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.