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Record W3096318977 · doi:10.1182/blood-2020-138943

Inferior Outcomes with a High LSC17 Score Can be Improved with Flag-IDA

2020· article· en· W3096318977 on OpenAlexaff
Tracy Murphy, Stanley Ng, Ian King, Tong Zhang, Andrea Arruda, Narmin Ibrahimova, Jaime O. Claudio, Fiona Ferrera, Kristele Pan, Chantal Rockwell, Zhibin Lu, Natalie Stickle, Carl Virtanen, Vikas Gupta, Dawn Maze, Caroline McNamara, Aaron D. Schimmer, Andre C. Schuh, Hassan Sibai, Karen Yee, Dina Khalaf, Brian Leber, Mitchell Sabloff, Anne Tierens, Mark D. Minden, Tracy Stockley, Steven M. Chan, Jean Wang

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsMcMaster UniversityJuravinski Cancer CentreOttawa HospitalPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsFlag (linear algebra)MedicineInternal medicineMathematics

Abstract

fetched live from OpenAlex

Introduction: Acute myeloid leukemia (AML) is driven by a subpopulation of leukemia stem cells (LSCs), which possess properties such as quiescence and self-renewal that are linked to therapy resistance and relapse. The LSC17 score was derived from genes differentially expressed between functionally validated LSC+ and LSC- fractions from 78 AML patients and is strongly associated with survival and response to standard therapy. A critical advantage of the LSC17 test over cytogenetic and molecular analysis is its rapid turnaround time (24-48h on a NanoString platform), providing clinicians with a rapid and powerful tool for upfront risk stratification. We have developed a clinical assay for the LSC17 score validated in a CAP/CLIA-lab setting. Methods: We conducted a prospective, multicenter validation and feasibility study to test the prognostic value of the LSC17 assay under real-world conditions in AML patients treated with curative intent. Patients with a possible new diagnosis of AML were eligible. Patients with a confirmed diagnosis of acute promyelocytic leukemia were excluded from analysis. Standard prognostic markers including cytogenetics, molecular studies and targeted sequencing using a standard AML panel were performed in parallel to the LSC17 score. Treatment was administered according to physician preference, based on patient history and results of standard prognostic assays, when available. Survival data was censored on June 14th, 2020. Results: 381 patients were recruited to the study between June 2016 and March 2020. 4 patients were excluded for quality control reasons (one sample had insufficient RNA and three samples failed quality control checks). 103 were excluded as they had alternative diagnoses. 84 patients were excluded because they did not receive intensive chemotherapy. LSC17 scores ranged from 0 to 1.25, and were classified as high or low according to the median score of 0.51 from a previously validated reference cohort (Ng et al, Nature 2016). Of the 190 patients included in this analysis, 84 had a low LSC17 score and 106 had a high LSC17 score. The median age was 61 years (range 18-79); 86 (45%) were female. When stratified according to ELN 2017 criteria, 48 (27%), 51 (29%), and 77 (44%) patients had favorable, intermediate, and adverse risk disease, respectively. Low LSC17 score was associated with normal cytogenetics (high vs low, 33% vs 58%; P <0.01) and low molecular risk disease (normal cytogenetics, NPM1 mutated, FLT3-ITD wildtype; high vs low, 4% vs 30%; P <0.01). High LSC17 score was associated with poor risk cytogenetics (high vs low, 41% vs 11%; P <0.01), myelodysplasia-related changes (high vs low, 36% vs 10%; P <0.01), and adverse risk by ELN criteria (high vs low, 66% vs 18%; P <0.01). We first considered response to induction chemotherapy (Table 1). 141 patients had standard induction chemotherapy with 3+7, 40 had Flag-IDA and 9 had CPX-351. High score patients had inferior responses to 3+7 with only 59% achieving complete remission (CR) after 1 cycle of chemotherapy compared to 96% of low score patients; responses for LSC17 high score patients were better in the Flag-IDA group with 80% achieving CR after 1 cycle. When considering overall CR rates after 2 cycles of induction, patients with a high LSC17 score were less likely to achieve CR (high vs low, 87% vs 98%; P=0.02). However, this difference was predominantly observed in patients treated with 3+7 (87% vs 99% CR rate in high vs low score patients, respectively); response rates to Flag-IDA were not significantly different between the 2 groups. Measurable residual disease (MRD) monitoring by flow cytometry was performed at the time of CR in 135 (71%) patients enrolled at Princess Margaret Cancer Centre. Patients with a high LSC17 score were significantly more likely to have MRD compared to low score patients (46% vs 10% respectively, P <0.0001). The initial poor response to 3+7 observed in high score patients was associated with worse survival compared to low score patients (Figure 1) (HR 1.8, P=0.09). Survival of high and low score patients treated with Flag-IDA was similar (HR 1.5, P=0.43). Conclusion: AML patients with a high LSC17 score have inferior outcomes following 3+7 induction chemotherapy. The LSC17 score should be considered as a tool to identify and stratify high-risk patients to alternative upfront therapies such as Flag-IDA. A risk adapted study is planned to validate these results. Disclosures Gupta: Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sierra Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy; Bristol MyersSquibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Incyte: Honoraria, Research Funding. Maze:Novartis: Honoraria; Takeda: Research Funding; Pfizer: Consultancy. McNamara:Novartis: Honoraria. Schimmer:Medivir AB: Research Funding; AbbVie Pharmaceuticals: Other: owns stock ; Takeda: Honoraria, Research Funding; Novartis: Honoraria; Jazz: Honoraria; Otsuka: Honoraria. Leber:Takeda/Palladin: Honoraria, Membership on an entity's Board of Directors or advisory committees; Treadwell: 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, Speakers Bureau; BMS/Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Pfizer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Otsuka Pharmaceutical: Honoraria, Membership on an entity's Board of Directors or advisory committees; Lundbeck: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Alexion: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Tierens:Amgen: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Astellas Pharma: Membership on an entity's Board of Directors or advisory committees. Wang:Trilium therapeutics: Patents & Royalties: There is an existing license agreement between TTI and University Health Network and J.C.Y.W. may be entitled to receive financial benefits further to this license and in accordance with UHN's intellectual property policies. .

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)0.002
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.259
Teacher spread0.238 · 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
Published2020
Admission routes1
Has abstractyes

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