A network analysis to identify mediators of germline-driven differences in breast cancer prognosis.

Identifying the underlying genetic drivers of the heritability of breast cancer prognosis remains elusive. We adapt a network-based approach to handle underpowered complex datasets to provide new insights into the potential function of germline variants in breast cancer prognosis. This network-based analysis studies ~7.3 million variants in 84,457 breast cancer patients in relation to breast cancer survival and confirms the results on 12,381 independent patients. Aggregating the prognostic effects of genetic variants across multiple genes, we identify four gene modules associated with survival in estrogen receptor (ER)-negative and one in ER-positive disease. The modules show biological enrichment for cancer-related processes such as G-alpha signaling, circadian clock, angiogenesis, and Rho-GTPases in apoptosis

Cigarette Smoking Before and After Breast Cancer Diagnosis: Mortality From Breast Cancer and Smoking-Related Diseases

Cigarette smoking increases overall mortality, but it is not established whether smoking is associated with breast cancer prognosis

The Prognostic Significance of Phosphatase and Tensin Homolog Loss in Breast Cancer

AIM: This study aims to determine the prognostic significance of phosphatase and tensin homolog (PTEN) loss in breast cancer. METHODS: We conducted a meta-analysis study. Sample of this study were research articles that evaluated PTEN loss and prognosis in breast cancer patients. We searched for relevant studies published in PubMed and Proquest from January 2010 to July 2018. We reviewed studies that examined the association between immunohistochemical expression of PTEN and breast cancer prognosis using meta-analysis methods. Pooled risk ratios (RR) were calculated using fixed and random-effect models. Data were processed using Review Manager 5.3 (RevMan 5.3). RESULTS: There were 7 studies conducted a systematic review then continued to evaluate the association of PTEN loss and breast cancer prognosis by meta-analysis. There was a significant association of PTEN loss with poor prognosis of breast cancer (RR = 0.76 [95% CI 0.59-0.98 p <0.07), and there was not any significant publication bias for studies included. CONCLUSION: This study confirmed PTEN loss is an important independent factor for breast cancer prognosis

Is breast cancer prognosis inherited?

Introduction: A genetic component is well established in the etiology of breast cancer. It is not well known, however, whether genetic traits also influence prognostic features of the malignant phenotype. Methods: We carried out a population-based cohort study in Sweden based on the nationwide Multi-Generation Register. Among all women with breast cancer diagnosed from 1961 to 2001, 2,787 mother-daughter pairs and 831 sister pairs with breast cancer were identified; we achieved complete follow-up and classified 5-year breast cancer-specific prognosis among proband (mother or oldest sister) into tertiles as poor, intermediary, or good. We used Kaplan-Meier estimates of survival proportions and Cox models to calculate relative risks of dying from breast cancer within 5 years depending on the proband's outcome. Results: The 5-year survival proportion among daughters whose mothers died within 5 years was 87% compared to 91% if the mother was alive (p = 0.03). Among sisters, the corresponding proportions were 70% and 88%, respectively (p = 0.001). After adjustment for potential confounders, daughters and sisters of a proband with poor prognosis had a 60% higher 5-year breast cancer mortality compared to those of a proband with good prognosis (hazard ratio [HR], 1.6; 95% confidence interval [CI], 1.2 to 2.2; p for trend 0.002). This association was slightly stronger among sisters (HR, 1.8; 95% CI, 1.0 to 3.4) than among daughters (HR, 1.6; 95% CI, 1.1 to 2.3). Conclusion: Breast cancer prognosis of a woman predicts the survival in her first-degree relatives with breast cancer. Our novel findings suggest that breast cancer prognosis might be inherited

A network analysis to identify mediators of germline-driven differences in breast cancer prognosis.

Identifying the underlying genetic drivers of the heritability of breast cancer prognosis remains elusive. We adapt a network-based approach to handle underpowered complex datasets to provide new insights into the potential function of germline variants in breast cancer prognosis. This network-based analysis studies ~7.3 million variants in 84,457 breast cancer patients in relation to breast cancer survival and confirms the results on 12,381 independent patients. Aggregating the prognostic effects of genetic variants across multiple genes, we identify four gene modules associated with survival in estrogen receptor (ER)-negative and one in ER-positive disease. The modules show biological enrichment for cancer-related processes such as G-alpha signaling, circadian clock, angiogenesis, and Rho-GTPases in apoptosis

A network analysis to identify mediators of germline-driven differences in breast cancer prognosis

cited By 0Identifying the underlying genetic drivers of the heritability of breast cancer prognosis remains elusive. We adapt a network-based approach to handle underpowered complex datasets to provide new insights into the potential function of germline variants in breast cancer prognosis. This network-based analysis studies similar to 7.3 million variants in 84,457 breast cancer patients in relation to breast cancer survival and confirms the results on 12,381 independent patients. Aggregating the prognostic effects of genetic variants across multiple genes, we identify four gene modules associated with survival in estrogen receptor (ER)-negative and one in ER-positive disease. The modules show biological enrichment for cancer-related processes such as G-alpha signaling, circadian clock, angiogenesis, and Rho-GTPases in apoptosis.Peer reviewe

Detection of gene pathways with predictive power for breast cancer prognosis

<p>Abstract</p> <p>Background</p> <p>Prognosis is of critical interest in breast cancer research. Biomedical studies suggest that genomic measurements may have independent predictive power for prognosis. Gene profiling studies have been conducted to search for predictive genomic measurements. Genes have the inherent pathway structure, where pathways are composed of multiple genes with coordinated functions. The goal of this study is to identify gene pathways with predictive power for breast cancer prognosis. Since our goal is fundamentally different from that of existing studies, a new pathway analysis method is proposed.</p> <p>Results</p> <p>The new method advances beyond existing alternatives along the following aspects. First, it can assess the predictive power of gene pathways, whereas existing methods tend to focus on model fitting accuracy only. Second, it can account for the joint effects of multiple genes in a pathway, whereas existing methods tend to focus on the marginal effects of genes. Third, it can accommodate multiple heterogeneous datasets, whereas existing methods analyze a single dataset only. We analyze four breast cancer prognosis studies and identify 97 pathways with significant predictive power for prognosis. Important pathways missed by alternative methods are identified.</p> <p>Conclusions</p> <p>The proposed method provides a useful alternative to existing pathway analysis methods. Identified pathways can provide further insights into breast cancer prognosis.</p

The Cure: Making a game of gene selection for breast cancer survival prediction

Motivation: Molecular signatures for predicting breast cancer prognosis could greatly improve care through personalization of treatment. Computational analyses of genome-wide expression datasets have identified such signatures, but these signatures leave much to be desired in terms of accuracy, reproducibility and biological interpretability. Methods that take advantage of structured prior knowledge (e.g. protein interaction networks) show promise in helping to define better signatures but most knowledge remains unstructured. Crowdsourcing via scientific discovery games is an emerging methodology that has the potential to tap into human intelligence at scales and in modes previously unheard of. Here, we developed and evaluated a game called The Cure on the task of gene selection for breast cancer survival prediction. Our central hypothesis was that knowledge linking expression patterns of specific genes to breast cancer outcomes could be captured from game players. We envisioned capturing knowledge both from the players prior experience and from their ability to interpret text related to candidate genes presented to them in the context of the game. Results: Between its launch in Sept. 2012 and Sept. 2013, The Cure attracted more than 1,000 registered players who collectively played nearly 10,000 games. Gene sets assembled through aggregation of the collected data clearly demonstrated the accumulation of relevant expert knowledge. In terms of predictive accuracy, these gene sets provided comparable performance to gene sets generated using other methods including those used in commercial tests. The Cure is available at http://genegames.org/cure