Molecular Profiles of Sentinel and Non-Sentinel Lymph Nodes in Breast Cancer Progression and Prognosis

Sentinel lymph nodes (SLN) receive lymphatic drainage from the primary tumour and have been regarded as sensitive and reliable indicators of disease progression. It has been postulated that absence of metastatic tumour in SLN might obviate the exigency of axillary clearance and avoid the associated morbidity. But debate continues about the need for complete axillary dissection in SLN positive breast cancer patients, and whether SLN positivity might reflect the involvement of non-SLN. In essence, SLN positivity might be an invaluable aid in patient management. Albeit fraught with much debate, it would be worthwhile examining importance of the presence of micrometastases in patient management and disease outcome. Some of these considerations have led to the identification of molecular markers and measurement of their expression in SLN and non-SLN using sensitive, specific and precise molecular biological techniques. Several molecular markers of cancer progression have been employed in this way. These are related to the state of differentiation, and cell proliferation and apoptosis of tumours. Tumour suppressor genes and oncogenes have also been seen as ideal markers of SLN involvement. Significant advances have also been made in the elucidation of pathways of signalling that angiogenic and lymphangiogenic factors use and these have also contributed to the identification of potential markers of lymphangiogenesis. The availability of such a wide spectrum of markers requires not only that their expression is assessed objectively but also that potential difficulties of interpreting the complex interactions and inter-relationships between these markers and their bearing on disease progression and prognosis are addressed and assessed, which robustly advocates the use of artificial intelligence systems for this purpose. Construction of molecular profiles of primary tumours and disseminated tumour in SLN and body fluids would be invaluable for assessing cancer progression, for the prediction of prognosis and in designing therapeutic clinical trials.