| 1. |
- Haugen, Mads H., et al.
(författare)
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Protein signature predicts response to neoadjuvant treatment with chemotherapy and bevacizumab in HER2-negative breast cancers
- 2021
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Ingår i: JCO Precision Oncology. - 2473-4284. ; 5, s. 286-306
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Tidskriftsartikel (refereegranskat)abstract
- PURPOSE Antiangiogenic therapy using bevacizumab has proven effective for a number of cancers; however, in breast cancer (BC), there is an unmet need to identify patients who benefit from such treatment. PATIENTS AND METHODS In the NeoAva phase II clinical trial, patients (N = 132) with large (= 25 mm) human epidermal growth factor receptor 2 (HER2)-negative primary tumors were randomly assigned 1:1 to treatment with neoadjuvant chemotherapy (CTx) alone or in combination with bevacizumab (Bev plus CTx). The ratio of the tumor size after relative to before treatment was calculated into a continuous response scale. Tumor biopsies taken prior to neoadjuvant treatment were analyzed by reverse-phase protein arrays (RPPA) for expression levels of 210 BC-relevant (phospho-) proteins. Lasso regression was used to derive a predictor of tumor shrinkage from the expression of selected proteins prior to treatment. RESULTS We identified a nine-protein signature score named vascular endothelial growth factor inhibition response predictor (ViRP) for use in the Bev plus CTx treatment arm able to predict with accuracy pathologic complete response (pCR) (area under the curve [AUC] = 0.85; 95% CI, 0.74 to 0.97) and low residual cancer burden (RCB 0/I) (AUC = 0.80; 95% CI, 0.68 to 0.93). The ViRP score was significantly lower in patients with pCR (P< .001) and in patients with low RCB (P<.001). The ViRP score was internally validated on mRNA data and the resultant surrogate mRNA ViRP score significantly separated the pCR patients (P = .016). Similarly, the mRNA ViRP score was validated (P < .001) in an independent phase II clinical trial (PROMIX). CONCLUSION Our ViRP score, integrating the expression of nine proteins and validated on mRNA data both internally and in an independent clinical trial, may be used to increase the likelihood of benefit from treatment with bevacizumab combined with chemotherapy in patients with HER2-negative BC.
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| 2. |
- Khademi, Naeem, et al.
(författare)
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Deliverable D2.2 - Core Transport System, with both Low-level and High-level Components
- 2017
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Rapport (refereegranskat)abstract
- This document presents the core transport system in NEAT, as used for development of thereference implementation of the NEAT System. The document describes the componentsnecessary to realise the basic Transport Services provided by the NEAT User API, with thedescription of a set of NEAT building blocks and their related design choices. The designof this core transport system takes into consideration the Transport Services and the API(defined in Task 1.3) and in close coordination with the overall architecture (Task 1.2).To realise the Transport Services provided by the API, a set of transport functionalitieshas to be provided by the NEAT Core Transport System. These functionalities take the formof several building blocks, or NEAT Components, each representing an associated implementationactivity. Some of the components are needed to ensure the basic operation ofthe NEAT System—e.g., a NEAT Flow Endpoint, a callback-based NEAT API Framework, theNEAT Logic and the functionality to Connect to a name. Additional components are neededfor: (a) ensuring connectivity, by means of mechanisms for discovery of path support fordifferent protocols; (b) supporting end-to-end security; (c) the ability to apply differentpolicies to influence the decision-making process of the transport system; (d) providingother important functionalities (e.g., a user-space SCTP stack, or gathering statistics forusers or system administrators).This document updates Deliverable D2.1; in particular, the descriptions of NEAT componentspresented here correspond to the implementation status at the time of writing,and as such they replace those in D2.1.
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| 3. |
- Khademi, Naeem, et al.
(författare)
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Deliverable D2.3 - Final Version of Core Transport System
- 2017
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Rapport (refereegranskat)abstract
- This document presents the core transport system in NEAT, as used for development of the reference implementation of the NEAT System. The document describes the components necessary to realise the basic Transport Services provided by the NEAT User API, with the description of a set of NEAT building blocks and their related design choices. The design of this core transport system, which is the final product ofWork Package 2, is driven by the Transport Services and API design from Task 1.4, and in close coordination with the overall NEAT architecture defined in Task 1.2. To realise the Transport Services provided by the API, a set of transport functions has to be provided by the NEAT Core Transport System. These functions take the form of several building blocks, or NEAT Components, each representing an associated implementation activity. Some components are needed to ensure the basic operation of the NEAT System—e.g., a NEAT Flow Endpoint, a callback-based NEAT API Framework, the NEAT Logic and the functionality to Connect to a name. Additional components are needed for: (a) ensuring connectivity, by means of mechanisms for discovery of path support for different protocols; (b) supporting end-to-end security; (c) the ability to apply different policies to influence the decision-making process of the transport system; (d) providing other important functionalities (e.g., a user-space SCTP stack, or gathering statistics for users or system administrators). This document updates Deliverable D2.2; in particular, the descriptions of NEAT components presented here correspond to their implementation status by the end of WP2, and as such they supersede those in D2.2.
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| 4. |
- Shlien, Adam, et al.
(författare)
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Direct Transcriptional Consequences of Somatic Mutation in Breast Cancer
- 2016
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Ingår i: Cell Reports. - : Elsevier BV. - 2211-1247. ; 16:7, s. 2032-2046
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Tidskriftsartikel (refereegranskat)abstract
- Disordered transcriptomes of cancer encompass direct effects of somatic mutation on transcription, coordinated secondary pathway alterations, and increased transcriptional noise. To catalog the rules governing how somatic mutation exerts direct transcriptional effects, we developed an exhaustive pipeline for analyzing RNA sequencing data, which we integrated with whole genomes from 23 breast cancers. Using X-inactivation analyses, we found that cancer cells are more transcriptionally active than intermixed stromal cells. This is especially true in estrogen receptor (ER)-negative tumors. Overall, 59% of substitutions were expressed. Nonsense mutations showed lower expression levels than expected, with patterns characteristic of nonsense-mediated decay. 14% of 4,234 rearrangements caused transcriptional abnormalities, including exon skips, exon reusage, fusions, and premature polyadenylation. We found productive, stable transcription from sense-to-antisense gene fusions and gene-to-intergenic rearrangements, suggesting that these mutation classes drive more transcriptional disruption than previously suspected. Systematic integration of transcriptome with genome data reveals the rules by which transcriptional machinery interprets somatic mutation.
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| 5. |
- Tekpli, Xavier, et al.
(författare)
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An independent poor-prognosis subtype of breast cancer defined by a distinct tumor immune microenvironment
- 2019
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- How mixtures of immune cells associate with cancer cell phenotype and affect pathogenesis is still unclear. In 15 breast cancer gene expression datasets, we invariably identify three clusters of patients with gradual levels of immune infiltration. The intermediate immune infiltration cluster (Cluster B) is associated with a worse prognosis independently of known clinicopathological features. Furthermore, immune clusters are associated with response to neoadjuvant chemotherapy. In silico dissection of the immune contexture of the clusters identified Cluster A as immune cold, Cluster C as immune hot while Cluster B has a pro-tumorigenic immune infiltration. Through phenotypical analysis, we find epithelial mesenchymal transition and proliferation associated with the immune clusters and mutually exclusive in breast cancers. Here, we describe immune clusters which improve the prognostic accuracy of immune contexture in breast cancer. Our discovery of a novel independent prognostic factor in breast cancer highlights a correlation between tumor phenotype and immune contexture.
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