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Abstract B170: Therapeutic implications of altered epigenetics and DNA damage responses in IDH2-mutated hematologic diseases

2019· article· en· W2911359328 on OpenAlexaff
Julie Leca

Bibliographic record

VenueCancer Immunology Research · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsOntario Institute for Cancer Research
Fundersnot available
KeywordsIDH2EpigeneticsCancer researchBiologyMyeloidDNA methylationHistoneMyeloid leukemiaIDH1HaematopoiesisMutationStem cellGeneticsGeneGene expression

Abstract

fetched live from OpenAlex

Abstract Acute myeloid leukemia (AML) and angioimmunoblastic T-cell lymphoma (AITL), are hematologic diseases requiring novel approaches to patient selection and therapy. In AITL, neoplastic cells express many markers of T follicular helper (TFH) cells, while in AML, myeloid stem and progenitor cells are affected. Tumor cells of AML and AITL patients frequently bear mutations affecting genes involved in epigenetic regulation, including isocitrate dehydrogenase (IDH) and ten-eleven translocation-2 (TET2). IDH mutations drive production of the rare metabolite D-2-hydroxyglutarate (2HG), which competitively inhibits α-ketoglutarate (α-KG)-dependent dioxygenases such as the TET proteins (affecting DNA methylation) and Jumonji histone demethylases (altering histone methylation). IDH mutations occur in 30% of AML and AITL cases but the spectrum of these mutations differs. In AML, IDH1R132 (41%), IDH2R140 (44%) and IDH2R172 (15%) dominate, and IDH and TET2 mutations are mutually exclusive. In AITL, IDH2R140 (2%) and IDH2R172 (98%) dominate (no IDH1 mutations), with co-mutation of TET2 in 82.1% of cases. In AML, loss of these dioxygenase activities causes epigenetic alterations to DNA and histones, leads to abnormal gene transcription that affects hematopoietic cell differentiation, and drives myeloid disease. However, in AITL disease, the impact of the IDH2 mutation is completely unknown. My goal is to dissect the effects of IDH2 and TET2 mutations in hematologic diseases to understand how IDH2 and TET2 mutations collaborate to drive malignancy in AITL, and why they cooperate differently in myeloid and lymphoid diseases. I focus on DDR signaling and epigenetics analysis to found novel therapeutic vulnerabilities arise from the altered epigenetic regulation and DDR linked to IDH2 and TET2 mutations. I used a CD4-Cre mouse model to introduce the IDH2R172K and TET2 mutations in the T-cell compartment. This would allow me to study the collaboration between IDH2 and TET2 mutations in the context of T-cells. Mice bearing both IDH2 and TET2 mutations show decreased survival, with a median survival of approximately 8 months accompanied by splenomegaly and lymphadenopathy. This is significantly different than CD4+ control mice or single mutant mice. Double mutant mice (DM) present a disruption of spleen and lymph node architecture. To understand this phenotype, I performed comprehensive flow cytometry staining and, surprisingly, I found that the increased spleen size is not due to T-cell infiltration, but is due to increased erythropoiesis and expansion of immature erythropoietic cells (CD71+, cKit+). We can explain this result by the fact that CD4-Cre is also expressed in some progenitor cells leading to stress induced erythropoiesis. However, if we focus on the T-cell population, we see that DM mice present a T-cell phenotype including a decrease of CD4+ naïve cells and an increase of CD4+ effector memory cells as early as 5 months. So, there is an imbalance in T-cell homeostasis, but only when both the IDH2 and TET2 mutations are present, suggesting a cooperative role for both mutations in T-cell development. Since both IDH2 and TET2 affect epigenetic regulation, I want to conduct experiments to understand how these changes modulate T-cells homeostasis. Moreover, emerging data support the hypothesis that connections exist between epigenetic regulators and DDR signaling in hematologic diseases. My final aim will be to attempt to evaluate the efficacy of treatment with IDH2 inhibitors, hypomethylating agents, or DDR-targeting drugs, alone or in combination in AITL disease and identify factors involved in responses to these therapies or in the development of resistance. Finally, I will compare results obtained in AML versus AITL mouse models and clinical samples to identify mechanisms that are shared, and those that are unique to each disease. Citation Format: Julie Leca. Therapeutic implications of altered epigenetics and DNA damage responses in IDH2-mutated hematologic diseases [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr B170.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.082
GPT teacher head0.422
Teacher spread0.340 · 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.

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
Published2019
Admission routes1
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