Analysis of Prognostic Factors Influencing Survival and Recurrence in Breast Cancer: A Hybrid Machine Learning Approach

Purpose: The present study sought to identify prognostic factors for breast cancer survival and recurrence using a machine learning approach and electronic medical record data.Methods: We used a machine learning technique called feature selection to identify factors influencing breast cancer prognosis, and factors affecting survival and recurrence in a Cox regression model. Results: History of relapse, type of surgery, diagnostic route, SEER stage, and hormone therapy all affected breast cancer survival. Recurrence of breast cancer was affected by age, history of diabetes, breast reconstruction, pain, breast lumps, nipple discharge, and the presence of other symptoms. According to the survival analysis based on feature selection, patients with diabetes had a significantly higher risk of early recurrence of breast cancer (hazard ratio, 4.8; 95% confidence interval, 2.04–11.2, p < 0.05). Conclusions: The present study identified several factors associated with breast cancer prognosis. While survival was affected by the diagnostic route, recurrence was primarily influenced by breast cancer symptoms and other underlying health conditions. A more accurate and standardized model considering time-to-event data could be developed in the future to evaluate prognostic factors and predict prognoses, and for clinical validation

Germline variants and breast cancer survival in patients with distant metastases at primary breast cancer diagnosis

Breast cancer metastasis accounts for most of the deaths from breast cancer. Identification of germline variants associated with survival in aggressive types of breast cancer may inform understanding of breast cancer progression and assist treatment. In this analysis, we studied the associations between germline variants and breast cancer survival for patients with distant metastases at primary breast cancer diagnosis. We used data from the Breast Cancer Association Consortium (BCAC) including 1062 women of European ancestry with metastatic breast cancer, 606 of whom died of breast cancer. We identified two germline variants on chromosome 1, rs138569520 and rs146023652, significantly associated with breast cancer-specific survival (P = 3.19 × 10−8 and 4.42 × 10−8). In silico analysis suggested a potential regulatory effect of the variants on the nearby target genes SDE2 and H3F3A. However, the variants showed no evidence of association in a smaller replication dataset. The validation dataset was obtained from the SNPs to Risk of Metastasis (StoRM) study and included 293 patients with metastatic primary breast cancer at diagnosis. Ultimately, larger replication studies are needed to confirm the identified associations.

Effect of Estradiol as a Continuous Variable on Breast Cancer Survival by Menopausal Status: A Cohort Study in China

BackgroundHigh levels of circulating estradiol (E2) are associated with increased risk of breast cancer, whereas its relationship with breast cancer prognosis is still unclear. We studied the effect of E2 concentration on breast cancer survival among pre- menopausal and post- menopausal patients in China.MethodsWe evaluated this association among 8766 breast cancer cases diagnosed between 2005 and 2017 from the Tianjin Breast Cancer Cases Cohort. Levels of serum E2 were measured in pre-menopausal and post-menopausal women. Multivariable-adjusted Cox proportional hazards models were used to estimate hazard ratios (HR) and 95% confidence intervals (95% CI) between quartile of E2 levels and overall survival (OS) and progression-free survival (PFS) of breast cancer. The penalized spline was then used to test for non-linear relationships between E2 (continuous variable) and survival endpoints.ResultsA total of 612 deaths and 982 progressions occurred over follow-up through 2017. Compared to women in the quartile 3, the highest quartile of E2 was associated with reduced risk of both PFS in pre-menopausal women (HR=1.79, 95% CI: 1.17-2.75, P=0.008) and OS in post-menopausal women (HR=1.35, 95% CI: 1.04-1.74, P=0.023). OS and PFS in pre-menopausal women exhibited a nonlinear relation (“L-shaped” and “U-shaped”, respectively) with E2 levels. However, there was a linear relationship in post-menopausal women, among whom increasing E2 was associated with escalating risks of death and progression. Moreover, patients with estrogen receptor-negative (ER-negative) breast cancer showed a “U-shaped” relationship with OS and PFS in pre-menopausal women.ConclusionsPre-menopausal breast cancer patients have a plateau stage of prognosis at the intermediate concentrations of E2, whereas post-menopausal patients have no apparent threshold, and ER status may have an impact on this relationship.

The comparison of censored quantile regression methods in prognosis factors of breast cancer survival

The Cox proportional hazards model is a widely used statistical method for the censored data that model the hazard rate rather than survival time. To overcome complexity of interpreting hazard ratio, quantile regression was introduced for censored data with more straightforward interpretation. Different methods for analyzing censored data using quantile regression model, have been introduced. The quantile regression approach models the quantile function of failure time and investigates the covariate effects in different quantiles. In this model, the covariate effects can be changed for patients with different risk and is a flexible model for controlling the heterogeneity of covariate effects. We illustrated and compared five methods in quantile regression for right censored data included Portnoy, Wang and Wang, Bottai and Zhang, Yang and De Backer methods. The comparison was made through the use of these methods in modeling the survival time of breast cancer. According to the results of quantile regression models, tumor grade and stage of the disease were identified as significant factors affecting 20th percentile of survival time. In Bottai and Zhang method, 20th percentile of survival time for a case with higher unit of stage decreased about 14 months and 20th percentile of survival time for a case with higher grade decreased about 13 months. The quantile regression models acted the same to determine prognostic factors of breast cancer survival in most of the time. The estimated coefficients of five methods were close to each other for quantiles lower than 0.1 and they were different from quantiles upper than 0.1.

Yap-Hippo Signaling Activates Mitochondrial Protection and Sustains Breast Cancer Viability under Hypoxic Stress

Yes-associated protein (Yap) is a transcriptional regulator that upregulates oncogenes and downregulates tumor repressor genes. In this study, we analyzed protein expression, RNA transcription, and signaling pathways to determine the function and mechanism of Yap in breast cancer survival during hypoxic stress. Yap transcription was drastically upregulated by hypoxia in a time-dependent manner. siRNA-mediated Yap knockdown attenuated breast cancer viability and impaired cell proliferation under hypoxic conditions. Yap knockdown induced mitochondrial stress, including mitochondrial membrane potential reduction, mitochondrial oxidative stress, and ATP exhaustion after exposure to hypoxia. It also repressed mitochondrial protective systems, including mitophagy and mitochondrial fusion upon exposure to hypoxia. Finally, our data showed that Yap knockdown suppresses MCF-7 cell migration by inhibiting F-actin transcription and promoting lamellipodium degradation under hypoxic stress. Taken together, Yap maintenance of mitochondrial function and activation of F-actin/lamellipodium signaling is required for breast cancer survival, migration, and proliferation under hypoxic stress.

An Artificial Intelligence-Enabled Pipeline for Medical Domain: Malaysian Breast Cancer Survivorship Cohort as a Case Study

Automated artificial intelligence (AI) systems enable the integration of different types of data from various sources for clinical decision-making. The aim of this study is to propose a pipeline to develop a fully automated clinician-friendly AI-enabled database platform for breast cancer survival prediction. A case study of breast cancer survival cohort from the University Malaya Medical Centre was used to develop and evaluate the pipeline. A relational database and a fully automated system were developed by integrating the database with analytical modules (machine learning, automated scoring for quality of life, and interactive visualization). The developed pipeline, iSurvive has helped in enhancing data management as well as to visualize important prognostic variables and survival rates. The embedded automated scoring module demonstrated quality of life of patients whereas the interactive visualizations could be used by clinicians to facilitate communication with patients. The pipeline proposed in this study is a one-stop center to manage data, to automate analytics using machine learning, to automate scoring and to produce explainable interactive visuals to enhance clinician-patient communication along the survivorship period to modify behaviours that relate to prognosis. The pipeline proposed can be modelled on any disease not limited to breast cancer.