The potential of epigenetic therapy to target the 3D epigenome in endocrine-resistant breast cancer.

Three-dimensional (3D) epigenome remodeling is an important mechanism of gene deregulation in cancer. However, its potential as a target to counteract therapy resistance remains largely unaddressed. Here, we show that epigenetic therapy with decitabine (5-Aza-mC) suppresses tumor growth in xenograft models of pre-clinical metastatic estrogen receptor positive (ER+) breast tumor. Decitabine-induced genome-wide DNA hypomethylation results in large-scale 3D epigenome deregulation, including de-compaction of higher-order chromatin structure and loss of boundary insulation of topologically associated domains. Significant DNA hypomethylation associates with ectopic activation of ER-enhancers, gain in ER binding, creation of new 3D enhancer-promoter interactions and concordant up-regulation of ER-mediated transcription pathways. Importantly, long-term withdrawal of epigenetic therapy partially restores methylation at ER-enhancer elements, resulting in a loss of ectopic 3D enhancer-promoter interactions and associated gene repression. Our study illustrates the potential of epigenetic therapy to target ER+ endocrine-resistant breast cancer by DNA methylation-dependent rewiring of 3D chromatin interactions, which are associated with the suppression of tumor growth.

Multi-modal transcriptomic analysis unravels enrichment of hybrid epithelial/mesenchymal state and enhanced phenotypic heterogeneity in basal breast cancer.

Intra-tumoral phenotypic heterogeneity promotes tumor relapse and therapeutic resistance and remains an unsolved clinical challenge. It manifests along multiple phenotypic axes and decoding the interconnections among these different axes is crucial to understand its molecular origins and to develop novel therapeutic strategies to control it. Here, we use multi-modal transcriptomic data analysis - bulk, single-cell and spatial transcriptomics - from breast cancer cell lines and primary tumor samples, to identify associations between epithelial-mesenchymal transition (EMT) and luminal-basal plasticity - two key processes that enable heterogeneity. We show that luminal breast cancer strongly associates with an epithelial cell state, but basal breast cancer is associated with hybrid epithelial/mesenchymal phenotype(s) and higher phenotypic heterogeneity. These patterns were inherent in methylation profiles, suggesting an epigenetic crosstalk between EMT and lineage plasticity in breast cancer. Mathematical modelling of core underlying gene regulatory networks representative of the crosstalk between the luminal-basal and epithelial-mesenchymal axes recapitulate and thus elucidate mechanistic underpinnings of the observed associations from transcriptomic data. Our systems-based approach integrating multi-modal data analysis with mechanism-based modeling offers a predictive framework to characterize intra-tumor heterogeneity and to identify possible interventions to restrict it.

Innovative thiosemicarbazones that induce multi-modal mechanisms to down-regulate estrogen-, progesterone-, androgen- and prolactin-receptors in breast cancer.

The estrogen receptor-α (ER-α) is a key driver of breast cancer (BC) and the ER-antagonist, tamoxifen, is a central pillar of BC treatment. However, cross-talk between ER-α, other hormone and growth factor receptors enables development of de novo resistance to tamoxifen. Herein, we mechanistically dissect the activity of a new class of anti-cancer agents that inhibit multiple growth factor receptors and down-stream signaling for the treatment of ER-positive BC. Using RNA sequencing and comprehensive protein expression analysis, we examined the activity of di-2-pyridylketone-4,4-dimethyl-3-thiosemicarbazone (Dp44mT) and di-2-pyridylketone-4-cyclohexyl-4-methyl-3-thiosemicarbazone (DpC), on the expression and activation of hormone and growth factor receptors, co-factors, and key resistance pathways in ER-α-positive BC. DpC differentially regulated 106 estrogen-response genes, and this was linked to decreased mRNA levels of 4 central hormone receptors involved in BC pathogenesis, namely ER, progesterone receptor (PR), androgen receptor (AR), and prolactin receptor (PRL-R). Mechanistic investigation demonstrated that due to DpC and Dp44mT binding metal ions, these agents caused a pronounced decrease in ER-α, AR, PR, and PRL-R protein expression. DpC and Dp44mT also inhibited activation and down-stream signaling of the epidermal growth factor (EGF) family receptors, and expression of co-factors that promote ER-α transcriptional activity, including SRC3, NF-κB p65, and SP1. In vivo, DpC was highly tolerable and effectively inhibited ER-α-positive BC growth. Through bespoke, non-hormonal, multi-modal mechanisms, Dp44mT and DpC decrease the expression of PR, AR, PRL-R, and tyrosine kinases that act with ER-α to promote BC, constituting an innovative therapeutic approach.

The androgen receptor is a tumor suppressor in estrogen receptor-positive breast cancer.

The role of the androgen receptor (AR) in estrogen receptor (ER)-α-positive breast cancer is controversial, constraining implementation of AR-directed therapies. Using a diverse, clinically relevant panel of cell-line and patient-derived models, we demonstrate that AR activation, not suppression, exerts potent antitumor activity in multiple disease contexts, including resistance to standard-of-care ER and CDK4/6 inhibitors. Notably, AR agonists combined with standard-of-care agents enhanced therapeutic responses. Mechanistically, agonist activation of AR altered the genomic distribution of ER and essential co-activators (p300, SRC-3), resulting in repression of ER-regulated cell cycle genes and upregulation of AR target genes, including known tumor suppressors. A gene signature of AR activity positively predicted disease survival in multiple clinical ER-positive breast cancer cohorts. These findings provide unambiguous evidence that AR has a tumor suppressor role in ER-positive breast cancer and support AR agonism as the optimal AR-directed treatment strategy, revealing a rational therapeutic opportunity.

Differential kynurenine pathway metabolism in highly metastatic aggressive breast cancer subtypes: beyond IDO1-induced immunosuppression.

BACKGROUND: Immunotherapy has recently been proposed as a promising treatment to stop breast cancer (BrCa) progression and metastasis. However, there has been limited success in the treatment of BrCa with immune checkpoint inhibitors. This implies that BrCa tumors have other mechanisms to escape immune surveillance. While the kynurenine pathway (KP) is known to be a key player mediating tumor immune evasion and while there are several studies on the roles of the KP in cancer, little is known about KP involvement in BrCa. METHODS: To understand how KP is regulated in BrCa, we examined the KP profile in BrCa cell lines and clinical samples (n = 1997) that represent major subtypes of BrCa (luminal, HER2-enriched, and triple-negative (TN)). We carried out qPCR, western blot/immunohistochemistry, and ultra-high pressure liquid chromatography on these samples to quantify the KP enzyme gene, protein, and activity, respectively. RESULTS: We revealed that the KP is highly dysregulated in the HER2-enriched and TN BrCa subtype. Gene, protein expression, and KP metabolomic profiling have shown that the downstream KP enzymes KMO and KYNU are highly upregulated in the HER2-enriched and TN BrCa subtypes, leading to increased production of the potent immunosuppressive metabolites anthranilic acid (AA) and 3-hydroxylanthranilic acid (3HAA). CONCLUSIONS: Our findings suggest that KMO and KYNU inhibitors may represent new promising therapeutic targets for BrCa. We also showed that KP metabolite profiling can be used as an accurate biomarker for BrCa subtyping, as we successfully discriminated TN BrCa from other BrCa subtypes.

MDM2 inhibition in combination with endocrine therapy and CDK4/6 inhibition for the treatment of ER-positive breast cancer.

BACKGROUND: Resistance to endocrine therapy is a major clinical challenge in the management of oestrogen receptor (ER)-positive breast cancer. In this setting, p53 is frequently wildtype and its activity may be suppressed via upregulation of its key regulator MDM2. This underlies our rationale to evaluate MDM2 inhibition as a therapeutic strategy in treatment-resistant ER-positive breast cancer. METHODS: We used the MDM2 inhibitor NVP-CGM097 to treat in vitro and in vivo models alone and in combination with fulvestrant or palbociclib. We perform cell viability, cell cycle, apoptosis and senescence assays to evaluate anti-tumour effects in p53 wildtype and p53 mutant ER-positive cell lines (MCF-7, ZR75-1, T-47D) and MCF-7 lines resistant to endocrine therapy and to CDK4/6 inhibition. We further assess the drug effects in patient-derived xenograft (PDX) models of endocrine-sensitive and endocrine-resistant ER-positive breast cancer. RESULTS: We demonstrate that MDM2 inhibition results in cell cycle arrest and increased apoptosis in p53-wildtype in vitro and in vivo breast cancer models, leading to potent anti-tumour activity. We find that endocrine therapy or CDK4/6 inhibition synergises with MDM2 inhibition but does not further enhance apoptosis. Instead, combination treatments result in profound regulation of cell cycle-related transcriptional programmes, with synergy achieved through increased antagonism of cell cycle progression. Combination therapy pushes cell lines resistant to fulvestrant or palbociclib to become senescent and significantly reduces tumour growth in a fulvestrant-resistant patient-derived xenograft model. CONCLUSIONS: We conclude that MDM2 inhibitors in combination with ER degraders or CDK4/6 inhibitors represent a rational strategy for treating advanced, endocrine-resistant ER-positive breast cancer, operating through synergistic activation of cell cycle co-regulatory programmes.

Non-canonical AR activity facilitates endocrine resistance in breast cancer.

The role of androgen receptor (AR) in endocrine-resistant breast cancer is controversial and clinical trials targeting AR with an AR antagonist (e.g., enzalutamide) have been initiated. Here, we investigated the consequence of AR antagonism using in vitro and in vivo models of endocrine resistance. AR antagonism in MCF7-derived tamoxifen-resistant (TamR) and long-term estrogen-deprived breast cancer cell lines were achieved using siRNA-mediated knockdown or pharmacological inhibition with enzalutamide. The efficacy of enzalutamide was further assessed in vivo in an estrogen-independent endocrine-resistant patient-derived xenograft (PDX) model. Knockdown of AR inhibited the growth of the endocrine-resistant cell line models. Microarray gene expression profiling of the TamR cells following AR knockdown revealed perturbations in proliferative signaling pathways upregulated in endocrine resistance. AR loss also increased some canonical ER signaling events and restored sensitivity of TamR cells to tamoxifen. In contrast, enzalutamide did not recapitulate the effect of AR knockdown in vitro, even though it inhibited canonical AR signaling, which suggests that it is the non-canonical AR activity that facilitated endocrine resistance. Enzalutamide had demonstrable efficacy in inhibiting AR activity in vivo but did not affect the growth of the endocrine-resistant PDX model. Our findings implicate non-canonical AR activity in facilitating an endocrine-resistant phenotype in breast cancer. Unlike canonical AR signaling which is inhibited by enzalutamide, non-canonical AR activity is not effectively antagonized by enzalutamide, and this has important implications in the design of future AR-targeted clinical trials in endocrine-resistant breast cancer.

Basal-like breast cancer: molecular profiles, clinical features and survival outcomes.

BACKGROUND: Basal-like constitutes an important molecular subtype of breast cancer characterised by an aggressive behaviour and a limited therapy response. The outcome of patients within this subtype is, however, divergent. Some individuals show an increased risk of dying in the first five years, and others a long-term survival of over ten years after the diagnosis. In this study, we aim at identifying markers associated with basal-like patients' survival and characterising subgroups with distinct disease outcome. METHODS: We explored the genomic and transcriptomic profiles of 351 basal-like samples from the METABRIC and ROCK data sets. Two selection methods, labelled Differential and Survival filters, were employed to determine genes/probes that are differentially expressed in tumour and control samples, and are associated with overall survival. These probes were further used to define molecular subgroups, which vary at the microRNA level and in DNA copy number. RESULTS: We identified the expression signature of 80 probes that distinguishes between two basal-like subgroups with distinct clinical features and survival outcomes. Genes included in this list have been mainly linked to cancer immune response, epithelial-mesenchymal transition and cell cycle. In particular, high levels of CXCR6, HCST, C3AR1 and FPR3 were found in Basal I; whereas HJURP, RRP12 and DNMT3B appeared over-expressed in Basal II. These genes exhibited the highest betweenness centrality and node degree values and play a key role in the basal-like breast cancer differentiation. Further molecular analysis revealed 17 miRNAs correlated to the subgroups, including hsa-miR-342-5p, -150, -155, -200c and -17. Additionally, increased percentages of gains/amplifications were detected on chromosomes 1q, 3q, 8q, 10p and 17q, and losses/deletions on 4q, 5q, 8p and X, associated with reduced survival. CONCLUSIONS: The proposed signature supports the existence of at least two subgroups of basal-like breast cancers with distinct disease outcome. The identification of patients at a low risk may impact the clinical decisions-making by reducing the prescription of high-dose chemotherapy and, consequently, avoiding adverse effects. The recognition of other aggressive features within this subtype may be also critical for improving individual care and for delineating more effective therapies for patients at high risk.

Life as an early career researcher: interview with Heloisa Helena Milioli.

Heloisa Helena Milioli speaks to Francesca Lake, Managing Editor: Heloisa received a BSc degree in Biological Sciences (2008) from the Universidade Federal de Santa Catarina (Brazil) and obtained a MSc degree in Genetics (2011) from Universidade Federal do Paraná (Brazil). In 2011 and 2012, she worked as a lecturer and tutor in the Department of Cell Biology, Embryology and Genetics (Universidade Federal de Santa Catarina). She moved to Australia in 2012 to obtain her PhD in Biological Sciences, with emphasis on Bioinformatics, from The University of Newcastle. Her doctoral work brings together new considerations in the breast cancer field by combining novel bioinformatics approaches with the study of intrinsic subtypes. She has been applying advanced methods and sophisticated algorithms in unconventional computer architecture for the molecular classification of breast cancer based on the genomic (single nucleotide polymorphisms, circulating nucleic acids and copy number variations) and transcriptomic (gene expression and miRNA) signatures. Fundamental research will allow her to identify biomarkers of use in translational medicine for the diagnosis, prognosis and disease management focused on group-based tailored therapies.

Extensive Transcriptomic and Genomic Analysis Provides New Insights about Luminal Breast Cancers.

Despite constituting approximately two thirds of all breast cancers, the luminal A and B tumours are poorly classified at both clinical and molecular levels. There are contradictory reports on the nature of these subtypes: some define them as intrinsic entities, others as a continuum. With the aim of addressing these uncertainties and identifying molecular signatures of patients at risk, we conducted a comprehensive transcriptomic and genomic analysis of 2,425 luminal breast cancer samples. Our results indicate that the separation between the molecular luminal A and B subtypes-per definition-is not associated with intrinsic characteristics evident in the differentiation between other subtypes. Moreover, t-SNE and MST-kNN clustering approaches based on 10,000 probes, associated with luminal tumour initiation and/or development, revealed the close connections between luminal A and B tumours, with no evidence of a clear boundary between them. Thus, we considered all luminal tumours as a single heterogeneous group for analysis purposes. We first stratified luminal tumours into two distinct groups by their HER2 gene cluster co-expression: HER2-amplified luminal and ordinary-luminal. The former group is associated with distinct transcriptomic and genomic profiles, and poor prognosis; it comprises approximately 8% of all luminal cases. For the remaining ordinary-luminal tumours we further identified the molecular signature correlated with disease outcomes, exhibiting an approximately continuous gene expression range from low to high risk. Thus, we employed four virtual quantiles to segregate the groups of patients. The clinico-pathological characteristics and ratios of genomic aberrations are concordant with the variations in gene expression profiles, hinting at a progressive staging. The comparison with the current separation into luminal A and B subtypes revealed a substantially improved survival stratification. Concluding, we suggest a review of the definition of luminal A and B subtypes. A proposition for a revisited delineation is provided in this study.

Iteratively refining breast cancer intrinsic subtypes in the METABRIC dataset.

BACKGROUND: Multi-gene lists and single sample predictor models have been currently used to reduce the multidimensional complexity of breast cancers, and to identify intrinsic subtypes. The perceived inability of some models to deal with the challenges of processing high-dimensional data, however, limits the accurate characterisation of these subtypes. Towards the development of robust strategies, we designed an iterative approach to consistently discriminate intrinsic subtypes and improve class prediction in the METABRIC dataset. FINDINGS: In this study, we employed the CM1 score to identify the most discriminative probes for each group, and an ensemble learning technique to assess the ability of these probes on assigning subtype labels using 24 different classifiers. Our analysis is comprised of an iterative computation of these methods and statistical measures performed on a set of over 2000 samples. The refined labels assigned using this iterative approach revealed to be more consistent and in better agreement with clinicopathological markers and patients' overall survival than those originally provided by the PAM50 method. CONCLUSIONS: The assignment of intrinsic subtypes has a significant impact in translational research for both understanding and managing breast cancer. The refined labelling, therefore, provides more accurate and reliable information by improving the source of fundamental science prior to clinical applications in medicine.

The Discovery of Novel Biomarkers Improves Breast Cancer Intrinsic Subtype Prediction and Reconciles the Labels in the METABRIC Data Set.

BACKGROUND: The prediction of breast cancer intrinsic subtypes has been introduced as a valuable strategy to determine patient diagnosis and prognosis, and therapy response. The PAM50 method, based on the expression levels of 50 genes, uses a single sample predictor model to assign subtype labels to samples. Intrinsic errors reported within this assay demonstrate the challenge of identifying and understanding the breast cancer groups. In this study, we aim to: a) identify novel biomarkers for subtype individuation by exploring the competence of a newly proposed method named CM1 score, and b) apply an ensemble learning, as opposed to the use of a single classifier, for sample subtype assignment. The overarching objective is to improve class prediction. METHODS AND FINDINGS: The microarray transcriptome data sets used in this study are: the METABRIC breast cancer data recorded for over 2000 patients, and the public integrated source from ROCK database with 1570 samples. We first computed the CM1 score to identify the probes with highly discriminative patterns of expression across samples of each intrinsic subtype. We further assessed the ability of 42 selected probes on assigning correct subtype labels using 24 different classifiers from the Weka software suite. For comparison, the same method was applied on the list of 50 genes from the PAM50 method. CONCLUSIONS: The CM1 score portrayed 30 novel biomarkers for predicting breast cancer subtypes, with the confirmation of the role of 12 well-established genes. Intrinsic subtypes assigned using the CM1 list and the ensemble of classifiers are more consistent and homogeneous than the original PAM50 labels. The new subtypes show accurate distributions of current clinical markers ER, PR and HER2, and survival curves in the METABRIC and ROCK data sets. Remarkably, the paradoxical attribution of the original labels reinforces the limitations of employing a single sample classifiers to predict breast cancer intrinsic subtypes.

Comparative proteomics of primary breast carcinomas and lymph node metastases outlining markers of tumor invasion.

BACKGROUND: Lymph node metastasis is an important clinicopathological parameter for breast cancer prognostication and treatment. Although the development of metastasis is common in axillary lymph nodes, the mechanisms underlying the locoregional spread are yet poorly understood. In the present study, we outline the involvement of proteins in tumor invasion by comparing the proteome profile of primary breast tumors (PBT) against that of lymph node metastasis (LNM). PATIENTS AND METHODS: The comparative proteome analyses of seven paired samples were performed using two-dimensional gel electrophoresis (2DE) and mass spectrometry (MS). RESULTS: Recurrent proteins were differentially expressed in PBT and LNM across patients. Higher levels of 1433G, 1433T, K2C8, PSME2, SNAA, TPM4, TRFE and VIME were observed in primary tumors compared to the metastatic site. On the other hand, higher levels of ALDH2 and GDIR2 were identified in metastasis related to tumors. These proteins provide a new insight on breast cancer research. CONCLUSION: Our achievements strengthened previous omics-based studies and also support the validation of potential markers of tumor invasion and metastasis.

The IMPAKT of breast cancer research: fundamental science and applied medicine.

The IMPAKT 2015 Breast Cancer Conference was designed for researchers and clinicians by the Breast International Group (BIG) and the European Society for Medical Oncology (ESMO). The event was held on 7-9 May in Brussels, Belgium, bringing together approximately 525 participants with a special interest in translational science and state-of-the-art applications in the clinical setting. Oncologists, pathologists and scientists collaborated to develop innovative ideas about breast cancer research and to enhance its relevance to patient care. This report highlights the most recent discussions in fundamental research and future clinical perspectives presented by professionals from around the world. It also covers the important issues regarding new technologies for biomarker discovery and the actual path to clinical utility.