Building a Model of Myelodysplastic Syndromes From the Ground Up

The diagnosis and classification of myelodysplastic syndromes (MDS) have undergone several iterations since their criteria were initially codified by the French-American-British Classification in 1982.¹  These sequential classification systems have adapted by incorporating new data (for example, the definition of MDS with isolated del[5q] was recently expanded to include up to one other cytogenetic abnormality) but have generally been faithful to the core precedent of prior classification systems. The current World Health Organization (WHO) classification of MDS still relies largely on morphologic parameters in establishing the disease subtypes.²  Indeed, specific dysplastic cytologic features of hematopoietic cells have been validated as both diagnostic and prognostic criteria in MDS.^3,4  However, evaluating dysplasia is inherently subjective, and the dysplastic lineage(s) identified are frequently incongruent with the patient’s actual cytopenias.⁵  Moreover, interrogation of MDS by next-generation sequencing data in recent years has elucidated a remarkable genetic heterogeneity of MDS^6,7 ; yet uncertainty remains regarding how to incorporate these new data into the current classification scheme and how the different morphologic appearances of MDS may relate to the myriad mutation patterns.

Dr. Yasunobu Nagata and colleagues applied machine learning algorithms to a series of 1,097 patients with genetically characterized myeloid neoplasms. Their samples included MDS as well as some myelodysplastic/myeloproliferative neoplasms (MDS/MPN) and acute myeloid leukemia that had evolved from MDS or MDS/MPN. Rather than using existing WHO disease subtypes, they distilled the morphology (and clinical features such as blood counts) into 24 distinct individual parameters that were each scored by an experienced pathologist and correlated with specific gene mutations. They identified several significant associations between individual morphologic/cytopenia parameters and mutations, indicating that the type of dysplasia we see through the microscope is indeed a manifestation of the specific mutation portfolio in each unique MDS case. Unsupervised analysis based on consensus clustering revealed five patterns of co-occurring morphologic features, termed morphologic “profiles” (Figure 1, P1-P5). While some of these profiles bore strong resemblance to well-established disease subtypes, such as MDS with excess blasts or chronic myelomonocytic leukemia (CMML), other profiles did not correspond to any WHO disease subtype. The authors also applied Bayesian machine learning techniques to create eight signatures of somatic mutations in their cohort and correlated these global signatures with the morphologic profiles. In cases lacking excess blasts (termed the “low-risk” group), they found six significant morphologic-genetic profile associations that were identified in both discovery and validation cohorts (Figure 1). These included well-known associations such as the correlation of morphologic features of the WHO entity MDS/MPN with ring sideroblasts, and thrombocytosis (MDS/MPN-RS-T) with co-mutation of SF3B1 and JAK2. However, they also found unexpected heterogeneity and overlap in some morphologic profiles: for example, the “CMML-like” profile included both the well-known TET2 and SRSF2 co-mutated signature, but also a signature characterized by TET2 mutation and wildtype SRSF2. Genetic signature heterogeneity was also observed in the subset of cases with excess blasts (termed the “high-risk” group), which included six discrete signatures such as TP53, U2AF1, or DNMT3A mutations; significant differences in survival were found among the different genetic signatures even within these excess blast cases (Figure 2).