• HR positive
• gut microbiome
• letrozole
• locally advanced breast cancer
• neoadjuvant
• ribociclib
• senescence
• single-cell RNA sequencing

• Adult
• Female
• Humans
• Middle Aged
• Aminopyridines/therapeutic use
• Aminopyridines/administration & dosage
• Antineoplastic Combined Chemotherapy Protocols/therapeutic use
• Breast Neoplasms/drug therapy
• Breast Neoplasms/pathology
• Letrozole/administration & dosage
• Letrozole/therapeutic use
• Neoadjuvant Therapy/methods
• Purines/administration & dosage
• Purines/therapeutic use
• Receptor, ErbB-2/metabolism
• Receptors, Estrogen/metabolism
• Treatment Outcome

• P30 CA008748 NCI NIH HHS

• Humans
• Female
• Breast Neoplasms/pathology
• Aromatase Inhibitors/pharmacology
• Aromatase Inhibitors/therapeutic use
• Peroxisomes/metabolism
• Peroxisomes/pathology
• Acetyl-CoA Carboxylase
• Lipid Droplets/metabolism
• Lipid Droplets/pathology
• Cell Line, Tumor
• Estrogens/metabolism
• Drug Resistance, Neoplasm

• DNA damage repair
• PRAME
• breast cancer
• machine learning
• single-cell

• Humans
• Female
• Breast Neoplasms/genetics
• Multiomics
• CD4-Positive T-Lymphocytes
• DNA Repair/genetics
• DNA Damage
• Antigens, Neoplasm

• breast cancer
• cell plasticity
• chromatin organization

• Humans
• Female
• Breast Neoplasms/genetics
• Breast Neoplasms/metabolism
• Cell Line, Tumor
• Breast/metabolism
• Chromatin/genetics
• Gene Expression Profiling
• Gene Expression Regulation, Neoplastic

• R01 CA206505 NCI NIH HHS
• R01 CA257502 NCI NIH HHS
• T32 GM135081 NIGMS NIH HHS
• R01CA257502 NIH HHS
• National Institutes of Health

• breast cancer
• cell biology

• Bayesian learning
• MCMC
• classification
• dynamic Bayesian networks
• gene expression
• time series

• Algorithms
• Bayes Theorem
• Computational Biology/methods
• Computer Simulation
• Gene Regulatory Networks

• 609883 European Research Council
• SystemsX.ch Research, Technology and Development

• CA12
• TFF3
• anthracycline
• breast cancer
• taxanes

In post-menopausal women, intra-mammary estrogen, which is converted from extra-ovarian estrone (E1), promotes the growth of breast cancer. Since the aromatase inhibitor letrozole does not suppress 17β-estradiol (E2) production from E1, high intra-mammary E1 concentrations impair letrozole's therapeutic efficacy. Progesterone receptor membrane component 1 (Pgrmc1) is a non-classical progesterone receptor associated with breast cancer progression. In the present study, we introduced a Pgrmc1 heterozygous knockout (hetero KO) murine model exhibiting low Pgrmc1 expression, and observed estrogen levels and steroidogenic gene expression. Naïve Pgrmc1 hetero KO mice exhibited low estrogen (E2 and E1) levels and low progesterone receptor (PR) expression, compared to wild-type mice. In contrast, Pgrmc1 hetero KO mice that have been ovariectomized (OVX), including letrozole-treated OVX mice (OVX-letrozole), exhibited high estrogen levels and PR expression. Increased extra-ovarian estrogen production in Pgrmc1 hetero KO mice was observed with the induction of steroid sulfatase (STS). In MCF-7 cell, letrozole suppressed PR expression, but PGRMC1 knockdown increased PR and STS expression. Our presented results highlight the important role of Pgrmc1 in modulating estrogen production when ovary-derived estrogen is limited, thereby suggesting a potential therapeutic approach for letrozole resistance.

• Pgrmc1
• estrogen
• extra-ovarian
• letrozole
• steroid sulfatase

• ER
• GSEA
• GSVA
• biomarker
• endocrine
• estrogen response
• survival
• treatment response

• R01CA160688 NIH HHS
• 19H03714 Scientific Research
• 18K19576 Scientific Research
• P30-CA016056 NCI NIH HHS

Machine learning (ML) methods still have limited applicability in personalized oncology due to low numbers of available clinically annotated molecular profiles. This doesn’t allow sufficient training of ML classifiers that could be used for improving molecular diagnostics.

We reviewed published datasets of high throughput gene expression profiles corresponding to cancer patients with known responses on chemotherapy treatments. We browsed Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Tumor Alterations Relevant for GEnomics-driven Therapy (TARGET) repositories.

We identified data collections suitable to build ML models for predicting responses on certain chemotherapeutic schemes. We identified 26 datasets, ranging from 41 till 508 cases per dataset. All the datasets identified were checked for ML applicability and robustness with leave-one-out cross validation. Twenty-three datasets were found suitable for using ML that had balanced numbers of treatment responder and non-responder cases.

We collected a database of gene expression profiles associated with clinical responses on chemotherapy for 2786 individual cancer cases. Among them seven datasets included RNA sequencing data (for 645 cases) and the others – microarray expression profiles. The cases represented breast cancer, lung cancer, low-grade glioma, endothelial carcinoma, multiple myeloma, adult leukemia, pediatric leukemia and kidney tumors. Chemotherapeutics included taxanes, bortezomib, vincristine, trastuzumab, letrozole, tipifarnib, temozolomide, busulfan and cyclophosphamide.

• Biomarkers detection
• Cancer
• Chemotherapy
• Clinical oncology
• Gene expression
• Machine learning
• Microarrays
• Molecular diagnostics
• Personalized medicine
• RNA sequencing
• Transcriptomics

Stress Granules (SGs) were discovered in 1999 and while the first decade of research has focused on some fundamental questions, the field is now investigating their role in human pathogenesis. Since then, evidences of a link between SGs and cancerology are accumulating in vitro and in vivo. In this work we summarized the role of SGs proteins in cancer development and their prognostic values. We find that level of expression of protein involved in SGs formation (and not mRNA level) could serve a prognostic marker in cancer. With this review we strongly suggest that SGs (proteins) could be targets of choice to block cancer development and counteract resistance to improve patients care.

Cancer treatments are constantly evolving with new approaches to improve patient outcomes. Despite progresses, too many patients remain refractory to treatment due to either the development of resistance to therapeutic drugs and/or metastasis occurrence. Growing evidence suggests that these two barriers are due to transient survival mechanisms that are similar to those observed during stress response. We review the literature and current available open databases to study the potential role of stress response and, most particularly, the involvement of Stress Granules (proteins) in cancer. We propose that Stress Granule proteins may have prognostic value for patients.

• CAPRIN-1
• G3BP1
• G3BP2
• TIA-1
• TIAR
• USP10
• biomarker
• cancer prognosis
• cell death
• metastasis
• pro-survival properties
• resistance
• stress granules

• Anthracyclines/pharmacology
• Antineoplastic Agents/pharmacology
• Biomarkers, Pharmacological/analysis
• Breast Neoplasms/drug therapy
• Breast Neoplasms/metabolism
• Cell Cycle Proteins/genetics
• Cell Cycle Proteins/metabolism
• Chemotherapy, Adjuvant/adverse effects
• Chemotherapy, Adjuvant/methods
• Drug Monitoring/methods
• Drug Resistance, Neoplasm
• Estrogen Receptor Antagonists/pharmacology
• Estrogen Receptor Modulators/pharmacology
• Female
• Gene Expression Profiling/methods
• Humans
• Middle Aged
• Neoplasm Staging
• Progression-Free Survival
• Receptors, Estrogen/metabolism

• 16942 Cancer Research UK

• Breast cancer
• Cancer therapeutic resistance

(1) Background: Machine learning (ML) methods are rarely used for an omics-based prescription of cancer drugs, due to shortage of case histories with clinical outcome supplemented by high-throughput molecular data. This causes overtraining and high vulnerability of most ML methods. Recently, we proposed a hybrid global-local approach to ML termed floating window projective separator (FloWPS) that avoids extrapolation in the feature space. Its core property is data trimming, i.e., sample-specific removal of irrelevant features. (2) Methods: Here, we applied FloWPS to seven popular ML methods, including linear SVM, k nearest neighbors (kNN), random forest (RF), Tikhonov (ridge) regression (RR), binomial naïve Bayes (BNB), adaptive boosting (ADA) and multi-layer perceptron (MLP). (3) Results: We performed computational experiments for 21 high throughput gene expression datasets (41–235 samples per dataset) totally representing 1778 cancer patients with known responses on chemotherapy treatments. FloWPS essentially improved the classifier quality for all global ML methods (SVM, RF, BNB, ADA, MLP), where the area under the receiver-operator curve (ROC AUC) for the treatment response classifiers increased from 0.61–0.88 range to 0.70–0.94. We tested FloWPS-empowered methods for overtraining by interrogating the importance of different features for different ML methods in the same model datasets. (4) Conclusions: We showed that FloWPS increases the correlation of feature importance between the different ML methods, which indicates its robustness to overtraining. For all the datasets tested, the best performance of FloWPS data trimming was observed for the BNB method, which can be valuable for further building of ML classifiers in personalized oncology.

• bioinformatics
• chemotherapy
• machine learning
• omics profiling
• oncology
• personalized medicine

• Antineoplastic Agents/therapeutic use
• High-Throughput Screening Assays/methods
• Humans
• Machine Learning
• Medical Oncology/methods
• Neoplasms/drug therapy
• Precision Medicine/methods

• Dormancy
• Epigenetics
• Letrozole
• Microarray
• Oestrogen deprivation therapy
• Proteomics
• Resistance
• Sequential samples

Invasive lobular breast carcinoma (ILC) is a histological subtype of breast cancer that is characterized by loss of E-cadherin and high expression of estrogen receptor alpha (ERα). In many cases, ILC is effectively treated with adjuvant aromatase inhibitors (AIs); however, acquired AI resistance remains a significant problem.

To identify underlying mechanisms of acquired anti-estrogen resistance in ILC, we recently developed six long-term estrogen-deprived (LTED) variant cell lines from the human ILC cell lines SUM44PE (SUM44; two lines) and MDA-MB-134VI (MM134; four lines). To better understand mechanisms of AI resistance in these models, we performed transcriptional profiling analysis by RNA-sequencing followed by candidate gene expression and functional studies.

MM134 LTED cells expressed ER at a decreased level and lost growth response to estradiol, while SUM44 LTED cells retained partial ER activity. Our transcriptional profiling analysis identified shared activation of lipid metabolism across all six independent models. However, the underlying basis of this signature was distinct between models. Oxysterols were able to promote the proliferation of SUM44 LTED cells but not MM134 LTED cells. In contrast, MM134 LTED cells displayed a high expression of the sterol regulatory element-binding protein 1 (SREBP1), a regulator of fatty acid and cholesterol synthesis, and were hypersensitive to genetic or pharmacological inhibition of SREBPs. Several SREBP1 downstream targets involved in fatty acid synthesis, including FASN, were induced, and MM134 LTED cells were more sensitive to etomoxir, an inhibitor of the rate-limiting enzyme in beta-oxidation, than their respective parental control cells. Finally, in silico expression analysis in clinical specimens from a neo-adjuvant endocrine trial showed a significant association between the increase of SREBP1 expression and lack of clinical response, providing further support for a role of SREBP1 in the acquisition of endocrine resistance in breast cancer.

Our characterization of a unique series of AI-resistant ILC models identifies the activation of key regulators of fatty acid and cholesterol metabolism, implicating lipid-metabolic processes driving estrogen-independent growth of ILC cells. Targeting these changes may prove a strategy for prevention and treatment of endocrine resistance for patients with ILC.

The online version of this article (10.1186/s13058-018-1041-8) contains supplementary material, which is available to authorized users.

• Cholesterol
• Endocrine resistance
• Fatty acid
• Invasive lobular breast
• LTED
• SREBP1

Ongoing clinical and research efforts seek to optimise the use of endocrine therapy in the treatment of breast cancer. Accurate biomarkers are needed that predict response for individual patients. The presence of the estrogen receptor (ER) as the direct (for tamoxifen and fulvestrant) or indirect (for aromatase inhibitors) target molecule for endocrine therapy remains the foremost biomarker and determinant of response. However, ER expression only poorly predicts outcome and further indicators of response or resistance are required. The development and application of molecular signature assays such as Oncotype Dx, Prosigna, Mammaprint and Endopredict have provided valuable information on prognosis and these are being used to support clinical decision making on whether endocrine therapy alone alongside surgery is sufficient for ER-positive early stage breast cancers or whether combination of endocrine with chemotherapy are also warranted. Ki67, the proliferation marker, has been widely used in the neo-adjuvant (pre-operative) setting to help predict response and long term outcome. Gene expression studies within the same setting have allowed monitoring of changes of potential predictive markers. These have identified frequent changes in estrogen-regulated and proliferation genes. Specific molecules such as mutant ER may also prove helpful biomarkers in predicting outcome and monitoring response to treatment.

• Estrogen
• IL6ST
• Biomarker
• Breast cancer
• Predictive

• Estrogen receptor-positive breast cancer
• Gene expression signature
• Ki67 labeling index
• Neoadjuvant endocrine therapy
• Predictive marker

Recent advances in cancer immunology revealed immune-related properties of cancer cells as novel promising therapeutic targets. The two TNF superfamily members, APRIL (TNFSF13), and BAFF (TNFSF13B), which are type II membrane proteins, released in active forms by proteolytic cleavage and are primarily involved in B-lymphocyte maturation, have also been associated with tumor growth and aggressiveness in several solid tumors, including breast cancer. In the present work we studied the effect of APRIL and BAFF on epithelial to mesenchymal transition, migration, and stemness of breast cancer cells. Our findings show that both molecules increase epithelial to mesenchymal transition and migratory capacity of breast cancer cells, as well as cancer stem cell numbers, by increasing the expression of pluripotency genes such as ALDH1A1, KLF4, and NANOG. These effects are mediated by their common receptor BCMA (TNFRSF17) and the JNK signaling pathway. Interestingly, transcriptional data analysis from breast cancer cells and patients revealed that androgens can increase APRIL transcription and subsequently, in an autocrine/paracrine manner, enhance its pluripotency effect. In conclusion, our data suggest a possible role of APRIL and BAFF in breast cancer disease progression and provide evidence for a new possible mechanism of therapy resistance, that could be particularly relevant in aromatase inhibitors-treated patients, were local androgen is increased.

• APRIL (TNFSF13)
• BAFF (TNFSF13B)
• BCMA (TNFRSF17)
• androgen
• aromatase inhibitors
• cancer stem cells
• pluripotency
• therapy resistance

• Breast cancer
• Endocrinology
• Obesity
• Oncology
• growth factors

• Adipose Tissue/metabolism
• Adipose Tissue/pathology
• Animals
• Breast Neoplasms/etiology
• Breast Neoplasms/genetics
• Diet
• Disease Progression
• Estrogens/metabolism
• Female
• Gene Expression Regulation, Neoplastic
• Homeodomain Proteins/genetics
• Homeodomain Proteins/metabolism
• Humans
• Loss of Function Mutation
• Mice
• Obesity/complications
• Obesity/metabolism
• Obesity/pathology
• Receptor, Fibroblast Growth Factor, Type 1/genetics
• Receptor, Fibroblast Growth Factor, Type 1/metabolism
• Receptors, Estrogen/metabolism
• Signal Transduction
• Tamoxifen/therapeutic use
• Tumor Microenvironment
• Weight Gain

• R01 CA164166 NCI NIH HHS
• R01 CA140985 NCI NIH HHS
• KL2 TR002534 NCATS NIH HHS
• U54 CA210184 NCI NIH HHS
• K01 DK109079 NIDDK NIH HHS
• P50 HD073063 NICHD NIH HHS
• R01 CA215797 NCI NIH HHS
• UL1 TR001082 NCATS NIH HHS
• P30 DK048520 NIDDK NIH HHS
• R01 CA205044 NCI NIH HHS
• P30 CA008748 NCI NIH HHS
• P30 CA046934 NCI NIH HHS
• National Cancer Institute
• Susan G. Komen
• National Institute of Diabetes and Digestive and Kidney Diseases
• National Center for Advancing Translational Sciences

One important use of genome-wide transcriptional profiles is to identify relationships between transcription levels and patient outcomes. These translational insights can guide the development of biomarkers for clinical application. Data from thousands of translational-biomarker studies have been deposited in public repositories, enabling reuse. However, data-reuse efforts require considerable time and expertise because transcriptional data are generated using heterogeneous profiling technologies, preprocessed using diverse normalization procedures, and annotated in non-standard ways. To address this problem, we curated 45 publicly available, translational-biomarker datasets from a variety of human diseases. To increase the data's utility, we reprocessed the raw expression data using a uniform computational pipeline, addressed quality-control problems, mapped the clinical annotations to a controlled vocabulary, and prepared consistently structured, analysis-ready data files. These data, along with scripts we used to prepare the data, are available in a public repository. We believe these data will be particularly useful to researchers seeking to perform benchmarking studies—for example, to compare and optimize machine-learning algorithms' ability to predict biomedical outcomes.

The purpose of this study was to test whether immunohistochemical (IHC) Ki67 levels after short-term preoperative hormone therapy (post-Ki67) predict similar numbers of patients with favorable prognoses as genomic markers.

Thirty paired cases (60 samples) were enrolled in this study. Post-Ki67 levels were significantly lower than pre-treatment Ki67 levels (P < 0.001). Post-Ki67 predicted more low-risk cases (83.3%, 25/30) than pre-genomic surrogate signature(GSS) (66.7%: 20/30), but the difference in predictive power was not significant (P = 0.233). Proliferation (MKI67, STK15, Survivin, CCNB1, and MYBL2) and estrogen (ER, PGR, BCL2, and SCUBE2) related signatures were significantly downregulated after therapy (P < 0.001 and 0.041, respectively).

Core needle biopsy specimens of primary breast cancer were collected at Okayama University Hospital from hormone receptor-positive and human epidermal growth factor 2-negative patients that subsequently received two weeks of neoadjuvant hormone therapy. Paired post-treatment specimens from surgical samples were also collected. IHC Ki67 levels and GSS were compared between pre- and post-hormone treatment samples. Changes of gene expression pattern in short-term hormone therapy were also assessed.

IHC based post-Ki67 levels may have distinct predictive power compared with the naïve IHC Ki67. Future studies with larger cohorts and longer follow-up periods may be needed to validate our results.

• IHC Ki67
• breast cancer
• genomic marker
• short-term hormone therapy

For premenopausal women with primary ER + breast cancer, oophorectomy (OvX) is an evidence-based cost-effective option and is standard treatment in many countries. However, there is virtually no data describing the effects of OvX on breast tumour biology. We therefore, characterised the endocrine and genome-wide transcriptional impact of OvX in 56 premenopausal women with ER + breast cancer for 2 weeks prior to mastectomy. Plasma estradiol concentrations decreased from 406 ± 41 to 20.7 ± 2.6 pmol/l (mean ± sem) 24 h after OvX, and to 8.1 ± 0.8 pmol/l 2 weeks later at mastectomy. Ki67 decreased in 33/36 (91.7%) tumours. The expression of 655 genes changed significantly (FDR < 1%) with an absolute mean fold-change (FC) ≥ 1.25 (257 up, 398 down). Archetypal oestrogen-regulated genes (TFF1, GREB1, PGR and PDZK1) showed large decreases in expression (FC = 0.20–0.69; p < 1e-6-1e-7). Proliferation-associated genes (e.g. TOP2A, AURKA and UBE2C) were also strongly downregulated (FC = 0.38–0.56; p < 1e-7) along with putative progesterone-regulated genes (e.g. FKBP4, MYB; FC = 0.64–0.68; p < 1e-4-1e-7). The gene expression changes did not differ according to HER2 status and correlated strongly with the changes reported previously after aromatase inhibitor (AI) treatment in postmenopausal women (rho = 0.55, p < 1e-04). However, after OvX the mean FC was significantly higher compared to AI (p < 1e-04). In conclusion, changes in tumoural gene expression after OvX were largely similar, but of a greater magnitude to those observed after AI in postmenopausal patients; however, OvX appeared to have a greater effect on progesterone-regulated genes than AI.

Surgical removal of the ovaries alters the expression of hundreds of genes in the tumour cells of premenopausal women with breast cancer. Ben Haynes from Royal Marsden Hospital in London, UK and colleagues characterised molecular changes in 56 premenopausal women from Vietnam with oestrogen receptor-positive breast cancer who underwent oophorectomies, a standard treatment for this patient population. They showed that blood levels of the hormone estradiol dropped precipitously following ovary-removal surgery. Levels of a protein that was indicative of tumour growth also went down, as did genes involved in regulating hormone signalling and cell proliferation. The results are consistent with those seen in postmenopausal women following treatment with an oestrogen-blocking drug, but oophorectomy had a more dramatic effect. The data could aid the search for predictive biomarkers of who stands to benefit most from ovary removal.

• Breast cancer
• Breast conserving surgery
• Endocrine therapy
• Estrogen receptor-positive
• Neoadjuvant treatment

The present explorative study was initiated to evaluate the clinical value of ¹⁸F-FES PET/CT in monitoring the change of estrogen receptor (ER) expression and potential predictive value in metastatic breast cancer patients. Twenty-two pathology-confirmed breast cancer patients were prospectively enrolled and randomly divided into two groups (T: docetaxel, n = 14 and TF: docetaxel + fulvestrant, n = 8). The percentage of patients without disease progression after 12 months (PFS > 12 months) was 62.5% in group TF compared with 21.4% in group T (P = 0.08). According to ¹⁸F-FES PET/CT scans, the SUVmax (maximum standard uptake value) of all the metastatic lesions decreased in group TF after 2 cycles of treatment (6 weeks ± 3 days). However, 6 of 9 patients in group T had at least one lesion with higher post-treatment SUVmax. There was a significant difference in the reduction of ER expression between these two groups (P = 0.028). In group TF, the patients with PFS > 12 months had significantly greater SUVmax changes of ¹⁸F-FES than those with PFS < 12 months (PFS > 12 months: 91.0 ± 12.0% versus PFS < 12 months: 20.7 ± 16.2%; t = −4.64, P = 0.01). Our preliminary study showed that ¹⁸F-FES PET/CT, as a noninvasive method to monitor ER expression, could be utilized to predict prognosis based on changes in SUVmax.

Breast cancer remains a common malignancy in women, but the take-up for breast cancer screening programs in Japan is still low, possibly due to its perceived inconvenience. TFF1 and TFF3 are expressed in both breast cancer tissue and normal breast. Serum trefoil proteins were reported as cancer screening markers for gastric, prostate, lung, pancreatic cancer and cholangio carcinoma. The purpose of this study was to examine whether serum trefoil proteins could be screening biomarkers for breast cancer. Serum trefoil proteins in 94 breast cancer patients and 84 health check females were measured by ELISA. Serum TFF1 and TFF3 were significantly higher and serum TFF2 was significantly lower in breast cancer patients. Area under the curve of receiver operating characteristic of TFF1, TFF2, and TFF3 was 0.69, 0.83, and. 0.72, respectively. AUC of the combination of TFF1, TFF2, and TFF3 was 0.96. Immunohistochemically, TFF1 expression was positive in 56.5% and TFF3 was positive in 73.9% of breast cancers, while TFF2 was negative in all tumors. Serum TFF1 had positive correlation with expression of TFF1 in breast cancer tissue. Serum concentrations of TFF1 and TFF3 but not TFF2 are higher in women with breast cancer than in women without 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.

• breast cancer
• endocrine resistance
• metabolomics
• transcriptomics

DNA replication is a critical step in cell proliferation. Overexpression of MCM2-7 genes correlated with poor prognosis in breast cancer patients. However, the roles of Cdc6 and Cdt1, which work with MCMs to regulate DNA replication, in breast cancers are largely unknown. In the present study, we have shown that the expression levels of Cdc6 and Cdt1 were both significantly correlated with an increasing number of MCM2-7 genes overexpression. Both Cdc6 and Cdt1, when expressed in a high level, alone or in combination, were significantly associated with poorer survival in the breast cancer patient cohort (n = 1441). In line with this finding, the expression of Cdc6 and Cdt1 was upregulated in breast cancer cells compared to normal breast epithelial cells. Expression of Cdc6 and Cdt1 was significantly higher in ER negative breast cancer, and was suppressed when ER signalling was inhibited either by tamoxifen in vitro or letrozole in human subjects. Importantly, breast cancer patients who responded to letrozole expressed significantly lower Cdc6 than those patients who did not respond. Our results suggest that Cdc6 is a potential prognostic marker and therapeutic target in breast cancer patients.

• Breast cancer
• First line aromatase inhibitors therapy
• LRG1
• PIK3CA mutations

Interleukin-6 (IL-6) is an important growth factor for estrogen receptor-α (ERα)-positive breast cancer, and elevated serum IL-6 is associated with poor prognosis.

The role of the phosphorylated signal transducer and activator of transcription 3 pathway was investigated in ERα-positive breast cancer. A panel of cell lines was treated with exogenous IL-6. An IL-6 specific gene signature was generated by profiling ten ERα-positive breast cancer cell lines alone or following treatment with 10 ng/mL recombinant IL-6 or human marrow stromal cell-conditioned media, with or without siltuximab (a neutralizing anti-IL-6 antibody) and grown in three-dimensional tumor microenvironment-aligned cultures for 4 days, 5 days, or 6 days. The established IL-6 signature was validated against 36 human ERα-positive breast tumor samples with matched serum. A comparative MCF-7 xenograft murine model was utilized to determine the role of IL-6 in estrogen-supplemented ERα-positive breast cancer to assess the efficacy of anti-IL-6 therapy in vivo.

In eight of nine ERα-positive breast cancer cell lines, recombinant IL-6 increased phosphorylation of tyrosine 705 of STAT3. Differential gene expression analysis identified 17 genes that could be used to determine IL-6 pathway activation by combining their expression intensity into a pathway activation score. The gene signature included a variety of genes involved in immune cell function and migration, cell growth and apoptosis, and the tumor microenvironment. Validation of the IL-6 gene signature in 36 matched human serum and ERα-positive breast tumor samples showed that patients with a high IL-6 pathway activation score were also enriched for elevated serum IL-6 (≥10 pg/mL). When human IL-6 was provided in vivo, MCF-7 cells engrafted without the need for estrogen supplementation, and addition of estrogen to IL-6 did not further enhance engraftment. Subsequently, we prophylactically treated mice at MCF-7 engraftment with siltuximab, fulvestrant, or combination therapy. Siltuximab alone was able to blunt MCF-7 engraftment. Similarly, siltuximab alone induced regressions in 90% (9/10) of tumors, which were established in the presence which were established in the presence of hMSC expressing human IL-6 and estrogen.

Given the established role for IL-6 in ERα-positive breast cancer, these data demonstrate the potential for anti-IL-6 therapeutics in breast cancer.

• breast cancer
• estrogen receptor
• gene signature
• paracrine IL-6
• siltuximab

• Animals
• Antineoplastic Agents, Hormonal/pharmacology
• Antineoplastic Agents, Hormonal/therapeutic use
• Aromatase Inhibitors/pharmacology
• Aromatase Inhibitors/therapeutic use
• Biosynthetic Pathways/drug effects
• Biosynthetic Pathways/genetics
• Blotting, Western
• Breast Neoplasms/drug therapy
• Breast Neoplasms/genetics
• Breast Neoplasms/pathology
• Cholesterol/biosynthesis
• Chromatin Immunoprecipitation
• Drug Resistance, Neoplasm/drug effects
• Drug Resistance, Neoplasm/genetics
• Epigenesis, Genetic/genetics
• Estrogen Receptor alpha/metabolism
• Female
• Humans
• Hydroxycholesterols
• Hydroxymethylglutaryl-CoA Reductase Inhibitors/pharmacology
• Immunohistochemistry
• In Vitro Techniques
• MCF-7 Cells
• Mice
• Mice, SCID
• Neoplasm Invasiveness
• Neoplasm Transplantation
• Real-Time Polymerase Chain Reaction
• Up-Regulation

• Wellcome Trust
• 12011 Cancer Research UK
• 18784 Cancer Research UK
• 103034/Z/13/Z Wellcome Trust

• Aromatase inhibitors
• Biomarkers
• Breast cancer
• Endocrine resistance
• Estrogen receptor
• TFF3

• Aged
• Androstadienes/pharmacology
• Antineoplastic Agents/pharmacology
• Antineoplastic Agents/therapeutic use
• Aromatase Inhibitors/pharmacology
• Aromatase Inhibitors/therapeutic use
• Breast Neoplasms/drug therapy
• Breast Neoplasms/genetics
• Breast Neoplasms/metabolism
• Breast Neoplasms/pathology
• Cell Line, Tumor
• Chemotherapy, Adjuvant
• Drug Resistance, Neoplasm/genetics
• Eye Proteins/genetics
• Eye Proteins/metabolism
• Female
• Gene Expression Regulation, Neoplastic/drug effects
• Humans
• Letrozole
• Middle Aged
• Neoplasm Recurrence, Local/genetics
• Nitriles/therapeutic use
• Peptides/genetics
• Peptides/metabolism
• RGS Proteins/genetics
• RGS Proteins/metabolism
• Receptors, Estrogen/metabolism
• Retrospective Studies
• Transcription Factors/genetics
• Transcription Factors/metabolism
• Treatment Outcome
• Trefoil Factor-3
• Triazoles/therapeutic use

• Amphiregulin
• Animals
• Breast Neoplasms/metabolism
• Cell Line, Tumor
• EGF Family of Proteins/metabolism
• Enzyme-Linked Immunosorbent Assay
• Estrogen Receptor alpha/metabolism
• Estrogens/metabolism
• Female
• Heterografts
• Humans
• Immunohistochemistry
• Mice
• Reverse Transcriptase Polymerase Chain Reaction
• Signal Transduction/physiology
• Tissue Array Analysis

• K12 GM102779 NIGMS NIH HHS
• P30 CA013330 NCI NIH HHS
• 1K12GM102779-01 NIGMS NIH HHS

• Early breast cancer
• Endocrine treatment
• Neoadjuvant therapy
• Oestrogen-receptor positive

• Antineoplastic Agents, Hormonal/therapeutic use
• Apoptosis Regulatory Proteins/genetics
• Aromatase Inhibitors/therapeutic use
• Biomarkers, Tumor/genetics
• Biopsy
• Breast Neoplasms/diagnostic imaging
• Breast Neoplasms/drug therapy
• Breast Neoplasms/genetics
• Breast Neoplasms/pathology
• Chemotherapy, Adjuvant
• Cytokine Receptor gp130/genetics
• Disease-Free Survival
• Female
• Gene Expression Profiling/methods
• Gene Expression Regulation, Neoplastic
• Humans
• Immunohistochemistry
• Kaplan-Meier Estimate
• Letrozole
• Minichromosome Maintenance Complex Component 4/genetics
• Neoadjuvant Therapy
• Nerve Tissue Proteins/genetics
• Nitriles/therapeutic use
• Oligonucleotide Array Sequence Analysis
• Precision Medicine
• Predictive Value of Tests
• Reproducibility of Results
• Reverse Transcriptase Polymerase Chain Reaction
• Time Factors
• Treatment Outcome
• Triazoles/therapeutic use
• Ultrasonography

• 20406 Cancer Research UK

• Biomarkers, Tumor
• Breast Neoplasms/diagnosis
• Breast Neoplasms/genetics
• Breast Neoplasms/mortality
• Cluster Analysis
• Computational Biology/methods
• Datasets as Topic
• Female
• Gene Expression Profiling
• Gene Expression Regulation, Neoplastic
• Genomics/methods
• Humans
• Prognosis
• Reproducibility of Results
• Transcriptome