Resistance to abemaciclib is associated with increased metastatic potential and lysosomal protein deregulation in breast cancer cells.

Cyclin dependent kinase 4 and 6 inhibitors such as abemaciclib are routinely used to treat metastatic estrogen receptor positive (ER+) breast cancer. However, adaptive mechanisms inhibit their effectiveness and allow for disease progression. Using ER+ breast cancer cell models, we show that acquired resistance to abemaciclib is accompanied by increase in metastatic potential. Mass spectrometry-based proteomics from abemaciclib sensitive and resistant cells showed that lysosomal proteins including CTSD (cathepsin D), cathepsin A and CD68 were significantly increased in resistant cells. Combination of abemaciclib and a lysosomal destabilizer, such as hydroxychloroquine (HCQ) or bafilomycin A1, resensitized resistant cells to abemaciclib. Also, combination of abemaciclib and HCQ decreased migration and invasive potential and increased lysosomal membrane permeability in resistant cells. Prosurvival B cell lymphoma 2 (BCL2) protein levels were elevated in resistant cells, and a triple treatment with abemaciclib, HCQ, and BCL2 inhibitor, venetoclax, significantly inhibited cell growth compared to treatment with abemaciclib and HCQ. Furthermore, resistant cells showed increased levels of Transcription Factor EB (TFEB), a master regulator of lysosomal-autophagy genes, and siRNA mediated knockdown of TFEB decreased invasion in resistant cells. TFEB was found to be mutated in a subset of invasive human breast cancer samples, and overall survival analysis in ER+, lymph node-positive breast cancer showed that increased TFEB expression correlated with decreased survival. Collectively, we show that acquired resistance to abemaciclib leads to increased metastatic potential and increased levels of protumorigenic lysosomal proteins. Therefore, the lysosomal pathway could be a therapeutic target in advanced ER+ breast cancer.

Modeling breast cancer proliferation, drug synergies, and alternating therapies.

Estrogen receptor positive (ER+) breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of targeted therapy often results in resistance, driving the consideration of combination and alternating therapies. Toward this end, we developed a mathematical model that can simulate various mono, combination, and alternating therapies for ER + breast cancer cells at different doses over long time scales. The model is used to look for optimal drug combinations and predicts a significant synergism between Cdk4/6 inhibitors in combination with the anti-estrogen fulvestrant, which may help explain the clinical success of adding Cdk4/6 inhibitors to anti-estrogen therapy. Furthermore, the model is used to optimize an alternating treatment protocol so it works as well as monotherapy while using less total drug dose.

Mathematically Modeling the Effect of Endocrine and Cdk4/6 Inhibitor Therapies on Breast Cancer Cells.

Mathematical modeling of cancer systems is beginning to be used to design better treatment regimens, especially in chemotherapy and radiotherapy. The effectiveness of mathematical modeling to inform treatment decisions and identify therapy protocols, some of which are highly nonintuitive, is because it enables the exploration of a huge number of therapeutic possibilities. Considering the immense cost of laboratory research and clinical trials, these nonintuitive therapy protocols would likely never be found by experimental approaches. While much of the work to date in this area has involved high-level models, which look simply at overall tumor growth or the interaction of resistant and sensitive cell types, mechanistic models that integrate molecular biology and pharmacology can contribute greatly to the discovery of better cancer treatment regimens. These mechanistic models are better able to account for the effect of drug interactions and the dynamics of therapy. The aim of this chapter is to demonstrate the use of ordinary differential equation-based mechanistic models to describe the dynamic interactions between the molecular signaling of breast cancer cells and two key clinical drugs. In particular, we illustrate the procedure for building a model of the response of MCF-7 cells to standard therapies used in the clinic. Such mathematical models can be used to explore the vast number of potential protocols to suggest better treatment approaches.

Caveolin-1 controls mitochondrial damage and ROS production by regulating fission - fusion dynamics and mitophagy.

As essential regulators of mitochondrial quality control, mitochondrial dynamics and mitophagy play key roles in maintenance of metabolic health and cellular homeostasis. Here we show that knockdown of the membrane-inserted scaffolding and structural protein caveolin-1 (Cav-1) and expression of tyrosine 14 phospho-defective Cav-1 mutant (Y14F), as opposed to phospho-mimicking Y14D, altered mitochondrial morphology, and increased mitochondrial matrix mixing, mitochondrial fusion and fission dynamics as well as mitophagy in MDA-MB-231 triple negative breast cancer cells. Further, we found that interaction of Cav-1 with mitochondrial fusion/fission machinery Mitofusin 2 (Mfn2) and Dynamin related protein 1 (Drp1) was enhanced by Y14D mutant indicating Cav-1 Y14 phosphorylation prevented Mfn2 and Drp1 translocation to mitochondria. Moreover, limiting mitochondrial recruitment of Mfn2 diminished formation of the PINK1/Mfn2/Parkin complex required for initiation of mitophagy resulting in accumulation of damaged mitochondria and ROS (mtROS). Thus, these studies indicate that phospho-Cav-1 may be an important switch mechanism in cancer cell survival which could lead to novel strategies for complementing cancer therapies.

Resistance to CDK4/6 Inhibitors in Estrogen Receptor-Positive Breast Cancer.

Estrogen receptor-positive (ER+) breast cancer is the most common form of breast cancer. Antiestrogens were the first therapy aimed at treating this subtype, but resistance to these warranted the development of a new treatment option. CDK4/6 inhibitors address this problem by halting cell cycle progression in ER+ cells, and have proven to be successful in the clinic. Unfortunately, both intrinsic and acquired resistance to CDK4/6 inhibitors are common. Numerous mechanisms of how resistance occurs have been identified to date, including the activation of prominent growth signaling pathways, the loss of tumor-suppressive genes, and noncanonical cell cycle function. Many of these have been successfully targeted and demonstrate the ability to overcome resistance to CDK4/6 inhibitors in preclinical and clinical trials. Future studies should focus on the development of biomarkers so that patients likely to be resistant to CDK4/6 inhibition can initially be given alternative methods of treatment.

Targeting WEE1 Inhibits Growth of Breast Cancer Cells That Are Resistant to Endocrine Therapy and CDK4/6 Inhibitors.

Despite the success of antiestrogens in extending overall survival of patients with estrogen receptor positive (ER+) breast tumors, resistance to these therapies is prevalent. ER+ tumors that progress on antiestrogens are treated with antiestrogens and CDK4/6 inhibitors. However, 20% of these tumors never respond to CDK4/6 inhibitors due to intrinsic resistance. Here, we used endocrine sensitive ER+ MCF7 and T47D breast cancer cells to generate long-term estrogen deprived (LTED) endocrine resistant cells that are intrinsically resistant to CDK4/6 inhibitors. Since treatment with antiestrogens arrests cells in the G1 phase of the cell cycle, we hypothesized that a defective G1 checkpoint allows resistant cells to escape this arrest but increases their dependency on G2 checkpoint for DNA repair and growth, and hence, targeting the G2 checkpoint will induce cell death. Indeed, inhibition of WEE1, a crucial G2 checkpoint regulator, with AZD1775 (Adavosertib), significantly decreased cell proliferation and increased G2/M arrest, apoptosis and gamma-H2AX levels (a marker for DNA double stranded breaks) in resistant cells compared with sensitive cells. Thus, targeting WEE1 is a promising anti-cancer therapeutic strategy in standard therapy resistant ER+ breast cancer.

Hematologic safety of palbociclib in combination with endocrine therapy in patients with benign ethnic neutropenia and advanced breast cancer.

BACKGROUND: Cyclin-dependent kinase 4/6 (CDK4/6) inhibitors, including palbociclib, are approved to treat hormone receptor (HR)-positive/human epidermal growth factor receptor 2 (HER2)-negative advanced breast cancer (ABC) and are associated with hematologic toxicity. African American women, who are underrepresented in CDK4/6 inhibitor clinical trials, may experience worse neutropenia because of benign ethnic neutropenia. The authors specifically investigated the hematologic safety of palbociclib in African American women with HR-positive/HER2-negative ABC. METHODS: PALINA was a single-arm, open-label, investigator-initiated study of palbociclib (125 mg daily; 21 days on and 7 days off) plus endocrine therapy (ET) in African American women who had HR-positive/HER2-negative ABC and a baseline absolute neutrophil count ≥1000/mm3 (ClinicalTrials.gov identifier NCT02692755). The primary outcome was the proportion of patients who completed 12 months of therapy without experiencing febrile neutropenia or treatment discontinuation because of neutropenia. Single nucleotide polymorphism analysis was used to assess Duffy polymorphism status. RESULTS: Thirty-five patients received ≥1 dose of palbociclib plus ET; 19 had a Duffy null polymorphism (cytosine/cytosine). There were no reports of febrile neutropenia or permanent study discontinuation because of neutropenia. Significantly more patients with the Duffy null versus the wild-type variant had grade 3 and 4 neutropenia (72.2% vs 23.1%; P = .029) and required a palbociclib dose reduction (55.6% vs 7.7%; P = .008). Patients with the Duffy null versus the wild-type variant had lower overall relative dose intensity (mean ± SD, 81.89% ± 15.87 and 95.67% ± 5.89, respectively; P = .0026) and a lower clinical benefit rate (66.7% and 84.6%, respectively). CONCLUSIONS: These findings suggest that palbociclib is well tolerated in African American women with HR-positive/HER2-negative ABC. Duffy null status may affect the incidence of grade 3 neutropenia, dose intensity, and possibly clinical benefit.

Inhibition of DNA Repair Pathways and Induction of ROS Are Potential Mechanisms of Action of the Small Molecule Inhibitor BOLD-100 in Breast Cancer.

BOLD-100, a ruthenium-based complex, sodium trans-[tetrachloridobis (1H-indazole) ruthenate (III)] (also known as IT-139, NKP1339 or KP1339), is a novel small molecule drug that demonstrated a manageable safety profile at the maximum tolerated dose and modest antitumor activity in a phase I clinical trial. BOLD-100 has been reported to inhibit the upregulation of the endoplasmic reticulum stress sensing protein GRP78. However, response to BOLD-100 varies in different cancer models and the precise mechanism of action in high-response versus low-response cancer cells remains unclear. In vitro studies have indicated that BOLD-100 induces cytostatic rather than cytotoxic effects as a monotherapy. To understand BOLD-100-mediated signaling mechanism in breast cancer cells, we used estrogen receptor positive (ER+) MCF7 breast cancer cells to obtain gene-metabolite integrated models. At 100 µM, BOLD-100 significantly reduced cell proliferation and expression of genes involved in the DNA repair pathway. BOLD-100 also induced reactive oxygen species (ROS) and phosphorylation of histone H2AX, gamma-H2AX (Ser139), suggesting disruption of proper DNA surveillance. In estrogen receptor negative (ER-) breast cancer cells, combination of BOLD-100 with a PARP inhibitor, olaparib, induced significant inhibition of cell growth and xenografts and increased gamma-H2AX. Thus, BOLD-100 is a novel DNA repair pathway targeting agent and can be used with other chemotherapies in ER- breast cancer.

Mathematical modelling of breast cancer cells in response to endocrine therapy and Cdk4/6 inhibition.

Oestrogen receptor (ER)-positive breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of any targeted therapy often results in resistance to the therapy. Our ultimate goal is to use mathematical modelling to optimize alternating therapies that not only decrease proliferation but also stave off resistance. Toward this end, we measured levels of key proteins and proliferation over a 7-day time course in ER+ MCF-7 breast cancer cells. Treatments included endocrine therapy, either oestrogen deprivation, which mimics the effects of an aromatase inhibitor, or fulvestrant, an ER degrader. These data were used to calibrate a mathematical model based on key interactions between ER signalling and the cell cycle. We show that the calibrated model is capable of predicting the combination treatment of fulvestrant and oestrogen deprivation. Further, we show that we can add a new drug, palbociclib, to the model by measuring only two key proteins, cMyc and hyperphosphorylated RB1, and adjusting only parameters associated with the drug. The model is then able to predict the combination treatment of oestrogen deprivation and palbociclib. We illustrate the model's potential to explore protocols that limit proliferation and hold off resistance by not depending on any one therapy.

Glutamine Metabolism Drives Growth in Advanced Hormone Receptor Positive Breast Cancer.

Dependence on the glutamine pathway is increased in advanced breast cancer cell models and tumors regardless of hormone receptor status or function. While 70% of breast cancers are estrogen receptor positive (ER+) and depend on estrogen signaling for growth, advanced ER+ breast cancers grow independent of estrogen. Cellular changes in amino acids such as glutamine are sensed by the mammalian target of rapamycin (mTOR) complex, mTORC1, which is often deregulated in ER+ advanced breast cancer. Inhibitor of mTOR, such as everolimus, has shown modest clinical activity in ER+ breast cancers when given with an antiestrogen. Here we show that breast cancer cell models that are estrogen independent and antiestrogen resistant are more dependent on glutamine for growth compared with their sensitive parental cell lines. Co-treatment of CB-839, an inhibitor of GLS, an enzyme that converts glutamine to glutamate, and everolimus interrupts the growth of these endocrine resistant xenografts. Using human tumor microarrays, we show that GLS is significantly higher in human breast cancer tumors with increased tumor grade, stage, ER-negative and progesterone receptor (PR) negative status. Moreover, GLS levels were significantly higher in breast tumors from African-American women compared with Caucasian women regardless of ER or PR status. Among patients treated with endocrine therapy, high GLS expression was associated with decreased disease free survival (DFS) from a multivariable model with GLS expression treated as dichotomous. Collectively, these findings suggest a complex biology for glutamine metabolism in driving breast cancer growth. Moreover, targeting GLS and mTOR in advanced breast cancer may be a novel therapeutic approach in advanced ER+ breast cancer.

Promoting Scientist-Advocate Collaborations in Cancer Research: Why and How.

Advocates bring unique and important viewpoints to the cancer research process, ensuring that scientific and medical advances are patient-centered and relevant. In this article, we discuss the benefits of engaging advocates in cancer research and underscore ways in which both the scientific and patient communities can facilitate this mutually beneficial collaboration. We discuss how to establish and nurture successful scientist-advocate relationships throughout the research process. We review opportunities that are available to advocates who want to obtain training in the evaluation of cancer research. We also suggest practical solutions that can strengthen communication between scientists and advocates, such as introducing scientist-advocate interactions at the trainee level. Finally, we highlight the essential role social media can play in disseminating patient-supported cancer research findings to the patient community and in raising awareness of the importance of promoting cancer research. Our perspective offers a model that Georgetown Breast Cancer Advocates have found effective and which could be one option for those interested in developing productive, successful, and sustainable collaborations between advocates and scientists in cancer research. Cancer Res; 78(20); 5723-8. ©2018 AACR.

CDK4/6 inhibitors in breast cancer therapy: Current practice and future opportunities.

Dysregulation of the cyclin dependent kinase pathway in luminal breast cancer creates a new therapeutic opportunity for estrogen receptor positive breast cancer. Initial pan-CDK inhibitors were associated with extensive toxicities but in recent years, the development of potent specific CDK inhibitors with favorable tolerability has driven renewed interests in this class of targeted therapies. Palbociclib, ribociclib and abemaciclib are specific CDK4/6 inhibitors that have been approved by the U.S. Food and Drug Administration for use in combination with endocrine therapy for women with advanced hormone receptor positive breast cancer. These three anticancer therapeutics were approved based on progression free survival benefit seen on phase III trials with the most common grade 3 treatment-related side effects being neutropenia, fatigue, nausea and diarrhea. Except for estrogen receptor positivity, no biomarkers predictive of response to CDK4/6 inhibitors have been identified to date. Based on mechanistic insights here described, CDK4/6 inhibitors are currently being explored in combination with other agents, including targeted therapies, immunotherapy and chemotherapy.

EGR1 regulates cellular metabolism and survival in endocrine resistant breast cancer.

About 70% of all breast cancers are estrogen receptor alpha positive (ER+; ESR1). Many are treated with antiestrogens. Unfortunately, de novo and acquired resistance to antiestrogens is common but the underlying mechanisms remain unclear. Since growth of cancer cells is dependent on adequate energy and metabolites, the metabolomic profile of endocrine resistant breast cancers likely contains features that are deterministic of cell fate. Thus, we integrated data from metabolomic and transcriptomic analyses of ER+ MCF7-derived breast cancer cells that are antiestrogen sensitive (LCC1) or resistant (LCC9) that resulted in a gene-metabolite network associated with EGR1 (early growth response 1). In human ER+ breast tumors treated with endocrine therapy, higher EGR1 expression was associated with a more favorable prognosis. Mechanistic studies showed that knockdown of EGR1 inhibited cell growth in both cells and EGR1 overexpression did not affect antiestrogen sensitivity. Comparing metabolite profiles in LCC9 cells following perturbation of EGR1 showed interruption of lipid metabolism. Tolfenamic acid, an anti-inflammatory drug, decreased EGR1 protein levels and synergized with antiestrogens in inhibiting cell proliferation in LCC9 cells. Collectively, these findings indicate that EGR1 is an important regulator of breast cancer cell metabolism and is a promising target to prevent or reverse endocrine resistance.

MYC-Driven Pathways in Breast Cancer Subtypes.

The transcription factor MYC (MYC proto-oncogene, bHLH transcription factor) is an essential signaling hub in multiple cellular processes that sustain growth of many types of cancers. MYC regulates expression of RNA, both protein and non-coding, that control central metabolic pathways, cell death, proliferation, differentiation, stress pathways, and mechanisms of drug resistance. Activation of MYC has been widely reported in breast cancer progression. Breast cancer is a complex heterogeneous disease and treatment options are primarily guided by histological and biochemical evaluations of the tumors. Based on biochemical markers, three main breast cancer categories are ER+ (estrogen receptor alpha positive), HER2+ (human epidermal growth factor receptor 2 positive), and TNBC (triple-negative breast cancer; estrogen receptor negative, progesterone receptor negative, HER2 negative). MYC is elevated in TNBC compared with other cancer subtypes. Interestingly, MYC-driven pathways are further elevated in aggressive breast cancer cells and tumors that display drug resistant phenotype. Identification of MYC target genes is essential in isolating signaling pathways that drive tumor development. In this review, we address the role of MYC in the three major breast cancer subtypes and highlight the most promising leads to target MYC functions.

Breast cancer cell obatoclax response characterization using passivated-electrode insulator-based dielectrophoresis.

Inherent electrical properties of cells can be beneficial to characterize different cell lines and their response to experimental drugs. This paper presents a novel method to characterize the response of breast cancer cells to drug stimuli through use of off-chip passivated-electrode insulator-based dielectrophoresis (OπDEP) and the application of AC electric fields. This work is the first to demonstrate the ability of OπDEP to differentiate between two closely related breast cancer cell lines, LCC1 and LCC9 while assessing their drug sensitivity to an experimental anti-cancer agent, Obatoclax. Although both cell lines are derivatives of estrogen-responsive MCF-7 breast cancer cells, growth of LCC1 is estrogen independent and anti-estrogen responsive, while LCC9 is both estrogen-independent and anti-estrogen resistant. Under the same operating conditions, LCC1 and LCC9 had different DEP profiles. LCC1 cells had a trapping onset (crossover) frequency of 700 kHz and trapping efficiencies between 30-40%, while LCC9 cells had a lower crossover frequency (100 kHz) and showed higher trapping efficiencies of 40-60%. When exposed to the Obatoclax, both cell lines exhibited dose-dependent shifts in DEP crossover frequency and trapping efficiency. Here, DEP results supplemented with cell morphology and proliferation assays help us to understand the response of these breast cancer cells to Obatoclax.

PSSV: a novel pattern-based probabilistic approach for somatic structural variation identification.

MOTIVATION: Whole genome DNA-sequencing (WGS) of paired tumor and normal samples has enabled the identification of somatic DNA changes in an unprecedented detail. Large-scale identification of somatic structural variations (SVs) for a specific cancer type will deepen our understanding of driver mechanisms in cancer progression. However, the limited number of WGS samples, insufficient read coverage, and the impurity of tumor samples that contain normal and neoplastic cells, limit reliable and accurate detection of somatic SVs. RESULTS: We present a novel pattern-based probabilistic approach, PSSV, to identify somatic structural variations from WGS data. PSSV features a mixture model with hidden states representing different mutation patterns; PSSV can thus differentiate heterozygous and homozygous SVs in each sample, enabling the identification of those somatic SVs with heterozygous mutations in normal samples and homozygous mutations in tumor samples. Simulation studies demonstrate that PSSV outperforms existing tools. PSSV has been successfully applied to breast cancer data to identify somatic SVs of key factors associated with breast cancer development. AVAILABILITY AND IMPLEMENTATION: An R package of PSSV is available at http://www.cbil.ece.vt.edu/software.htm CONTACT: xuan@vt.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Systems Approaches to Cancer Biology.

Cancer systems biology aims to understand cancer as an integrated system of genes, proteins, networks, and interactions rather than an entity of isolated molecular and cellular components. The inaugural Systems Approaches to Cancer Biology Conference, cosponsored by the Association of Early Career Cancer Systems Biologists and the National Cancer Institute of the NIH, focused on the interdisciplinary field of cancer systems biology and the challenging cancer questions that are best addressed through the combination of experimental and computational analyses. Attendees found that elucidating the many molecular features of cancer inevitably reveals new forms of complexity and concluded that ensuring the reproducibility and impact of cancer systems biology studies will require widespread method and data sharing and, ultimately, the translation of important findings to the clinic. Cancer Res; 76(23); 6774-7. ©2016 AACR.

Caveolin-1 regulates cancer cell metabolism via scavenging Nrf2 and suppressing MnSOD-driven glycolysis.

Aerobic glycolysis is an indispensable component of aggressive cancer cell metabolism. It also distinguishes cancer cells from most healthy cell types in the body. Particularly for this reason, targeting the metabolism to improve treatment outcomes has long been perceived as a potentially valuable strategy. In practice, however, our limited knowledge of why and how metabolic reprogramming occurs has prevented progress towards therapeutic interventions that exploit the metabolic peculiarities of tumors. We recently described that in breast cancer, MnSOD upregulation is both necessary and sufficient to activate glycolysis. Here, we focused on determining the molecular mechanisms of MnSOD upregulation. We found that Caveolin-1 (Cav-1) is a central component of this mechanism due to its suppressive effects of NF-E2-related factor 2 (Nrf2), a transcription factor upstream of MnSOD. In transformed MCF10A(Er/Src) cells, Cav-1 loss preceded the activation of Nrf2 and its induction of MnSOD expression. Consistently, with previous observations, MnSOD expression secondary to Nrf2 activation led to an increase in the glycolytic rate dependent on mtH2O2 production and the activation of AMPK. Moreover, rescue of Cav-1 expression in a breast cancer cell line (MCF7) suppressed Nrf2 and reduced MnSOD expression. Experimental data were reinforced by epidemiologic nested case-control studies showing that Cav-1 and MnSOD are inversely expressed in cases of invasive ductal carcinoma, with low Cav-1 and high MnSOD expression being associated with lower 5-year survival rates and molecular subtypes with poorest prognosis.

ChIP-BIT: Bayesian inference of target genes using a novel joint probabilistic model of ChIP-seq profiles.

Chromatin immunoprecipitation with massively parallel DNA sequencing (ChIP-seq) has greatly improved the reliability with which transcription factor binding sites (TFBSs) can be identified from genome-wide profiling studies. Many computational tools are developed to detect binding events or peaks, however the robust detection of weak binding events remains a challenge for current peak calling tools. We have developed a novel Bayesian approach (ChIP-BIT) to reliably detect TFBSs and their target genes by jointly modeling binding signal intensities and binding locations of TFBSs. Specifically, a Gaussian mixture model is used to capture both binding and background signals in sample data. As a unique feature of ChIP-BIT, background signals are modeled by a local Gaussian distribution that is accurately estimated from the input data. Extensive simulation studies showed a significantly improved performance of ChIP-BIT in target gene prediction, particularly for detecting weak binding signals at gene promoter regions. We applied ChIP-BIT to find target genes from NOTCH3 and PBX1 ChIP-seq data acquired from MCF-7 breast cancer cells. TF knockdown experiments have initially validated about 30% of co-regulated target genes identified by ChIP-BIT as being differentially expressed in MCF-7 cells. Functional analysis on these genes further revealed the existence of crosstalk between Notch and Wnt signaling pathways.

BMRF-Net: a software tool for identification of protein interaction subnetworks by a bagging Markov random field-based method.

UNLABELLED: Identification of protein interaction subnetworks is an important step to help us understand complex molecular mechanisms in cancer. In this paper, we develop a BMRF-Net package, implemented in Java and C++, to identify protein interaction subnetworks based on a bagging Markov random field (BMRF) framework. By integrating gene expression data and protein-protein interaction data, this software tool can be used to identify biologically meaningful subnetworks. A user friendly graphic user interface is developed as a Cytoscape plugin for the BMRF-Net software to deal with the input/output interface. The detailed structure of the identified networks can be visualized in Cytoscape conveniently. The BMRF-Net package has been applied to breast cancer data to identify significant subnetworks related to breast cancer recurrence. AVAILABILITY AND IMPLEMENTATION: The BMRF-Net package is available at http://sourceforge.net/projects/bmrfcjava/. The package is tested under Ubuntu 12.04 (64-bit), Java 7, glibc 2.15 and Cytoscape 3.1.0.