Molecular patterns identify distinct subclasses of myeloid neoplasia

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).

Classification and prognostic stratification based on genomic features in myelodysplastic neoplasms, myeloproliferative neoplasms, and their overlapping conditions

“…Recent genomic studies have introduced an alternative paradigm for the classi cation of myeloid neoplasms (MNs), focusing on shared and distinct genomic patterns (1)(2)(3). Landmark studies in myeloproliferative neoplasms (MPN) (4), myelodysplastic neoplasms (MDS) (5,6), and their overlapping diseases (7) demonstrate the potential of genomic classi cation systems for disease subclassi cation and personalized prognostication. This has led to a fundamental shift towards genetics-based approaches in characterizing these diseases, with recent classi cation systems increasingly incorporating genomic attributes to align more closely with their underlying biology (2,8).…”

“…Nevertheless, current frameworks still prioritize clinical features over biological characteristics in distinguishing major classes of MNs (3,8). The morphological and phenotypic criteria have subjective elements, leading to inter-observer variability in diagnosis (5). This results in biological heterogeneity within disease groups and hinders insights into pathogenic mechanisms that traverse boundaries.…”

Network-based clustering unveils interconnected landscapes of genomic and clinical features across myeloid malignancies

“…Leveraging a data-driven approach, previous studies have clustered the mutational and cytogenetic profiles of myeloid malignancies to derive risk scores in AML 10,13,15,16 , MDS 14,17,18 , CMML 19 , and MPS 20,21 . Outside of myeloid malignancies, network-based approaches have been employed to effectively condense heterogeneous genomic profiles for prediction and patient stratification [22][23][24][25] .…”

“…However, most previous clustering strategies did not integrate clinical covariates, such as blood and bone marrow values and demographic factors, into their clustering approaches, despite their established relevance for prognosis and clinical decisionmaking [26][27][28][29] . For instance, a previous study focused exclusively on genomic profiles to cluster MDS patients with secondary AML patients 14 . While insightful, as the authors acknowledged, this approach was unable to integrate clinical data for more nuanced patient stratification.…”

Using machine learning to unravel the intricacy of acute myeloid leukemia

“…Having said that, this study complements other key results achieved through ML in the field of AML in recent years. The interconnection of several variables in large cohorts of patients has allowed researchers to explore, through ML, different patient stratifications 2 , 3 and integrated prognostic algorithms, 4 to identify biomarkers, 5 and support cytomorphological diagnosis. 6 This huge amount of data has offered insights into different aspects of AML management, respecting the granularities of disease features, and suggesting the possibilities of adding new factors or classifiers to consider in the tailoring of treatment strategy.…”