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Invasive disease-free survival, but not OS, was significantly superior for patients with node-positive early BC who received the adjuvant standard schedule EC-T than for those who received the experimental ET-X regimen. Toxicity profiles differed substantially across arms.

Metastatic breast cancer is a heterogeneous disease that presents in varying forms, and a growing number of therapeutic options makes it difficult to determine the best choice in each particular situation. When selecting a systemic treatment, it is important to consider the medication administered in the previous stages, such as acquired resistance, type of progression, time to relapse, tumor aggressiveness, age, comorbidities, pre-and post-menopausal status, and patient preferences. Moreover, tumor genomic signatures can identify different subtypes, which can be used to create patient profiles and design specific therapies. However, there is no consensus regarding the best treatment sequence for each subgroup of patients. During the SABCC Congress of 2014, specialized breast cancer oncologists from referral hospitals in Europe met to define patient profiles and to determine specific treatment sequences for each one. Conclusions were then debated in a final meeting in which a relative degree of consensus for each treatment sequence was established. Four patient profiles were defined according to established breast cancer phenotypes: pre-menopausal patients with luminal subtype, post-menopausal patients with luminal subtype, patients with triple-negative subtype, and patients with HER2-positive subtype. A treatment sequence was then defined, consisting of hormonal therapy with tamoxifen, aromatase inhibitors, fulvestrant, and mTOR inhibitors for pre-and post-menopausal patients; a chemotherapy sequence for the first, second, and further lines for luminal and triple-negative patients; and an optimal sequence for treatment with new antiHER2 therapies. Finally, a document detailing all treatment sequences, that had the agreement of all the oncologists, was drawn up as a guideline and advocacy tool for professionals treating patients with this disease.

2029 Background: Glioblastoma (GBM) gene expression subtypes have been described in last years, data in homogeneously treated patients is lacking. Methods: Clinical, molecular and immunohistochemistry (IHC) analysis from patients with newly diagnosed GBM homogeneously treated with standard radiochemotherapy were studied. Samples were classified based on the expression profiles into three different subtypes (classical, mesenchymal, proneural) using Support Vector Machine (SVM), the K-nearest neighbor (K-NN) and the single sample Gene Set Enrichment Analysis (ssGSEA) classification algorithms provided by GlioVis web application. Results: GLIOCAT Project recruited 432 patients from 6 catalan institutions, all of whom received standard first-line treatment (2004 -2015). Best paraffin tissue samples were selected for RNAseq and reliable data were obtained from 124. 82 cases (66%) were classified into the same subtype by all three classification algorithms. SVM and ssGEA algorithms obtain more similar results (87%). No differences in clinical variables were found between the 3 GBM subtypes. Proneural subtype was enriched with IDH1 mutated and G-CIMP positive tumors. Mesenchymal subtype (SVM) was enriched in unmethylated MGMT tumors (p = 0.008), and classical (SVM) in methylated MGMT tumors (p = 0.008). Long survivors ( > 30 months) were rarely classified as mesenchymal (0-7.5%) and were more frequently classified as Proneural (23.1-26.). Clinical (age, resection, KPS) and molecular ( IDH1, MGMT) known prognostic factors were confirmed in this serie. Overall, no differences in prognosis were observed between 3 subtypes, but a trend to worse survival in mesenchymal was observed in K-NN (9.6 vs 15 ). Mesenchymal subtype presented less expression of Olig2 (p < 0.001) and SOX2 (p = 0.003) by IHC, but more YLK-40 expression (p = 0.023, SVM). On the other hand, classical subtype expressed more Nestin (p = 0.004) compared to the other subtypes (K-NN). Conclusions: In our study we have not found correlation between glioblastoma expression subtype and outcome. This large serie provides reproducible data regarding clinical-molecular-immunohistochemistry features of glioblastoma genetic subtypes.