Harnessing the power of AI in precision medicine: NGS-based therapeutic insights for colorectal cancer cohort

Our study highlights three distinct approaches for identifying potential therapeutic targets in cancer patients. Each approach yielded valuable insights into our cohort, underscoring the relevance and utility of these methodologies in the development of precision and personalized cancer therapies. Importantly, we developed a novel AI model that aligns tumors with representative cell lines using RNAseq and methylation data. This model enables us to identify cell lines closely resembling patient tumors, facilitating accurate selection of models needed for in vitro validation.

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Publication Details

Journal
Frontiers in Oncology
Published
2024-10-07
DOI
https://doi.org/10.3389/fonc.2024.1407465
Citations
3
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
1.21
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article

Harnessing the power of AI in precision medicine: NGS-based therapeutic insights for colorectal cancer cohort

Michał Warchoł, Weronika Czarnocka, Martyna Balawejder, Anna Sanecka-Duin et al.
3 citations
Frontiers in Oncology
Radiomics and Machine Learning in Medical Imaging
1.21
article

Harnessing the power of AI in precision medicine: NGS-based therapeutic insights for colorectal cancer cohort

Michał Warchoł, Weronika Czarnocka, Martyna Balawejder, Anna Sanecka-Duin, Maciej Jasiński, Joanna Marczyńska-Grzelak, Agnieszka Blum, Mirosław Szura, Piotr Skoczylas, Joanna Welanyk, Marek Karczewski, Rafał Szatanek, Zofia Orzeszko, Bartłomiej Król-Józaga, Grzegorz Torbicz, Stanisław Kłęk, Giovanni Mazzocco, Łukasz Wohadło, Marek J. Piatek, Oskar Gniewek, Łukasz Grochowalski, Michał Wysocki, Beata Markowska, Victor Murcia Pienkowski, Maciej Borys, Paweł Biernat, Tomasz Kucharczyk, Małgorzata Kamuda, Jan Kaczmarczyk, Jakub Widawski, Agata Zaremba
article en
3 citations

Abstract

Our study highlights three distinct approaches for identifying potential therapeutic targets in cancer patients. Each approach yielded valuable insights into our cohort, underscoring the relevance and utility of these methodologies in the development of precision and personalized cancer therapies. Importantly, we developed a novel AI model that aligns tumors with representative cell lines using RNAseq and methylation data. This model enables us to identify cell lines closely resembling patient tumors, facilitating accurate selection of models needed for in vitro validation.

Frontiers in OncologyVol. 14
Poznan University of Medical Sciences (PL), Jagiellonian University (PL), Holy Cross University (PL), Andrzej Frycz Modrzewski Krakow University (PL), The Maria Sklodowska-Curie National Research Institute of Oncology (PL)
Industry, innovation and infrastructure
Openalex Percentile: Top 20%
Radiomics and Machine Learning in Medical Imaging
1.21
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