A multimodal analysis of genomic and RNA splicing features in myeloid malignancies
Arda Durmaz,
Carmelo Gurnari,
Courtney E. Hershberger,
Simona Pagliuca,
Noah Daniels,
Hassan Awada,
Hussein Awada,
Vera Adema,
Minako Mori,
Ben Ponvilawan,
Yasuo Kubota,
Tariq Kewan,
Waled S. Bahaj,
John Barnard,
Jacob Scott,
Richard A. Padgett,
Torsten Haferlach,
Jaroslaw P. Maciejewski,
Valeria Visconte
Affiliations
Arda Durmaz
Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA; Systems Biology and Bioinformatics Department, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
Carmelo Gurnari
Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA; Department of Biomedicine and Prevention, PhD in Immunology, Molecular Medicine and Applied Biotechnology, University of Rome Tor Vergata, Rome, Italy
Courtney E. Hershberger
Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA
Simona Pagliuca
Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA; Department of Clinical Hematology, CHRU de Nancy, Nancy, France
Noah Daniels
Department of Cardiovascular & Metabolic Sciences, Cleveland Clinic, Cleveland, OH, USA
Hassan Awada
Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
Hussein Awada
Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
Vera Adema
MD Anderson Cancer Center, Houston, TX, USA
Minako Mori
Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
Ben Ponvilawan
Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
Yasuo Kubota
Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
Tariq Kewan
Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
Waled S. Bahaj
Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
John Barnard
Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA
Jacob Scott
Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA; Systems Biology and Bioinformatics Department, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
Richard A. Padgett
Department of Cardiovascular & Metabolic Sciences, Cleveland Clinic, Cleveland, OH, USA
Torsten Haferlach
MLL Munich Leukemia Laboratory, Munich, Germany
Jaroslaw P. Maciejewski
Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
Valeria Visconte
Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA; Corresponding author
Summary: RNA splicing dysfunctions are more widespread than what is believed by only estimating the effects resulting by splicing factor mutations (SFMT) in myeloid neoplasia (MN). The genetic complexity of MN is amenable to machine learning (ML) strategies. We applied an integrative ML approach to identify co-varying features by combining genomic lesions (mutations, deletions, and copy number), exon-inclusion ratio as measure of RNA splicing (percent spliced in, PSI), and gene expression (GE) of 1,258 MN and 63 normal controls. We identified 15 clusters based on mutations, GE, and PSI. Different PSI levels were present at various extents regardless of SFMT suggesting that changes in RNA splicing were not strictly related to SFMT. Combination of PSI and GE further distinguished the features and identified PSI similarities and differences, common pathways, and expression signatures across clusters. Thus, multimodal features can resolve the complex architecture of MN and help identifying convergent molecular and transcriptomic pathways amenable to therapies.
