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A. Martinez

Researcher at University of Barcelona

Publications -  18
Citations -  2180

A. Martinez is an academic researcher from University of Barcelona. The author has contributed to research in topics: Lenalidomide & Follicular lymphoma. The author has an hindex of 10, co-authored 18 publications receiving 1951 citations.

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Stromal gene signatures in large-B-cell lymphomas

TL;DR: Survival after treatment of diffuse large-B-cell lymphoma is influenced by differences in immune cells, fibrosis, and angiogenesis in the tumor microenvironment, and a multivariate model created from three gene-expression signatures predicted survival both in patients who received CHOP and patients who receive R-CHOP.
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The Follicular Lymphoma International Prognostic Index (FLIPI) and the histological subtype are the most important factors to predict histological transformation in follicular lymphoma

TL;DR: FLIPI and histology were the most important variables predicting HT, and only patients achieving CR reached prolonged survival, thus emphasizing the need for effective therapies once this event occurs.
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The EMT activator ZEB1 promotes tumor growth and determines differential response to chemotherapy in mantle cell lymphoma.

TL;DR: It is shown that ZEB1, a transcription factor better known for promoting metastasis in carcinomas, is expressed in primary MCLs with active Wnt signaling, and this gene could potentially become a predictive biomarker and therapeutic target in this lymphoma.
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Synergistic antitumor activity of lenalidomide with the BET bromodomain inhibitor CPI203 in bortezomib-resistant mantle cell lymphoma

TL;DR: It is suggested that exacerbated IRF4/MYC signaling is associated to bortezomib resistance in MCL in vivo and warrant clinical evaluation of lenalidomide plus BET inhibitor combination in Mcl cases refractory to proteasome inhibition.
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Comparison of four prognostic scores in peripheral T-cell lymphoma

TL;DR: All the scores demonstrated their usefulness to assess the outcome of patients with PTCL, with IPTCLP being the most significant to predict OS.