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Research
Research Topics
Functional and Structural Genomics
Molecular Genome Analysis
Research
Clinical Proteomics

Research

Pathway activities for informed decision making

Clinical decision making in molecular tumor boards, like the NCT MASTER study, mostly relies on genomic analysis (WGS/WES, RNA-seq) of patient tumors. We hypothesized that proteomic data should complement genome and transcript information, as proteins are closer to pathway activities and to drug effects.
We applied the RPPA targeted proteomic technology to test central kinases in pathways representative of the interventional baskets regarded within the NCT MASTER study (Wahjudi et al. 2021 Int J Cancer, 148(6):1438-51). There, proteomic data retrospectively collected from MASTER patients were used to compare recommendations based on this data, with those that had been based on genomic/transcriptomic information (WGS/WES, RNA-seq) alone. We observed that some of the original MASTER therapy recommendations were indeed supported by proteomic data. However, the protein and phosphoprotein data on pathway and drug-target activities would have suggested other treatment schemes in many other cases, some were in concordance with patient responses to the therapies having been applied to these patients. This retrospective study thus demonstrated that proteomic analysis of tumors provides information that could aid therapy decision in a molecular tumor board.

Pathway activities for informed decision making.
© dkfz.de

Figure 1: We currently test if unbiased mass spectrometry could replace RPPA, particularly in the timely analysis of single tumor samples. To this end, we apply unbiased total and phosphoproteomic analysis of tumors. Kinase and pathway activities are inferred from the data, to prioritize interventional baskets for individual patients. Along these lines, the SVRACAS ranking score has been developed to integrate multi-omics (WGS/WES, RNA-seq, proteomic) datasets from individual patients, and prioritize interventional baskets based on the molecular as well as of clinical data.

Along the same lines, we have developed a computational ranking scheme (SVRACAS, available at GitHub), which aggregates and interprets multi-Omic datasets for prioritization of actionable alterations in individual patients (Vlachavas et al. 2021 Int J Mol Sci, 22(6):2822; Kontogianni et al. 2023 Cancers, 15(3):815).

Collaborations

NCT MASTER: Stefan Fröhling, Peter Horak, Hanno Glimm, Bruno Köhler (NCT Heidelberg)

MS Proteomics: Dominic Helm (DKFZ)

SVRACAS: Bruno Köhler, Peter Horak (Heidelberg), Aristotelis Chatziioannou, Konstantinos Voutetakis, Olga Papadodima (Athens), Ryangguk Kim (Washington), Rachel Karchin (Baltimore)

 

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