Prognosis in myelodysplastic syndromes: The attractions and limitations of simplicity
Authors: David P. Steensma
Journal: American Journal of Hematology (2018)

Legendary physicist Enrico Fermi (1901–1954) was known for making surprisingly accurate quantitative estimates on the fly, often with few definitive data from which to work. He famously estimated to within a few kilotons the strength of the initial atomic bomb burst at the 1945 Trinity test site, merely by tossing some papers into the New Mexico desert air at the moment the atomic wind reached his observing station and measuring how far they flew.1 Fermi's back-of-the-envelope style of estimates, which became known as “Fermi problems,” are still widely used by scientists and engineers today, since they provide a first approximation that can reveal whether subsequent calculations are likely to be erroneous. Fermi problems can be used to guess everything from the number of piano tuners in St. Louis to the quantity of leaves in Central Park or hairs on a human scalp. In the natural sciences, sometimes just being in the ballpark with an estimate can provide an interesting insight or can sketch the outlines of a more definitive solution. When it comes to estimating prognosis for a human disease, there is a tension between such quick calculations versus results generated using more detailed, but time-consuming, approaches. This tension is heightened by the increasingly hectic environment of clinical practice, especially in the United States, where documentation burden and poorly chosen payer metrics mean that the average patient encounter requires doctors to spend more than twice as much time charting as face-to-face with the patient.2 Under such malignant time pressures, practicing clinicians prefer to make calculations for patient management using simple tools they can memorize or post on the wall of a clinic workroom, rather than needing to take the time to log in to a dedicated website or a mobile app. For example, while frailty indices that incorporate multiple tests and lengthy depression screening questionnaires are all well validated, geriatricians can learn a lot just from watching an older person get up from a chair and walk, and palliative care physicians know that asking a patient, “Are you depressed?” is a screening tool with startlingly high sensitivity and specificity.3, 4 In the context of myelodysplastic syndromes (MDS), risk stratification is especially important for counseling patients and selecting treatment, because MDS are highly heterogeneous with a diverse natural history.5 Some patients will die of complications of cytopenias within just a few months of diagnosis, while others will live more than a decade with smoldering, indolent disease that has only a modest impact on their lifestyle. The most widely used risk stratification tool in MDS to distinguish between these groups is the revised International Prognostic Scoring System (IPSS-R), published by the International Working Group for Prognosis in MDS (IWG-PM) in 2012.6 The IPSS-R includes the number and depth of cytopenias, marrow blast proportion, and karyotype, and separates patients into five distinct risk groups. The IPSS-R does not include molecular genetic data, since it was developed from datasets created before the contemporary era in which next generation sequencing is widely used in clinical practice. Therefore, it is a high priority to integrate molecular data into existing risk stratification systems such as the IPSS-R, as well as to try to overcome other limitations of the IPSS-R, such as its validity only at the time of initial diagnosis in untreated patients with de novo disease. But genomics in particular is challenging to incorporate in a simple fashion into familiar clinicopathological risk stratification systems, since a binary yes/no answer about a mutation provides only limited information about disease biology, and mutation status is highly dependent on context. As Einstein allegedly said, “Everything should be made as simple as possible—but not simpler.” MDS are associated with tremendous molecular heterogeneity, with dozens of genes recurrently mutated; none of the individual alterations are observed in more than 25%-30% of patients.7 These diseases are also often oligoclonal with a complex clonal architecture,8 and some altered proteins encoded by mutant genes may interact while others have no cross-talk. In addition, while for some genes there are recurrent hotspots (eg, almost all U2AF1 mutations are S34 or Q157P, and NRAS mutations cluster in codons 12, 13, and 61), for other genes such as TP53 there are myriad variations including point mutations that differ from each other in prognosis and that differ from gene deletions. Further, some variants picked up on current sequencing platforms are even germline, which is not always straightforward to determine but can have important implications both for treatment approach and for counseling of families.9 Gangat et al. from Mayo Clinic have studied 300 patients with primary MDS and analyzed the implications of mutations detected by a targeted capture assay of 27 genes in the context of the Mayo cytogenetic risk stratification system.10 The investigators had previously reported this karyotyping risk model, which recognizes the high risk of monosomal karyotype and weights such karyotypes accordingly.11 For simplicity's sake, the Mayo cytogenetic system groups IPSS-R-associated karyotype changes into two groups (Mayo intermediate risk = IPSS-R intermediate/poor/very poor, while Mayo low risk = IPSS-R good/very good). In the new Mayo series incorporating targeted DNA sequencing, RUNX1 and ASXL1 mutations were associated with independent adverse prognosis, while SF3B1 mutations were more favorable. TP53 mutations—identified in most other series as indicative of a grim prognosis (and uncommon in de novo disease compared with therapy-related MDS/AML)—were identified as adverse in univariate analysis; however, TP53 did not have independent significance in multivariable analysis, probably because these mutations cluster with complex and monosomal karyotype already accounted for by the cytogenetic component of the Mayo system. The current Mayo series benefits from high-quality hematopathology and cytogenetics, and detailed clinical characterization of patients by the Mayo hematology consultants. Because of the mutational complexity in MDS, properly accounting for clonal heterogeneity will require a very large dataset indeed. The IWG-PM is currently coordinating a multinational effort that will include sequencing data for 17 genes more than 3500 patients from 19 centers and result in an IPSS-R with molecular data (“IPSS-RM”). Bejar et al. presented a first pass of these data in abstract form in 2015, at which time mutation in SF3B1 was reported as independently favorable and TP53 unfavorable; illustrating the potential interaction between co-mutations, DNMT3A was moderately unfavorable with respect to overall survival, but when co-mutated with SF3B1, DNMT3A was associated with outcomes just as good as for SF3B1 mutation alone.12 But even this large IWG-PM dataset will not include substantial numbers of patients with all possible co-mutation patterns, or dynamic information obtained across the disease course—nor can it include relevant non-cell-autonomous information such as the status of the marrow microenvironment or immune response to the mutant cells, which are clearly important in risk of clonal progression and clinical outcome, but are currently not easily measurable.13 There will always be a place for back-of-the-envelope estimates such as that proposed by Gangat et al., but the complexity of clonal architecture in a disease like MDS mean that machine learning will be required to sort out what gene mutations mean, and electronic computational tools will be needed in the clinic to apply next-generation sequencing results in individual cases. The major electronic medical record tools as currently designed cannot even automatically calculate something as basic as the IPSS-R, and they make physicians' lives more difficult, but ultimately well-designed electronic tools will be our salvation. Even Fermi used computers when available: ENIAC, the first general-purpose computer, debuted in 1946, and Fermi was a member of the very first team granted access to this “Great Brain.” Nothing to report. The authors have no relevant disclosures.