Dataset Information

Prognostic models in male breast cancer.

ABSTRACT:

Purpose

Breast cancer in men is uncommon; it accounts for 1 % of all patients with primary breast cancer. Its treatment is mostly extrapolated from its female counterpart. Accurate predictions are essential for adjuvant systemic treatment decision-making and informing patients. Several predictive models are available for female breast cancer (FBC) including the Morphometric Prognostic Index (MPI), Nottingham Prognostic Index (NPI), Adjuvant! Online and Predict. The aim of this study was to examine and compare the prognostic performance of these models for male breast cancer (MBC).

Methods

The population of this study consists of 166 MBC patients. The prognostic scores of the patients are categorized by good, (moderate) and poor, defined by the test itself (MPI and NPI) or based on tertiles (Adjuvant! Online and Predict). Survival according to prognostic score was compared by Kaplan-Meier analysis and differences were tested by logRank. The prognostic performances were evaluated with C-statistics. Calibration was done with the aim to estimate to what extent the survival rates predicted by Predict were similar to the observed survival rates.

Results

All prediction models were capable of discriminating between good, moderate and poor survivors. P-values were highly significant. Comparison between the models using C-statistics (n = 88) showed equal performance of MPI (0.67), NPI (0.68), Adjuvant! Online (0.69) and Predict (0.69). Calibration of Predict showed overestimation for MBC patients.

Conclusion

In conclusion, MPI, NPI, Adjuvant! and Predict prognostic models, originally developed and validated for FBC patients, also perform quite well for MBC patients.

SUBMITTER: van der Pol CC

PROVIDER: S-EPMC5065611 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Publications

Prognostic models in male breast cancer.

van der Pol Carmen C CC Lacle Miangela M MM Witkamp Arjen J AJ Kornegoor Robert R Miao Hui H Bouchardy Christine C Borel Rinkes Inne I van der Wall Elsken E Verkooijen Helena M HM van Diest Paul J PJ

Breast cancer research and treatment 20160926 2

<h4>Purpose</h4>Breast cancer in men is uncommon; it accounts for 1 % of all patients with primary breast cancer. Its treatment is mostly extrapolated from its female counterpart. Accurate predictions are essential for adjuvant systemic treatment decision-making and informing patients. Several predictive models are available for female breast cancer (FBC) including the Morphometric Prognostic Index (MPI), Nottingham Prognostic Index (NPI), Adjuvant! Online and Predict. The aim of this study was ...[more]

PMID: 27671991

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Project description:BackgroundBreast cancer is the most common cancer in women worldwide, with a great diversity in outcomes among individual patients. The ability to accurately predict a breast cancer outcome is important to patients, physicians, researchers, and policy makers. Many models have been developed and tested in different settings. We systematically reviewed the prognostic models developed and/or validated for patients with breast cancer.MethodsWe conducted a systematic search in four electronic databases and some oncology websites, and a manual search in the bibliographies of the included studies. We identified original studies that were published prior to 1st January 2017, and presented the development and/or validation of models based mainly on clinico-pathological factors to predict mortality and/or recurrence in female breast cancer patients.ResultsFrom the 96 articles selected from 4095 citations found, we identified 58 models, which predicted mortality (n = 28), recurrence (n = 23), or both (n = 7). The most frequently used predictors were nodal status (n = 49), tumour size (n = 42), tumour grade (n = 29), age at diagnosis (n = 24), and oestrogen receptor status (n = 21). Models were developed in Europe (n = 25), Asia (n = 13), North America (n = 12), and Australia (n = 1) between 1982 and 2016. Models were validated in the development cohorts (n = 43) and/or independent populations (n = 17), by comparing the predicted outcomes with the observed outcomes (n = 55) and/or with the outcomes estimated by other models (n = 32), or the outcomes estimated by individual prognostic factors (n = 8). The most commonly used methods were: Cox proportional hazards regression for model development (n = 32); the absolute differences between the predicted and observed outcomes (n = 30) for calibration; and C-index/AUC (n = 44) for discrimination. Overall, the models performed well in the development cohorts but less accurately in some independent populations, particularly in patients with high risk and young and elderly patients. An exception is the Nottingham Prognostic Index, which retains its predicting ability in most independent populations.ConclusionsMany prognostic models have been developed for breast cancer, but only a few have been validated widely in different settings. Importantly, their performance was suboptimal in independent populations, particularly in patients with high risk and in young and elderly patients.

Project description:BackgroundThe successful identification of breast cancer (BRCA) prognostic biomarkers is essential for the strategic interference of BRCA patients. Recently, various methods have been proposed for exploring a small prognostic gene set that can distinguish the high-risk group from the low-risk group.MethodsRegularized Cox proportional hazards (RCPH) models were proposed to discover prognostic biomarkers of BRCA from gene expression data. Firstly, the maximum connected network with 1142 genes by mapping 956 differentially expressed genes (DEGs) and 677 previously BRCA-related genes into the gene regulatory network (GRN) was constructed. Then, the 72 union genes of the four feature gene sets identified by Lasso-RCPH, Enet-RCPH, [Formula: see text]-RCPH and SCAD-RCPH models were recognized as the robust prognostic biomarkers. These biomarkers were validated by literature checks, BRCA-specific GRN and functional enrichment analysis. Finally, an index of prognostic risk score (PRS) for BRCA was established based on univariate and multivariate Cox regression analysis. Survival analysis was performed to investigate the PRS on 1080 BRCA patients from the internal validation. Particularly, the nomogram was constructed to express the relationship between PRS and other clinical information on the discovery dataset. The PRS was also verified on 1848 BRCA patients of ten external validation datasets or collected cohorts.ResultsThe nomogram highlighted that the importance of PRS in guiding significance for the prognosis of BRCA patients. In addition, the PRS of 301 normal samples and 306 tumor samples from five independent datasets showed that it is significantly higher in tumors than in normal tissues ([Formula: see text]). The protein expression profiles of the three genes, i.e., ADRB1, SAV1 and TSPAN14, involved in the PRS model demonstrated that the latter two genes are more strongly stained in tumor specimens. More importantly, external validation illustrated that the high-risk group has worse survival than the low-risk group ([Formula: see text]) in both internal and external validations.ConclusionsThe proposed pipelines of detecting and validating prognostic biomarker genes for BRCA are effective and efficient. Moreover, the proposed PRS is very promising as an important indicator for judging the prognosis of BRCA patients.

Project description:BackgroundMale breast cancer (MBC) is a rare disease for which there is limited understanding of treatment patterns and prognostic factors.MethodsMen with TNM stage I to stage III breast cancer diagnosed between 2004 and 2014 in the National Cancer Data Base were included. Trends in treatment modalities were described using the average annual percentage change (AAPC) and estimated using Joinpoint software for the analysis of trends. Kaplan-Meier curves and the multivariate Cox proportional hazards regression model were used to compare survival between subgroups and to identify prognostic factors.ResultsA total of 10,873 MBC cases were included, with a median age at diagnosis of 64 years. Breast-conserving surgery was performed in 24% of patients, and 70% of patients undergoing breast conservation received radiotherapy. Approximately 44% of patients received chemotherapy, and 62% of patients with estrogen receptor-positive disease received endocrine therapy. Oncotype DX was ordered in 35% of patients with lymph node-negative, estrogen receptor-positive/human epidermal growth factor receptor 2 (HER2)-negative tumors. During the study period, there was a significant increase in the rates of total mastectomy, contralateral prophylactic mastectomy, radiotherapy after breast conservation, ordering of Oncotype DX, and the use of endocrine therapy (P < .05). On multivariate analysis, factors found to be associated with worse overall survival were older age, black race, higher Charlson Comorbidity Index, high tumor grade and stage of disease, and undergoing total mastectomy. Residing in a higher income area; having progesterone receptor-positive tumors; and receipt of chemotherapy, radiotherapy, and endocrine therapy were associated with better overall survival.ConclusionsDespite the lack of prospective randomized trials in patients with MBC, the results of the current study demonstrated that the treatment of this disease has evolved over the years. These findings further the understanding of the modern treatment and prognosis of MBC, and identify several areas for further research.

Dataset Information

Prognostic models in male breast cancer.

Purpose

Methods

Results

Conclusion

Publications

Prognostic models in male breast cancer.

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