A multistate model of survival prediction and event monitoring in prefibrotic myelofibrosis

SOHO State of the Art Updates and Next Questions | Diagnosis, Outcomes, and Management of Prefibrotic Myelofibrosis

Prefibrotic primary myelofibrosis (prefibrotic PMF) is a myeloproliferative neoplasm with distinct characteristics comprising histopathological and clinico-biological parameters. It is classified as a subtype of primary myelofibrosis. In clinical practice, it is essential to correctly distinguish prefibrotic PMF from essential thrombocythemia especially but also overt PMF besides other myeloid neoplasms. Risk stratification and survival outcomes for prefibrotic PMF are worse than that of ET but better than that of overt PMF. Rates of progression to overt PMF and blast phase disease are also higher for prefibrotic PMF than ET. In this review we first discuss the historical context to the evolution of prefibrotic PMF as an entity, its presenting features and diagnostic criteria. We emphasize the differences between prefibrotic PMF, ET, and overt PMF with regards to presenting features and disease outcomes including thrombohemorrhagic events and progression to fibrotic and blast phase disease. Next, we discuss the risk stratification models and contextualize these in the setting of clinical management. We share our view of personalizing treatment to address unique patient needs in the context of currently available management options. Lastly, we discuss areas of critical need in clinical research and speculate on the possibility of future disease course modifying therapies in prefibrotic PMF.

Molecular Genetic Profile of Myelofibrosis: Implications in the Diagnosis, Prognosis, and Treatment Advancements

Myelofibrosis (MF) is an essential element of primary myelofibrosis, whereas secondary MF may develop in the advanced stages of other myeloid neoplasms, especially polycythemia vera and essential thrombocythemia. Over the last two decades, advances in molecular diagnostic techniques, particularly the integration of next-generation sequencing in clinical laboratories, have revolutionized the diagnosis, classification, and clinical decision making of myelofibrosis. Driver mutations involving JAK2, CALR, and MPL induce hyperactivity in the JAK-STAT signaling pathway, which plays a central role in cell survival and proliferation. Approximately 80% of myelofibrosis cases harbor additional mutations, frequently in the genes responsible for epigenetic regulation and RNA splicing. Detecting these mutations is crucial for diagnosing myeloproliferative neoplasms (MPNs), especially in cases where no mutations are present in the three driver genes (triple-negative MPNs). While fibrosis in the bone marrow results from the disturbance of inflammatory cytokines, it is fundamentally associated with mutation-driven hematopoiesis. The mutation profile and order of acquiring diverse mutations influence the MPN phenotype. Mutation profiling reveals clonal diversity in MF, offering insights into the clonal evolution of neoplastic progression. Prognostic prediction plays a pivotal role in guiding the treatment of myelofibrosis. Mutation profiles and cytogenetic abnormalities have been integrated into advanced prognostic scoring systems and personalized risk stratification for MF. Presently, JAK inhibitors are part of the standard of care for MF, with newer generations developed for enhanced efficacy and reduced adverse effects. However, only a minority of patients have achieved a significant molecular-level response. Clinical trials exploring innovative approaches, such as combining hypomethylation agents that target epigenetic regulators, drugs proven effective in myelodysplastic syndrome, or immune and inflammatory modulators with JAK inhibitors, have demonstrated promising results. These combinations may be more effective in patients with high-risk mutations and complex mutation profiles. Expanding mutation profiling studies with more sensitive and specific molecular methods, as well as sequencing a broader spectrum of genes in clinical patients, may reveal molecular mechanisms in cases currently lacking detectable driver mutations, provide a better understanding of the association between genetic alterations and clinical phenotypes, and offer valuable information to advance personalized treatment protocols to improve long-term survival and eradicate mutant clones with the hope of curing MF.

International Consensus Classification of myeloid and lymphoid neoplasms: myeloproliferative neoplasms

The recently published International Consensus Classification (ICC) of myeloid neoplasms summarized the results of an in-depth effort by pathologists, oncologists, and geneticists aimed to update the 2017 World Health Organization classification system for hematopoietic tumors. Along these lines, several important modifications were implemented in the classification of myeloproliferative neoplasms (MPNs). For chronic myeloid leukemia, BCR::ABL1-positive, the definition of accelerated and blast phase was simplified, and in the BCR::ABL1-negative MPNs, the classification was slightly updated to improve diagnostic specificity with a more detailed and better validated morphologic approach and the recommendation of more sensitive molecular techniques to capture in particular early stage diseases. In this regard, high sensitive single target (RT-qPCR, ddPCR) or multi-target next-generation sequencing assays with a minimal sensitivity of VAF 1% are now important for a proper diagnostic identification of MPN cases with low allelic frequencies at initial presentation. This review discusses the updated diagnostic criteria of MPN according to the ICC, particularly by highlighting the new concepts and how they can be applied in clinical settings to obtain an appropriate prognostic relevant diagnosis.

The international consensus classification of myeloid neoplasms and acute Leukemias: myeloproliferative neoplasms

A group of international experts, including hematopathologists, oncologists, and geneticists were recently summoned (September 2021, Chicago, IL, USA) to update the 2016/17 World Health Organization classification system for hematopoietic tumors. After careful deliberation, the group introduced the new International Consensus Classification (ICC) for Myeloid Neoplasms and Acute Leukemias. This current in-depth review focuses on the ICC-2022 category of JAK2 mutation-prevalent myeloproliferative neoplasms (MPNs): essential thrombocythemia, polycythemia vera, primary myelofibrosis, and MPN, unclassifiable. The ICC MPN subcommittee chose to preserve the primary role of bone marrow morphology in disease classification and diagnostics, while also acknowledging the complementary role of genetic markers for establishing clonality, facilitating MPN subtype designation, and disease prognostication.

Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis

Myelofibrosis (MF) is a myeloproliferative neoplasm (MPN) with heterogeneous clinical course. Allogeneic hematopoietic cell transplantation remains the only curative therapy, but its morbidity and mortality require careful candidate selection. Therefore, accurate disease risk prognostication is critical for treatment decision-making. We obtained registry data from patients diagnosed with MF in 60 Spanish institutions (N = 1386). These were randomly divided into a training set (80%) and a test set (20%). A machine learning (ML) technique (random forest) was used to model overall survival (OS) and leukemia-free survival (LFS) in the training set, and the results were validated in the test set. We derived the AIPSS-MF (Artificial Intelligence Prognostic Scoring System for Myelofibrosis) model, which was based on 8 clinical variables at diagnosis and achieved high accuracy in predicting OS (training set c-index, 0.750; test set c-index, 0.744) and LFS (training set c-index, 0.697; test set c-index, 0.703). No improvement was obtained with the inclusion of MPN driver mutations in the model. We were unable to adequately assess the potential benefit of including adverse cytogenetics or high-risk mutations due to the lack of these data in many patients. AIPSS-MF was superior to the IPSS regardless of MF subtype and age range and outperformed the MYSEC-PM in patients with secondary MF. In conclusion, we have developed a prediction model based exclusively on clinical variables that provides individualized prognostic estimates in patients with primary and secondary MF. The use of AIPSS-MF in combination with predictive models that incorporate genetic information may improve disease risk stratification.

The different variant allele frequencies of type I/type II mutations and the distinct molecular landscapes in CALR-mutant essential thrombocythaemia and primary myelofibrosis

OBJECTIVE

Calreticulin (CALR) mutations have been identified as driver mutations in a quarter of patients with essential thrombocythaemia (ET) and primary myelofibrosis (PMF), which are subgroups of myeloproliferative neoplasms (MPNs). A 52-bp deletion (type I mutation) and a 5-bp insertion (type II mutation) are the most frequent variants. To better understand the impact of different CALR mutant variants, with or without nondriver mutations, on the clinical subtypes of MPN needs further investigation.

METHODS

The clinical characteristics, laboratory parameters and genetic mutation statuses were analysed in a cohort of 77 MPN patients with CALR mutations (ET = 24, prePMF = 33, and overt PMF = 20). Targeted NGS using a 38-gene panel was performed to evaluate the variant allele frequency (VAF) of CALR type I/type II mutations and assess the molecular landscape of nondriver gene mutations.

RESULTS

A lower VAF of type I vs. type II was observed in CALR-mutant ET, prePMF and overt PMF, and a higher frequency of type I vs. type II was found in CALR-mutant overt PMF. Additional somatic mutations were indicated to be useful in understanding the pathogenesis of MPN. In this study, the mutation landscape was more complex in overt PMF than in ET or in prePMF. Mutations in epigenetic regulators (ASXL1, EZH2 and TET2) were more common in overt PMF.

CONCLUSIONS

The two different subtypes of CALR mutations may have different impacts on MPN. A lower VAF of CALR type I indicates a greater contribution to disease progression in MPN, and increased nondriver mutations may be important in myelofibrosis progression.

Challenges of diagnosing and managing pre-fibrotic myelofibrosis: A case-based and practical approach

Pre-Fibrotic Myelofibrosis is a frequently under-recognised entity that has distinct features separate to those of both Essential Thrombocythaemia and overt Primary Myelofibrosis. Misdiagnosis is relatively common due to subtle differences in bone marrow trephine morphology and multidisciplinary approaches are required. The clinical phenotype and disease course is heterogeneous and hence management approaches tend to vary widely. Although patients may initially be asymptomatic, disease-related complications can include troublesome symptom burdens, increased incidence of both arterial and venous thromboses, haemorrhage, anaemia and an inherent risk of disease evolution to either overt myelofibrosis or blastic phase disease. Specific prognostic tools with high discriminatory power are lacking. Within this review we use case-based approaches to review the current literature, highlight challenges in both diagnostics and disease management and suggest contemporary approaches to improve patient outcomes.

Diabetes and Second Neoplasia Impact on Prognosis in Pre-Fibrotic Primary Myelofibrosis

Simple Summary

The 2016 WHO-revised classification of MPNs recognized pre-fibrotic PMF (pre-PMF) as a distinct clinical entity from both overt fibrotic PMF (overt PMF) and essential thrombocythemia (ET). In fact, while the initial presentation of pre-PMF is often an isolated thrombocytosis, thus mimicking ET, its course may be symptomatic in a non-negligible number of cases. Conversely, overt PMF patients are enriched in higher-risk categories, thus suggesting a greater propensity for disease progression than pre-PMF. Importantly, median survival is significantly reduced in overt PMF vs. pre-PMF, thereby reinforcing the appropriateness of making this distinction in clinical practice. Nevertheless, a specific prognostic model for pre-PMF is still lacking, except for thrombotic risk. The aim of the present study was therefore to identify covariates other than those commonly related to PMF, which can better define prognosis in pre-PMF patients in the real-world setting, thus resulting in more personalized and efficient therapeutic approaches.

Abstract

The 2016 WHO classification recognized pre-fibrotic primary myelofibrosis (pre-PMF) as a distinct entity. Nevertheless, a prognostic model specific for pre-PMF is still lacking. Our aim was to identify the most relevant clinical, histological, and driver mutation information at diagnosis to evaluate outcomes in pre-PMF patients in the real-world setting. We firstly assessed the association between IPSS or DIPSS at diagnosis and response variables in 378 pre-PMF patients. A strict association was observed between IPSS and DIPSS and occurrence of death. Other analyzed endpoints were not associated with IPSS or DIPSS as thrombo-hemorrhagic events at diagnosis or during follow-up, or did not show a clinical plausibility, as transformation into acute leukemia or overt PMF. The only covariates which were significantly associated with death were diabetes and second neoplasia, and were therefore included in two different prognostic settings: the first based on IPSS at diagnosis [class 1 vs. 0, OR (95%CIs): 3.34 (1.85–6.04); class 2 vs. 0, OR (95%CIs): 12.55 (5.04–31.24)], diabetes [OR (95%CIs): 2.95 (1.41–6.18)], and second neoplasia [OR (95%CIs): 2.88 (1.63–5.07)]; the second with DIPSS at diagnosis [class 1 vs. 0, OR (95%CIs): 3.40 (1.89–6.10); class 2 vs. 0, OR (95%CIs): 25.65 (7.62–86.42)], diabetes [OR (95%CIs): 2.89 (1.37–6.09)], and second neoplasia [OR (95%CIs): 2.97 (1.69–5.24)]. In conclusion, our study underlines the importance of other additional risk factors, such as diabetes and second neoplasia, to be evaluated, together with IPSS and DIPSS, to better define prognosis in pre-PMF patients.

Multistate Models for the Recovery Process in the Covid-19 Context: An Empirical Study of Chinese Enterprises

The Covid-19 pandemic has severely affected enterprises worldwide. It is thus of practical significance to study the process of enterprise recovery from Covid-19. However, the research on the effects of relevant determinants of business recovery is limited. This article presents a multistate modeling framework that considers the determinants, recovery time, and transition likelihood of Chinese enterprises by the state of those enterprises as a result of the pandemic (recovery state), with the help of an accelerated failure time model. Empirical data from 750 enterprises were used to evaluate the recovery process. The results indicate that the main problems facing non-manufacturing industries are supply shortages and order cancellations. With the increase of supplies and orders, the probability of transition between different recovery states gradually increases, and the recovery time of enterprises becomes shorter. For manufacturing industries, the factors that hinder recovery are more complex. The main problems are employee panic and order cancellations in the initial stage, employee shortages in the middle stage, and raw material shortages in the full recovery stage. This study can provide a reference for enterprise recovery in the current pandemic context and help policymakers and business managers take necessary measures to accelerate recovery.

Progression of Myeloproliferative Neoplasms (MPN): Diagnostic and Therapeutic Perspectives

Classical BCR-ABL-negative myeloproliferative neoplasms (MPN) are a heterogeneous group of hematologic malignancies, including essential thrombocythemia (ET), polycythemia vera (PV), and primary myelofibrosis (PMF), as well as post-PV-MF and post-ET-MF. Progression to more symptomatic disease, such as overt MF or acute leukemia, represents one of the major causes of morbidity and mortality. There are clinically evident but also subclinical types of MPN progression. Clinically evident progression includes evolution from ET to PV, ET to post-ET-MF, PV to post-PV-MF, or pre-PMF to overt PMF, and transformation of any of these subtypes to myelodysplastic neoplasms or acute leukemia. Thrombosis, major hemorrhage, severe infections, or increasing symptom burden (e.g., pruritus, night sweats) may herald progression. Subclinical types of progression may include increases in the extent of bone marrow fibrosis, increases of driver gene mutational allele burden, and clonal evolution. The underlying causes of MPN progression are diverse and can be attributed to genetic alterations and chronic inflammation. Particularly, bystander mutations in genes encoding epigenetic regulators or splicing factors were associated with progression. Finally, comorbidities such as systemic inflammation, cardiovascular diseases, and organ fibrosis may augment the risk of progression. The aim of this review was to discuss types and mechanisms of MPN progression and how their knowledge might improve risk stratification and therapeutic intervention. In view of these aspects, we discuss the potential benefits of early diagnosis using molecular and functional imaging and exploitable therapeutic strategies that may prevent progression, but also highlight current challenges and methodological pitfalls.

Aberrant expression of SPAG6 may affect the disease phenotype and serve as a tumor biomarker in BCR/ABL1-negative myeloproliferative neoplasms

Sperm-associated antigen 6 (SPAG6) is a newly identified cancer-testis antigen that has been revealed to contribute to the occurrence and development of various types of human cancer, such as ovarian, bladder, breast and lung cancer. However, to the best of our knowledge, the expression levels of SPAG6 in breakpoint cluster region (BCR)/ABL1-negative myeloproliferative neoplasms (MPNs) have not been investigated previously. Using reverse transcription-quantitative PCR and different tissue staining techniques, the present study revealed that SPAG6 was expressed by MPN cells, both at the mRNA and protein levels, and that nucleated erythroid precursors and megakaryocytes expressed the highest levels of SPAG6. In addition, SPAG6, which is known as a microtubule-associated protein, was found to exhibit nucleic, cytoplasmic or both cytoplasmic and nucleic subcellular localization patterns within the same patient or cell type; however, it did not always co-localize with β-tubulin. Furthermore, SPAG6 expression was revealed to be associated with fewer splenomegaly [P=0.015 for polycythemia vera (PV) and essential thrombocythemia (ET); and P=0.012 for primary myelofibrosis (PMF)] and myelofibrosis events (P=0.014 for PV and ET; and P=0.004 for PMF). In patients with PMF, upregulated expression levels of SPAG6 were also found to be associated with lower white blood cell counts (P=0.042) and lactate dehydrogenase levels (P=0.012), and higher hemoglobin levels (P=0.031) and platelet counts (P=0.025). In addition, the receiver operating characteristic curve analysis indicated that SPAG6 may be a potential biomarker for distinguishing MPN cases from healthy individuals. In conclusion, to the best of our knowledge, the present study is the first to report that aberrant SPAG6 expression may affect the disease phenotype and serve as a tumor biomarker in BCR/ABL1-negative MPNs.

Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques

This paper identifies prognosis factors for survival in patients with acute myeloid leukemia (AML) using machine learning techniques. We have integrated machine learning with feature selection methods and have compared their performances to identify the most suitable factors in assessing the survival of AML patients. Here, six data mining algorithms including Decision Tree, Random Forrest, Logistic Regression, Naive Bayes, W-Bayes Net, and Gradient Boosted Tree (GBT) are employed for the detection model and implemented using the common data mining tool RapidMiner and open-source R package. To improve the predictive ability of our model, a set of features were selected by employing multiple feature selection methods. The accuracy of classification was obtained using 10-fold cross-validation for the various combinations of the feature selection methods and machine learning algorithms. The performance of the models was assessed by various measurement indexes including accuracy, kappa, sensitivity, specificity, positive predictive value, negative predictive value, and area under the ROC curve (AUC). Our results showed that GBT with an accuracy of 85.17%, AUC of 0.930, and the feature selection via the Relief algorithm has the best performance in predicting the survival rate of AML patients.