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The Clonal Origins of Leukemic Progression of Myelodysplasia

2016· article· en· W2593342731 on OpenAlexaff
Tae-Hyung Kim, Marc S. Tyndel, Hyeoung Joon Kim, Jae‐Sook Ahn, Seung Hyun Choi, Hee Jeong Park, Yeo‐Kyeoung Kim, Deok‐Hwan Yang, Je‐Jung Lee, Sung‐Hoon Jung, Soo Young Kim, Yoo Hong Min, June‐Won Cheong, Sang Kyun Sohn, Joon Ho Moon, Murim Choi, Moses Lee, Zhaolei Zhang, Dennis Dong Hwan Kim

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsExome sequencingDeep sequencingOncologyBiologyExomeInternal medicineMedicineMutationGeneticsGeneGenome

Abstract

fetched live from OpenAlex

Abstract Introduction Acute myeloid leukemia (AML) that develops from pre-existing hematologic diseases, rather than developing de novo, is known as secondary AML (sAML). A number of hematologic malignancies can progress to sAML. However, the molecular and genetic characteristics behind the progression of hematologic malignancies to sAML remain unclear. To address this question and dissect the order of mutation acquisition throughout the course of the disease, we performed whole-exome sequencing and targeted deep sequencing on serial samples. Patients and Methods This study examined several cohorts with a combined total of 124 patients. This study was approved by research ethics boards at relevant institutions and samples were taken after informed consent. The discovery cohort (C1) consisted of 31 patients diagnosed with myelodysplasia who all progressed to sAML. Whole-exome sequencing (WXS) was performed for each case on bone marrow samples taken at the diagnosis of the antecedent malignancy and after sAML progression, as well as fractionated T-cell samples (CD3+). WXS (Agilent SureSelect v4) was performed on the 93 samples as per the manufacturer's protocol using an Illumina HiSeq 2000. The other cohorts included 72 non-progressed MDS patients (C2a, median follow-up of 3.5 years) and an additional 21 sAML patients (C2b) progressed to sAML from MDS, for whom samples from the MDS stage were not available. Targeted sequencing was performed using an Agilent custom probe set of the selected 92 genes. We multiplexed and sequenced the samples using an Illumina Hiseq 2000. Targeted deep sequencing was performed on all cohorts. Genomon-ITD was used to detect FLT3-ITD. Results The mean read depth retrieved for target regions for WXS data was 73x. After calling and prioritizing variants, we found a mean and median of 7.7 and 6 significant variants per patient at the time of initial diagnosis, and 12.4 and 10 variants after sAML progression, respectively. We also detected that FLT3-ITD emerged in 2 patients after sAML progression. The presence of variants in T-cell samples in 5 C1 and 20 C2a patients provides evidence on the relative timing of early events for a subset of patients. Both cohorts notably lack activated signaling pathway variants at this stage (Figure A). The T-cell variants in 5 C1 patients with pathway associations were all in genes involved in DNA methylation (DNMT3A, IDH1/2, and TET2) or splicing machinery (SRSF2 and SF3B1). This pattern was verified in 20 C2a patients except for a single case that had an NRAS-G13D mutation. These patients showed evidence of having clonal hematopoiesis. At MDS, there were a significant number of cases with variants in genes involved in DNA methylation and/or splicing machinery (35.5% and 48.3%, respectively). However, the portion of cases with variants affecting these pathways increased significantly at the MDS stage, but did not change much by the sAML stage (Figure C-D). On the other hand, variants in genes involved in activated signaling pathways showed a distinctive pattern. The portion of cases with variants affecting activated signaling pathways noticeably increased at the sAML step (25.8% to 54.8%) (Figure B). The changes in VAF between stages within cases of these variants revealed a similar pattern. In summary, clonal evolution patterns can be postulated based on the acquisition/expansion of mutations related to the three signature pathways (Figure E). Forty-eight percent of patients showed growth or development of clones containing activated signaling pathway variants at the sAML stage. Sixteen percent of patients developed the MDS from preleukemic mutations associated with DNA methylation or splicing machinery, and 26% first developed clones of this category at the MDS stage (a total of 42%). Conclusion Mutations in DNA methylation and splicing machinery genes are early disease events, expanding at the MDS stage but not during progression. On the other hand, activated signaling pathway mutations expand during progression, demonstrating that distinct categories of genetic lesions play roles at different stages of sAML in a generally fixed order. 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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

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)0.0010.000

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 head0.014
GPT teacher head0.302
Teacher spread0.288 · 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 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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Citations1
Published2016
Admission routes1
Has abstractyes

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