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Record W2417041000 · doi:10.1182/blood.v122.21.744.744

NGS-Based Detection Of Multiple RAS-Mutated Clones In MLL-Rearranged Leukemias Suggests Strong Oncogenic Collaboration

2013· article· en· W2417041000 on OpenAlexaff
Vincent‐Philippe Lavallée, Patrick Gendron, Geneviève Boucher, Marianne Arteau, Brian T. Wilhelm, Sébastien Lemieux, Josée Hébert, Guy Sauvageau

Bibliographic record

VenueBlood · 2013
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsInstitute for Research in Immunology and CancerUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsCEBPABiologyKRASExome sequencingNPM1ExomeTranscriptomeMutationGeneticsLeukemiaCancer researchDeep sequencingGeneKaryotypeGenomeGene expression

Abstract

fetched live from OpenAlex

Abstract Background Recent development in sequencing technologies with deep coverage for mutation analysis has enabled the identification of clonal architecture in some cancers. RAS mutations are observed in a large proportion of MLL leukemias. Our hypothesis is that determination of RAS mutation status in MLL leukemias should provide insights into the clonal make up of this disease and clues about the nature of clones that overcome therapy. Methods We combined exome and transcriptome sequencing in 32 adult MLL leukemias and results were compared to our cohort of 48 normal karyotype (NK) AML. Exome capture and paired-end sequencing (2 x 100bp, Illumina HiSeq 2000) were performed using TruSeq (Illumina) protocols. Mean coverage was 165X for transcriptome and 42X for exome. Initial analysis was focused on 25 known AML-associated genes and excluded all other novel mutations. Average transcriptome and exome coverage for N/KRAS alleles was 287X (25-846) and 42X (9-151), respectively. Clones were defined based on the identification of N/KRAS mutations in at least 1% of the reads. Results Figure 1 shows mutation status, MLL partners and FAB classification for each MLL leukemia. No mutations were observed in NPM1, FLT3 (ITD), CEBPA (biallelic), RUNX1, DNMT3A, IDH1, KIT, BCOR, SF3B1, U2AF1 or RAD21. On average, 1 mutated gene (range: 0-4) per sample was found compared to 3 (range 0-5) in NK-AML (p < 0.0001). We observed that 13/32 MLL leukemias (which include 2 paired samples) harbored N/KRAS mutations. There were no association between RAS mutation status and MLL partner, FAB classification, age, white blood cell count and overall survival. RAS mutations were found in 15% of NK-AML which contained on average 2.3 additional mutations in leukemia-associated genes compared to only 0.3 (p<0.0001) in MLL leukemias. Excluding 2 paired relapse specimens, a total of 24 N/KRAS mutated clones were identified in 11 of the 30 MLL leukemias. The first sample included 5 clones each containing different NRAS mutations (e.g. G13R, G13D, etc.) contributing to 17, 9, 4, 2 and 2 % of the reads. Since RAS mutations are mostly heterozygous, we estimated that the contribution of each clones varied between 34 (i.e. 17% x 2) to 4%. A similar analysis revealed 4 clones in another specimen, contributing to 22, 12, 12 and 4 % of the cells. In 4 additional samples, the proportions of N/KRAS mutated clones were 1) 42, 38 and 8% 2) 92, 4 and 2 %, 3) 78 and 12% and 4) 52 and 32%, establishing that 20% (6/30) of these MLL leukemias were oligo- to polyclonal. In comparison, our NK-AML cohort of 48 patients included 7 specimens mutated for N/KRAS in which a total of 10 different clones were identified for an average of 0.2 RAS mutated clones per NK-AML versus 0.8 in MLL leukemias (p=0.007). This result further strengthens the hypothesis that RAS and MLL-fusion genes are strong collaborators in human AML. Grossmann et al recently showed that RAS mutated clones can be lost at relapse (Leukemia, 2013), possibly suggesting that other genes are at play in collaborating with MLL-fusions and causing drug resistance. To identify such genes, we further analyzed paired diagnosis and relapse samples in 2 patients. In the first patient, the KRAS mutation that was found in 66% of the cells at diagnosis was identified in all cells at relapse. In the second patient, while KRAS G12V and G12D mutations were found in 78% and 12 % of the cells at diagnosis, only the G12V clone was detected in 100% of the cells at relapse indicating in vivo clonal selection in both cases. We then performed a comparative analysis of mutated/wild type allele ratios for other coding genes. This analysis enabled us to identify a subset of mutations in candidate genes that are present at relapse in the dominant clone but that were undetectable or at lower frequency at presentation, indicating they might be specifically involved into occurrence of relapse (i.e. drug resistance). Conclusion NRAS and KRAS are mutated in 37% of MLL leukemias in this cohort. In contrast to NK-AML, these leukemias are frequently oligo- to polyclonal and contain few additional mutations suggesting that RAS activation may be sufficient to induce AML in the presence of MLL fusions. Evidence from our limited number of relapse patients, and that of others, suggests that RAS does not confer drug resistance which could be explained by novel mutations in genes that were specifically detected in the dominant clones at 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)0.001
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 score0.002
Threshold uncertainty score0.008

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.278
Teacher spread0.262 · 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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Citations0
Published2013
Admission routes1
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