Phosphatidylinositol 3-kinase (PI3K), protein kinase B (PKB/AKT) and mechanistic target of rapamycin (mTOR) (PAM) pathways play important roles in breast tumorigenesis and confer worse prognosis in breast cancer patients. The inhibitors targeting three key nodes of these pathways, PI3K, AKT and mTOR, are continuously developed. For breast cancer patients to truly benefit from PAM pathway inhibitors, it is necessary to clarify the frequency and mechanism of abnormal alterations in the PAM pathway in different breast cancer subtypes, and further explore reliable biomarkers to identify the appropriate population for precision therapy. Some PI3K and mTOR inhibitors have been approved by regulatory authorities for the treatment of specific breast cancer patient populations, and many new-generation PI3K/mTOR inhibitors and AKT isoform inhibitors have also been shown to have good prospects for cancer therapy. This review summarizes the changes in the PAM signaling pathway in different subtypes of breast cancer, and the latest research progress about the biomarkers and clinical application of PAM-targeted inhibitors.

Objective: Some excellent prognostic models based on survival analysis methods for breast cancer have been proposed and extensively validated, which provide an essential means for clinical diagnosis and treatment to improve patient survival. To analyze clinical and follow-up data of 12119 breast cancer patients, derived from the Clinical Research Center for Breast (CRCB) in West China Hospital of Sichuan University, we developed a gradient boosting algorithm, called EXSA, by optimizing survival analysis of XGBoost framework for ties to predict the disease progression of breast cancer. Methods: EXSA is based on the XGBoost framework in machine learning and the Cox proportional hazards model in survival analysis. By taking Efron approximation of partial likelihood function as a learning objective for ties, EXSA derives gradient formulas of a more precise approximation. It optimizes and enhances the ability of XGBoost for survival data with ties. After retaining 4575 patients (3202 cases for training, 1373 cases for test), we exploit the developed EXSA method to build an excellent prognostic model to estimate disease progress. Risk score of disease progress is evaluated by the model, and the risk grouping and continuous functions between risk scores and disease progress rate at 5- and 10-year are also demonstrated. Results: Experimental results on test set show that the EXSA method achieves competitive performance with concordance index of 0.83454, 5-year and 10-year AUC of 0.83851 and 0.78155, respectively. Conclusion: The proposed EXSA method can be utilized as an effective method for survival analysis. Significance: The proposed method in this paper can provide an important means for follow-up data of breast cancer or other disease research.

Background: Risk-based breast cancer screening is a cost-effective intervention for controlling breast cancer in China, but the successful implementation of such intervention requires an accurate breast cancer prediction model for Chinese women. Objective: This study aimed to evaluate and compare the performance of four machine learning algorithms on predicting breast cancer among Chinese women using 10 breast cancer risk factors. Methods: A dataset consisting of 7127 breast cancer cases and 7127 matched healthy controls was used for model training and testing. We used repeated 5-fold cross-validation and calculated AUC, sensitivity, specificity, and accuracy as the measures of the model performance. Results: The three novel machine-learning algorithms (XGBoost, Random Forest and Deep Neural Network) all achieved significantly higher area under the receiver operating characteristic curves (AUCs), sensitivity, and accuracy than logistic regression. Among the three novel machine learning algorithms, XGBoost (AUC 0.742) outperformed deep neural network (AUC 0.728) and random forest (AUC 0.728). Main residence, number of live births, menopause status, age, and age at first birth were considered as top-ranked variables in the three novel machine learning algorithms. Conclusions: The novel machine learning algorithms, especially XGBoost, can be used to develop breast cancer prediction models to help identify women at high risk for breast cancer in developing countries.

Little is known about how health insurance policies, particularly in developing countries, influence breast cancer prognosis. Here, we examined the association between individual health insurance and breast cancer‐specific mortality in China. We included 7436 women diagnosed with invasive breast cancer between 2009 and 2016, at West China Hospital, Sichuan University. The health insurance plan of patient was classified as either urban or rural schemes and was also categorized as reimbursement rate (ie, the covered/total charge) below or above the median. Breast cancer‐specific mortality was the primary outcome. Using Cox proportional hazards models, we calculated hazard ratios (HRs) for cancer‐specific mortality, contrasting rates among patients with a rural insurance scheme or low reimbursement rate to that of those with an urban insurance scheme or high reimbursement rate, respectively. During a median follow‐up of 3.1 years, we identified 326 deaths due to breast cancer. Compared to patients covered by urban insurance schemes, patients covered by rural insurance schemes had a 29% increased cancer‐specific mortality (95% CI 0%‐65%) after adjusting for demographics, tumor characteristics and treatment modes. Reimbursement rate below the median was associated with a 42% increased rate of cancer‐specific mortality (95% CI 11%‐82%). Every 10% increase in the reimbursement rate is associated with a 7% (95% CI 2%‐12%) reduction in cancer‐specific mortality risk, particularly in patients covered by rural insurance schemes (26%, 95% CI 9%‐39%). Our findings suggest that underinsured patients face a higher risk of breast cancer‐specific mortality in developing countries.

Purpose: Poly (ADP-ribose) polymerase (PARP) inhibitors have shown promising results in metastatic triple-negative breast cancers (TNBCs). We therefore performed a systematic review and meta-analysis to evaluate the efficacy and safety of this drug in patients with advanced or metastatic TNBC.Methods: A systematic literature search of PubMed, Embase, Scopus, Web of Science, and Cochrane Central Register of Controlled Trials for synonyms of “PARP inhibitors” and “breast cancer” was carried out. All published phase II/III clinical studies of PARP inhibitors in patients with advanced/metastatic TNBC were screened. Data were extracted independently by two authors and analyzed using Review Manager software version 5.3. End points include overall response rate (ORR), progression-free survival (PFS), and adverse events.Results: Ten clinical trials were identified, with a total of 1,495 patients included. Pooled analyses showed that PARP inhibitors could provide a significant improvement of ORR [risk ratio (RR) = 2.00; 95% confidence interval (CI), 1.14–3.50; p = 0.02) and PFS [hazard ratio (HR) = 0.68; 95%Cl, 0.59–0.77; p < 0.0001) compared to chemotherapy in the whole population. In subgroup analysis, patients with BRCA mutation had a higher objective response to PARP inhibitor, with an RR of 2.85 (95%CI, 1.34–6.06; p = 0.007) compared to BRCA wild-type patients. However, no significant difference in ORR was observed between the homologous recombination deficiency (HRD) positive and non-HRD subgroups (RR = 1.82; 95%CI, 0.81–4.08; p = 0.14). Hematological toxicity is a common adverse event of PARP inhibitors.Conclusions: PARP inhibitors are effective options for the treatment of patients with advanced or metastatic TNBC. Compared with patients without germline BRCA mutation, patients with germline BRCA mutation could benefit more from PARP inhibitors. In clinical setting, hematological toxicity associated with PARP inhibitors should be monitored regularly.

Neoadjuvant chemotherapy (NAC) is widely used in advanced breast cancer patients. However, there is little known about conversion frequency of estrogen receptor (ER) and/or progesterone receptor (PR) status for hormone receptor positive-breast cancer patients after NAC and their correlation with prognosis. In this study, 231 breast cancer patients with residual disease after NAC were enrolled and divided into receptor stable group (having no conversion in both ER and PR status pre- and post-NAC) and any receptor conversion group (having any conversion in either ER or PR status). Univariate and multivariate survival analyses were used to compare survival differences between the two groups. Fifty-five patients (23.8%) had ER and/or PR conversion after NAC. Younger patients (≤ 50 years) were more likely to have receptor conversion (P = 0.014). For 213 patients (92.2%) who received adjuvant endocrinotherapy after surgery, the 5-year disease free survival (DFS) estimates for patients in the any receptor conversion group (55.2%) was worse than patients in the receptor stable group (73.7%, Log-rank test, P = 0.015). While the 5-year overall survival estimates for patients with or without receptor conversion were not statistically different (86.0 vs. 82.4%, Log-rank test, P = 0.587). In multivariate Cox proportional hazard analyses, patients with any receptor conversion had worse DFS (hazard ratio, 1.995; 95% confidence interval, 1.130–3.521, P = 0.031). It is necessary to recommend patients to test biomarkers in residual disease and pay more attention to patients who have any receptor conversion. These patients may need more individual therapy after surgery.