Detection of circulating tumor cells in the cerebrospinal fluid of a patient with a solitary metastasis from breast cancer: A case report

External validation of a published nomogram for prediction of brain metastasis in patients with extra-cerebral metastatic breast cancer and risk regression analysis

BACKGROUND

Survival of patients with metastatic breast cancer (MBC) suffering from brain metastasis (BM) is limited and this event is usually fatal. In 2010, the Graesslin's nomogram was published in order to predict subsequent BM in patients with breast cancer (BC) with extra-cerebral metastatic disease. This model aims to select a patient population at high risk for BM and thus will facilitate the design of prevention strategies and/or the impact of early treatment of BM in prospective clinical studies.

PATIENTS AND METHODS

Nomogram external validation was retrospectively applied to patients with BC and later BM between January 2005 and December 2012, treated in our institution. Moreover, risk factors of BM appearance were studied by Fine and Gray's competing risk analysis.

RESULTS

Among 492 patients with MBC, 116 developed subsequent BM. Seventy of them were included for the nomogram validation. The discrimination is good (area under curve = 0.695 [95% confidence interval, 0.61-0.77]). Risk factors of BM appearance are: human epidermal growth factor receptor 2 (HER2) overexpression/amplification, triple-negative BC and number of extra-cerebral metastatic sites (>1). With a competing risk model, we highlight the nomogram interest for HER2+ tumour subgroup exclusively.

CONCLUSION

Graesslin's nomogram external validation demonstrates exportability and reproducibility. Importantly, the competing risk model analysis provides additional information for the design of prospective trials concerning the early diagnosis of BM and/or preventive treatment on high risk patients with extra-cerebral metastatic BC.

Translational Research and Plasma Proteomic in Cancer

Proteomics is a recent field of research in molecular biology that can help in the fight against cancer through the search for biomarkers that can detect this disease in the early stages of its development. Proteomic is a speedily growing technology, also thanks to the development of even more sensitive and fast mass spectrometry analysis. Although this technique is the most widespread for the discovery of new cancer biomarkers, it still suffers of a poor sensitivity and insufficient reproducibility, essentially due to the tumor heterogeneity. Common technical shortcomings include limitations in the sensitivity of detecting low abundant biomarkers and possible systematic biases in the observed data. Current research attempts are trying to develop high-resolution proteomic instrumentation for high-throughput monitoring of protein changes that occur in cancer. In this review, we describe the basic features of the proteomic tools which have proven to be useful in cancer research, showing their advantages and disadvantages. The application of these proteomic tools could provide early biomarkers detection in various cancer types and could improve the understanding the mechanisms of tumor growth and dissemination.