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Record W4417007088 · doi:10.1182/blood-2025-2046

Clonal persistence and early cooperative mutations shape transformation in myelofibrosis

2025· article· en· W4417007088 on OpenAlexaff
Noushin Farnoud, Kamal Menghrajani, Paola Guglielmelli, Christopher Famulare, Erin McGovern, Giuseppe Gaetano Loscocco, Andriy Derkach, Andrew Dunbar, Naseema Gangat, Vikas Gupta, Andrea Arruda, Taghi Manshouri, Srđan Verstovšek, Andrew Kuykendall, Vincent T. Ho, Auro Viswabandya, Joachim Deeg, Thomas S. Monahan, Tania Jain, Jeanne Palmer, Alla Keyzner, Aaron T. Gerds, Alexandra Gomez-Arteaga, Nikolai A. Podoltsev, Roni Tamari, Helen Ajufo, Satyajit Kosuri, Haris Ali, Idoroenyi Amanam, Jordan Chervin, Minal Patel, Jesús Gutiérrez‐Abril, Sarun Sereewattanawoot, Juan Arango Ossa, Francesco Passamonti, Francesco Maura, Maymona Abdelmagid, Ahmed Abdelrheem, Yassin Bashir, Muhammad Yousuf, Animesh Pardanani, Kapila Viges, David Shoultz, Rick Winneker, Omar Abdel‐Wahab, John Mascarenhas, Ronald Hoffman, Ross Levine, Elli Papaemmanuil, Alessandro M. Vannucchi, Ayalew Tefferi, Raajit K. Rampal

Bibliographic record

VenueBlood · 2025
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMyelofibrosisLoss of heterozygosityMutationPoint mutationSomatic cellMyeloidGeneCopy-number variationSomatic evolution in cancer

Abstract

fetched live from OpenAlex

Abstract Background Myeloproliferative neoplasms (MPNs) can progress from chronic phase to accelerated or blast phase (MPN-AP/BP), a transition with poor prognosis and limited treatment options. While some abnormalities have been described, the genomic and clonal drivers of transformation remain poorly defined. We characterized mutational and structural alterations in MPN-AP/BP, leveraging paired samples to trace clonal evolution. Methods We profiled 186 samples from 160 individuals, including 159 MPN-AP/BP and 27 chronic-phase MPN samples (26 MF, 1 PV), using targeted sequencing of a 588 gene panel (mean depth: 709x). Paired pre- and post-transformation samples were available for 26 patients (n= 52). A consensus pipeline was used to detect somatic mutations, copy number variations (CNVs), and copy-neutral loss of heterozygosity (cnLOH). Clinical cytogenetic data were integrated when available. Single-cell validation is ongoing. Results Genomic complexity was widespread: 99% of patients harbored ≥1 oncogenic mutation (median 4; range 1–10). In total, 859 mutations were identified, most commonly in JAK2 (68%), ASXL1 (28%), TET2 (26%), SRSF2 (24%), TP53 (22%), and RUNX1 (22%). 684 structural alterations were identified in 86% of patients (median 3; range 1-20), including arm-level (22.5%), segmental (>5 Mb; 60%), and focal (<2 Mb; 9%) alterations. Recurrent events included 9p cnLOH (16%), del(5q) (13%), del(17p) (10%), and focal deletion of 21q22.12 involving RUNX1 (10%). Complex karyotypes (≥3 abnormalities) were observed in 55% of patients. Biallelic mutations accounted for 21% (183/859) of mutations, affecting 58% of patients across 21 genes, most frequently JAK2 V617F (28%), TP53 (17%) and TET2 (14%). Patients with biallelic mutations in these genes had significantly higher blast percentages compared to those with monoallelic mutations (p<0.05), highlighting an association with aggressive disease. In 26 patients with longitudinal samples (chronic phase MPN and MPN-AP/BP), 87% (125/144) of mutations were already detectable during the chronic phase, on average more than 7 years prior to transformation. Only 11% (16/144) of mutations were newly acquired, including additional RUNX1 and TP53 hits, or structural changes that converted pre-existing JAK2 V617F and TP53 R248Q mutations from monoallelic to biallelic via 9p cnLOH or 17p loss, respectively. These findings suggest transformation is often driven by outgrowth of pre-existing high-risk clones, indicating that these clones likely have a competitive advantage and that the mutations within these clones have a cooperative biological interaction which underlies this competitive advantage. To investigate this hypothesis, we examined whether patterns of recurrent co-mutations were identifiable in this cohort. We identified recurrent co-occurring mutations in SRSF2-IDH2, ASXL1-SRSF2, and ASXL1-EZH2, which were enriched at transformation (p < 0.05; Fisher exact test). Importantly, presence of these co-mutations was consistently associated with a higher transformation risk in two independent cohorts of MF patients from Mayo Clinic (n= 405) and University of Florence (n= 518). Clonal architecture analysis revealed that these co-mutations frequently arose within the same malignant clone: 100% for SRSF2-IDH2, 80% for ASXL1-SRSF2, and 85% for ASXL1-EZH2, and are often within the dominant clone. Prior studies have demonstrated mechanistic interplay between SRSF2 and IDH2 (Yoshimi et al.), as well as ASXL1 and SRSF2 (Sui et al.) mutations in myeloid malignancies. We assessed functional synergy of ASXL1-EZH2 using a murine model and identified that dual deletion caused a rapidly fatal myeloid neoplasm with shorter survival than single-gene loss. These results reinforce that functional synergy emerges within a shared cellular context, supporting clonal co-dependence as a driver of progression. Single-cell RNA sequencing of ASXL1-EZH2 mutant cases is ongoing and will be presented. Conclusions Transformation to MPN-AP/BP is driven by gradual clonal remodeling and expansion of pre-existing high-risk abnormalities. Biallelic and multi-hit alterations in TP53, JAK2, TET2, and RUNX1 promote clonal dominance and blast progression. Recurrent co-mutations such as IDH2-SRSF2 and ASXL1-EZH2 arise within the same clone, exhibit functional synergy, and are linked to poor outcomes. These findings support the clinical utility of early clonal and co-mutational profiling to guide risk-adapted intervention.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.016
GPT teacher head0.262
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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