Molecular patterns identify distinct subclasses of myeloid neoplasia

Tariq Kewan (), Arda Durmaz, Waled Bahaj, Carmelo Gurnari, Laila Terkawi, Hussein Awada, Olisaemeka D. Ogbue, Ramsha Ahmed, Simona Pagliuca, Hassan Awada, Yasuo Kubota, Minako Mori, Ben Ponvilawan, Bayan Al-Share, Bhumika J. Patel, Hetty E. Carraway, Jacob Scott, Suresh K. Balasubramanian, Taha Bat, Yazan Madanat, Mikkael A. Sekeres, Torsten Haferlach, Valeria Visconte () and Jaroslaw P. Maciejewski ()
Additional contact information
Tariq Kewan: Taussig Cancer Institute, Cleveland Clinic
Arda Durmaz: Taussig Cancer Institute, Cleveland Clinic
Waled Bahaj: Taussig Cancer Institute, Cleveland Clinic
Carmelo Gurnari: Taussig Cancer Institute, Cleveland Clinic
Laila Terkawi: Taussig Cancer Institute, Cleveland Clinic
Hussein Awada: Taussig Cancer Institute, Cleveland Clinic
Olisaemeka D. Ogbue: Taussig Cancer Institute, Cleveland Clinic
Ramsha Ahmed: Taussig Cancer Institute, Cleveland Clinic
Simona Pagliuca: Taussig Cancer Institute, Cleveland Clinic
Hassan Awada: Roswell Park Comprehensive Cancer Center
Yasuo Kubota: Taussig Cancer Institute, Cleveland Clinic
Minako Mori: Taussig Cancer Institute, Cleveland Clinic
Ben Ponvilawan: Taussig Cancer Institute, Cleveland Clinic
Bayan Al-Share: Wayne State University
Bhumika J. Patel: Taussig Cancer Institute, Cleveland Clinic
Hetty E. Carraway: Taussig Cancer Institute, Cleveland Clinic
Jacob Scott: Taussig Cancer Institute, Cleveland Clinic
Suresh K. Balasubramanian: Wayne State University
Taha Bat: University of Texas Southwestern Medical Center
Yazan Madanat: University of Texas Southwestern Medical Center
Mikkael A. Sekeres: University of Miami
Torsten Haferlach: MLL Munich Leukemia Laboratory
Valeria Visconte: Taussig Cancer Institute, Cleveland Clinic
Jaroslaw P. Maciejewski: Taussig Cancer Institute, Cleveland Clinic

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: Abstract Genomic mutations drive the pathogenesis of myelodysplastic syndromes and acute myeloid leukemia. While morphological and clinical features have dominated the classical criteria for diagnosis and classification, incorporation of molecular data can illuminate functional pathobiology. Here we show that unsupervised machine learning can identify functional objective molecular clusters, irrespective of anamnestic clinico-morphological features, despite the complexity of the molecular alterations in myeloid neoplasia. Our approach reflects disease evolution, informed classification, prognostication, and molecular interactions. We apply machine learning methods on 3588 patients with myelodysplastic syndromes and secondary acute myeloid leukemia to identify 14 molecularly distinct clusters. Remarkably, our model shows clinical implications in terms of overall survival and response to treatment even after adjusting to the molecular international prognostic scoring system (IPSS-M). In addition, the model is validated on an external cohort of 412 patients. Our subclassification model is available via a web-based open-access resource ( https://drmz.shinyapps.io/mds_latent ).

Date: 2023
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DOI: 10.1038/s41467-023-38515-4

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