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Record W2988198068 · doi:10.1182/blood-2019-130923

HSCs Fated to Progress to Blast Phase Can be Detected in Myelofibrosis Patients Several Years Prior to Leukemic Transformation

2019· article· en· W2988198068 on OpenAlexaffabout
Jenny Ho, Jessie J.F. Medeiros, Michelle Chan‐Seng‐Yue, Lauren Hummel, Andrea Arruda, Amanda Mitchell, Caroline McNamara, James A. Kennedy, Anne Tierens, Dawn Maze, Hubert Tsui, Mark D. Minden, Vikas Gupta, John E. Dick

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsSunnybrook Health Science CentreUniversity of TorontoPrincess Margaret Cancer CentreHealth Sciences CentreUniversity Health Network
Fundersnot available
KeywordsMyelofibrosisMyeloid leukemiaBiologyclone (Java method)Germline mutationMyeloidMutationSomatic cellGeneticsLeukemiaCancer researchBone marrowGeneImmunology

Abstract

fetched live from OpenAlex

In myeloproliferative neoplasms (MPN), somatic mutations in genes recurrently mutated in myeloid leukemia, apart from known MPN driver mutations (JAK2, MPL, CALR), are common and can increase the risk of transformation to acute leukemia, also called blast phase (BP). However, when mutations in these genes are acquired, the nature of the cells bearing these mutations, and how each mutation cooperates to promote blast transformation remains largely unknown. We therefore examined serially collected blood and bone marrow samples from patients with myelofibrosis (MF) who progressed to BP to further elucidate the genetic basis of blast transformation. The aims of our study were to determine the temporal acquisition of mutations during chronic phase (CP) that contribute to blast transformation and to define the cellular origins within the hematopoietic hierarchy of the clone fated to progress to BP (termed BP-fated clone). Our cohort included 9 MF patients (8 JAK2V617Fpositiveand 1 MPLW515Lpositive) who progressed to BP and for whom CP and BP samples were previously collected and banked. The time interval between CP and BP collections ranged between 1.5 to 6.6 years. In 4 patients, additional CP samples were available from intervening time points. Whole genome sequencing (WGS) of leukemic blasts (BP sample), the MPN clone (CP sample), and germline control (T-cells or buccal DNA) was performed to identify somatic mutations. We detected somatic mutations at BP in 19 genes recurrently mutated in myeloid leukemia (ML) with an average of 5.3 (range 2-8) genes mutated per patient. Genes that were mutated in 2 or more BP samples include SRSF2 (n=5), ASXL1 (n=4), TET2 (n=4), IDH1/2 (n=4), RUNX1 (n=4), NRAS (n=4), KRAS (n=2), U2AF1 (n=2), PHF6 (n=2), and STAG2 (n=2). Notably, 73% of ML gene mutations identified at BP were already acquired and present at ≥5% variant allele frequency in CP. The remaining (27%) ML gene mutations were not detected at CP and were, thus, termed BP-specific mutations. To achieve our study aims, we sorted hematopoietic stem and progenitor cell (HSPC) populations (HSC, MPP, LMPP, CMP, MEP, GMP), as well as, mature cell populations (myeloid, erythroid, T-cell, B-cell and NK) from CP samples and performed DNA whole genome amplification. We then interrogated sorted cell populations for BP-specific mutations by droplet digital PCR (ddPCR) to identify low frequency mutations. Our results revealed that BP-specific ML gene mutations could be detected at low frequencies (range between 0.2-5%) in one or more cell populations several years (1.5-3 years) prior to BP diagnosis. Importantly, we detected these low frequency mutations within the HSC population from several patients, indicating that BP-fated clones were derived from an HSC. This finding is being verified in all patients by targeted sequencing of additional BP-specific mutations that were identified by WGS (average of 300 variants per patient, range 37 to 659). In one patient analyzed to date, additional low frequency BP-specific mutations have been detected within the HSC population, and thereby confirm the BP cell of origin as an HSC in this individual. Generalization of this finding will be confirmed by targeted sequencing of sorted populations from the remaining patients. In summary, BP-fated clones often appear several years prior to blast transformation and can be traced back to HSCs. Identification of BP-fated clones that remain dormant strongly suggests that mechanisms beyond the acquisition of somatic mutations in ML genes (including but not limited to epigenetic alterations, acquisition of non-coding mutations, inflammation) are necessary to effectively promote full leukemic transformation. Disclosures McNamara: Novartis Pharmaceutical Canada Inc.: Consultancy. Maze:Pfizer Inc: Consultancy; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees. Tsui:Novartis: Consultancy, Honoraria. Minden:Trillium Therapetuics: Other: licensing agreement. Gupta:Incyte: Honoraria, Research Funding; Sierra Oncology: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Incyte: Honoraria, Research Funding; Sierra Oncology: Honoraria, Membership on an entity's Board of Directors or advisory committees.

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 score0.001
Threshold uncertainty score0.003

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.266
Teacher spread0.256 · 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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Citations1
Published2019
Admission routes2
Has abstractyes

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