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Differential Gene Expression Using RNA Sequencing Between Elderly Acute Myeloid Leukemia (AML) Patients with Long Versus Short-Term Survival

2016· article· en· W2912279633 on OpenAlexaff
Amy M. Trottier, Adnan Mansoor, Carolyn Owen, Ariz Akhter, Etienne Mahé, Michelle Geddes

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsCalgary Laboratory ServicesUniversity of Calgary
Fundersnot available
KeywordsOncologyMyeloid leukemiaMedicineInternal medicinePopulationSurvival analysisTranscriptomeBioinformaticsBiologyGeneGeneticsGene expression

Abstract

fetched live from OpenAlex

Abstract Introduction Advances in genetic sequencing have shed light on the biological underpinnings of AML, however, the vast majority of previous work has focused on young patients, with little evidence-based data in the elderly population. Older age at diagnosis is a well-known poor prognostic factor, however prognostication within the older age group itself remains a challenge making therapeutic decision making particularly difficult. In this pilot project we studied the transcriptomes of short and long-lived elderly AML patients to gain insight into the potential molecular differences and signaling pathways that may help better prognosticate patients within this unique population. Methods Elderly patients (age > 65 and not fit for induction chemotherapy) with newly diagnosed AML (excluding APL) between 2011 - 2015, inclusive, with an available diagnostic bone marrow biopsy sample were considered for inclusion in this retrospective analysis. Patients were divided into two groups: long-term survivors (survival ≥ 6 months) and short-term survivors (survival < 2 months). RNA sequencing was performed on 12 patients in the long-term survivors group and 24 patients in the short-term survivors group. RNA sequencing was conducted using the Illumina platform (Illumina NextSeq 500) and data analysis was performed with TopHat and Cufflinks software. Results Baseline clinical characteristics were similar between the short-term and long-term survival groups as shown in Table 1. RNA sequencing revealed 41 genes with statistically significant (p-values < 0.001 and false-discovery q-values < 0.001) differential expression between the long-term and short-term survival groups. See Figure 1 for a heat map reflecting the gene expression values between groups. Of these 41 genes several are known to be involved in key cellular functions and signaling pathways including RNA post-transcription regulation, apoptosis, p53 regulation, and the mTOR pathway. However, only a few have previously been studied in AML (e.g. ERG, PCK2, and ABCG1) and none have been examined in the context of elderly AML patients. Twelve of the 41 differentially expressed genes were small nucleolar RNAs (snoRNAs), a class of regulatory RNAs involved in post-transcriptional modification of ribosomal RNA. These were found to be down regulated in the short-term survivors compared to the long-term survivors (p-value range 0.00005 - 0.001). This is a novel finding. Although recent studies have found differences in snoRNA expression in AML and ALL compared to healthy donors there are no published studies examining the role of snoRNA in the prognosis of AML. CYFIP2 is involved in caspase activation and cellular apoptosis and was found to be relatively under expressed in the short-term survivors group (p-value 0.008). WRAP53 plays an important role in the regulation of p53 expression and was found to be under-expressed in the long-term survivors. PRR5L is associated with mTORC2 and was found to be relatively over-expressed in the long-term survivors (p-value 0.00002). Due to the small sample sizes of this pilot project multivariate analysis was not conducted. In addition to the individual genes, these results highlight differences in several pathways, namely the mTOR, and p53 tumor suppressor/caspase apoptotic pathways, which may be associated with prognosis for elderly patients with newly diagnosed AML and deserve further investigation. The finding of down regulation of numerous snoRNAs in elderly patients with poor outcome also warrants further detailed study with larger sample sizes to fully elucidate their potential prognostic value. Figure 1: Heat Map highlighting the differential gene expressions from RNA sequencing for long-term compared to short-term survivors. Conclusion We have identified distinctly different gene expression profiles in elderly AML patients with long-term compared to short-term survival. These differentially expressed genes provide biologic insight into AML in the elderly as well as highlight candidate pathways and cellular mechanisms on which to base future detailed study to enable accurate prognostication and improved therapeutic decision making in this understudied population. Figure 1 Figure 1. Disclosures Owen: Roche: Honoraria, Research Funding; Janssen: Honoraria; Lundbeck: Honoraria, Research Funding; Abbvie: Honoraria; Novartis: Honoraria; Gilead: Honoraria, Research Funding; Pharmacyclics: Research Funding; Celgene: Honoraria, Research Funding. Geddes:Celgene: Other: Advisory Board, Research Funding.

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
Bibliometrics0.0010.001
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 head0.037
GPT teacher head0.290
Teacher spread0.253 · 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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Citations0
Published2016
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