Inflammation in Myeloproliferative Philadelphia Chromosome Negative Neoplasms - Relationship with Biomarkers of Disease Activity
Bibliographic record
Abstract
Introduction Myeloproliferative neoplasms (MPN) are characterized by activating somatic mutations, most commonly in the Janus kinase 2 (JAK2) gene, and are associated with increased risk of thrombosis. Inflammation, measured by biomarkers such as high sensitivity C-reactive protein (hs-CRP), may be seen in patients with MPN (Barbui et. al. Haematologica 2011). Inflammation has also been associated with cardiovascular events. In ambulatory patients with cancer, elevated cardiac troponin levels may indicate a higher risk of major adverse cardiovascular events (Kavsak et. al. JACC CardioOncology 2024). The relationship between the genetic characteristics of MPN and biochemical phenotype - specifically, markers of inflammation and cardiovascular risk - has not been well established. Objectives This study evaluated associations between JAK2 V617F variant allele frequency (VAF) and the presence of additional somatic mutations, with biomarkers of inflammation (hs-CRP) and biomarkers of cardiovascular risk including high sensitivity troponin T (hs-TnT), D-Dimer, amino-terminal pro-brain natriuretic peptide (NT-proBNP), in patients with MPN. Methods A cross-sectional study of adult patients with a diagnosis of Philadelphia chromosome negative MPN (based on 2016 World Health Organization or British Committee for Standards criteria) was conducted across three Canadian centres. Participants were prospectively enrolled from clinical lists or databases. Patient characteristics were documented. Peripheral blood study samples were collected and processed for hs-CRP, hs-TnT, NT-proBNP, D-Dimer, and JAK2 V617F VAF. A custom targeted gene sequencing panel was employed for detection of other somatic mutations and their corresponding allele frequencies, with a lower bound detection limit of 2%. Log transformation of biomarker data was conducted due to skewed distributions. Target recruitment was 100 patients. Results A total of 109 patients were recruited, of whom 62% were female. 57 patients had a diagnosis of polycythemia vera (PV), 38 patients had essential thrombocythemia (ET), and 14 had primary myelofibrosis. Mean ± standard deviation (SD) for age was 67 ± 14 years. A history of cardiovascular disease was identified in 28% of patients. JAK2 V617F (n=80) was present in 89% of PV, 58% of ET, and 50% of primary myelofibrosis cases. Somatic mutations were characterized with one mutation in 42% of patients, two in 36%, and three in 6%. 17% of patients had no recognized mutation. A sample size of 109 has 86% power to detect an R2 value of 0.08 or greater. There was no clear association between JAK2 V617F VAF and age, relative to mean cohort age (mean ± SD VAF 0.31 ± 0.30 in older patients versus 0.26 ± 0.27 in younger patients, p=0.40), nor with a history of cardiovascular disease (mean ± SD VAF 0.29 ± 0.28 with a history versus 0.28 ± 0.31 in those without, p=0.87). There was also no relationship between JAK2 V617F VAF and hs-CRP, hs-TnT, D-Dimer, or NT-proBNP concentrations. There was a trend towards higher hs-TnT levels in patients with more than one somatic mutation, compared to patients with one mutation or less (mean ± SD hs-TnT 13 ± 13ng/L versus 10 ± 7ng/L, p=0.053). These patients were also older (mean ± SD age 71 ± 9 years versus 64 ± 16 years, p=0.007). 20% of patients (n=21) were on ruxolitinib at the time of the study. Findings were consistent after excluding these participants. Conclusions Our study demonstrates no association between JAK2 V617 VAF and biomarker parameters in patients with MPN. Inflammation and cardiovascular risk in MPN may be mediated by factors beyond VAF alone. More research is needed to understand the mechanisms of increased thrombotic risk seen in patients with MPN. Additional considerations that may have influenced study findings include frequency of homozygous JAK2 V617F variants (> 50% VAF), and participant disease subtype at time of study enrolment, notably prevalence of myelofibrosis (13%, n=14). There was a trend toward higher hs-TnT levels in patients with multiple somatic mutations, as well as with advanced age. This supports the hypothesis that multiple somatic mutations comprise a biomarker phenotype associated with adverse cardiovascular outcomes in patients with MPN.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".