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Record W2590633005 · doi:10.1182/blood.v128.22.769.769

Chemo-Transcriptomic Analysis of Complex Karyotype AML Reveals Increased Expression of Cell Cycle Components and Exquisite Dependency on Polo-like Kinase 1

2016· article· en· W2590633005 on OpenAlexaff
Vincent‐Philippe Lavallée, Clarisse Thiollier, Céline Moison, Marie-Ève Bordeleau, Isabel Boivin, Geneviève Boucher, Patrick Gendron, Sébastien Lemieux, Anne Marinier, Josée Hébert, Guy Sauvageau

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsInstitute for Research in Immunology and CancerLeukemia & Lymphoma Society of CanadaUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsNeuroblastoma RAS viral oncogene homologBiologyRUNX1Cancer researchTranscriptomeMyeloidInternal medicineOncologyHaematopoiesisGeneMedicineGeneticsKRASStem cellMutationGene expression

Abstract

fetched live from OpenAlex

Abstract Background: Acute myeloid leukemias (AML) with complex karyotype (CK) are associated with an adverse patient prognosis with current therapies, in particular when they include TP53 mutations. Identification of novel therapeutic strategies is urgently needed for this subgroup of patients. Methods and aims:We have performed an RNA-Sequencing transcriptomic analysis of 68 CK AML included in the Leucegene cohort comprising 415 primary AML samples and performed correlative targeted chemical screening aiming at the identification of active agents in this subgroup. Cell culture and analysis of mutations, gene expression and chemical screening were performed as previously described (Pabst et al., Nature Methods, 2014; Lavallée et al, Nature Genetics, 2015). Results:Genes mutated at a frequency greater than 5% in the CK cohort were: TP53 (43/68, 63%), NRAS (9/68, 13%), DNMT3A (8/68, 12%), TET2 and NF1 (7/68 each, 10%), RUNX1 (6/68), FLT3, MLL and PTPN11 (5/68 each) and JAK2 and U2AF1 (4/68 each). Of those, only TP53 (p < 0.0001) and NF1 (p = 0.02) were preferentially associated with CK AML. HMGA2 (High Mobility Group AT-hook 2) was the most differentially expressed gene in CK AML compared to other AML (median: 1.35 vs 0.008 RPKM, q = 2 x 10-14). TP53-mutated samples were in addition characterized by low expression levels of Ectodysplasin A2 Receptor or EDA2R, a known target of TP53. We next tested in dose-response studies 267 compounds enriched in approved anti-cancer drugs on 27 primary CK AML samples and in 11 intermediate-risk karyotype controls. Four percent of these compounds were very active (median IC50 < 10 nM) in CK AML (red dots in Fig A). Surprisingly, this small subset of compounds comprised the only 2 inhibitors of Polo-Like Kinase 1 (PLK1), volasertib (median IC50: 6.6 nM) and rigosertib (median IC50: 8.5 nM). Rigosertib was, in addition, more active in CK AML than in intermediate-risk AML (p = 0.0035) supporting the hypothesis that choice of treatment, rather than genetic anomalies, determine prognosis (Fig. B-C). Most interestingly, expression levels of PLK1 correlated with the measured sensitivity to both rigosertib and volasertib raising the possibility that PLK1 expression determines sensitivity to these inhibitors. PLK1 is more highly expressed in CK AML (p=0.009) and in TP53-mutated AML (p = 0.015) than in control samples. A threshold of 5 RPKM was particularly predictive for drug sensitivity (rigosertib: p < 0.001, Fig C; volasertib: p = 0.037) suggesting that it could become a companion biomarker. Rigosertib and volasertib also potently inhibit PLK2 and PLK3 in cell-free assays, but no association was observed between PLK2/PLK3 expression and response to inhibitors, suggesting that cell lethality was mediated by PLK1 inhibition. PLK1 is a protein kinase involved in cell cycle. Accordingly, expression of this gene in our dataset defines a cluster of very highly correlated genes comprising mostly cell cycle components (e.g. CDC20, CCNB2, CENPA and CTSE1: > 0.90). Volasertib was recently studied in a phase 2 clinical trial in AML patients in combination with low-dose cytarabine (LDAC) (Döhner et al, Blood 2014). In this analysis, and in line with our data, responses were seen across all genetic groups including in adverse-risk AML (1/14 in LDAC vs 5/14 in LDAC + volasertib). Conclusion:Our chemo-transcriptomic analysis revealed that CK AML are characterized by high levels of HMGA2 expression and, in addition for the TP53 mutated subset, low levels of EDA2R. Most importantly our results show that CK AML with high expression levels of PLK1 are uniformly and preferentially sensitive to PLK1 inhibitors. Our data thus support the hypothesis that sensitivity to PLK1 inhibitors is associated to PLK1 expression, and that this gene may represent a promising biomarker to predict biologicalresponse to these agents. Our analysis provides for the first time a strong rationale for investigating PLK1 inhibitors in the context of CK and/or TP53 mutated AML in which correlative PLK1 expression analyses are commanded. Figure. Figure. 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.021
GPT teacher head0.273
Teacher spread0.252 · 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 designBench or experimental
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
Published2016
Admission routes1
Has abstractyes

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