Lymphatic mapping and sentinel node biopsy in gynecological cancers: a critical review of the literature

Although it does not have a long history of sentinel node evaluation (SLN) in female genital system cancers, there is a growing number of promising study results, despite the presence of some aspects that need to be considered and developed. It has been most commonly used in vulvar and uterine cervivcal cancer in gynecological oncology. According to these studies, almost all of which are prospective, particularly in cases where Technetium-labeled nanocolloid is used, sentinel node detection rate sensitivity and specificity has been reported to be 100%, except for a few cases. In the studies on cervical cancer, sentinel node detection rates have been reported around 80–86%, a little lower than those in vulva cancer, and negative predictive value has been reported about 99%. It is relatively new in endometrial cancer, where its detection rate varies between 50 and 80%. Studies about vulvar melanoma and vaginal cancers are generally case reports. Although it has not been supported with multicenter randomized and controlled studies including larger case series, study results reported by various centers around the world are harmonious and mutually supportive particularly in vulva cancer, and cervix cancer. Even though it does not seem possible to replace the traditional approaches in these two cancers, it is still a serious alternative for the future. We believe that it is important to increase and support the studies that will strengthen the weaknesses of the method, among which there are detection of micrometastases and increasing detection rates, and render it usable in routine clinical practice

A comprehensive overview of radioguided surgery using gamma detection probe technology

The concept of radioguided surgery, which was first developed some 60 years ago, involves the use of a radiation detection probe system for the intraoperative detection of radionuclides. The use of gamma detection probe technology in radioguided surgery has tremendously expanded and has evolved into what is now considered an established discipline within the practice of surgery, revolutionizing the surgical management of many malignancies, including breast cancer, melanoma, and colorectal cancer, as well as the surgical management of parathyroid disease. The impact of radioguided surgery on the surgical management of cancer patients includes providing vital and real-time information to the surgeon regarding the location and extent of disease, as well as regarding the assessment of surgical resection margins. Additionally, it has allowed the surgeon to minimize the surgical invasiveness of many diagnostic and therapeutic procedures, while still maintaining maximum benefit to the cancer patient. In the current review, we have attempted to comprehensively evaluate the history, technical aspects, and clinical applications of radioguided surgery using gamma detection probe technology

Benchmarking and audit of breast units improves quality of care

Quality Indicators (QIs) are measures of health care quality that make use of readily available hospital inpatient administrative data. Assessment quality of care can be performed on different levels: national, regional, on a hospital basis or on an individual basis. It can be a mandatory or voluntary system. In all cases development of an adequate database for data extraction, and feedback of the findings is of paramount importance. In the present paper we performed a Medline search on “QIs and breast cancer” and “benchmarking and breast cancer care”, and we have added some data from personal experience. The current data clearly show that the use of QIs for breast cancer care, regular internal and external audit of performance of breast units, and benchmarking are effective to improve quality of care. Adherence to guidelines improves markedly (particularly regarding adjuvant treatment) and there are data emerging showing that this results in a better outcome. As quality assurance benefits patients, it will be a challenge for the medical and hospital community to develop affordable quality control systems, which are not leading to excessive workload

A Core Invasiveness Gene Signature Reflects Epithelial-to-Mesenchymal Transition but Not Metastatic Potential in Breast Cancer Cell Lines and Tissue Samples

<div><p>Introduction</p><p>Metastases remain the primary cause of cancer-related death. The acquisition of invasive tumour cell behaviour is thought to be a cornerstone of the metastatic cascade. Therefore, gene signatures related to invasiveness could aid in stratifying patients according to their prognostic profile. In the present study we aimed at identifying an invasiveness gene signature and investigated its biological relevance in breast cancer.</p><p>Methods & Results</p><p>We collected a set of published gene signatures related to cell motility and invasion. Using this collection, we identified 16 genes that were represented at a higher frequency than observed by coincidence, hereafter named the core invasiveness gene signature. Principal component analysis showed that these overrepresented genes were able to segregate invasive and non-invasive breast cancer cell lines, outperforming sets of 16 randomly selected genes (all P<0.001). When applied onto additional data sets, the expression of the core invasiveness gene signature was significantly elevated in cell lines forced to undergo epithelial-mesenchymal transition. The link between core invasiveness gene expression and epithelial-mesenchymal transition was also confirmed in a dataset consisting of 2420 human breast cancer samples. Univariate and multivariate Cox regression analysis demonstrated that CIG expression is not associated with a shorter distant metastasis free survival interval (HR = 0.956, 95%C.I. = 0.896–1.019, P = 0.186).</p><p>Discussion</p><p>These data demonstrate that we have identified a set of core invasiveness genes, the expression of which is associated with epithelial-mesenchymal transition in breast cancer cell lines and in human tissue samples. Despite the connection between epithelial-mesenchymal transition and invasive tumour cell behaviour, we were unable to demonstrate a link between the core invasiveness gene signature and enhanced metastatic potential.</p></div

Association between published gene signatures and the CIG signature in human breast cancer.

<p>Heatmap showing the association between the expressions of several published gene signatures and the CIG signature in a set of approximately 2.500 breast tumour samples. The rows and columns represent the set of analysed gene expression signatures organized into groups related to prognosis, EMT, pathway activation, stem cell biology, breast tumour heterogeneity and stromal involvement. The cells at the intersection between the rows and the columns are colour-coded with red indicating a positive correlation between the respective gene signatures and white indicating a negative correlation. Colour saturation is associated the magnitude the correlation coefficient. The dendrogram is divided in 3 groups (red, blue and green) of strongly associated gene signatures. Underneath the heatmap the Spearman correlation coefficients between the CIG signature and the remaining signatures is represented as well as the ten signatures most strongly associated with the CIG signature.</p

Boxplots showing the relation between CIG expression and EMT.

<p>The top row (A–B) represents a time series of different cell lines treated with EMT-inducing factors. These data demonstrate that CIG expression increases by incubation time. The lower left boxplot (C) indicates that CIG expression is induced by all of the known EMT-inducing factors, but most strongly downstream of GSC. The lower right boxplot (D) indicates that CIG expression does not necessarily correlate with metastatic capability as the cell line with the highest metastatic capability has the lowest CIG expression.</p

Collection of gene signatures used for overrepresentation analysis.

<p>Collection of gene signatures used for overrepresentation analysis.</p

Survival analysis.

<p>Survival analysis.</p

Gene expression data sets used throughout this study.

<p>Gene expression data sets used throughout this study.</p