Identification of a minimum number of genes to predict triple-negative breast cancer subgroups from gene expression profiles.

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 amount of information from several data sets has allowed us to identify a well-determined minimal number of genes that may help in the recognition of TNBC subtypes. These genes, most of which have been previously found to be associated with breast cancer, have the potential to become novel diagnostic markers and/or therapeutic targets for specific TNBC subsets.

Molecular characterisation and clinical correlation of papillary thyroid microcarcinoma.

PURPOSE: Papillary thyroid microcarcinoma (mPTC) is defined as a papillary thyroid cancer sized 10 mm or less. Despite their generally indolent clinical course and good prognosis, a subset of mPTCs shows potentially aggressive behaviour. METHODS: To search for predictors of clinical outcome of mPTCs, we retrospectively evaluated the genetic tumour profile of 100 patients (23 M/77 F, mean age ± SD 53.8 ± 13.4 years) with histologically confirmed mPTCs through analysis of BRAF, NRAS and TERT promoter mutations as well as RET/PTC translocations. RESULTS: Mean follow-up period was 8.4 ± 3.6 years. In 55 cases, mPTC were detected incidentally after surgery. Capsular invasion, bilateralism and multifocality were found in 11/100, 17/100 and 24/100 cases, respectively, while lymph-nodes metastases were present at diagnosis in 9/100 cases. After 3.5 ± 2.0 years, tumour relapse occurred in 6/100 cases and was locoregional in five (two in the thyroid bed, three in laterocervical lymph-nodes), while lung metastasis occurred in one case. Biochemical persistence of disease was seen in 1/100 case. Mutations occurred in 55/100 cases; BRAFV600E was the most frequently detected (49/100) and was associated with higher tumour size, bilateralism and follicular variant but not with capsular invasion. RET/PTC rearrangements were found in 2/100 cases, NRASQ61R in 4/100, while no mutations of TERT promoter gene were detected. Despite the observed association between BRAFV600E mutation and unfavourable histopathological features, we found no direct association with tumour recurrence, distant metastases and mortality. CONCLUSION: In our study, the search for the most frequent genetic alterations as prognostic markers in mPTCs would not have changed the therapeutic strategy.