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- Carrera, Caty, et al.
(author)
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Validation of a clinical-genetics score to predict hemorrhagic transformations after rtPA.
- 2019
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In: Neurology. - 1526-632X. ; 93:9
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Journal article (peer-reviewed)abstract
- To validate the Genot-PA score, a clinical-genetic logistic regression score that stratifies the thrombolytic therapy safety, in a new cohort of patients with stroke.We enrolled 1,482 recombinant tissue plasminogen activator (rtPA)-treated patients with stroke in Spain and Finland from 2003 to 2016. Cohorts were analyzed on the basis of ethnicity and therapy: Spanish patients treated with IV rtPA within 4.5 hours of onset (cohort A and B) or rtPA in combination with mechanical thrombectomy within 6 hours of onset (cohort C) and Finnish participants treated with IV rtPA within 4.5 hours of onset (cohort D). The Genot-PA score was calculated, and hemorrhagic transformation (HT) and parenchymal hematoma (PH) risks were determined for each score stratum.Genot-PA score was tested in 1,324 (cohort A, n = 726; B, n = 334; C, n = 54; and D, n = 210) patients who had enough information to complete the score. Of these, 213 (16.1%) participants developed HT and 85 (6.4%) developed PH. In cohorts A, B, and D, HT occurrence was predicted by the score (p = 2.02 × 10-6, p = 0.023, p = 0.033); PH prediction was associated in cohorts A through C (p = 0.012, p = 0.034, p = 5.32 × 10-4). Increased frequency of PH events from the lowest to the highest risk group was found (cohort A 4%-15.7%, cohort B 1.5%-18.2%, cohort C 0%-100%). The best odds ratio for PH prediction in the highest-risk group was obtained in cohort A (odds ratio 5.16, 95% confidence interval 1.46-18.08, p = 0.009).The Genot-PA score predicts HT in patients with stroke treated with IV rtPA. Moreover, in an exploratory study, the score was associated with PH risk in mechanical thrombectomy-treated patients.
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| 3. |
- Diez, Paula, et al.
(author)
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Integration of Proteomics and Transcriptomics Data Sets for the Analysis of a Lymphoma B-Cell Line in the Context of the Chromosome-Centric Human Proteome Project
- 2015
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In: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 14:9, s. 3530-3540
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Journal article (peer-reviewed)abstract
- A comprehensive study of the molecular active landscape of human cells can be undertaken to integrate two different but complementary perspectives: transcriptomics, and proteomics. After the genome era, proteomics has emerged as a powerful tool to simultaneously identify and characterize the compendium of thousands of different proteins active in a cell. Thus, the Chromosome-centric Human Proteome Project (C-HPP) is promoting a full characterization of the human proteome combining high-throughput proteomics with the data derived from genome-wide expression profiling of protein-coding genes. Here we present a full proteomic profiling of a human lymphoma B-cell line (Ramos) performed using a nanoUPLC-LTQ-Orbitrap Velos proteomic platform, combined to an in-depth transcriptomic profiling of the same cell type. Data are available via ProteomeXchange with identifier PXD001933. Integration of the proteomic and transcriptomic data sets revealed a 94% overlap in the proteins identified by both -omics approaches. Moreover, functional enrichment analysis of the proteomic profiles showed an enrichment of several functions directly related to the biological and morphological characteristics of B-cells. In turn, about 30% of all protein-coding genes present in the whole human genome were identified as being expressed by the Ramos cells (stable average of 30% genes along all the chromosomes), revealing the size of the protein expression-set present in one specific human cell type. Additionally, the identification of missing proteins in our data sets has been reported, highlighting the power of the approach. Also, a comparison between neXtProt and UniProt database searches has been performed. In summary, our transcriptomic and proteomic experimental profiling provided a high coverage report of the expressed proteome from a human lymphoma B-cell type with a clear insight into the biological processes that characterized these cells. In this way, we demonstrated the usefulness of combining -omics for a comprehensive characterization of specific biological systems.
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| 4. |
- Horvatovich, Peter, et al.
(author)
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Quest for Missing Proteins : Update 2015 on Chromosome-Centric Human Proteome Project
- 2015
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In: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 14:9, s. 3415-3431
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Journal article (other academic/artistic)abstract
- This paper summarizes the recent activities of the Chromosome-Centric Human Proteome Project (C-HPP) consortium, which develops new technologies to identify yet-to-be annotated proteins (termed "missing proteins") in biological samples that lack sufficient experimental evidence at the protein level for confident protein identification. The C-HPP also aims to identify new protein forms that may be caused by genetic variability, post-translational modifications, and alternative splicing. Proteogenomic data integration forms the basis of the C-HPP's activities; therefore, we have summarized some of the key approaches and their roles in the project. We present new analytical technologies that improve the chemical space and lower detection limits coupled to bioinformatics tools and some publicly available resources that can be used to improve data analysis or support the development of analytical assays. Most of this paper's content has been compiled from posters, slides, and discussions presented in the series of C-HPP workshops held during 2014. All data (posters, presentations) used are available at the C-HPP Wild (http://c-hpp.webhosting.rug.nl/) and in the Supporting Information.
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| 5. |
- Perez-Gracia, Jose Luis, et al.
(author)
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Strategies to design clinical studies to identify predictive biomarkers in cancer research
- 2017
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In: Cancer Treatment Reviews. - : Elsevier BV. - 0305-7372. ; 53, s. 79-97
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Research review (peer-reviewed)abstract
- The discovery of reliable biomarkers to predict efficacy and toxicity of anticancer drugs remains one of the key challenges in cancer research. Despite its relevance, no efficient study designs to identify promising candidate biomarkers have been established. This has led to the proliferation of a myriad of exploratory studies using dissimilar strategies, most of which fail to identify any promising targets and are seldom validated. The lack of a proper methodology also determines that many anti-cancer drugs are developed below their potential, due to failure to identify predictive biomarkers. While some drugs will be systematically administered to many patients who will not benefit from them, leading to unnecessary toxicities and costs, others will never reach registration due to our inability to identify the specific patient population in which they are active. Despite these drawbacks, a limited number of outstanding predictive biomarkers have been successfully identified and validated, and have changed the standard practice of oncology. In this manuscript, a multidisciplinary panel reviews how those key biomarkers were identified and, based on those experiences, proposes a methodological framework—the DESIGN guidelines—to standardize the clinical design of biomarker identification studies and to develop future research in this pivotal field.
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