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Record W2911986426 · doi:10.1182/blood-2018-99-118889

AML-Fated Clones Arise in Stem and Progenitor Cells in Myelofibrosis Patients Several Years Prior to AML Diagnosis

2018· article· en· W2911986426 on OpenAlexaff
Jenny Ho, Jessie James Franco Medeiros, Michelle Chan‐Seng‐Yue, Lauren Hummel, Andrea Arruda, Amanda Mitchell, Caroline McNamara, James A. Kennedy, Anne Tierens, Hubert Tsui, Mark D. Minden, Vikas Gupta, John E. Dick

Bibliographic record

VenueBlood · 2018
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsSunnybrook Health Science CentreUniversity of TorontoPrincess Margaret Cancer CentreHealth Sciences CentreUniversity Health Network
Fundersnot available
KeywordsMyelofibrosisMyeloidMyeloid leukemiaBiologyclone (Java method)Cancer researchCD34Stem cellBone marrowImmunologyGeneGenetics

Abstract

fetched live from OpenAlex

Abstract In myeloproliferative neoplasms (MPN), somatic mutations in recurrently mutated myeloid leukemia genes, apart from MPN driver genes, are common and can increase transformation risk to acute myeloid leukemia (AML). However, the temporal acquisition of these mutations, the properties of the cells bearing these mutations, and how each mutation cooperates to promote leukemic transformation remains largely unknown. We therefore examined blood and bone marrow samples collected serially from patients with myelofibrosis (MF) who developed AML to further elucidate the genetic basis of leukemic transformation. Specific aims include: 1) determining the timeline of acquisition of mutations that contribute to AML in chronic phase and 2) defining the cell type that harbours the clone fated to initiate AML. Ten patients who transformed to AML following a diagnosis of MF (6 PMF, 3 post-PV MF, 1 post-ET MF) were studied. The time interval from the first MPN collection to AML diagnosis ranged between 1.5 to 6.6 years; and in 4 patients additional MPN samples collected between these time points were available for study. Somatic mutations were identified from whole genome sequencing (WGS) data of AML blasts (CD45dim cells), the MPN clone (CD45highCD33+ and CD34+ cells), and germline references (T-cells or Buccal DNA); and from targeted sequencing results of 54 recurrently mutated myeloid leukemia genes performed on AML samples as part of another study. Stem and progenitor populations (HSC, MPP, LMPP, CMP, MEP, GMP), as well as, mature cell populations (myeloid, erythroid, T-lymphoid, B-lymphoid and NK) were sorted from MPN time points and whole genome amplified using the REPLI-g Single Cell Kit (Qiagen). Somatic mutations were tracked by droplet digital PCR (ddPCR) and/or targeted sequencing. We identified an average of 5 (range 1-8) recurrently mutated myeloid leukemia genes that were somatically mutated in each patient at AML diagnosis, with an average of 5.5 (range 1-11) different mutations affecting these genes. Many of these mutations were present at ≥5% variant allele frequency (VAF) in the MPN clone at the earliest chronic phase time point. In 8 patients, the MPN driver mutation was detected in the MPN clone and AML blasts (MPNdriver-concordant), while in 2 other patients there was discordance as JAK2V617F was present in the MPN clone, but not the AML blasts. Myeloid leukemia genes that were mutated in 2 or more patients included 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). We tracked mutations by ddPCR in populations sorted from MPN time points and demonstrated that 10 out of 13 mutations not identified by WGS were in fact present at low VAF in one or more cell populations. Moreover, in 5 patients with MPNdriver-concordant AML, the AML-fated clone was detectable 1.5 to 3 years before AML was diagnosed; and in 4 of these patients the AML-fated clone resided among cells with a HSC or MPP immunophenotype. In order to better establish the reservoir harbouring AML-fated clones, clonal tracking within the stem and progenitor populations by targeted sequencing of a greater number of AML-specific variants is underway. Among the two patients with JAK2V617F-negative AML, we observed in one patient the emergence of the AML-fated clone within one year of AML diagnosis. In summary, we show that clones fated to intiate AML in patients with MF often reside among cells with a HSC or MPP immunophenotype and can appear several years prior to the diagnosis of AML. This strongly suggests that mechanisms beyond the acquisition of somatic driver mutations (including but not limited to epigenetic alterations, acquisition of non-coding mutations, inflammation) are necessary to effectively promote full leukemic transformation. Interestingly, in one patient with JAK2V617F-negative AML (following JAK2V617F-positive MF), the latency period between the appearance of the AML-fated clone and AML diagnosis was much shorter, suggesting that the mechanisms underlying transformation are likely different for these 2 clonal evolutionary pathways. Disclosures Gupta: Novartis: Consultancy, Honoraria, Research Funding; Incyte: Research Funding.

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 score0.004

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 head0.011
GPT teacher head0.244
Teacher spread0.233 · 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
Published2018
Admission routes1
Has abstractyes

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