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PS981 LONG TERM AML SURVIVORS HAVE INCREASED MORTALITY AND HIGH PREVALENCE OF CLONAL HEMATOPOIESIS

2019· article· en· W3005831402 on OpenAlexaff
Noa Chapal Ilani, Elisabeth Niemeyer, Netta Mendelson Cohen, Yevgeny Moskovitz, Barak Oron, Amanda Mitchell, M.D. Minden, Amos Tanay, Ran D. Balicer, Tamir Biezuner, D. Cilloni, Klaus H. Metzeler, Yishai Ofran, Nathali Kaushansky, Liran Slush

Bibliographic record

VenueHemaSphere · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineMyeloid leukemiaBone marrowHaematopoiesisOncologyLeukemiaInternal medicineclone (Java method)ImmunologyStem cellBiologyGene

Abstract

fetched live from OpenAlex

Background: Acute myeloid leukemia (AML) is defined by the accumulation of immature blasts in the bone marrow (BM) resulting, if left untreated, in acute BM failure. While AML presents in an acute form, it is clear today that most AML cases are preceded by a long phase of age related clonal hematopoiesis (ARCH) with almost no symptoms. The clinical and molecular trajectories of AML both before and after diagnosis are highly predictable. Recent studies identified the high risk individuals destined to evolve from ARCH to AML based on blood counts parameters (mainly red cell distribution width (RDW)) and molecular attributes such as clone size and the number of ARCH defining events. Most AML patients will achieve complete remission after induction chemotherapy, however most will relapse within a median time of 11 months despite post remission consolidation or bone marrow transplantation (BMT). A small fraction of AML patients will maintain at long term remission (LTR). Aims: The course of AML from ARCH to relapse is well characterized both clinically and molecularly, however, this information is missing for long term AML survivors, specifically for AML patients who did not receive BMT. The fact that AML has a long chronic history before its acute presentation might suggest that a recovery from the acute phase will not necessary result in resetting the hematopoietic system but rather to turn it back to its chronic phase before AML presentation. Methods: To answer the question whether preleukemic events are still present at LTR and what important clinical implications it might have, we studied morbidity, mortality and molecular structure among two unique LTR cohorts. One cohort is the electronic health records (HER) of 4.2 million individuals covering over 15 years of follow up and include 61 LTR survivors who did not undergo BMT (LTSnoBMT). The second cohort which contains molecular data, included analysis of genetic variation in 30 LTR cases at diagnosis and LTR, and was compared to a group of lymphoid malignancies patients at remission. Results: We found that the mortality among LTSnoBMT 10 years after diagnosis was 20%>50% higher in comparison to controls. In addition, several lab results were different between the LTSnoBMT and controls including higher red cell distribution width (RDW) among older (>55) LTSnoBMT. Recent studies found a correlation between high RDW and mortality among individuals carrying age related clonal hematopoiesis (ARCH) mutations. We also found that ARCH defining events were significantly more prevalent among LTSnoBMT in comparison to controls (63% vs 37% p = 0.039). Furthermore of the ARCH defining events among the LTSnoBMT 62% were recurrent AML variants as oppose to 21% among the controls (p = 0.002). Of the recurrent mutations IDH1/2 were the most common mutations and 2 out of 7 IDH1/2 cases experienced a late relapse (Figure 1). Summary/Conclusion: Altogether, even after the sustainable eradication of the malignant clone, the hematopoietic system/microenvironment does not normalize (abnormal blood counts, high prevalence of ARCH and higher mortality). image These results suggest that AML at LTR is a unique situation and that this group of patients are not fully recovered and should be further studied and treated.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.023
GPT teacher head0.302
Teacher spread0.279 · 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 teacher head, not a consensus.

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
Published2019
Admission routes1
Has abstractyes

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