Biology subjects

Korf, U.

Publications and source records attributed to Korf, U..

3 recordsLinked to original sources

bioRxiv2021

Multicenter reverse-phase protein array data integration

Among the technologies available for protein biomarker discovery and validation, reverse-phase protein array (RPPA) benefits from unequalled sample throughput. Panels of high-quality antibodies enable the quantification by RPPA of protein abundance and posttranslational modifications in biological specimens with high precision and sensitivity. Incorporation of RPPA technology into clinical and drug development pipelines requires robust assays that generate reproducible results across multiple laboratories. We implemented the first international multicenter pilot study to investigate RPPA workflow variability. We characterized the proteomic responses of a series of breast cancer cells to two cancer drugs. This analysis quantified 86,832 sample spots, representing 108 biological samples, arrayed at three independent RPPA platforms. This unique integrated set of data is publicly available as a resource to the proteomic and cancer research communities to catalyse further analysis and investigation. We anticipate that this dataset will form a reference for the comparison of RPPA workflows and reagents, which can be expanded in the future, and will aid the identification of platform-robust treatment-marker antigens in breast cancer cells. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=95 SRC="FIGDIR/small/458377v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@e2fb2forg.highwire.dtl.DTLVardef@1d8f425org.highwire.dtl.DTLVardef@84dd60org.highwire.dtl.DTLVardef@e1ccf7_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2021

Coordinated regulation of WNT/β-catenin, c-Met, and Integrin signalling pathways by miR-193b controls triple negative breast cancer metastatic traits

BackgroundTriple Negative Breast Cancer (TNBC) is the most aggressive subtype of Breast Cancer (BC). Treatment options for TNBC patients are limited and further insights into disease aetiology are needed to develop better therapeutic approaches. microRNAs ability to regulate multiple targets could hold a promising discovery approach to pathways relevant for TNBC aggressiveness. Thus, we address the role of miRNAs in controlling signalling pathways and phenotypes relevant to the biology of TNBC. MethodsTo identify miRNAs regulating WNT/{beta}-catenin, c-Met, and integrin signalling pathways, we performed a high-throughput targeted proteomic approach, investigating the effect of 800 miRNAs on the expression of 62 proteins in the MDA-MB-231 TNBC cell line. We then developed a novel network analysis, Pathway Coregulatory (PC) score, to detect miRNAs regulating the three pathways. Using in vitro assays for cell growth, migration, apoptosis, and stem-cell content, we validated the function of candidate miRNAs. Bioinformatic analyses using BC patients datasets were employed to assess expression of miRNAs as well as their pathological relevance in TNBC patients. ResultsWe identified six candidate miRNAs coordinately regulating the three signalling pathways. Quantifying cell growth of three TNBC cell lines upon miRNA gain-of-function experiments, we characterised miR-193b as a strong and consistent repressor of this phenotype. Importantly, the effects of miR-193b were stronger than chemical inhibition of the individual pathways. We further demonstrated that miR-193b induced apoptosis, repressed migration, and regulated stem-cell markers in MDA-MB-231 cells. Furthermore, miR-193b expression was the lowest in patients classified as TNBC or Basal compared to other subtypes when classified by PAM50 signatures. Gene Set Enrichment Analysis showed that miR-193b expression was significantly associated with reduced activity of of WNT/{beta}-catenin and c-Met signalling pathways in TNBC patients. ConclusionsIntegrating miRNA-mediated effects and protein functions on networks, we show that miRNAs predominantly act in a coordinated fashion to activate or repress signalling pathways responsible for metastatic traits in TNBC. We further demonstrate that our top candidate, miR-193b, regulates these phenotypes to an extent stronger than individual pathway inhibition, thus proving that its effect on TNBC aggressiveness is mediated by repressing multiple interconnected pathways.

cancer biology↗

bioRxiv2019

Integrative analysis of multi-platform reverse-phase protein array data for the pharmacodynamic assessment of response to targeted therapies

Reverse-phase protein array (RPPA) technology uses panels of high-specificity antibodies to measure proteins and protein post-translational modifications in cells and tissues. The approach offers sensitive and precise quantification of large numbers of samples and has thus found applications in the analysis of clinical and pre-clinical samples. For effective integration into drug development and clinical practice, robust assays with consistent results are essential. Leveraging a collaborative RPPA model, we set out to assess the variability between three different RPPA platforms using distinct instrument set-ups and workflows. Employing multiple RPPA-based approaches operated across distinct laboratories, we characterised a range of human breast cancer cells and their protein-level responses to two clinically relevant cancer drugs. We integrated multi-platform RPPA data and used unsupervised learning to identify protein expression and phosphorylation signatures that were not dependent on RPPA platform and analysis workflow. Our findings indicate that proteomic analyses of cancer cell lines using different RPPA platforms can identify concordant profiles of response to pharmacological inhibition, including when using different antibodies to measure the same target antigens. These results highlight the robustness and the reproducibility of RPPA technology and its capacity to identify protein markers of disease or response to therapy.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=58 SRC=\"FIGDIR/small/769158v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (19K):\norg.highwire.dtl.DTLVardef@3b72aeorg.highwire.dtl.DTLVardef@e62fe4org.highwire.dtl.DTLVardef@5238c3org.highwire.dtl.DTLVardef@ca925_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗