ranges between 20%-53%, the morbidity of ALND may far exceed the likelihood of detecting further nodal involvement in women with micrometastatic disease: 7.1% in this series.