[Prognostic and predictive factors of invasive breast cancer: update 2009]

Relationship between Clinicopathologic Variables in Breast Cancer Overall Survival Using Biogeography-Based Optimization Algorithm

Breast cancer is the most common cancer among women and is considered a major public health concern worldwide. Biogeography-based optimization (BBO) is a novel metaheuristic algorithm. This study analyzed the relationship between the clinicopathologic variables of breast cancer using Cox proportional hazard (PH) regression on the basis of the BBO algorithm. The dataset is prospectively maintained by the Division of Breast Surgery at Kaohsiung Medical University Hospital. A total of 1896 patients with breast cancer were included and tracked from 2005 to 2017. Fifteen general breast cancer clinicopathologic variables were collected. We used the BBO algorithm to select the clinicopathologic variables that could potentially contribute to predicting breast cancer prognosis. Subsequently, Cox PH regression analysis was used to demonstrate the association between overall survival and the selected clinicopathologic variables. C-statistics were used to test predictive accuracy and the concordance of various survival models. The BBO-selected clinicopathologic variables model obtained the highest C-statistic value (80%) for predicting the overall survival of patients with breast cancer. The selected clinicopathologic variables included tumor size (hazard ratio [HR] 2.372, p = 0.006), lymph node metastasis (HR 1.301, p = 0.038), lymphovascular invasion (HR 1.606, p = 0.096), perineural invasion (HR 1.546, p = 0.168), dermal invasion (HR 1.548, p = 0.028), total mastectomy (HR 1.633, p = 0.092), without hormone therapy (HR 2.178, p = 0.003), and without chemotherapy (HR 1.234, p = 0.491). This number was the minimum number of discriminators required for optimal discrimination in the breast cancer overall survival model with acceptable prediction ability. Therefore, on the basis of the clinicopathologic variables, the survival prediction model in this study could contribute to breast cancer follow-up and management.

Impact of cyclooxygenase-2 over-expression on the prognosis of breast cancer patients

OBJECTIVE

The aim of this present study was to assess the impact of COX-2 over-expression on breast cancer survival.

MATERIAL AND METHODS

Non-metastatic invasive breast cancer patients who received adequate loco-regional and systemic treatments were evaluated. Patients' demographic, clinical, pathologic, and treatment-related and survival data were retrieved from their hospital files. COX-2, estrogen/progesterone receptor (ER/PR), HER-2/neu expression and Ki67 index of the tumors were determined immunohistochemically. As the primary objective, COX-2 positive and negative patients were compared in terms of overall (OS), disease-free (DFS) and breast cancer-specific survival (BCSS). Secondary objectives were to assess the independent prognostic factors for survival. In addition, the correlation of COX-2 expression with conventional prognostic and predictive factors of breast cancer was assessed.

RESULTS

Two hundred and seventeen patients who underwent adequate breast cancer treatment between November 2004 and December 2013 were included in the study. The median follow-up was 37 months (range: 5-107). Eighty-one (37%) patients were COX-2 positive. OS, DFS, and BCSS were similar in COX-2 positive and negative patients. Ki67 index and age were significantly correlated with COX-2 expression (r=-0.116; p=0.02; r=0.159; p=0.02). PR expression was found to be the only independent factor for predicting OS, tumor size and molecular subtype classification were found to be the only independent factors for predicting DFS, and PR expression was found to be the only independent factor for predicting BCSS.

CONCLUSION

Among the independent predictive and prognostic factors of breast cancer, COX-2 over-expression was only correlated with Ki67 index and age.

Urokinase plasminogen activator (uPA) and plasminogen activator inhibitor type-1 (PAI-1) in breast cancer - correlation with traditional prognostic factors

BACKGROUND

Urokinase plasminogen activator (uPA) and plasminogen activator inhibitor type-1 (PAI-1) play a key role in tumour invasion and metastasis. High levels of both proteolytic enzymes are associated with poor prognosis in breast cancer patients. The purpose of this study was to evaluate the correlation between traditional prognostic factors and uPA and PAI-1 expression in primary tumour of breast cancer patients.

PATIENTS AND METHODS

606 primary breast cancer patients were enrolled in the prospective study in the Department of gynaecological oncology and breast oncology at the University Medical Centre Maribor between the years 2004 and 2010. We evaluated the traditional prognostic factors (age, menopausal status, tumour size, pathohistological type, histologic grade, lymph node status, lymphovascular invasion and hormone receptor status), together with uPA and PAI-1. We used Spearman's rank correlation, Mann Whitney U test and χ(2) test for statistical analysis.

RESULTS

Our findings indicate a positive correlation between uPA and tumour size (p < 0.001), grade (p < 0.001), histological type (p < 0.001), lymphovascular invasion (p = 0.01) and a negative correlation between uPA and hormone receptor status (p < 0.001). They also indicate a positive correlation between PAI-1 and tumour size (p = 0.004), grade (p < 0.001), pathohistological type (p < 0.001) and negative correlation between PAI-1 and hormone receptor status (p = 0.002).

CONCLUSIONS

Our study showed a relationship between uPA and PAI-1 and traditional prognostic factors. Their role as prognostic and predictive factors remains to be further evaluated.

The changing role of pathology in breast cancer diagnosis and treatment

Pathological examination has been the gold standard for diagnosis in cancer and its role has also included the elucidation of etiology, pathogenesis, clinicopathological correlation, and prognostication. The advent of newer technologies and the realization that breast cancer is heterogeneous has shifted the focus to prognostication, with increased attention being paid to the identification of morphological features and immunohistochemical markers of prognostic relevance. However, despite the massive efforts invested in the identification of immunohistochemical biomarkers in breast cancer the majority have not proven to be of value in multivariate analyses and only estrogen receptor, progesterone receptor, and Her2/neu expression have remained essential components of pathological examination. These 3 markers were initially employed for prognostication but their role in treatment also rendered them of predictive value. Newer molecular methods, especially high-throughput technologies, have shown that even morphologically similar subtypes of breast cancer can show molecular heterogeneity; moreover, infiltrating ductal carcinoma can be separated into at least 4 molecular subtypes designated luminal (ER+, PR+, and Her2/neu-), Her2 overexpressing (ER-, PR-, and Her2/neu+), basal-like (ER-, PR-, Her2/neu-, and CK5/6+, EGFR+), and normal breast-like (ER-, PR-, and Her2/neu-), each with different clinical outcomes. The importance of proliferative gene expression in these subtypes has been demonstrated and surrogate immunohistochemical markers include ER, PR, Her2/neu, and Ki67 for the more expensive molecular tests. Molecular technologies, importantly, have not only provided further insights into the heterogeneity of breast cancer but have also opened new avenues for treatment through the identification of signaling molecules important in the proliferation and survival of the neoplastic cells. The treatment of cancer thus shifts from the conventional approach of 'one size fits all' to one of personalized treatment tailored to the specific characteristics of the tumor. Pathologists continue to play their traditional role in diagnosis but, as purveyors of the excised tissue, pathologists now have the additional role of identifying biomarkers responsive to therapeutic manipulation, thus playing an inextricable role as diagnostic oncologists in the management of breast cancer.

A Normalized Tree Index for identification of correlated clinical parameters in microarray experiments

Background

Measurements on gene level are widely used to gain new insights in complex diseases e.g. cancer. A promising approach to understand basic biological mechanisms is to combine gene expression profiles and classical clinical parameters. However, the computation of a correlation coefficient between high-dimensional data and such parameters is not covered by traditional statistical methods.

Methods

We propose a novel index, the Normalized Tree Index (NTI), to compute a correlation coefficient between the clustering result of high-dimensional microarray data and nominal clinical parameters. The NTI detects correlations between hierarchically clustered microarray data and nominal clinical parameters (labels) and gives a measurement of significance in terms of an empiric p-value of the identified correlations. Therefore, the microarray data is clustered by hierarchical agglomerative clustering using standard settings. In a second step, the computed cluster tree is evaluated. For each label, a NTI is computed measuring the correlation between that label and the clustered microarray data.

Results

The NTI successfully identifies correlated clinical parameters at different levels of significance when applied on two real-world microarray breast cancer data sets. Some of the identified highly correlated labels confirm the actual state of knowledge whereas others help to identify new risk factors and provide a good basis to formulate new hypothesis.

Conclusions

The NTI is a valuable tool in the domain of biomedical data analysis. It allows the identification of correlations between high-dimensional data and nominal labels, while at the same time a p-value measures the level of significance of the detected correlations.