International external quality assurance of JAK2 V617F quantification

Superior detection of low-allele burden Janus kinase 2 V617F mutation and monitoring clonal evolution in myeloproliferative neoplasms using chip-based digital PCR

The JAK2 V617F is a prevalent driver mutation in Philadelphia chromosome-negative myeloproliferative neoplasms (Ph-MPNs), significantly affecting disease progression, immunophenotype, and patient outcomes. The World Health Organization (WHO) guidelines highlight the JAK2 V617F mutation as one of the key diagnostic criterions for Ph-MPNs. In this study, we analyzed 283 MPN samples with the JAK2 V617F mutation to assess the effectiveness of three detection technologies: chip-based digital PCR (cdPCR), real-time quantitative PCR (qPCR), and next-generation sequencing (NGS). Additionally, we investigated the relationship between JAK2 V617F mutant allele burden (% JAK2 V617F) and various laboratory characteristics to elucidate potential implications in MPN diagnosis. Our findings demonstrated high conformance of cdPCR with qPCR/NGS for detecting % JAK2 V617F, but the mutant allele burdens detected by qPCR/NGS were lower than those detected by cdPCR. Moreover, the cdPCR exhibited high sensitivity with a limit of detection (LoD) of 0.08% and a limit of quantification (LoQ) of 0.2% for detecting % JAK2 V617F in MPNs. Clinical implications were explored by correlating % JAK2 V617F with various laboratory characteristics in MPN patients, revealing significant associations with white blood cell counts, lactate dehydrogenase levels, and particularly β2-microglobulin (β2-MG) levels. Finally, a case report illustrated the application of cdPCR in detecting low-allele burdens in a de novo chronic myeloid leukemia (CML) patient with a hidden JAK2 V617F subclone, which expanded during tyrosine kinase inhibitor (TKI) treatment. Our findings underscore the superior sensitivity and accuracy of cdPCR, making it a valuable tool for early diagnosis and monitoring clonal evolution.

Mathematical modelling of stem and progenitor cell dynamics during ruxolitinib treatment of patients with myeloproliferative neoplasms

Introduction

The Philadelphia chromosome-negative myeloproliferative neoplasms are a group of slowly progressing haematological malignancies primarily characterised by an overproduction of myeloid blood cells. Patients are treated with various drugs, including the JAK1/2 inhibitor ruxolitinib. Mathematical modelling can help propose and test hypotheses of how the treatment works.

Materials and methods

We present an extension of the Cancitis model, which describes the development of myeloproliferative neoplasms and their interactions with inflammation, that explicitly models progenitor cells and can account for treatment with ruxolitinib through effects on the malignant stem cell response to cytokine signalling and the death rate of malignant progenitor cells. The model has been fitted to individual patients' data for the JAK2 V617F variant allele frequency from the COMFORT-II and RESPONSE studies for patients who had substantial reductions (20 percentage points or 90% of the baseline value) in their JAK2 V617F variant allele frequency (n = 24 in total).

Results

The model fits very well to the patient data with an average root mean square error of 0.0249 (2.49%) when allowing ruxolitinib treatment to affect both malignant stem and progenitor cells. This average root mean square error is much lower than if allowing ruxolitinib treatment to affect only malignant stem or only malignant progenitor cells (average root mean square errors of 0.138 (13.8%) and 0.0874 (8.74%), respectively).

Discussion

Systematic simulation studies and fitting of the model to the patient data suggest that an initial reduction of the malignant cell burden followed by a monotonic increase can be recapitulated by the model assuming that ruxolitinib affects only the death rate of malignant progenitor cells. For patients exhibiting a long-term reduction of the malignant cells, the model predicts that ruxolitinib also affects stem cell parameters, such as the malignant stem cells' response to cytokine signalling.

Calibration of cell-free DNA measurements by next-generation sequencing

OBJECTIVES

Accurate monitoring of disease burden depends on accurate disease marker quantification. Although next-generation sequencing (NGS) is a promising technology for noninvasive monitoring, plasma cell-free DNA levels are often reported in misleading units that are confounded by non-disease-related factors. We proposed a novel strategy for calibrating NGS assays using spiked normalizers to improve precision and to promote standardization and harmonization of analyte concentrations.

METHODS

In this study, we refined our NGS protocol to calculate absolute analyte concentrations to (1) adjust for assay efficiency, as judged by recovery of spiked synthetic normalizer DNAs, and (2) calibrate NGS values against droplet digital polymerase chain reaction (ddPCR). As a model target, we chose the Epstein-Barr virus (EBV) genome. In patient (n = 12) and mock (n = 12) plasmas, NGS and 2 EBV ddPCR assays were used to report EBV load in copies per mL of plasma.

RESULTS

Next-generation sequencing was equally sensitive to ddPCR, with improved linearity when NGS values were normalized for spiked DNA read counts (R2 = 0.95 for normalized vs 0.91 for raw read concentrations). Linearity permitted NGS calibration to each ddPCR assay, achieving equivalent concentrations (copies/mL).

CONCLUSIONS

Our novel strategy for calibrating NGS assays suggests potential for a universal reference material to overcome biological and preanalytical variables hindering traditional NGS strategies for quantifying disease burden.

Quantification of JAK2V617F mutation load by droplet digital PCR can aid in diagnosis of myeloproliferative neoplasms

INTRODUCTION

This study developed a method for quantifying the JAK2V617F mutation load in patients with myeloproliferative neoplasm (MPN) using droplet digital PCR (ddPCR), which provides a new laboratory method for diagnosing polycythemia vera (PV), essential thrombocythemia (ET), and pre-primary myelofibrosis (pre-PMF).

METHODS

Patients with MPN who had JAK2V617F mutations from March 2013 to August 2019 were enrolled in this study. JAK2V617F mutation loads were quantified using ddPCR technology.

RESULTS

The study examined 225 patients, including 135 with ET, 58 with PV, and 32 with PMF. JAK2V617F mutation loads significantly differed (P < .001) between the ET and PV groups and between the ET and PMF groups. Bone marrow biopsies were reclassified in accordance with the 2016 World Health Organization diagnostic criteria, which revealed 132 patients with MPN: 62 with ET, 35 with PV, 17 with pre-PMF, and 18 with overt-PMF. JAK2V617F mutation loads significantly differed (P < .001) between the ET and PV groups and between the ET and pre-PMF groups. The cutoff value between the ET and pre-PMF groups was 49.9.

CONCLUSION

JAK2V617F mutation loads provide an additional basis for diagnosis of ET, PV, and PMF, particularly regarding differentiation between ET and pre-PMF.

The Genetic Basis of Primary Myelofibrosis and Its Clinical Relevance

Among classical BCR-ABL-negative myeloproliferative neoplasms (MPN), primary myelofibrosis (PMF) is the most aggressive subtype from a clinical standpoint, posing a great challenge to clinicians. Whilst the biological consequences of the three MPN driver gene mutations (JAK2, CALR, and MPL) have been well described, recent data has shed light on the complex and dynamic structure of PMF, that involves competing disease subclones, sequentially acquired genomic events, mostly in genes that are recurrently mutated in several myeloid neoplasms and in clonal hematopoiesis, and biological interactions between clonal hematopoietic stem cells and abnormal bone marrow niches. These observations may contribute to explain the wide heterogeneity in patients' clinical presentation and prognosis, and support the recent effort to include molecular information in prognostic scoring systems used for therapeutic decision-making, leading to promising clinical translation. In this review, we aim to address the topic of PMF molecular genetics, focusing on four questions: (1) what is the role of mutations on disease pathogenesis? (2) what is their impact on patients' clinical phenotype? (3) how do we integrate gene mutations in the risk stratification process? (4) how do we take advantage of molecular genetics when it comes to treatment decisions?