MSIMEP: Predicting microsatellite instability from microarray DNA methylation tumor profiles

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  • Martín Santamarina-García, University of Santiago de Compostela
  • ,
  • Jenifer Brea-Iglesias, University of Santiago de Compostela, Álvaro Cunqueiro Hospital
  • ,
  • Jesper Bertram Bramsen
  • Mar Fuentes-Losada, University of Santiago de Compostela
  • ,
  • Francisco Javier Caneiro-Gómez, University of Santiago de Compostela
  • ,
  • José Ángel Vázquez-Bueno, Complexo Hospitalario Universitario de Ferrol
  • ,
  • Héctor Lázare-Iglesias, University of Santiago de Compostela
  • ,
  • Natalia Fernández-Díaz, University of Santiago de Compostela
  • ,
  • Laura Sánchez-Rivadulla, Complexo Hospitalario Universitario de Ferrol
  • ,
  • Yoel Z. Betancor, University of Santiago de Compostela
  • ,
  • Miriam Ferreiro-Pantín, University of Santiago de Compostela
  • ,
  • Pablo Conesa-Zamora, Santa Lucía University Hospital
  • ,
  • José Ramón Antúnez-López, University of Santiago de Compostela
  • ,
  • Masahito Kawazu, Chiba Cancer Center Hospital, National Cancer Center Research Institute
  • ,
  • Manel Esteller, Josep Carreras Leukaemia Research Institute , ICREA, Pompeu Fabra University, Centro de Investigación Biomédica en Red
  • ,
  • Claus Lindbjerg Andersen
  • Jose M.C. Tubio, University of Santiago de Compostela
  • ,
  • Rafael López-López, University of Santiago de Compostela, Centro de Investigación Biomédica en Red
  • ,
  • Juan Ruiz-Bañobre, University of Santiago de Compostela, Centro de Investigación Biomédica en Red

Deficiency in DNA MMR activity results in tumors with a hypermutator phenotype, termed microsatellite instability (MSI). Beyond its utility in Lynch syndrome screening algorithms, today MSI has gained importance as predictive biomarker for various anti-PD-1 therapies across many different tumor types. Over the past years, many computational methods have emerged to infer MSI using either DNA- or RNA-based approaches. Considering this together with the fact that MSI-high tumors frequently exhibit a hypermethylated phenotype, herein we developed and validated MSIMEP, a computational tool for predicting MSI status from microarray DNA methylation tumor profiles of colorectal cancer samples. We demonstrated that MSIMEP optimized and reduced models have high performance in predicting MSI in different colorectal cancer cohorts. Moreover, we tested its consistency in other tumor types with high prevalence of MSI such as gastric and endometrial cancers. Finally, we demonstrated better performance of both MSIMEP models vis-à-vis a MLH1 promoter methylation-based one in colorectal cancer.

OriginalsprogEngelsk
Artikelnummer106127
TidsskriftiScience
Vol/bind26
Nummer3
Antal sider16
ISSN2589-0042
DOI
StatusUdgivet - mar. 2023

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