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Gimeno M, San José-Enériz E, Villar S, Agirre X, Prosper F, Rubio A, Carazo F. Explainable artificial intelligence for precision medicine in acute myeloid leukemia. Front Immunol 2022; 13:977358. [PMID: 36248800 PMCID: PMC9556772 DOI: 10.3389/fimmu.2022.977358] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 09/13/2022] [Indexed: 12/02/2022] Open
Abstract
Artificial intelligence (AI) can unveil novel personalized treatments based on drug screening and whole-exome sequencing experiments (WES). However, the concept of “black box” in AI limits the potential of this approach to be translated into the clinical practice. In contrast, explainable AI (XAI) focuses on making AI results understandable to humans. Here, we present a novel XAI method -called multi-dimensional module optimization (MOM)- that associates drug screening with genetic events, while guaranteeing that predictions are interpretable and robust. We applied MOM to an acute myeloid leukemia (AML) cohort of 319 ex-vivo tumor samples with 122 screened drugs and WES. MOM returned a therapeutic strategy based on the FLT3, CBFβ-MYH11, and NRAS status, which predicted AML patient response to Quizartinib, Trametinib, Selumetinib, and Crizotinib. We successfully validated the results in three different large-scale screening experiments. We believe that XAI will help healthcare providers and drug regulators better understand AI medical decisions.
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Affiliation(s)
- Marian Gimeno
- Departamento de Ingeniería Biomédica y Ciencias, TECNUN, Universidad de Navarra, San Sebastián, Spain
| | - Edurne San José-Enériz
- Programa Hemato-Oncología, Centro de Investigación Médica Aplicada, Instituto de Investigación Sanitaria de Navarra (IDISNA), Universidad de Navarra, Pamplona, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
| | - Sara Villar
- Departamento de Hematología and CCUN (Cancer Center University of Navarra), Clínica Universidad de Navarra, Universidad de Navarra, Pamplona, Spain
| | - Xabier Agirre
- Programa Hemato-Oncología, Centro de Investigación Médica Aplicada, Instituto de Investigación Sanitaria de Navarra (IDISNA), Universidad de Navarra, Pamplona, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
| | - Felipe Prosper
- Programa Hemato-Oncología, Centro de Investigación Médica Aplicada, Instituto de Investigación Sanitaria de Navarra (IDISNA), Universidad de Navarra, Pamplona, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
- Departamento de Hematología and CCUN (Cancer Center University of Navarra), Clínica Universidad de Navarra, Universidad de Navarra, Pamplona, Spain
| | - Angel Rubio
- Departamento de Ingeniería Biomédica y Ciencias, TECNUN, Universidad de Navarra, San Sebastián, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Universidad de Navarra, Pamplona, Spain
- *Correspondence: Angel Rubio, ; Fernando Carazo,
| | - Fernando Carazo
- Departamento de Ingeniería Biomédica y Ciencias, TECNUN, Universidad de Navarra, San Sebastián, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Universidad de Navarra, Pamplona, Spain
- *Correspondence: Angel Rubio, ; Fernando Carazo,
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