EpiGe: A machine-learning strategy for rapid classification of medulloblastoma using PCR-based methyl-genotyping

Soledad Gómez-González; Joshua Llano; Marta Garcia; Alicia Garrido-Garcia; Mariona Suñol; Isadora Lemos; Sara Perez-Jaume; Noelia Salvador; Nagore Gene-Olaciregui; Raquel Arnau Galán; Vicente Santa-María; Marta Perez-Somarriba; Alicia Castañeda; José Hinojosa; Ursula Winter; Francisco Barbosa Moreira; Fabiana Lubieniecki; Valeria Vazquez; Jaume Mora; Ofelia Cruz; Andrés Morales La Madrid; Alexandre Perera; Cinzia Lavarino

doi:10.1016/j.isci.2023.107598

EpiGe: A machine-learning strategy for rapid classification of medulloblastoma using PCR-based methyl-genotyping

iScience. 2023 Aug 12;26(9):107598. doi: 10.1016/j.isci.2023.107598. eCollection 2023 Sep 15.

Authors

Soledad Gómez-González^{1

2}, Joshua Llano^{2

3

4}, Marta Garcia^{1

2}, Alicia Garrido-Garcia^{1

2}, Mariona Suñol⁵, Isadora Lemos⁶, Sara Perez-Jaume^{1

2}, Noelia Salvador⁶, Nagore Gene-Olaciregui⁶, Raquel Arnau Galán⁵, Vicente Santa-María⁷, Marta Perez-Somarriba⁸, Alicia Castañeda⁹, José Hinojosa¹⁰, Ursula Winter¹¹, Francisco Barbosa Moreira¹¹, Fabiana Lubieniecki¹¹, Valeria Vazquez¹¹, Jaume Mora^{1

9}, Ofelia Cruz⁷, Andrés Morales La Madrid⁷, Alexandre Perera^{2

3

4}, Cinzia Lavarino^{1

2

6}

Affiliations

¹ Laboratory of Developmental Tumor Biology, Institut de Recerca Sant Joan de Déu, Pediatric Cancer Center Barcelona, Hospital Sant Joan de Déu, Barcelona, Spain.
² Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain.
³ B2SLab, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, Spain.
⁴ Networking Biomedical Research Centre in the Subject Area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.
⁵ Department of Pathology, Hospital Sant Joan de Déu, Barcelona, Spain.
⁶ Laboratory of Molecular Oncology, Pediatric Cancer Center Barcelona, Hospital Sant Joan de Déu, Barcelona, Spain.
⁷ Neuro Oncology Unit, Pediatric Cancer Center Barcelona, Hospital Sant Joan de Déu, Barcelona, Spain.
⁸ Children & Young People's Unit, The Royal Marsden NHS Foundation Trust, London, UK.
⁹ Pediatric Solid Tumor Unit, Pediatric Cancer Center Barcelona, Hospital Sant Joan de Déu, Barcelona, Spain.
¹⁰ Department of Neurosurgery, Hospital Sant Joan de Déu, Barcelona, Spain.
¹¹ Department of Pathology, Pediatric Hospital S.A.M.I.C. Prof. Dr. Juan P. Garrahan, Buenos Aires, Argentina.

Abstract

Molecular classification of medulloblastoma is critical for the treatment of this brain tumor. Array-based DNA methylation profiling has emerged as a powerful approach for brain tumor classification. However, this technology is currently not widely available. We present a machine-learning decision support system (DSS) that enables the classification of the principal molecular groups-WNT, SHH, and non-WNT/non-SHH-directly from quantitative PCR (qPCR) data. We propose a framework where the developed DSS appears as a user-friendly web-application-EpiGe-App-that enables automated interpretation of qPCR methylation data and subsequent molecular group prediction. The basis of our classification strategy is a previously validated six-cytosine signature with subgroup-specific methylation profiles. This reduced set of markers enabled us to develop a methyl-genotyping assay capable of determining the methylation status of cytosines using qPCR instruments. This study provides a comprehensive approach for rapid classification of clinically relevant medulloblastoma groups, using readily accessible equipment and an easy-to-use web-application.t.

Keywords: Cancer; Health technology; Machine learning.