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Record W2552867110 · doi:10.1182/blood.v126.23.223.223

On the Origins of AML Relapse

2015· article· en· W2552867110 on OpenAlexaff
Liran I. Shlush, Amanda Mitchell, Lawrence E. Heisler, Sagi Abelson, Monica Doedens, McLeod Jessica, John D. McPherson, Thomas J. Hudson, Jean Wang, Mark D. Minden, John E. Dick

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsOntario Institute for Cancer ResearchPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMyeloid leukemiaPopulationclone (Java method)BiologyOncologyLeukemiaCD33Stem cellMyeloidImmunologyMedicineInternal medicineCD34GeneticsGene

Abstract

fetched live from OpenAlex

Abstract While induction into remission is effective in the majority of acute myeloid leukemia (AML) patients, disease recurrence is common, especially among the elderly. Understanding the origins of AML relapse would permit better treatments targeting the specific cells that survive chemotherapy. While some evidence suggests that AML relapse can originate either from a minor or a major clone that is already present at diagnosis, the exact origins of AML relapse are still obscure. In the current study we aimed at identifying the origins of AML by identifying genetic variants that appear at relapse, and to then track these variants back into specific cell populations present at diagnosis. We hypothesized that relapse might have multiple origins: from the major blast population, from rare leukemia imitating cells (LIC) as detected using xenografting, or from preleukemic stem cells (preL-HSCs). Methods: The bulk diagnosis and relapse samples of peripheral blood from eleven AML patients were analyzed, first by whole genome sequencing (50X coverage) to identify somatic mutations and genetic variants which were specifically present at relapse (relapse variants-RVs). The presence RVs was then reassessed in phenotypically defined sub-fractions sorted from the diagnosis samples, at a sensitivity of 1 in 1000 by digital PCR. The following sub-populations were genotyped: 1) isolated CD33+ blasts (the major population) 2) phenotypically defined leukemic and preleukemic stem cells 3) functionally defined leukemia initiating cells (LICs) harvested from xenografts (an average of 30 xenografts were generated from each diagnosis and relapse sample). Results: LICs, but not the dominant blast population from diagnosis carried the RVs in 3 of 11 cases. In these patients CD33-CD34+CD45RA+ immature cells from diagnosis also carried the RVs. In a second subset of 3 of 11 AML samples, relapse originated from a minor clone present within the CD33+ leukemic blasts; these samples did not produce exnografts. Other samples (2/11) exhibited relapse samples that arose from a combined origin (both LICs, and CD33+ blasts, or from the major clone (1/11). In two cases we could not identify the origins of relapse. As our initial results suggested that the cells responsible for AML relapse can come from distinct origins within the diagnosis sample, we next asked whether other functional and phenotypic differences might be present between the patients that have different relapse origins. RNA sequencing analysis of bulk cells from diagnosis demonstrated a remarkable clustering of the global gene expression that correlated with the origin of relapse. Unsupervised hierarchical clustering grouped together the AML samples who relapsed from the LICs, while all other samples were in a very distinct second cluster. The gene expression signature of the samples that relapsed from LICs was consistent with a monocytic phenotypic signature, while the other samples were more progenitor-like. To further expand and validate our findings we used the same unsupervised clustering on the RNA sequencing data of AML samples who relapsed in the TCGA dataset (n=86). Remarkably, the similar two main clusters were generated; comparison by GSEA provided evidence that the gene expression clusters in our study were generated by the same genes as in the TCGA clusters. Conclusion: Our results provide for the first time evidence that AML can relapse from distinct, predictable and pre-existing origins: AMLs with a monocytic phenotype relapse from chemo-resistant LICs; and AMLs with a progenitor gene expression pattern (yet lacking xenografting capacity) that relapse from CD33+ cells. These results pose a series of predictions as to the success of different therapies. For example, in the former group the major monocytic clone is sensitive to chemotherapy, yet relapse originates from CD33-CD34+CD45RA+ cells and would therefore be predicted to be resistant to Anti-CD33 therapeutics. On the other hand, relapse in the latter group originates from CD33+ cells and these are predicted to be sensitive to Anti-CD33 therapeutics. The results of this study document the complexity in origins of AML relapse and have important implications for the design of future more effective and personalized strategies for preventing AML relapse. Disclosures No relevant conflicts of interest to declare.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.042
GPT teacher head0.306
Teacher spread0.264 · 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 designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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