Inhibition of poly(ADP-ribose) polymerase (PARP) enzymes is a potential synthetic lethal therapeutic strategy in cancers harbouring specific DNA-repair defects, including those arising in carriers of BRCA1 or BRCA2 mutations. Since the development of first-generation PARP inhibitors more than a decade ago, numerous clinical trials have been performed to validate their safety and efficacy, bringing us to the stage at which adjuvant therapy with PARP inhibitors is now being considered as a viable treatment option for patients with breast cancer. Nevertheless, the available data do not provide clear proof that these drugs are efficacious in the setting of metastatic disease. Advancement of a therapy to the neoadjuvant and adjuvant settings without such evidence is exceptional, but seems reasonable in the case of PARP inhibitors because the target population that might benefit from this class of drugs is small and well defined. This Review describes the evolution of PARP inhibitors from bench to bedside, and provides an up-to-date description of the key published or otherwise reported clinical trials of these agents. The specific considerations and challenges that might be encountered when implementing these compounds in the adjuvant treatment of breast cancer in the clinic are also highlighted.

PURPOSE Young women with germline BRCA mutations have unique reproductive challenges. Pregnancy after breast cancer does not increase the risk of recurrence; however, very limited data are available in patients with BRCA mutations. This study investigated the impact of pregnancy on breast cancer outcomes in patients with germline BRCA mutations. PATIENTS AND METHODS This is an international, multicenter, hospital-based, retrospective cohort study. Eligible patients were diagnosed between January 2000 and December 2012 with invasive early breast cancer at age ≤ 40 years and harbored deleterious germline BRCA mutations. Primary end points were pregnancy rate, and disease-free survival (DFS) between patients with and without a pregnancy after breast cancer. Pregnancy outcomes and overall survival (OS) were secondary end points. Survival analyses were adjusted for guarantee-time bias controlling for known prognostic factors. RESULTS Of 1,252 patients with germline BRCA mutations ( BRCA1, 811 patients; BRCA2, 430 patients; BRCA1/2, 11 patients) included, 195 had at least 1 pregnancy after breast cancer (pregnancy rate at 10 years, 19%; 95% CI, 17% to 22%). Induced abortions and miscarriages occurred in 16 (8.2%) and 20 (10.3%) patients, respectively. Among the 150 patients who gave birth (76.9%; 170 babies), pregnancy complications and congenital anomalies occurred in 13 (11.6%) and 2 (1.8%) cases, respectively. Median follow-up from breast cancer diagnosis was 8.3 years. No differences in DFS (adjusted hazard ratio [HR], 0.87; 95% CI, 0.61 to 1.23; P = .41) or OS (adjusted HR, 0.88; 95% CI, 0.50 to 1.56; P = .66) were observed between the pregnancy and nonpregnancy cohorts. CONCLUSION Pregnancy after breast cancer in patients with germline BRCA mutations is safe without apparent worsening of maternal prognosis and is associated with favorable fetal outcomes. These results provide reassurance to patients with BRCA-mutated breast cancer interested in future fertility.

The past years have witnessed a rapid increase in the amount of large-scale tumor datasets. The challenge has now become to find a way to obtain useful information from these masses of data that will allow to determine which combination of FDA-approved drugs is best suited to treat the specific tumor. Various statistical analyses are being developed to extract significant signals from cancer datasets. However, tumors are still being assigned to pre-defined categories (breast luminal A, triple negative, etc.), conceptually contradicting the vast heterogeneity that is known to exist among tumors, and likely overlooking unique tumors that must be addressed and treated individually. We present herein an approach based on information theory that, rather than searches for what makes a tumor similar to other tumors, addresses tumors individually and unbiasedly, and impartially decodes the critical patient-specific molecular network reorganization in every tumor. Methods : Using a large dataset obtained from ~3500 tumors of 11 types we decipher the altered protein network structure in each tumor, namely the patient-specific signaling signature. Each signature can harbor several altered protein subnetworks. We suggest that simultaneous targeting of central proteins from every altered subnetwork is essential to efficiently disturb the altered signaling in each tumor. We experimentally validate our ability to dissect sample-specific signaling signatures and to rationally design personalized drug combinations. Results : We unraveled a surprisingly simple order that underlies the extreme apparent complexity of tumor tissues, demonstrating that only 17 altered protein subnetworks characterize ~3500 tumors of 11 types. Each tumor was described by a specific subset of 1-4 subnetworks out of 17, i.e. a tumor-specific altered signaling signature. We show that the majority of tumor-specific signaling signatures are extremely rare, and are shared by only 5 tumors or less, supporting a personalized, comprehensive study of tumors in order to design the optimal combination therapy for every patient. We validate the results by confirming that the processes identified in the 11 original cancer types characterize patients harboring a different cancer type as well. We show experimentally, using different cancer cell lines, that the individualized combination therapies predicted by us achieved higher rates of killing than the clinically prescribed treatments. Conclusions : We present a new strategy to deal with the inter-tumor heterogeneity and to break down the high complexity of cancer systems into simple, easy to crack, patient-specific signaling signatures that guide the rational design of personalized drug therapies.

Previous studies have suggested an association between metformin use and improved outcome in patients with diabetes and breast cancer. In the current study, we aimed to explore this association in human epidermal growth factor receptor 2 (HER2 ) -positive primary breast cancer in the context of a large, phase III adjuvant trial. Patients and MethodsThe ALTTO trial randomly assigned patients with HER2-positive breast cancer to receive 1 year of either trastuzumab alone, lapatinib alone, their sequence, or their combination. In this substudy, we evaluated whether patients with diabetes at study entry-with or without metformin treatment-were associated with different disease-free survival (DFS), distant disease-free survival (DDFS), and overall survival (OS) compared with patients without diabetes. ResultsA total of 8,381 patients were included in the current analysis: 7,935 patients (94.7%) had no history of diabetes at diagnosis, 186 patients (2.2%) had diabetes with no metformin treatment, and 260 patients (3.1%) were diabetic and had been treated with metformin. Median follow-up was 4.5 years (0.16 to 6.31 years), at which 1,205 (14.38%), 929 (11.08%), and 528 (6.3%) patients experienced DFS, DDFS, and OS events, respectively. Patients with diabetes who had not been treated with metformin experienced worse DFS (multivariable hazard ratio [HR], 1.40; 95% CI, 1.01 to 1.94; P = .043), DDFS (multivariable HR, 1.56; 95% CI, 1.10 to 2.22; P = .013), and OS (multivariable HR, 1.87; 95% CI, 1.23 to 2.85; P = .004). This effect was limited to hormone receptor-positive patients. Whereas insulin treatment was associated with a detrimental effect, metformin had a salutary effect in patients with diabetes who had HER2-positive and hormone receptor-positive breast cancer. Conclusion Metformin may improve the worse prognosis that is associated with diabetes and insulin treatment, mainly in patients with primary HER2-positive and hormone receptor-positive breast cancer.

Microphthalmia transcription factor (MITF) and STAT3 are two transcription factors that play a major role in the regulation of growth and function in mast cells and melanocytes. In the present study, we explored the MITF-PIAS3-STAT3 network of interactions, how these interactions regulate gene expression, and how cytokine-mediated phosphorylation of MITF and STAT3 is involved in the in vivo interplay between these three proteins. In NIH 3T3 cells stimulated via gp130 receptor, transfected MITF was found to be phosphorylated at S409. Such phosphorylation of MITF leads to PIAS3 dissociation from MITF and its association with STAT3. Activation of mouse melanoma and mast cells through gp130 or c-Kit receptors induced the mobilization of PIAS3 from MITF to STAT3. In mast cells derived from MITF di/di mice, whose MITF lacks the Zip domain (PIAS3-binding domain), we found downregulation in mRNA levels of genes regulated by either MITF or STAT3. This regulatory mechanism is of considerable importance since it is likely to advance the deciphering of a role for MITF and STAT3 in mast cells and melanocytes.Microphthalmia transcription factor (MITF) is a basic helixloop-helix leucine zipper (bHLH-Zip) DNA-binding protein (17). Its gene resides at the mi locus in mice (19), and mutation of this gene results in deafness, bone loss, small eyes, and poorly pigmented eyes and skin (32). The primary cell types affected in MITF-deficient mice are mast cells, osteoclasts, and melanocytes (32). In humans, mutation in this gene causes Waardenburg syndrome type II (40). MITF regulates the expression of mouse mast cell protease 6 (mMCP) (35), mMCP5 (33), c-Kit (20), p75 nerve growth factor (33), granzyme B (9), and tryptophan hydroxylase (21). MITF regulates gene transcription by binding to E-box-type enhancers in the 5Ј-flanking regions of MITF-responsive genes (35). Like many other DNA-binding proteins, the transcription-enhancing activity of MITF is influenced in a complex manner by an array of different intracellular proteins. We previously identified two MITF-interacting proteins, PKCI/Hint (37) and PIAS3 (27), by using the yeast two-hybrid system with the bHLH-Zip domain as a bait. These two MITF-associated proteins were shown to be repressors of MITF transcriptional activity (27,37). We have also shown by pull-down assay and by using cells derived from MITF truncated mice (di/di) that the MITF Zip domain plays an essential role in the interaction between MITF and PIAS3 (28).Three serine sites for MITF phosphorylation have been reported (14,42,46). Phosphorylation of S73 (14) and S409 (46) occurs as a result of kit ligand stimulation and activation of mitogen-activated protein kinase and Rsk-1, respectively. These serine sites are of interest since, upon their phosphorylation, MITF transcriptional activity is upregulated, and this phosphorylation also serves as a signal for the degradation of MITF by ubiquitin-dependent proteolysis in melanocytes (46).In our previous study, we observed that phosphorylation of MITF at S73 and S409...

Breast cancer classification has been the focus of numerous worldwide efforts, analyzing the molecular basis of breast cancer subtypes and aiming to associate them with clinical outcome and to improve the current diagnostic routine. Genomic and transcriptomic profiles of breast cancer have been well established, however the proteomic contribution to these profiles has yet to be elucidated. In this work, we utilized mass spectrometry-based proteomic analysis on more than 130 clinical breast samples to demonstrate intertumor heterogeneity across three breast cancer subtypes and healthy tissue. Unsupervised analysis identified four proteomic clusters, among them, one that represents a novel luminal subtype characterized by increased PI3K signaling. This subtype was further validated using an independent protein-based dataset, but not in two independent transcriptome cohorts. These results demonstrate the importance of deep proteomic analysis, which may affect cancer treatment decision making. These findings utilize extensive proteomics to identify a novel luminal breast cancer subtype, highlighting the added value of clinical proteomics in breast cancer to identify unique features not observable by genomic approaches. .

BackgroundThe likelihood of recurrence in patients with breast cancer who have HER2-positive tumors is relatively high, although trastuzumab is a remarkably effective drug in this setting. Signal transducer and activator of transcription 3 protein (STAT3), a transcription factor that is persistently tyrosine-705 phosphorylated (pSTAT3) in response to numerous oncogenic signaling pathways, activates downstream proliferative and anti-apoptotic pathways. We hypothesized that pSTAT3 expression in HER2-positive breast cancer will confer trastuzumab resistance.MethodsWe integrated reverse phase protein array (RPPA) and gene expression data from patients with HER2-positive breast cancer treated with trastuzumab in the adjuvant setting.ResultsWe show that a pSTAT3-associated gene signature (pSTAT3-GS) is able to predict pSTAT3 status in an independent dataset (TCGA; AUC = 0.77, P = 0.02). This suggests that STAT3 induces a characteristic set of gene expression changes in HER2-positive cancers. Tumors characterized as high pSTAT3-GS were associated with trastuzumab resistance (log rank P = 0.049). These results were confirmed using data from the prospective, randomized controlled FinHer study, where the effect was especially prominent in HER2-positive estrogen receptor (ER)-negative tumors (interaction test P = 0.02). Of interest, constitutively activated pSTAT3 tumors were associated with loss of PTEN, elevated IL6, and stromal reactivation.ConclusionsThis study provides compelling evidence for a link between pSTAT3 and trastuzumab resistance in HER2-positive primary breast cancers. Our results suggest that it may be valuable to add agents targeting the STAT3 pathway to trastuzumab for treatment of HER2-positive breast cancer.Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-015-0416-2) contains supplementary material, which is available to authorized users.

Mutation of microphthalmia transcription factor (MITF) results in deafness, bone loss, small eyes, and poorly pigmented eyes and skin. A search for MITF-associated proteins, using a mast cell library that was screened with a construct that encodes the basic helix-loop-helix leucine zipper (Zip) domain of MITF, resulted in the isolation of the STAT3 inhibitor, PIAS3. PIAS3 functions in vivo as a key molecule in suppressing the transcriptional activity of MITF. Here, we report that the Zip domain is the region of MITF that is involved in the direct interaction between MITF and PIAS3. Additionally, we investigated the effect of phosphorylation of MITF on its interaction with PIAS3. We found that phosphorylation of MITF on serines in positions 73 and 409 plays an important role in its association with PIAS3. This effect was profound with phosphorylation on Ser409, which significantly reduced the inhibitory effect of PIAS3 on MITF and also modulated the transcriptional activity of MITF. Thus, phosphorylation of MITF could be considered a fine, and alternative, tuning of its transcriptional machinery.