Julio Saez-Rodriguez

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About Julio Saez-Rodriguez:

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SEEK ID: https://seek.lisym.org/people/199

Location: Germany

ORCID: https://orcid.org/0000-0002-8552-8976

Expertise: Not specified

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LiSyM-Krebs - Systemmedizinisches Forschungsnetz zur Früherkennung und Prävention von Leberkrebs

LiSyM-Krebs ist ein nationales Forschungsnetz zur Früherkennung und Prävention von Leberkrebs, das unter Verwendung des systemmedizinischen Forschungsansatzes die komplexen, dynamischen Prozesse der Krankheitsprogression analysiert, um ausgehend von den Erkenntnissen aus dem Forschungsnetz LiSyM die Entstehung von Leberkrebs besser zu verstehen, vorherzusagen und im besten Fall sogar zu verhindern. LiSyM-Krebs setzt die erfolgreichen Forschungsaktivitäten der BMBF-Vorgängerprogramme ...

Projects: LiSyM-Krebs Partnering, Forschungsnetzwerk LiSyM-Krebs, SMART-NAFLD, DEEP-HCC network, C-TIP-HCC network

Web page: https://www.lisym-cancer.org

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Forschungsnetzwerk LiSyM-Krebs

Forschungsnetzwerk zur Früherkennung und Prävention- LiSyM-Krebs

Ein Netzwerk von Klinikern, Wissenschaftlern und Datenmanagern hat sich zur Aufgabe gemacht, Methoden zu entwickeln, um Patienten mit einem hohen Risiko für ein Leberkarzinom frühzeitig, in Vorstadien der Tumorentwicklung, identifizieren zu können. Gemeinsam bilden sie das „Systemmedizinische Forschungsnetzwerk zur Früherkennung und Prävention von Leberkrebs“, LiSym-Krebs, das vom Bundesministerium für Bildung und Forschung ...

Programme: LiSyM-Krebs - Systemmedizinisches Forschungsnetz zur Früherkennung und Prävention von Leberkrebs

Public web page: Not specified

Organisms: Homo sapiens, Mus musculus, Rattus norvegicus, Rattus rattus

C-TIP-HCC network

C-TIP-HCC- Mechanism-based Multiscale Model to Dissect the Tipping Point from Liver Cirrhosis to Hepatocellular Carcinoma

The ultimate goal of the C-TIP-HCC is a „mechanistic multiscale model to describe dynamic changes in regenerative nodes across a tipping point (TIP) towards development in patients with cirrhosis to facilitate early monitoring and intervention“ and facilitate intervention“. To achieve this, we will conduct in-depth studies on cirrhotic regenerative nodes.

Programme: LiSyM-Krebs - Systemmedizinisches Forschungsnetz zur Früherkennung und Prävention von Leberkrebs

Public web page: Not specified

Organisms: Homo sapiens, Mus musculus

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Heidelberg University - Institute for Computational Biomedicine

Country: Germany

City: Heidelberg

Web page: Not specified

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Comprehensive evaluation of phosphoproteomic-based kinase activity inference (preprint)

Forschungsnetzwerk LiSyM-Krebs, C-TIP-HCC network

Abstract (Expand)

Kinases play a central role in regulating cellular processes, making their study essential for understanding cellular function and disease mechanisms. To investigate the regulatory state of a kinase, … numerous methods have been, and continue to be, developed to infer kinase activities from phosphoproteomics data. These methods usually rely on a set of kinase targets collected from various kinase-substrate libraries. However, only a small percentage of measured phosphorylation sites can usually be attributed to an upstream kinase in these libraries, limiting the scope of kinase activity inference. In addition, the inferred activities from different methods can vary making it crucial to evaluate them for accurate interpretation. Here, we present a comprehensive evaluation of kinase activity inference methods using multiple kinase-substrate libraries combined with different inference algorithms. Additionally, we try to overcome the coverage limitations for measured targets in kinase substrate libraries by adding predicted kinase-substrate interactions for activity inference. For the evaluation, in addition to classical cell-based perturbation experiments, we introduce a tumor-based benchmarking approach that utilizes multi-omics data to identify highly active or inactive kinases per tumor type. We show that while most computational algorithms perform comparably regardless of their complexity, the choice of kinase-substrate library can highly impact the inferred kinase activities. Hereby, manually curated libraries, particularly PhosphoSitePlus, demonstrate superior performance in recapitulating kinase activities from phosphoproteomics data. Additionally, in the tumor-based evaluation, adding predicted targets from NetworKIN further boosts the performance, while normalizing sites to host protein levels reduces kinase activity inference performance. We then showcase how kinase activity inference can help in characterizing the response to kinase inhibitors in different cell lines. Overall, the selection of reliable kinase activity inference methods is important in identifying deregulated kinases and novel drug targets. Finally, to facilitate the evaluation of novel methods in the future, we provide both benchmarking approaches in the R package benchmarKIN.

Authors: Sophia Müller-Dott, Eric J. Jaehnig, Khoi Pham Munchic, Wen Jiang, Tomer M. Yaron-Barir, Sara R. Savage, Martin Garrido-Rodriguez, Jared L. Johnson, Alessandro Lussana, Evangelia Petsalaki, Jonathan T. Lei, Aurélien Dugourd, Karsten Krug, Lewis C. Cantley, D. R. Mani, Bing Zhang, Julio Saez-Rodriguez

Date Published: 2nd Jul 2024

Publication Type: Journal

DOI: 10.1101/2024.06.27.601117

Citation: biorxiv;2024.06.27.601117v1,[Preprint]

Created: 5th Jul 2024 at 07:42

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