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001-es BibID:	BIBFORM109812
035-os BibID:	(scopus)85149918954
Első szerző:	Vodicska Barbara
Cím:	Real-world performance analysis of a novel computational method in the precision oncology of pediatric tumors / Vodicska Barbara, Déri Júlia, Tihanyi Dóra, Várkondi Edit, Kispéter Enikő, Dóczi Róbert, Lakatos Dóra, Dirner Anna, Vidermann Mátyás, Filotás Péter, Szalkai-Dénes Réka, Szegedi István, Bartyik Katalin, Gábor Krisztina Míta, Simon Réka, Hauser Péter, Péter György, Kiss Csongor, Garami Miklós, Peták István
Dátum:	2023
ISSN:	1708-8569 1867-0687
Megjegyzések:	Background The utility of routine extensive molecular profling of pediatric tumors is a matter of debate due to the high number of genetic alterations of unknown signifcance or low evidence and the lack of standardized and personalized deci sion support methods. Digital drug assignment (DDA) is a novel computational method to prioritize treatment options by aggregating numerous evidence-based associations between multiple drivers, targets, and targeted agents. DDA has been validated to improve personalized treatment decisions based on the outcome data of adult patients treated in the SHIVA01 clinical trial. The aim of this study was to evaluate the utility of DDA in pediatric oncology. Methods Between 2017 and 2020, 103 high-risk pediatric cancer patients (<21 years) were involved in our precision oncol ogy program, and samples from 100 patients were eligible for further analysis. Tissue or blood samples were analyzed by whole-exome (WES) or targeted panel sequencing and other molecular diagnostic modalities and processed by a software system using the DDA algorithm for therapeutic decision support. Finally, a molecular tumor board (MTB) evaluated the results to provide therapy recommendations. Results Of the 100 cases with comprehensive molecular diagnostic data, 88 yielded WES and 12 panel sequencing results. DDA identifed matching of-label targeted treatment options (actionability) in 72/100 cases (72%), while 57/100 (57%) showed potential drug resistance. Actionability reached 88% (29/33) by 2020 due to the continuous updates of the evidence database. MTB approved the clinical use of a DDA-top-listed treatment in 56 of 72 actionable cases (78%). The approved therapies had signifcantly higher aggregated evidence levels (AELs) than dismissed therapies. Filtering of WES results for targeted panels missed important mutations afecting therapy selection. Conclusions DDA is a promising approach to overcome challenges associated with the interpretation of extensive molecular profling in the routine care of high-risk pediatric cancers. Knowledgebase updates enable automatic interpretation of a con tinuously expanding gene set, a "virtual" panel, fltered out from genome-wide analysis to always maximize the performance of precision treatment planning
Tárgyszavak:	Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	World Journal of Pediatrics. - [Epub ahead of print] (2023). -
További szerzők:	Déri Júlia Tihanyi Dóra Várkondi Edit Kispéter Enikő Dóczi Róbert Lakatos Dóra Dirner Anna Vidermann Mátyás Filotás Péter Szalkai-Dénes Réka Szegedi István (1969-) (hematológus, onkológus, nefrológus) Bartyik Katalin (haematológus) Gábor Krisztina Míta Simon Réka Hauser Péter Péter György Kiss Csongor (1956-) (hematológus, onkológus) Garami Miklós Peták István
Pályázati támogatás:	NVKP_16-1-2016-0005 Egyéb KFI_16-1-2016-0048 Egyéb NKFIH K_22 143021 NKFIH 2019-1.1.1-PIACI-KFI-2019-00367 Egyéb K_143021 Egyéb
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