Clonal dynamics monitoring during clinical evolution in chronic lymphocytic leukaemia

Chronic lymphocytic leukaemia is the most prevalent leukaemia in Western countries. It is an incurable disease characterized by a highly variable clinical course. Chronic lymphocytic leukaemia is an ideal model for studying clonal heterogeneity and dynamics during cancer progression, response to therapy and/or relapse because the disease usually develops over several years. Here we report an analysis by deep sequencing of sequential samples taken at different times from the affected organs of two patients with 12- and 7-year disease courses, respectively. One of the patients followed a linear pattern of clonal evolution, acquiring and selecting new mutations in response to salvage therapy and/or allogeneic transplantation, while the other suffered loss of cellular tumoral clones during progression and histological transformation.This work was supported by the Spanish Ministry of Economy and Competence (MINECO): SAF2013-47416-R; ISCIII-MINECO AES-FEDER (Plan Estatal de I + D + I 2008–2011 and 2013–2016) (PI14/00221 (MSB), PIE14/0064 (MSB), PIE15/0081 (MAP), PI16/01294 (MAP), CIBERONC CB16/12/00291 (MAP)), Madrid Autonomous Community, B2017/BMD3778 (MAP, MSB, FA-S), F Hoffmann-La Roche (JAGM); CNIO Bioinformatics Unit work has been supported by Marie-Curie Career Integration Grant CIG334361. J.G.-R. is a recipient of a i-PFIS predoctoral fellowship (IFI14/00003); NM was supported by AECC Scientific Foundation; S.D. was supported by the Torres Quevedo subprogramme (MICINN) under grant agreement PTQ-12-05391. K.T. and J.P.-P. are supported by Severo Ochoa FPI grant doctoral fellowship by the Spanish MINECO. MSB currently holds a Miguel Servet II contract (CPII16/00024) supported by ISCIII-MINECO AES-FEDER (Plan Estatal I + D + I 2013–2016) and the Fundación de Investigación Biomédica Puerta de Hierro

An improved machine learning protocol for the identification of correct Sequest search results

<p>Abstract</p> <p>Background</p> <p>Mass spectrometry has become a standard method by which the proteomic profile of cell or tissue samples is characterized. To fully take advantage of tandem mass spectrometry (MS/MS) techniques in large scale protein characterization studies robust and consistent data analysis procedures are crucial. In this work we present a machine learning based protocol for the identification of correct peptide-spectrum matches from Sequest database search results, improving on previously published protocols.</p> <p>Results</p> <p>The developed model improves on published machine learning classification procedures by 6% as measured by the area under the ROC curve. Further, we show how the developed model can be presented as an interpretable tree of additive rules, thereby effectively removing the 'black-box' notion often associated with machine learning classifiers, allowing for comparison with expert rule-of-thumb. Finally, a method for extending the developed peptide identification protocol to give probabilistic estimates of the presence of a given protein is proposed and tested.</p> <p>Conclusions</p> <p>We demonstrate the construction of a high accuracy classification model for Sequest search results from MS/MS spectra obtained by using the MALDI ionization. The developed model performs well in identifying correct peptide-spectrum matches and is easily extendable to the protein identification problem. The relative ease with which additional experimental parameters can be incorporated into the classification framework, to give additional discriminatory power, allows for future tailoring of the model to take advantage of information from specific instrument set-ups.</p

Impact of the Microenvironment on Tumour Budding in Colorectal Cancer.

Tumour Budding (TB) is recognized as an adverse prognostic factor in colorectal cancer (CRC). TB is the detachment of isolated cancer cells or small clusters of such cells mainly at the invasion front. One question that arises is of the role of the tumour stroma regarding the permissiveness of the formation and progression of TB. In this review, we will examine potential factors affecting TB, in particular we will analyse the potential effect of inflammation, hypoxia, extracellular matrix and Cancer-Associated Fibroblasts (CAFs).info:eu-repo/semantics/publishe