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Record W2983035886 · doi:10.1182/blood-2019-125780

Prognostic Impact of a Composite Genetic Profile Defined By Cytogenetics and Next Generation Sequencing at Diagnosis on Treatment Outcomes Following Allogeneic Hematopoietic Stem Cell Transplantation in Acute Myeloid Leukemia

2019· article· en· W2983035886 on OpenAlexaffabout
Georgina S. Daher-Reyes, Tae‐Hyung Kim, Kyoung Ha Kim, Jae-Sook Ahn, Tracy Stockley, Jose Mario Capo-Chichi, Zeyad Al‐Shaibani, Arjun Law, Wilson Lam, Fotios V. Michelis, Auro Viswabandya, Jeffrey H. Lipton, Rajat Kumar, Jonas Mattsson, Aaron D. Schimmer, Caroline McNamara, Tracy Murphy, Dawn Maze, Vikas Gupta, Hassan Sibai, Steven M. Chan, Karen Yee, Mark D. Minden, Zhaolei Zhang, Andre C. Schuh, Dennis Dong Hwan Kim

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsUniversity Health NetworkUniversity of TorontoPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineOncologyInternal medicineNPM1TransplantationHematopoietic stem cell transplantationMyeloidFludarabineMyeloid leukemiaHazard ratioProportional hazards modelChemotherapyCyclophosphamideBiologyKaryotypeGenetics

Abstract

fetched live from OpenAlex

Introduction: The introduction of next-generation sequencing (NGS) has expedited the discovery of novel genetic lesions in acute myeloid leukemia (AML), thereby allowing better risk stratification with respect to overall survival (OS). We have previously reported that AML patients with PTPN11 and NPM1 mutations had longer OS following chemotherapy, while those carrying mutations in ASXL1, JAK2, RUNX1, TP53 and SRSF2 had a shorter OS (Daher-Reyes,ASH 2018). Little is known, however, regarding the impact of genetic profiles (somatic mutations and cytogenetic abnormalities) at initial AML diagnosis on the treatment outcomes following allogeneic hematopoietic stem cell transplantation (HCT). Methods & Patients: We enrolled AML patients who had available NGS data at time of initial diagnosis as part of the AGILE project between February 2015 and December 2018, and who subsequently underwent allogeneic HCT. NGS was performed on DNA samples isolated from peripheral blood or bone marrow samples at diagnosis. Analysis was performed using the TruSight Myeloid Sequencing Panel on the MiSeq sequencer (Illumina; San Diego, CA). Transplant outcomes (overall survival (OS), relapse-free survival (RFS), relapse incidence (RI), and non-relapse mortality (NRM)) after HCT were compared according to genetic profiles defined at diagnosis. Survival analysis for OS and RFS was performed using Cox's proportional hazard model, while the Fine-Gray model was used for RI and NRM analyses. Variables considered in the model included CR status prior to HCT (CR1 vs. beyond CR1), de novo AML (vs. secondary/therapy-related AML), induction chemotherapy used (3+7 vs. others), conditioning regimen (myeloablative vs. reduced intensity), WBC, age, donor type, mutation status of commonly mutated genes, and the composite adverse genetic profile (defined as having at least one of monosomal karyotype (MK), TP53 mutation, del(5), complex karyotype (CK), and monosomy 7), given that these 5 features were highly co-occurring, adverse prognostic factors (Figure 1A). Results: We identified 435 patients in whom frontline NGS was performed, of whom a total of 178 patients (40.9%) received HCT and were included in the final analysis. A total of 598 (median 4, IQR 2-5) mutations were identified in 165 patients (n=165/178, 92.7%). Among 54 genes in the panel, 12 genes were mutated in more than 10% of the cohort, with the most commonly mutated genes being DNMT3A (30.3%), TET2 (25.3%), NPM1 (22.5%), RUNX1 (18.5%), IDH2 (16.9%), FLT3 (15.7%), ASXL1 (12.4%), BCOR (12.4%), CEBPA (11.2%), NRAS (11.2%), IDH1 (10.1%), and SRSF2 (10.1%). In univariate analysis, the groups with a composite adverse genetic profile (n=30/178, 16.9%) showed decreased OS (HR 2.19 [1.30-3.67]; p=0.003), while patients harbouring spliceosome gene (SF3B1, SRSF2, U2AF1, and ZRSR2) mutations (n=37/178, 20.8%) had longer OS (HR 0.39 [0.18-0.85]; p=0.018), with 2-year OS rates of 24.9% and 57.9%, respectively (p=0.002)) (Figure 1B). The composite adverse genetic profile was also associated with shorter RFS (HR 2.23 [1.34-3.69]; p=0.002), while spliceosome gene mutations were associated with longer RFS (HR 0.42 [0.20-0.88]; p=0.022), with 2-year RFS rates of 23.7% vs. 57.9%, respectively (p=0.001)). The composite adverse genetic profile was also associated with higher RI (HR 2.94 [1.52-5.66]; p=0.001), with 2-year RI rates of 47.2% vs. 17.2%, respectively, for patients with and without adverse genetic features (p=0.002) (Figure 1C). Neither the composite adverse genetic profile, nor spliceosome gene mutations, were associated with NRM, with HR of 1.21 [0.55-2.65], p=0.64) and 0.45 [0.16-1.31], p=0.15, respectively (Figure 1D). Multivariate analyses confirmed that the composite adverse genetic profile and spliceosome gene mutations were independent prognostic factors for OS, RFS, and RI (p=0.004, p=0.002, and p=0.001, respectively) and for OS and RFS (p=0.020 and p=0.022, respectively). Conclusion: In our cohort, the composite adverse genetic profile (i.e. having at least one of MK, TP53 mutation, del(5), CK and monosomy 7 remained as a poor prognostic factor even after allogeneic HCT. To clarify the role of genetic risk stratification in HCT, further analysis using a larger cohort is warranted. In addition, a comparative analysis between HCT vs no-HCT groups according to the genetic profile, is ongoing in a in a larger patient cohort. Figure 1 Disclosures Michelis: CSL Behring: Other: Financial Support. Mattsson:Gilead: Honoraria; Celgene: Honoraria; Therakos: Honoraria. Schimmer:Novartis Pharmaceuticals: Consultancy; Otsuka Pharmaceuticals: Consultancy; Jazz Pharmaceuticals: Consultancy; Medivir Pharmaceuticals: Research Funding. McNamara:Novartis Pharmaceutical Canada Inc.: Consultancy. Maze:Pfizer Inc: Consultancy; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees. Gupta:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sierra Oncology: Honoraria, Membership on an entity's Board of Directors or advisory committees; Incyte: Honoraria, Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Yee:Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astex: Research Funding; Hoffman La Roche: Research Funding; MedImmune: Research Funding; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Merck: Research Funding; Millennium: Research Funding; Astellas: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Minden:Trillium Therapetuics: Other: licensing agreement. Schuh:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Agios: Honoraria; Teva Canada Innovation: Honoraria, Membership on an entity's Board of Directors or advisory committees; Astellas: Honoraria, Membership on an entity's Board of Directors or advisory committees; Jazz: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees.

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)0.001
metaresearch head score (Gemma)0.002
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
Metaresearch0.0010.002
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 head0.031
GPT teacher head0.280
Teacher spread0.249 · 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
Published2019
Admission routes2
Has abstractyes

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