Bone marrow pathology in essential thrombocythemia: interobserver reliability and utility for identifying disease subtypes

Evolution of WHO diagnostic criteria in "Classical Myeloproliferative Neoplasms" compared with the International Consensus Classification

A lively discussion persists regarding the diagnostic criteria for essential thrombocythemia (ET), primary myelofibrosis (PMF) and polycythemia vera (PV), particularly in relation to early/pre-fibrotic myelofibrosis (pre-PMF), a disease entity initially introduced in 2001 by the 3rd edition of the World Health Organization (WHO) classification. The definition and criteria used to diagnose pre-PMF have been progressively modified over time. The most update definition of pre-PMF can be found in the International Consensus Classification (ICC) published in 2022. An updated largely similar definition is also incorporated in the recently published 5th edition of WHO classification (2024). Diagnostic criteria for ET have undergone changes up to 2016/17 for the revised 4th edition of the WHO. In particular the threshold value for platelets were lowered and the important discrimination between "true" and "false" ET (in reality pre-PMF) been widely acknowledged. To avoid misdiagnose in early phase PV, the criteria for gender-adjusted thresholds for hemoglobin/ hematocrit have been lowered and the identification of an appropriate bone marrow (BM) morphology was upgraded as a major diagnostic criterion. Given the prominent role of morphology in MPN-related diagnostic algorithms, the diagnostic adequacy of the BM biopsy (sample procurement and proper laboratory handling) as emphasized in former WHO editions and in the ICC, was not addressed by the WHO 5th. The essential role of genetic markers is recognized by both classifications. A comparison between the revised 4th edition WHO classification and the ICC versus the WHO 5th reveals no significant differences, with the exception of the occurrence of leukoerythroblastosis in pre-PMF considered by the latter as one of the minor diagnostic criteria which seems unwarranted. In contrast to the revised 4th edition, the majority of the microscopic images used for the WHO 5th due to their low magnification and poor technique, do not highlight the diagnosis differences among these entities.

Development and validation of a deep learning model for morphological assessment of myeloproliferative neoplasms using clinical data and digital pathology

The subjectivity of morphological assessment and the overlapping pathological features of different subtypes of myeloproliferative neoplasms (MPNs) make accurate diagnosis challenging. To improve the pathological assessment of MPNs, we developed a diagnosis model (fusion model) based on the combination of bone marrow whole-slide images (deep learning [DL] model) and clinical parameters (clinical model). Thousand and fifty-one MPN and non-MPN patients were divided into the training, internal testing and one internal and two external validation cohorts (the combined validation cohort). In the combined validation cohort, fusion model achieved higher areas under curve (AUCs) than clinical or DL model or both for MPNs and subtype identification. Compared with haematopathologists with different experience, clinical model achieved AUC which was comparable to seniors and higher than juniors (p = 0.0208) for polycythaemia vera. The AUCs of fusion model were comparable to seniors and higher than juniors for essential thrombocytosis (p = 0.0141), prefibrotic primary myelofibrosis (p = 0.0085) and overt primary myelofibrosis (p = 0.0330) identification. In conclusion, the performances of our proposed models are equivalent to senior haematopathologists and better than juniors, providing a new perspective on the utilization of DL algorithms in MPN morphological assessment.

Myeloproliferative Neoplasms: Challenging Dogma

Myeloproliferative neoplasms, polycythemia vera, essential thrombocytosis, and primary myelofibrosis are a unique group of clonal hematopoietic stem cell neoplasms that share somatic, gain-in-function driver mutations in JAK2, CALR, and MPL. As a consequence, these disorders exhibit similar phenotypic features, the most common of which are the ceaseless production of normal erythrocytes, myeloid cells, platelets alone or in combination, extramedullary hematopoiesis, myelofibrosis, and a potential for leukemic transformation. In the case of polycythemia vera and essential thrombocytosis, however, prolonged survival is possible. With an incidence value in the range of 0.5-2.0/100,000, myeloproliferative neoplasms are rare disorders, but they are not new disorders, and after a century of scrutiny, their clinical features and natural histories are well-defined, though their individual management continues to be controversial. With respect to polycythemia vera, there has been a long-standing dispute between those who believe that the suppression of red blood cell production by chemotherapy is superior to phlebotomy to prevent thrombosis, and those who do not. With respect to essential thrombocytosis, there is a similar dispute about the role of platelets in veinous thrombosis, and the role of chemotherapy in preventing thrombosis by suppressing platelet production. Linked to these disputes is another: whether therapy with hydroxyurea promotes acute leukemia in disorders with a substantial possibility of longevity. The 21st century revealed new insights into myeloproliferative neoplasms with the discovery of their three somatic, gain-of-function driver mutations. Almost immediately, this triggered changes in the diagnostic criteria for myeloproliferative neoplasms and their therapy. Most of these changes, however, conflicted with prior well-validated, phenotypically driven diagnostic criteria and the management of these disorders. The aim of this review is to examine these conflicts and demonstrate how genomic discoveries in myeloproliferative neoplasms can be used to effectively complement the known phenotypic features of these disorders for their diagnosis and management.

Identifying disease-modifying potential in myelofibrosis clinical trials

ABSTRACT

The ultimate goal of bringing most new drugs to the clinic in hematologic malignancy is to improve overall survival. However, the use of surrogate end points for overall survival is increasingly considered standard practice, because a well validated surrogate end point can accelerate the outcome assessment and facilitate better clinical trial design. Established examples include monitoring minimal residual disease in chronic myeloid leukemia and acute leukemia, and metabolic response assessment in lymphoma. However, what happens when a clinical trial end point that is not a good surrogate for disease-modifying potential becomes ingrained as an expected outcome, and new agents are expected or required to meet this end point to demonstrate "efficacy"? Janus kinase (JAK) inhibitors for myelofibrosis (MF) have a specific impact on reducing symptom burden and splenomegaly but limited impact on the natural history of the disease. Since the introduction of ruxolitinib more than a decade ago there has been modest incremental success in clinical trials for MF but no major leap forward to alter the natural history of the disease. We argue that the clinical development of novel agents for MF will be accelerated by moving away from using end points that are specifically tailored to measure the beneficial effects of JAK inhibitors. We propose that specific measures of relevant disease burden, such as reduction in mutation burden as determined by molecular end points, should replace established end points. Careful reanalysis of existing data and trials in progress is needed to identify the most useful surrogate end points for future MF trials and better serve patient interest.

Clinical utility of investigations in triple-negative thrombocytosis: A real-world, multicentre evaluation of UK practice

Diagnosis of essential thrombocythaemia (ET) is challenging in patients lacking JAK2/CALR/MPL mutations. In a retrospective evaluation of 320 patients with 'triple-negative thrombocytosis', we assessed utility of bone marrow histology (90.9% of patients) and myeloid gene panel (MGP, 55.6%). Supportive histology ('myeloproliferative neoplasm-definite/probable', 36.8%) was associated with higher platelet counts and varied between centres. 14.6% MGP revealed significant variants: 3.4% JAK2/CALR/MPL and 11.2% other myeloid genes. Final clinical diagnosis was strongly predicted by histology, not MGP. 23.7% received cytoreduction (17.6% under 60 years). Real-world 'triple-negative' ET diagnosis currently depends heavily on histology; we advocate caution in MGP-negative cases and that specific guidelines are needed.

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.

Development of an Artificial-Intelligence-Based Tool for Automated Assessment of Cellularity in Bone Marrow Biopsies in Ph-Negative Myeloproliferative Neoplasms

The cellularity assessment in bone marrow biopsies (BMBs) for the diagnosis of Philadelphia chromosome (Ph)-negative myeloproliferative neoplasms (MPNs) is a key diagnostic feature and is usually performed by the human eyes through an optical microscope with consequent inter-observer and intra-observer variability. Thus, the use of an automated tool may reduce variability, improving the uniformity of the evaluation. The aim of this work is to develop an accurate AI-based tool for the automated quantification of cellularity in BMB histology. A total of 55 BMB histological slides, diagnosed as Ph- MPN between January 2018 and June 2023 from the archives of the Pathology Unit of University "Luigi Vanvitelli" in Naples (Italy), were scanned on Ventana DP200 or Epredia P1000 and exported as whole-slide images (WSIs). Fifteen BMBs were randomly selected to obtain a training set of AI-based tools. An expert pathologist and a trained resident performed annotations of hematopoietic tissue and adipose tissue, and annotations were exported as .tiff images and .png labels with two colors (black for hematopoietic tissue and yellow for adipose tissue). Subsequently, we developed a semantic segmentation model for hematopoietic tissue and adipose tissue. The remaining 40 BMBs were used for model verification. The performance of our model was compared with an evaluation of the cellularity of five expert hematopathologists and three trainees; we obtained an optimal concordance between our model and the expert pathologists' evaluation, with poorer concordance for trainees. There were no significant differences in cellularity assessments between two different scanners.

Variability of bone marrow biopsy reporting affects accuracy of diagnosis of myeloproliferative neoplasms: data from the ALLG MPN01 registry

The Philadelphia-negative myeloproliferative neoplasms (MPN) are a heterogeneous group of overlapping bone marrow disorders defined by characteristic peripheral blood counts and bone marrow morphological findings in conjunction with recurrent somatic mutations. The accurate diagnosis and subclassification of MPN relies upon careful reporting of bone marrow morphology combined with ancillary information in an integrated pathology report. This co-operative trial group study ALLG MPN01 (ANZCTR:12613000138785), led by the Australasian Leukaemia & Lymphoma Group (ALLG), aimed to describe the current approach to diagnosis of MPN in routine practice. Specifically, we assessed the frequency with which bone marrow biopsies were performed, and the adherence of reporting pathologists to recommendations contained in the revised 2016 WHO classification pertaining to MPN. We reviewed the diagnosis of 152 patients from eight institutions who were enrolled in a national MPN registry of the ALLG between 2010 and 2016. The ALLG MPN01 registry is now closed to recruitment. Key features were extracted from pathology reports provided to the registry. Bone marrow biopsies were performed in 112/152 cases (74%). The pathological information entered was concordant with the stated clinical diagnosis in 75/112 cases (67%). The main reasons for discordant results were incomplete descriptions of megakaryocyte topography and morphology, inconsistent grading of reticulin fibrosis, and failure to integrate the available morphological and ancillary clinicopathological information. In this retrospective audit, 26% of MPN patients did not undergo a diagnostic bone marrow biopsy. In those who did, the specific MPN subtype may not have been reported correctly in 33% of cases, as evidenced by inconsistent features reported or insufficient information to assess. A more standardised approach to bone marrow reporting is required to ensure accuracy of MPN diagnoses and consistent reporting to cancer registries and clinical trials.

Significant heterogeneity in management of calreticulin-mutated essential thrombocythemia and its progression to myelofibrosis: results of a national survey

Despite the recent publication of calreticulin (CALR)-mutated essential thrombocythemia (ET) management guidelines by the European Leukemia Net (ELN), there remains a paucity of data regarding the optimal way to manage this condition. To determine practice around Australia, we constructed a survey asking investigation and treatment questions in a hypothetical case of a young woman with CALR-mutated ET and subsequent progression to myelofibrosis. 51 of 88 hematologists replied. The responses demonstrated significant heterogeneity in specific issues such as the use of aspirin, when to initiate cytoreduction, the preferred type of cytoreduction, and platelet targets. These observations support the ELN acknowledgment that a strong evidence base for many management recommendations is lacking in this disease, and that substantial further research is needed.

Revisiting Circulating Extracellular Matrix Fragments as Disease Markers in Myelofibrosis and Related Neoplasms

Philadelphia chromosome-negative chronic myeloproliferative neoplasms (MPNs) arise due to acquired somatic driver mutations in stem cells and develop over 10-30 years from the earliest cancer stages (essential thrombocythemia, polycythemia vera) towards the advanced myelofibrosis stage with bone marrow failure. The JAK2V617F mutation is the most prevalent driver mutation. Chronic inflammation is considered to be a major pathogenetic player, both as a trigger of MPN development and as a driver of disease progression. Chronic inflammation in MPNs is characterized by persistent connective tissue remodeling, which leads to organ dysfunction and ultimately, organ failure, due to excessive accumulation of extracellular matrix (ECM). Considering that MPNs are acquired clonal stem cell diseases developing in an inflammatory microenvironment in which the hematopoietic cell populations are progressively replaced by stromal proliferation-"a wound that never heals"-we herein aim to provide a comprehensive review of previous promising research in the field of circulating ECM fragments in the diagnosis, treatment and monitoring of MPNs. We address the rationales and highlight new perspectives for the use of circulating ECM protein fragments as biologically plausible, noninvasive disease markers in the management of MPNs.

Easily Applicable Predictive Score for Differential Diagnosis of Prefibrotic Primary Myelofibrosis from Essential Thrombocythemia

Essential thrombocythemia (ET) and prefibrotic primary myelofibrosis (prePMF) initially have a similar phenotypic presentation with thrombocytosis. The aim of our study was to determine significant clinical-laboratory parameters at presentation to differentiate prePMF from ET as well as to develop and validate a predictive diagnostic prePMF model. This retrospective study included 464 patients divided into ET (289 pts) and prePMF (175 pts) groups. The model was built using data from a development cohort (229 pts; 143 ET, 86 prePMF), which was then tested in an internal validation cohort (235 pts; 146 ET, 89 prePMF). The most important prePMF predictors in the multivariate logistic model were age ≥ 60 years (RR = 2.2), splenomegaly (RR = 13.2), and increased lactat-dehidrogenase (RR = 2.8). Risk scores were assigned according to derived relative risk (RR) for age ≥ 60 years (1 point), splenomegaly (2 points), and increased lactat-dehidrogenase (1 point). Positive predictive value (PPV) for pre-PMF diagnosis with a score of ≥points was 69.8%, while for a score of ≥3 it was 88.2%. Diagnostic performance had similar values in the validation cohort. In MPN patients with thrombocytosis at presentation, the application of the new model enables differentiation of pre-PMF from ET, which is clinically relevant considering that these diseases have different prognoses and treatments.

Morphology of myeloproliferative neoplasms

Myeloproliferative neoplasms (MPN) are a group of clonal haematological malignancies first described by Dameshek in 1957. The Philadelphia-negative MPN that will be described are polycythaemia vera (PV), essential thrombocythaemia (ET), pre-fibrotic myelofibrosis and primary myelofibrosis (PMF). The blood and bone marrow morphology are essential in diagnosis, for WHO classification, establishing a baseline, monitoring response to treatment and identifying changes that may indicate disease progression. The blood film changes may be in any of the cellular elements. The key bone marrow features are architecture and cellularity, relative complement of individual cell types, reticulin content and bony structure. Megakaryocytes are the most abnormal cell and key to classification, as their number, location, size and cytology are all disease-defining. Reticulin content and grade are integral to assignment of the diagnosis of myelofibrosis. Even with careful assessment of all these features, not all cases fit neatly into the diagnostic entities; there is frequent overlap reflecting the biological disease continuum rather than distinct entities. Notwithstanding this, an accurate morphologic diagnosis in MPN is crucial due to the significant differences in prognosis between different subtypes and the availability of different therapies in the era of novel agents. The distinction between "reactive" and MPN is also not always straightforward and caution needs to be exercised given the prevalence of "triple negative" MPN. Here we describe the morphology of MPN including comments on changes with disease evolution and with treatment.

Secondary erythrocytosis

INTRODUCTION

Erythrocytosis is associated with an elevation of the hemoglobin level above 16.5 g/dL in men and above 16 g/dL in women and an elevation of the hematocrit level above 49% in men and > 48% in women. In primary erythrocytosis, the defect is a clonal disorder in the myeloid compartment of the bone marrow, leading to an increased red cell production. Secondary erythrocytosis is the result of external stimuli to the bone marrow, leading to the production of red cells in excess. Secondary erythrocytosis is more common than primary erythrocytosis and has a broad differential diagnosis.

AREAS COVERED

This review will discuss secondary erythrocytosis, its causes, clinical presentation, and both diagnostic and therapeutic approaches.

EXPERT OPINION

Although secondary erythrocytosis is more common than PV, there are still challenges and difficulties associated with the distinction between these two conditions. Moreover, there is a paucity of data and guidance when it comes to the management of certain congenital and acquired conditions. A pragmatic approach is recommended in order to identify the cause for this condition. Treatment should be directed at the management of the underlying cause.

Myeloproliferative neoplasms and splanchnic vein thrombosis: Contemporary diagnostic and therapeutic strategies

Myeloproliferative neoplasms (MPNs) are the most common etiologies of primary splanchnic vein thrombosis, present in almost forty percent of patients with Budd-Chiari syndrome or portal vein thrombosis. Diagnosis of MPNs can be difficult in these patients because key characteristics, such as elevated blood cell counts and splenomegaly, are confounded by portal hypertension or bleeding complications. In recent years, diagnostic tools have improved to provide more accurate diagnosis and classification of MPNs. Although bone marrow biopsy findings remain a major diagnostic criterion, molecular markers are playing an increasing role not only in diagnosis but also in better estimating prognosis. Therefore, though screening for JAK2V617F mutation should be the starting point of the diagnostic workup performed in all patients with splanchnic vein thrombosis, a multidisciplinary approach is needed to accurately diagnose the subtype of myeloproliferative neoplasm, recommend the useful additional tests (bone marrow biopsy, search for an additional mutation using targeted next-generation sequencing), and suggest the best treatment strategy. Indeed, providing a specific expert care pathway for patients with splanchnic vein thrombosis and underlying myeloproliferative neoplasm is crucial to determine the optimal management to reduce the risk of both hematological and hepatic complications.

Continuous Indexing of Fibrosis (CIF): improving the assessment and classification of MPN patients

The grading of fibrosis in myeloproliferative neoplasms (MPN) is an important component of disease classification, prognostication and monitoring. However, current fibrosis grading systems are only semi-quantitative and fail to fully capture sample heterogeneity. To improve the quantitation of reticulin fibrosis, we developed a machine learning approach using bone marrow trephine (BMT) samples (n = 107) from patients diagnosed with MPN or a reactive marrow. The resulting Continuous Indexing of Fibrosis (CIF) enhances the detection and monitoring of fibrosis within BMTs, and aids MPN subtyping. When combined with megakaryocyte feature analysis, CIF discriminates between the frequently challenging differential diagnosis of essential thrombocythemia (ET) and pre-fibrotic myelofibrosis with high predictive accuracy [area under the curve = 0.94]. CIF also shows promise in the identification of MPN patients at risk of disease progression; analysis of samples from 35 patients diagnosed with ET and enrolled in the Primary Thrombocythemia-1 trial identified features predictive of post-ET myelofibrosis (area under the curve = 0.77). In addition to these clinical applications, automated analysis of fibrosis has clear potential to further refine disease classification boundaries and inform future studies of the micro-environmental factors driving disease initiation and progression in MPN and other stem cell disorders.

Single-cell methods in myeloproliferative neoplasms: old questions, new technologies

Myeloproliferative neoplasms (MPN) are a group of clonal stem cell-derived hematopoietic malignancies driven by aberrant Janus kinase-signal transducer and activator of transcription proteins (JAK/STAT) signaling. Although these are genetically simple diseases, MPNs are phenotypically heterogeneous, reflecting underlying intratumoral heterogeneity driven by the interplay of genetic and nongenetic factors. Their evolution is determined by factors that enable certain cellular subsets to outcompete others. Therefore, techniques that resolve cellular heterogeneity at the single-cell level are ideally placed to provide new insights into MPN biology. With these insights comes the potential to uncover new approaches to predict the clinical course and treat these cancers, ultimately improving outcomes for patients. MPNs present a particularly tractable model of cancer evolution, because most patients present in an early disease phase and only a small proportion progress to aggressive disease. Therefore, it is not surprising that many groundbreaking technological advances in single-cell omics have been pioneered by their application in MPNs. In this review article, we explore how single-cell approaches have provided transformative insights into MPN disease biology, which are broadly applicable across human cancers, and discuss how these studies might be swiftly translated into clinical pathways and may eventually underpin precision medicine.

The Application of Morphogo in the Detection of Megakaryocytes from Bone Marrow Digital Images with Convolutional Neural Networks

The evaluation of megakaryocytes is an important part of the work up on bone marrow smear examination. It has significance in the differential diagnosis, therapeutic efficacy assessment, and predication of prognosis of many hematologic diseases. The process of manual identification of megakaryocytes are tedious and lack of reproducibility; therefore, a reliable method of automated megakaryocytic identification is urgently needed. Three hundred and thirty-three bone marrow aspirate smears were digitized by Morphogo system. Pathologists annotated megakaryocytes on the digital images of marrow smears are applied to construct a large dataset for testing the system's predictive performance. Subsequently, we obtained megakaryocyte count and classification for each sample by different methods (system-automated analysis, system-assisted analysis, and microscopic examination) to study the correlation between different counting and classification methods. Morphogo system localized cells likely to be megakaryocytes on digital smears, which were later annotated by pathologists and the system, respectively. The system showed outstanding performance in identifying megakaryocytes in bone marrow smears with high sensitivity (96.57%) and specificity (89.71%). The overall correlation between the different methods was confirmed the high consistency (r ≥ 0.7218, R² ≥ 0.5211) with microscopic examination in classifying megakaryocytes. Morphogo system was proved as a reliable screen tool for analyzing megakaryocytes. The application of Morphogo system shows promises to advance the automation and standardization of bone marrow smear examination.

Impact of Molecular Biology in Diagnosis, Prognosis, and Therapeutic Management of BCR::ABL1-Negative Myeloproliferative Neoplasm

BCR::ABL1-negative myeloproliferative neoplasms (MPNs) include three major subgroups-polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF)-which are characterized by aberrant hematopoietic proliferation with an increased risk of leukemic transformation. Besides the driver mutations, which are JAK2, CALR, and MPL, more than twenty additional mutations have been identified through the use of next-generation sequencing (NGS), which can be involved with pathways that regulate epigenetic modifications, RNA splicing, or DNA repair. The aim of this short review is to highlight the impact of molecular biology on the diagnosis, prognosis, and therapeutic management of patients with PV, ET, and PMF.

Pro106Leu MPL mutation is associated with thrombocytosis and a low risk of thrombosis, splenomegaly and marrow fibrosis

The P106L mutation in the human myeloproliferative leukemia virus oncogene (MPL) was shown to be associated with hereditary thrombocythemia in Arabs. The clinical and bone marrow (BM) features of P106L mutation are unknown. Genetic databases at two tertiary hospitals in Saudi Arabia were searched to identify patients with the MPL P106L mutation. Clinical data were collected retrospectively and the BM aspirates and biopsies were independently reviewed by two hematopathologists. In total, 115 patients were included. Median age was 33 years of which 31 patients were pediatric and 65 were female. The mutation was homozygous in 87 patients. Thrombocytosis was documented in 107 patients, with a median platelet count of 667 × 10⁹/L. The homozygous genotype was associated with a higher platelet count. Thirty-three patients had an evaluable BM and clustering of megakaryocytes was observed in 30/33 patients. At the time of last follow-up, 114 patients were alive. The median follow-up was 7.8 years from the time of thrombocytosis. No patients developed disease progression to myelofibrosis. The P106L mutation was associated with marked thrombocytosis at a younger age and with a low risk of thrombosis, splenomegaly, and marrow fibrosis. The BM demonstrated normal or hypocellular marrow with megakaryocyte clusters.

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.

Analysis of genetic variants in myeloproliferative neoplasms using a 22-gene next-generation sequencing panel

Background

The Philadelphia (Ph)-negative myeloproliferative neoplasms (MPNs), namely essential thrombocythaemia (ET), polycythaemia vera (PV) and primary myelofibrosis (PMF), are a group of chronic clonal haematopoietic disorders that have the propensity to advance into bone marrow failure or acute myeloid leukaemia; often resulting in fatality. Although driver mutations have been identified in these MPNs, subtype-specific markers of the disease have yet to be discovered. Next-generation sequencing (NGS) technology can potentially improve the clinical management of MPNs by allowing for the simultaneous screening of many disease-associated genes.

Methods

The performance of a custom, in-house designed 22-gene NGS panel was technically validated using reference standards across two independent replicate runs. The panel was subsequently used to screen a total of 10 clinical MPN samples (ET n = 3, PV n = 3, PMF n = 4). The resulting NGS data was then analysed via a bioinformatics pipeline.

Results

The custom NGS panel had a detection limit of 1% variant allele frequency (VAF). A total of 20 unique variants with VAFs above 5% (4 of which were putatively novel variants with potential biological significance) and one pathogenic variant with a VAF of between 1 and 5% were identified across all of the clinical MPN samples. All single nucleotide variants with VAFs ≥ 15% were confirmed via Sanger sequencing.

Conclusions

The high fidelity of the NGS analysis and the identification of known and novel variants in this study cohort support its potential clinical utility in the management of MPNs. However, further optimisation is needed to avoid false negatives in regions with low sequencing coverage, especially for the detection of driver mutations in MPL.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-021-01145-0.

An Expandable Informatics Framework for Enhancing Central Cancer Registries with Digital Pathology Specimens, Computational Imaging Tools, and Advanced Mining Capabilities

BACKGROUND

Population-based state cancer registries are an authoritative source for cancer statistics in the United States. They routinely collect a variety of data, including patient demographics, primary tumor site, stage at diagnosis, first course of treatment, and survival, on every cancer case that is reported across all U.S. states and territories. The goal of our project is to enrich NCI's Surveillance, Epidemiology, and End Results (SEER) registry data with high-quality population-based biospecimen data in the form of digital pathology, machine-learning-based classifications, and quantitative histopathology imaging feature sets (referred to here as Pathomics features).

MATERIALS AND METHODS

As part of the project, the underlying informatics infrastructure was designed, tested, and implemented through close collaboration with several participating SEER registries to ensure consistency with registry processes, computational scalability, and ability to support creation of population cohorts that span multiple sites. Utilizing computational imaging algorithms and methods to both generate indices and search for matches makes it possible to reduce inter- and intra-observer inconsistencies and to improve the objectivity with which large image repositories are interrogated.

RESULTS

Our team has created and continues to expand a well-curated repository of high-quality digitized pathology images corresponding to subjects whose data are routinely collected by the collaborating registries. Our team has systematically deployed and tested key, visual analytic methods to facilitate automated creation of population cohorts for epidemiological studies and tools to support visualization of feature clusters and evaluation of whole-slide images. As part of these efforts, we are developing and optimizing advanced search and matching algorithms to facilitate automated, content-based retrieval of digitized specimens based on their underlying image features and staining characteristics.

CONCLUSION

To meet the challenges of this project, we established the analytic pipelines, methods, and workflows to support the expansion and management of a growing repository of high-quality digitized pathology and information-rich, population cohorts containing objective imaging and clinical attributes to facilitate studies that seek to discriminate among different subtypes of disease, stratify patient populations, and perform comparisons of tumor characteristics within and across patient cohorts. We have also successfully developed a suite of tools based on a deep-learning method to perform quantitative characterizations of tumor regions, assess infiltrating lymphocyte distributions, and generate objective nuclear feature measurements. As part of these efforts, our team has implemented reliable methods that enable investigators to systematically search through large repositories to automatically retrieve digitized pathology specimens and correlated clinical data based on their computational signatures.

Learning Cellular Phenotypes through Supervision

Image-based cell phenotyping is an important and open problem in computational pathology. The two principal challenges are: 1) making the cell cluster properties insensitive to experimental settings (like seed point and feature selection) and 2) ensuring that the phenotypes emerging are biologically relevant and support clinical reporting. To gauge robustness, we first compare the consistency of the phenotypes using self-supervised and supervised features. Through case classification, we analyse the relevance of the self-supervised and supervised feature sets with respect to the clinical diagnosis. In addition, we demonstrate how we can add model explainability through Shapley values to identify more disease relevant cellular phenotypes and measure their importance in context of the disease. Here, myeloproliferative neoplasms, a haematopoietic stem cell disorder, where one particular cell type is of diagnostic relevance is used as an exemplar. The experiments conducted on a set of bone marrow trephines demonstrate an improvement of 7.4 % in accuracy for case classification using cellular phenotypes derived from the supervised scenario.

Deep learning for bone marrow cell detection and classification on whole-slide images

Bone marrow (BM) examination is an essential step in both diagnosing and managing numerous hematologic disorders. BM nucleated differential count (NDC) analysis, as part of BM examination, holds the most fundamental and crucial information. However, there are many challenges to perform automated BM NDC analysis on whole-slide images (WSIs), including large dimensions of data to process, complicated cell types with subtle differences. To the authors best knowledge, this is the first study on fully automatic BM NDC using WSIs with 40x objective magnification, which can replace traditional manual counting relying on light microscopy via oil-immersion 100x objective lens with a total 1000x magnification. In this study, we develop an efficient and fully automatic hierarchical deep learning framework for BM NDC WSI analysis in seconds. The proposed hierarchical framework consists of (1) a deep learning model for rapid localization of BM particles and cellular trails generating regions of interest (ROI) for further analysis, (2) a patch-based deep learning model for cell identification of 16 cell types, including megakaryocytes, mitotic cells, and four stages of erythroblasts which have not been demonstrated in previous studies before, and (3) a fast stitching model for integrating patch-based results and producing final outputs. In evaluation, the proposed method is firstly tested on a dataset with a total of 12,426 annotated cells using cross validation, achieving high recall and accuracy of 0.905 ± 0.078 and 0.989 ± 0.006, respectively, and taking only 44 seconds to perform BM NDC analysis for a WSI. To further examine the generalizability of our model, we conduct an evaluation on the second independent dataset with a total of 3005 cells, and the results show that the proposed method also obtains high recall and accuracy of 0.842 and 0.988, respectively. In comparison with the existing small-image-based benchmark methods, the proposed method demonstrates superior performance in recall, accuracy and computational time.

Essential thrombocythaemia with aggressive megakaryocytosis after myelofibrotic transformation

BACKGROUND

Among myeloproliferative neoplasms, it is often difficult to distinguish essential thrombocythaemia (ET) from prefibrotic-stage primary myelofibrosis (PMF) with thrombocytosis given their overlapping clinicopathological phenotypes.

CASE PRESENTATION

We encountered a 45-year-old male who was initially diagnosed with ET and eventually became transformed to secondary myelofibrosis 20 years later. Two distinct types of aberrant megakaryocytes were observed at diagnosis: one type characteristic of ET and the other type characteristic of PMF. With a proliferation in the bone marrow, aberrant megakaryocytes were infiltrated into the extramedullary organs and were even present in the thrombus were observed at autopsy. As a result of next-generation sequencing, the significant increase of variant allele frequency (VAF) of JAK2 V617F and U2AF1 S34Y mutations was observed in the bone marrow cells at the final stage.

CONCLUSIONS

This patient could be recognized as an atypical case of aggressive megakaryocytosis transformed from ET.

Synoptic Diagnostics of Myeloproliferative Neoplasms: Morphology and Molecular Genetics

Simple Summary

The diagnosis of myeloproliferative neoplasms requires assessment of a combination of clinical, morphological, immunophenotypic and genetic features, and this integrated, multimodal approach forms the basis for precise classification. Evaluation includes cell counts and morphology in the peripheral blood, bone marrow aspiration and trephine biopsy, and may encompass flow cytometry for specific questions. Diagnosis nowadays is completed by targeted molecular analysis for the detection of recurrent driver and, optionally, disease-modifying mutations. According to the current World Health Organization classification, all myeloproliferative disorders require assessment of molecular features to support the diagnosis or confirm a molecularly defined entity. This requires a structured molecular analysis workflow tailored for a rapid and cost-effective diagnosis. The review focuses on the morphological and molecular features of Ph-negative myeloproliferative neoplasms and their differential diagnoses, addresses open questions of classification, and emphasizes the enduring role of histopathological assessment in the molecular era.

Abstract

The diagnosis of a myeloid neoplasm relies on a combination of clinical, morphological, immunophenotypic and genetic features, and an integrated, multimodality approach is needed for precise classification. The basic diagnostics of myeloid neoplasms still rely on cell counts and morphology of peripheral blood and bone marrow aspirate, flow cytometry, cytogenetics and bone marrow trephine biopsy, but particularly in the setting of Ph− myeloproliferative neoplasms (MPN), the trephine biopsy has a crucial role. Nowadays, molecular studies are of great importance in confirming or refining a diagnosis and providing prognostic information. All myeloid neoplasms of chronic evolution included in this review, nowadays feature the presence or absence of specific genetic markers in their diagnostic criteria according to the current WHO classification, underlining the importance of molecular studies. Crucial differential diagnoses of Ph− MPN are the category of myeloid/lymphoid neoplasms with eosinophilia and gene rearrangement of PDGFRA, PDGFRB or FGFR1, or with PCM1-JAK2, and myelodysplastic/myeloproliferative neoplasms (MDS/MPN). This review focuses on morphological, immunophenotypical and molecular features of BCR-ABL1-negative MPN and their differential diagnoses. Furthermore, areas of difficulties and open questions in their classification are addressed, and the persistent role of morphology in the area of molecular medicine is discussed.

Artificial intelligence-based morphological fingerprinting of megakaryocytes: a new tool for assessing disease in MPN patients

Accurate diagnosis and classification of myeloproliferative neoplasms (MPNs) requires integration of clinical, morphological, and genetic findings. Despite major advances in our understanding of the molecular and genetic basis of MPNs, the morphological assessment of bone marrow trephines (BMT) is critical in differentiating MPN subtypes and their reactive mimics. However, morphological assessment is heavily constrained by a reliance on subjective, qualitative, and poorly reproducible criteria. To improve the morphological assessment of MPNs, we have developed a machine learning approach for the automated identification, quantitative analysis, and abstract representation of megakaryocyte features using reactive/nonneoplastic BMT samples (n = 43) and those from patients with established diagnoses of essential thrombocythemia (n = 45), polycythemia vera (n = 18), or myelofibrosis (n = 25). We describe the application of an automated workflow for the identification and delineation of relevant histological features from routinely prepared BMTs. Subsequent analysis enabled the tissue diagnosis of MPN with a high predictive accuracy (area under the curve = 0.95) and revealed clear evidence of the potential to discriminate between important MPN subtypes. Our method of visually representing abstracted megakaryocyte features in the context of analyzed patient cohorts facilitates the interpretation and monitoring of samples in a manner that is beyond conventional approaches. The automated BMT phenotyping approach described here has significant potential as an adjunct to standard genetic and molecular testing in established or suspected MPN patients, either as part of the routine diagnostic pathway or in the assessment of disease progression/response to treatment.

Application of Single-Cell Approaches to Study Myeloproliferative Neoplasm Biology

Philadelphia-negative myeloproliferative neoplasms (MPNs) are an excellent tractable disease model of a number of aspects of human cancer biology, including genetic evolution, tissue-associated fibrosis, and cancer stem cells. In this review, we discuss recent insights into MPN biology gained from the application of a number of new single-cell technologies to study human disease, with a specific focus on single-cell genomics, single-cell transcriptomics, and digital pathology.

Myeloid somatic mutation panel testing in myeloproliferative neoplasms

Myeloproliferative neoplasms are characterised by somatic mutations in pathways that regulate cell proliferation, epigenetic modifications, RNA splicing or DNA repair. Assessment of the mutational profile assists diagnosis and classification, but also aids assessment of prognosis, and may guide the use of emerging targeted therapies. The most practical way to provide information on numerous genetic variants is by using massively parallel sequencing, commonly in the form of disease specific next generation sequencing (NGS) panels. This review summarises the diagnostic and prognostic value of somatic mutation testing in Philadelphia-negative myeloproliferative neoplasms: polycythaemia vera, essential thrombocythaemia, primary myelofibrosis, chronic neutrophilic leukaemia, systemic mastocytosis, and chronic eosinophilic leukaemia. NGS panel testing is increasing in routine practice and promises to improve the accuracy and efficiency of pathological diagnosis and prognosis.

Disease and Clinical Characteristics of Patients With a Clinical Diagnosis of Essential Thrombocythemia Enrolled in the MOST Study

Few data exist regarding the disease and clinical characteristics of patients with essential thrombocythemia (ET) in the United States. The ongoing, multicenter, noninterventional, prospective, Myelofibrosis and Essential Thrombocythemia Observational STudy (MOST) was designed to collect data pertaining to the demographics, clinical management, and patient-reported outcomes in patients with myelofibrosis or ET in the United States (NCT02953704). This analysis examines the clinical characteristics of patients with clinical diagnoses of high-risk or low-risk ET receiving ET-directed therapy at enrollment. At data cutoff (June 17, 2019), 1207 of 1234 enrolled patients were eligible for this analysis (median age, 70 years; 65% female; 88% white); 917 patients (76%) had mutation testing results available. The median time from ET diagnosis to study enrollment was 4.2 years. The majority of patients (87%) had high-risk ET. Of 333 patients with a history of thrombotic events, 247 had at least 1 event classified as arterial and/or venous. Platelet count was above normal range in 54% of patients. Hypertension (56%) was the most common comorbidity. At enrollment, the majority of patients (low-risk ET, 94%; high-risk ET, 79%) were receiving ET-directed monotherapy. Additional prospective analyses from MOST will help to identify areas of unmet need.

Low-Risk Essential Thrombocythemia: A Comprehensive Review

Essential thrombocythemia (ET) is a chronic myeloproliferative neoplasm characterized by a persistently elevated platelet count in the absence of a secondary cause. The clinical consequences of uncontrolled thrombocytosis can include both thrombosis and hemorrhage. Patients with features conferring a “high risk” of vascular events benefit from reduction of the platelet count through cytoreductive therapy. The management of patients who lack such high-risk features has until recently been less well defined, but it is now apparent that many require minimal or even no intervention. In this review, we discuss the diagnostic pathway for younger patients with unexplained thrombocytosis, including screening molecular investigations, the role of bone marrow biopsy, and investigations in those patients negative for the classic myeloproliferative neoplasm driver mutations (JAK2, CALR, MPL). We discuss conventional and novel risk stratification methods in essential thrombocythemia and how these can be best applied in clinical practice, particularly in the era of more comprehensive genomic testing. The treatment approach for “low risk” patients is discussed including antiplatelets and the options for cytoreductive therapy, if indicated, together with areas of clinical need for future study.

[Pathological characteristics of megakaryocytes in myeloproliferative neoplasms and their correlation with driver gene mutations]

Objective: To investigate the pathological characteristics of megakaryocytes in myeloproliferative neoplasms(MPN)and their correlations with driver gene mutations. Methods: Trephine specimens administered for 160 patients with MPN from February 2012 to October 2017 were reevaluated according to the World Health Organization(WHO)'s(2016)diagnostic criteria. Results: This cohort of patients included 72(45.0%)men, with the median age of 59(range, 13-87)years, comprising 39 with polycythemia vera(PV), 33 with essential thrombocythemia(ET), 37 with prefibrotic/early-primary myelofibrosis(pre-PMF), 37 with overt PMF, 1 with post-ET MF, 2 with post-PV MF, and 11 with MPN-unclassifiable(MPN-U)after the re-diagnosis. With PV, ET, pre-PMF, and overt PMF changes, proportions of dense clusters, hypolobulated nuclei, and naked nuclei of megakaryocytes gradually increased, whereas erythropoiesis gradually decreased. Proportions of reticulin, collagen, and osteosclerosis grades of ≥1 also increased. Dense clusters, hypolobulated nuclei, and naked nuclei of megakaryocytes were negatively correlated with erythropoiesis and positively correlated with granulopoiesis and fibrosis. In patients with pre- and overt PMF, dense clusters and naked nuclei of megakaryocytes were positively correlated with fibrosis. Patients with JAK2V617F MPN had significantly increased erythropoiesis(P=0.022). Patients with CALR-mutated MPN were characterized by increased loose and dense clusters; paratrabecular distribution and naked nuclei of megakaryocytes(P=0.055, P=0.002, P=0.018, P=0.008); and increased reticulin, collagen, and osteosclerosis(P=0.003, P<0.001, P=0.001). In patients with pre- and overt PMF, patients with JAK2V617F had increased cellularity(P=0.037). CALR-mutated patients had increased dense clusters and giant sizes of megakaryocytes, collagen, and osteosclerosis(P=0.055, P=0.059, P=0.011, P=0.046). Conclusion: Megakaryocytes showed abnormal MPN morphology and distribution, which were related to fibrosis. CALR mutation was probably associated with abnormal morphology and distribution of megakaryocytes and fibrosis.

The long-term oral exposure to titanium dioxide impaired immune functions and triggered cytotoxic and genotoxic impacts in rats

BACKGROUND

Titanium dioxide "TiO₂, E171″ is a widely used food additive that exists in various everyday food products all over the world together with vast applications in cosmetics and industry. However, many toxicological aspects particularly following oral exposure still unclear.

METHODS

Hence, this study was planned to examine the effect of oral exposure of male Wistar rats to two doses of TiO₂ (20 or 40 mg/kg b.wt.) through oral gavage once daily for 90 consecutive days on the blood components, immunity, cytotoxic, and genotoxic indicators.

RESULTS

A dose-dependent leukopenia, eosinophilia, neutrophilia, and thrombocytopenia were noted. Also, the immunoglobins G (IgG) and IgM were significantly elevated in TiO₂ treated rats. The phagocytic activities, lysozyme, nitric oxide, and immunoglobulin levels were significantly depleted following TiO₂ exposure. A significantly reduced lymphocyte proliferation but elevated LDH activity was prominent in TiO₂ treated rats. Different pathological perturbations were observed in both splenic tissue and bone marrow. A marked increase in CD4⁺ and CD8⁺ immunolabeling was evident. A significant increase in the comet variables was recorded in response to the exposure of rats to the increasing level of TiO₂ at both levels.

CONCLUSION

Overall, these results indicated that TiO₂ could induce hematotoxicity, genotoxic, and immunotoxic alterations with exposure for long durations.

The new WHO classification for essential thrombocythemia calls for revision of available evidences

In the 2016 revised classification of myeloproliferative neoplasms pre-fibrotic primary myelofibrosis (pre-PMF) was recognized as a separate entity, distinct from essential thrombocythemia (ET). Owing that the majority of cases falling in the pre-PMF category were previously diagnosed as ET, one may question about the need to re-evaluate the results of epidemiologic, clinical, and molecular studies, and the results of clinical trials in the two entities. Based on a critical review of recently published studies, pre-PMF usually presents with a distinct clinical and hematological presentation and higher frequency of constitutional symptoms. JAK2V617F and CALR mutations in pre-PMF patients are superimposable to ET, whereas non-driver high-risk mutations are enriched in pre-PMF compared with ET. Thrombosis is not significantly different, whereas bleeding is more frequent in pre-PMF. Median survival is significantly shorter in pre-PMF and 10-year cumulative rates progression to overt myelofibrosis is 0-1% vs. 10-12%, and leukemic transformation is 1-2% vs. 2-6%, in ET and pre-fibrotic-PMF, respectively. Most patients fall in the lower prognostic IPSS group in which observation alone can be recommended. Patients at intermediate risk may require a symptom-driven treatment for anemia, splenomegaly or constitutional symptoms while cytoreductive drugs are indicated in the high-risk category.

Automated Osteosclerosis Grading of Clinical Biopsies Using Infrared Spectroscopic Imaging

Osteosclerosis and myefibrosis are complications of myeloproliferative neoplasms. These disorders result in excess growth of trabecular bone and collagen fibers that replace hematopoietic cells, resulting in abnormal bone marrow function. Treatments using imatinib and JAK2 pathway inhibitors can be effective on osteosclerosis and fibrosis; therefore, accurate grading is critical for tracking treatment effectiveness. Current grading standards use a four-class system based on analysis of biopsies stained with three histological stains: hematoxylin and eosin (H&E), Masson's trichrome, and reticulin. However, conventional grading can be subjective and imprecise, impacting the effectiveness of treatment. In this Article, we demonstrate that mid-infrared spectroscopic imaging may serve as a quantitative diagnostic tool for quantitatively tracking disease progression and response to treatment. The proposed approach is label-free and provides automated quantitative analysis of osteosclerosis and collagen fibrosis.

Multi-objective Parameter Auto-tuning for Tissue Image Segmentation Workflows

We propose a software platform that integrates methods and tools for multi-objective parameter auto-tuning in tissue image segmentation workflows. The goal of our work is to provide an approach for improving the accuracy of nucleus/cell segmentation pipelines by tuning their input parameters. The shape, size, and texture features of nuclei in tissue are important biomarkers for disease prognosis, and accurate computation of these features depends on accurate delineation of boundaries of nuclei. Input parameters in many nucleus segmentation workflows affect segmentation accuracy and have to be tuned for optimal performance. This is a time-consuming and computationally expensive process; automating this step facilitates more robust image segmentation workflows and enables more efficient application of image analysis in large image datasets. Our software platform adjusts the parameters of a nuclear segmentation algorithm to maximize the quality of image segmentation results while minimizing the execution time. It implements several optimization methods to search the parameter space efficiently. In addition, the methodology is developed to execute on high-performance computing systems to reduce the execution time of the parameter tuning phase. These capabilities are packaged in a Docker container for easy deployment and can be used through a friendly interface extension in 3D Slicer. Our results using three real-world image segmentation workflows demonstrate that the proposed solution is able to (1) search a small fraction (about 100 points) of the parameter space, which contains billions to trillions of points, and improve the quality of segmentation output by × 1.20, × 1.29, and × 1.29, on average; (2) decrease the execution time of a segmentation workflow by up to 11.79× while improving output quality; and (3) effectively use parallel systems to accelerate parameter tuning and segmentation phases.

How I treat polycythemia vera

Since its discovery, polycythemia vera (PV) has challenged clinicians responsible for its diagnosis and management and scientists investigating its pathogenesis. As a clonal hematopoietic stem cell (HSC) disorder, PV is a neoplasm but its driver mutations result in overproduction of morphologically and functionally normal blood cells. PV arises in an HSC but it can present initially as isolated erythrocytosis, leukocytosis, thrombocytosis, or any combination of these together with splenomegaly or myelofibrosis, and it can take years for a true panmyelopathy to appear. PV shares the same JAK2 mutation as essential thrombocytosis and primary myelofibrosis, but erythrocytosis only occurs in PV. However, unlike secondary causes of erythrocytosis, in PV, the plasma volume is frequently expanded, masking the erythrocytosis and making diagnosis difficult if this essential fact is ignored. PV is not a monolithic disorder: female patients deregulate fewer genes and clinically behave differently than their male counterparts, while some PV patients are genetically predisposed to an aggressive clinical course. Nevertheless, based on what we have learned over the past century, most PV patients can lead long and productive lives. In this review, using clinical examples, I describe how I diagnose and manage PV in an evidence-based manner without relying on chemotherapy.

Distinguishing essential thrombocythemia JAK2V617F from polycythemia vera: limitations of erythrocyte values

Distinguishing essential thrombocythemia JAK2V617F from polycythemia vera is difficult because of shared mutation and phenotypic characteristics. The World Health Organization suggested hemoglobin and hematocrit values to diagnose polycythemia vera (PV), but their sensitivity and specificity were not tested. Moreover, red cell values do not accurately predict red cell mass, which we use to discriminate essential thrombocythemia JAK2V617F from PV. Eighty-three PV and 39 essential thrombocythemia JAK2V617F patients were diagnosed based on JAK2V617F positivity, chromium-51 red cell mass, and marrow biopsy findings. Red cell values used to construct a receiver operating characteristic analysis determined optimal thresholds for distinguishing essential thrombocythemia JAK2V617F from PV. Red cell value frequencies were plotted determining if overlap existed. Chromium-51 red cell mass separated PV from essential thrombocythemia JAK2V617F, but red cell values overlapped in 25.0-54.7%. Our data indicate that a significant proportion of PV patients may be underdiagnosed by using only red cell values. A bone marrow biopsy was performed in 199 of 410 (48.5%) and a serum erythropoietin value was measured in 225 of 410 (54.9%) of potential PV patients at our institution. Without isotope studies, marrow biopsies and serum erythropoietin values should improve diagnostic accuracy and become mandatory, but clinical data suggest these tests have not been routinely performed. Therefore, the clinical hematologist must be aware of imperfect accuracy when using only red cell values for distinguishing essential thrombocythemia JAK2V617F from PV.

β-catenin and PPAR-γ levels in bone marrow of myeloproliferative neoplasm: an immunohistochemical and ultrastructural study

In accordance with increased proliferation in myeloproliferative neoplasm (MPN), the goal is to evaluate the immunoexpression of: β-catenin, PPAR-γ and Ki67 protein, to compare them with bone marrow ultrastructural characteristics in patients with MPN. Immunoexpression and electron microscopy of bone marrow was analyzed in 30 Ph-negative MPN patients, including per 10 patients with polycythemia vera (PV), essential thrombocythemia (ET) and primary myelofibrosis (PMF). The quantity of β-catenin immunoreactive cells was significantly higher in PV then in ET (p < 0.01) or PMF group of patients (p < 0.01) and also in ET versus PMF group of patients (p < 0.01). Erythroid lineage showed absent β-catenin staining without immunoreactivity in nucleus. In contrast, immunoreactivity for PPAR-γ was localized mostly in megakaryocytes and the highest number of PPAR-γ immunopositive cells was detected in PMF group of patients. In addition, the proliferative Ki67 index was significantly increased in the PMF and PV patients compared to patients with ET. Also, the megakaryocytes showed abnormal maturation in PMF group of patients as determined by ultrastructural analysis. These results indicated that PV dominantly expressed β-catenin and proliferation marker Ki67 in bone marrow, while PMF is linked preferentially to PPAR-γ immunopositive megakaryocytes characterized by abnormal maturation.

Analysis of clinical characteristics of bone marrow proliferative tumor progression to acute myeloid leukemia

OBJECTIVE

This study aims to analyze Chinese patients who developed acute leukemia after being diagnosed and treated for Philadelphia chromosome (Ph)-negative chronic myeloproliferative neoplasms (MPNs), and compare the findings of this series with similar studies from literature.

METHODS

Nine patients who progressed to leukemia after being diagnosed with MPN were included into the present study. Clinical data including age, treatment modalities and duration of use in the myeloproliferative phase, latency to leukemic transformation (LT), characteristics of leukemia, chemotherapy administration, and survival after LT were examined. Furthermore, factors associated with leukemia transformation were analyzed.

RESULTS

Over a 13-year period, nine patients had LT in 192 Ph-negative MPNs. Among these patients, two patients had polycythemia vera (PV), three patients had essential thrombocythemia (ET), and four patients had myelofibrosis (MF). The median age at MPN diagnosis was 51 years old (range: 42-69 years old), and the median age upon reaching LT was 57 years old (range: 46-72 years old). Furthermore, the median latency to LT was 72.8 months (range: 7-144 months). Five patients had cytogenetic abnormalities (62.5%), with abnormalities in chromosomes -5, +8 and -7 being common. Eight patients underwent the JAK2 V617F gene test when diagnosed with MPN. The prognosis of patients with LT was poor, and the average survival time was 6.7 months. This was not correlated with the treatment.

CONCLUSION

LT in Ph-negative MPNs is rare, and has poor prognosis, which has been consistently reported in a number of studies, However, this needs to be further confirmed through larger studies.

Simultaneous Cell Detection and Classification in Bone Marrow Histology Images

Recently, deep learning frameworks have been shown to be successful and efficient in processing digital histology images for various detection and classification tasks. Among these tasks, cell detection and classification are key steps in many computer-assisted diagnosis systems. Traditionally, cell detection and classification is performed as a sequence of two consecutive steps by using two separate deep learning networks: one for detection and the other for classification. This strategy inevitably increases the computational complexity of the training stage. In this paper, we propose a synchronized deep autoencoder network for simultaneous detection and classification of cells in bone marrow histology images. The proposed network uses a single architecture to detect the positions of cells and classify the detected cells, in parallel. It uses a curve-support Gaussian model to compute probability maps that allow detecting irregularly shape cells precisely. Moreover, the network includes a novel neighborhood selection mechanism to boost the classification accuracy. We show that the performance of the proposed network is superior than traditional deep learning detection methods and very competitive compared to traditional deep learning classification networks. Runtime comparison also shows that our network requires less time to be trained.

[Protein-dysregulation in human and murine myeloproliferative neoplasms]

In this work different types of dysregulation of signaling proteins in the context of myeloproliferative neoplasms are examined. In this heterogeneous disease group, uncontrolled cell proliferation plays a crucial role for the initiation of tumorigenesis, which Robert Weinberg described as a "hallmark" for the development of cancer. Protein dysregulation in form of overexpression of GAB2, a protein involved in formation of the CML-pathognomonic BCR/ABL-translocation complex, results in an enhanced disease phenotype in a Bcr/Abl-positive mouse model and disease acceleration is associated with a change of the subcellular localization of GAB2 in human blasts in CML-bone marrow biopsies. Furthermore, analyses of a mouse model show that a protein dysregulation caused by a distinct translocation (Tel-Syk) leads to the formation of a specific and morphologically very characteristic phenotype in the bone marrow of diseased mice. Moreover, results were presented which show that in certain subgroups of Myeloproliferative Neoplasms the protein NFE2, which is initially known only as a translocating factor, is apparently regulated by altering its subcellular localization. The difference in the subcellular localization of NFE2 in erythroid bone marrow cells is so clear between Essential Thrombocythemia and Primary Myelofibrosis that quantitative NFE2 immunohistochemistry can be used as an ancillary tool to diagnostically discriminate these two entities in an early stage.

Classification and Personalized Prognosis in Myeloproliferative Neoplasms

BACKGROUND

Myeloproliferative neoplasms, such as polycythemia vera, essential thrombocythemia, and myelofibrosis, are chronic hematologic cancers with varied progression rates. The genomic characterization of patients with myeloproliferative neoplasms offers the potential for personalized diagnosis, risk stratification, and treatment.

METHODS

We sequenced coding exons from 69 myeloid cancer genes in patients with myeloproliferative neoplasms, comprehensively annotating driver mutations and copy-number changes. We developed a genomic classification for myeloproliferative neoplasms and multistage prognostic models for predicting outcomes in individual patients. Classification and prognostic models were validated in an external cohort.

RESULTS

A total of 2035 patients were included in the analysis. A total of 33 genes had driver mutations in at least 5 patients, with mutations in JAK2, CALR, or MPL being the sole abnormality in 45% of the patients. The numbers of driver mutations increased with age and advanced disease. Driver mutations, germline polymorphisms, and demographic variables independently predicted whether patients received a diagnosis of essential thrombocythemia as compared with polycythemia vera or a diagnosis of chronic-phase disease as compared with myelofibrosis. We defined eight genomic subgroups that showed distinct clinical phenotypes, including blood counts, risk of leukemic transformation, and event-free survival. Integrating 63 clinical and genomic variables, we created prognostic models capable of generating personally tailored predictions of clinical outcomes in patients with chronic-phase myeloproliferative neoplasms and myelofibrosis. The predicted and observed outcomes correlated well in internal cross-validation of a training cohort and in an independent external cohort. Even within individual categories of existing prognostic schemas, our models substantially improved predictive accuracy.

CONCLUSIONS

Comprehensive genomic characterization identified distinct genetic subgroups and provided a classification of myeloproliferative neoplasms on the basis of causal biologic mechanisms. Integration of genomic data with clinical variables enabled the personalized predictions of patients' outcomes and may support the treatment of patients with myeloproliferative neoplasms. (Funded by the Wellcome Trust and others.).