TY - JOUR

T1 - An artificial intelligence-assisted clinical framework to facilitate diagnostics and translational discovery in hematologic neoplasia

AU - Tang, Ming

AU - Antić, Željko

AU - Fardzadeh, Pedram

AU - Pietzsch, Stefan

AU - Schröder, Charlotte

AU - Eberhardt, Adrian

AU - van Bömmel, Alena

AU - Escherich, Gabriele

AU - Hofmann, Winfried

AU - Horstmann, Martin A.

AU - Illig, Thomas

AU - McCrary, J. Matt

AU - Lentes, Jana

AU - Metzler, Markus

AU - Nejdl, Wolfgang

AU - Schlegelberger, Brigitte

AU - Schrappe, Martin

AU - Miarka-Walczyk, Karolina

AU - Patsorczak, Agata

AU - Cario, Gunnar

AU - Renard, Bernhard Y.

AU - Stanulla, Martin

AU - Bergmann, Anke Katharina

AU - Zimmermann, Martin

N1 - Publisher Copyright: © 2024 The Authors

PY - 2024/6

Y1 - 2024/6

N2 - Background: The increasing volume and intricacy of sequencing data, along with other clinical and diagnostic data, like drug responses and measurable residual disease, creates challenges for efficient clinical comprehension and interpretation. Using paediatric B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) as a use case, we present an artificial intelligence (AI)-assisted clinical framework clinALL that integrates genomic and clinical data into a user-friendly interface to support routine diagnostics and reveal translational insights for hematologic neoplasia. Methods: We performed targeted RNA sequencing in 1365 cases with haematological neoplasms, primarily paediatric B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) from the AIEOP-BFM ALL study. We carried out fluorescence in situ hybridization (FISH), karyotyping and arrayCGH as part of the routine diagnostics. The analysis results of these assays as well as additional clinical information were integrated into an interactive web interface using Bokeh, where the main graph is based on Uniform Manifold Approximation and Projection (UMAP) analysis of the gene expression data. At the backend of the clinALL, we built both shallow machine learning models and a deep neural network using Scikit-learn and PyTorch respectively. Findings: By applying clinALL, 78% of undetermined patients under the current diagnostic protocol were stratified, and ambiguous cases were investigated. Translational insights were discovered, including IKZF1plus status dependent subpopulations of BCR::ABL1 positive patients, and a subpopulation within ETV6::RUNX1 positive patients that has a high relapse frequency. Our best machine learning models, LDA and PASNET-like neural network models, achieve F1 scores above 97% in predicting patients’ subgroups. Interpretation: An AI-assisted clinical framework that integrates both genomic and clinical data can take full advantage of the available data, improve point-of-care decision-making and reveal clinically relevant insights promptly. Such a lightweight and easily transferable framework works for both whole transcriptome data as well as the cost-effective targeted RNA-seq, enabling efficient and equitable delivery of personalized medicine in small clinics in developing countries. Funding: German Ministry of Education and Research (BMBF), German Research Foundation (DFG) and Foundation for Polish Science.

AB - Background: The increasing volume and intricacy of sequencing data, along with other clinical and diagnostic data, like drug responses and measurable residual disease, creates challenges for efficient clinical comprehension and interpretation. Using paediatric B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) as a use case, we present an artificial intelligence (AI)-assisted clinical framework clinALL that integrates genomic and clinical data into a user-friendly interface to support routine diagnostics and reveal translational insights for hematologic neoplasia. Methods: We performed targeted RNA sequencing in 1365 cases with haematological neoplasms, primarily paediatric B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) from the AIEOP-BFM ALL study. We carried out fluorescence in situ hybridization (FISH), karyotyping and arrayCGH as part of the routine diagnostics. The analysis results of these assays as well as additional clinical information were integrated into an interactive web interface using Bokeh, where the main graph is based on Uniform Manifold Approximation and Projection (UMAP) analysis of the gene expression data. At the backend of the clinALL, we built both shallow machine learning models and a deep neural network using Scikit-learn and PyTorch respectively. Findings: By applying clinALL, 78% of undetermined patients under the current diagnostic protocol were stratified, and ambiguous cases were investigated. Translational insights were discovered, including IKZF1plus status dependent subpopulations of BCR::ABL1 positive patients, and a subpopulation within ETV6::RUNX1 positive patients that has a high relapse frequency. Our best machine learning models, LDA and PASNET-like neural network models, achieve F1 scores above 97% in predicting patients’ subgroups. Interpretation: An AI-assisted clinical framework that integrates both genomic and clinical data can take full advantage of the available data, improve point-of-care decision-making and reveal clinically relevant insights promptly. Such a lightweight and easily transferable framework works for both whole transcriptome data as well as the cost-effective targeted RNA-seq, enabling efficient and equitable delivery of personalized medicine in small clinics in developing countries. Funding: German Ministry of Education and Research (BMBF), German Research Foundation (DFG) and Foundation for Polish Science.

KW - Clinical framework

KW - Data integration

KW - Leukaemia

KW - Machine learning

UR - http://www.scopus.com/inward/record.url?scp=85194156680&partnerID=8YFLogxK

U2 - 10.1016/j.ebiom.2024.105171

DO - 10.1016/j.ebiom.2024.105171

M3 - Article

AN - SCOPUS:85194156680

VL - 104

JO - eBioMedicine

JF - eBioMedicine

M1 - 105171

ER -