DIA-MS identifies proteins associated with a poor response of metastatic renal cell carcinoma to tyrosine kinase inhibitor treatment

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Publikace nespadá pod Ústav výpočetní techniky, ale pod Přírodovědeckou fakultu. Oficiální stránka publikace je na webu muni.cz.
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ŠIMONÍK Jan LAPČÍK Petr BOUCHALOVÁ Pavla JANÁČOVÁ Lucia POTĚŠIL David PODHOREC Ján HORA Milan POPRACH Alexandr FIALA Ondřej BOUCHAL Pavel

Rok publikování 2022
Druh Konferenční abstrakty
Fakulta / Pracoviště MU

Přírodovědecká fakulta

Citace
Popis Metastatic renal cell carcinoma (mRCC) represents a systemic disease with very poor prognosis. mRCC patients have been typically treated by tyrosine kinase inhibitors (TKI), however, many patients respond poorly, or do not respond to this treatment. To understand the molecular mechanisms associated with a poor response to TKI and to identify patients who would benefit from an alternate treatment (e.g. immunotherapy or clinical trial), we performed a proteomics study on the set of 75 tumor tissues and 17 adjacent non-cancerous tissues from patients treated by TKI inhibitors sunitinib or pazopanib in the first line, of which half did not respond to the treatment. The tumors were further divided into training (n=53; 23 responders and 30 non-responders) and validation (n=22; 10 responders and 12 non-responders)) set. Tryptic digests of fresh frozen tissues were analyzed in data independent acquisition mode (DIA) on QExactive HF-X nanoLC-MS/MS system. The spectral library generated in Spectronaut 13.9 software (Biognosys) covered 7960 protein groups represented by 77817 peptides (FDR=0.01). Of these, 6252 protein groups were consistently quantified in the set of tissues (FDR=0.01). Using statistical analysis, we selected proteins which were deregulated between responders and non-responders’ tissues, were differentially abundant between tumor and normal tissue and unrelated to tumor grade (|log2FC| ?0.58 and q?0.05) in both the training and validation set, were significantly associated with progression free survival (FDR<0.05) and were able to divide patients between responders and non-responders based on area-under-curve (AUC; p<0.05). This gave us panel of 14 proteins that exhibited the best characteristics. We are now working of functional characterization of these targets using CRISPR/Cas9 to investigate their role in RCC cell migration and metastasis and confirm their roles as potential therapeutic targets. This work was supported by Ministry of Health of the Czech Republic, grant No. NV19- 08-00250. The first author is holder of a travel grant for 14th European Summer School: Advanced Proteomics from Proteomic section of Czech Society for Biochemistry and Molecular Biology
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