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Background: Triple-negative breast cancer (TNBC) is a very heterogeneous disease. Several gene expression and mutation profiling approaches were used to classify it, and all converged to the identification of distinct molecular subtypes, with some overlapping across different approaches. However, a standardised tool to routinely classify TNBC in the clinics and guide personalised treatment is lacking. We aimed at defining a specific gene signature for each of the six TNBC subtypes proposed by Lehman et al. in 2011 (basal-like 1 (BL1); basal-like 2 (BL2); mesenchymal (M); immunomodulatory (IM); mesenchymal stem-like (MSL); and luminal androgen receptor (LAR)), to be able to accurately predict them. Methods: Lehman’s TNBCtype subtyping tool was applied to RNA-sequencing data from 482 TNBC (GSE164458), and a minimal subtype-specific gene signature was defined by combining two class comparison techniques with seven attribute selection methods. Several machine learning algorithms for subtype prediction were used, and the best classifier was applied on microarray data from 72 Italian TNBC and on the TNBC subset of the BRCA-TCGA data set. Results: We identified two signatures with the 120 and 81 top up- and downregulated genes that define the six TNBC subtypes, with prediction accuracy ranging from 88.6 to 89.4%, and even improving after removal of the least important genes. Network analysis was used to identify highly interconnected genes within each subgroup. Two druggable matrix metalloproteinases were found in the BL1 and BL2 subsets, and several druggable targets were complementary to androgen receptor or aromatase in the LAR subset. Several secondary drug–target interactions were found among the upregulated genes in the M, IM and MSL subsets. Conclusions: Our study took full advantage of available TNBC data sets to stratify samples and genes into distinct subtypes, according to gene expression profiles. The development of a data mining approach to acquire a large amo

Sentinel lymph node (SLN) biopsy in apparent early-stage ovarian cancer may spare the surgical staging with extensive retroperitoneal dissection and its associated morbidity. However, SLN biopsy in ovarian cancer is still experimental and under investigation. A 46-year-old post-menopausal woman with bilateral apparent stage IC1 endometrioid ovarian cancer underwent surgical staging by SLN biopsy and subsequent comprehensive laparoscopic pelvic and para-aortic lymphadenectomy. Out of 4 SLNs submitted to ultrastaging, one was positive for isolated tumour cells (ITCs). We submitted to ultra-staging all the other 24 pelvic and para-aortic non-SLNs, which were reported negative for disease. This is the first reported case of comprehensive lymphadenectomy after SLN biopsy with universal ultrastaging of all non-SLNs in ovarian cancer. The presence of ITCs in only one SLN, with all other 27 lymph nodes negative at ultrastaging, is consistent with the SLN concept and the assumption of a reliable lymphatic pathway in ovarian cancer.