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Prospective Next-Generation Sequencing Molecular Profiling of Myeloid Malignancies: Assessment of Information Benefit and Impact on Patient Care

2015· article· en· W2552245275 on OpenAlexaff
Suzanne Kamel‐Reid, Mariam Thomas, Mahadeo A. Sukhai, Dwayne L. Barber, Swati Garg, Maksym Misyura, Roozbeh Dolatshahi, Djamel Harbi, Tong Zhang, Anna Porwit, Jan Delabie, Narmin Ibrahimova, Mohamed Shanavas, Karen Yee, Trevor J. Pugh, Philippe L. Bédard, Aaron D. Schimmer, Tracy Stockley, Vikas Gupta, Mark D. Minden, Andre C. Schuh

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsOntario Institute for Cancer ResearchPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsCEBPAMyeloid leukemiaMedicineDNA sequencingSanger sequencingMyeloidOncologyComputational biologyBioinformaticsInternal medicineGeneBiologyGeneticsMutation

Abstract

fetched live from OpenAlex

Abstract Introduction. Recent genome profiling studies have increased our understanding of the mutation landscapes of myeloid malignancies. Molecular testing of AMLs (NPM1, FLT3-ITD, KIT) and MPNs (JAK2, CALR) constitute current diagnostic standard-of-care. Evidence for the diagnostic, prognostic and/or therapeutic impact of a growing set of genes and variants in myeloid malignancies allows for more accurate patient stratification and enhanced patient management. This has led to consideration of next-generation sequencing (NGS) approaches to simultaneously detect multiple variants in myeloid malignancies for use in the clinical diagnostic setting, to supplant single-gene molecular assays. We designed the Princess Margaret Advanced Genomics in Leukemia (AGILE) trial to prospectively assess the utility of NGS molecular profiling in the management of patients with myeloid malignancies. Methods. Patients for the AGILE trial are consented at the time of diagnosis using an REB approved written consent. Bone marrow or peripheral blood samples are collected at consent, accessioned within CoPath, and DNA extracted for NGS testing. NGS molecular profiling was performed using the TruSight Myeloid Sequencing Panel (TMSP; Illumina) on the MiSeq benchtop genome sequencer (Illumina) by the University Health Network Advanced Molecular Diagnostics Laboratory. The TMSP enables profiling of 54 genes (39 hotspot region; 15 complete coding region coverage) using amplicon-based library preparation and sequencing by synthesis. The TMSP detects the CALR 52 base pair deletion relevant to myelofibrosis, but not FLT3 internal tandem duplications greater than 30 base pair in size. Data were analyzed by NextGENe (v.2.3.1, SoftGenetics) and MiSeq Reporter v2.4.60. A specific script enabling alignment and calling of CALR deletions was added to the analysis to ensure there were no false negative calls. Additional testing and verification of CEBPA variants was performed by Sanger sequencing. Variants were interpreted according to Sukhai et al (Genetics in Medicine, 2015), reviewed by lab directors and reported in the Electronic Patient Record. Impact on patient care was defined as: potential for post-consolidation clinical trials; changes to frequency of monitoring; and, changes to transplant management. Cases were discussed in an interdisciplinary Genomic Tumor Board setting, at which NGS profiling data were reviewed in the context of all other diagnostic information for the patient, to determine impact on patient care. Results. Between February 11 and July 24, 2015, 162 patients were consented for AGILE; 148/162 were profiled by NGS, and to date 124/148 have been reviewed and interpreted. 62/124 (50%) of interpreted cases had a diagnosis of acute myeloid leukemia (AML); 21/124 (20%) with myeloproliferative neoplasms (MPNs); 13/124 (10%) with myelodysplastic syndromes (MDS); 6/124 (5%) with MDS/MPN; and, 15% with other hematologic malignancies. 90% of all cases profiled were informative for at least one variant (range 1-9 variants, average 3.1 variants/case). AML, MDS and MDS/MPN cases exhibited slightly more variants (3.4-4.4 variants/case) than did MPN cases (2.6 variants/case). Overall, 69% of variants were potentially actionable (Sukhai et al, 2015: 23% class 1; 8% class 2; 38% class 3), with a large fraction of cases (90/124, 72.6%) demonstrating at least one class 1 or class 3 variant. Additionally, 73/124 (58.9%) of patients exhibited actionable, class 1, variants not currently being identified by routine molecular diagnostics. In AMLs and MPNs, 88-90% of cases exhibited at least one potentially actionable variant; NGS profiling was more informative in AMLs (62% of cases exhibiting potentially actionable variants not profiled in standard of care testing, compared to 12% of MPN cases). Conclusions. We report the results of a prospective analysis of integrated NGS profiling in the context of diagnosis and management of patients with myeloid malignancies. Using a targeted NGS panel, molecular profiling of patients yielded significant information benefit over current standard approaches in 58.9% of cases analyzed, enabling potential impact on patient management. These data highlight the utility of NGS profiling to complement the initial diagnostic evaluation of myeloid malignancies. Disclosures Gupta: Incyte: Honoraria, Research Funding; Novartis: Consultancy, 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)0.009
metaresearch head score (Gemma)0.013
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.031
GPT teacher head0.286
Teacher spread0.255 · 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
Published2015
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