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Record W3095188934 · doi:10.1182/blood-2020-138683

Identification of Prognostic Immunophenotypes at First Diagnosis in Patients with Acute Myeloid Leukemia (AML) By a Standardized Multicolor Flow Cytometry (MFC) Panel Originally Designed to Detect Measurable Residual Disease (MRD) at Follow-up

2020· article· en· W3095188934 on OpenAlexaff
Maximilian Alexander Röhnert, Malte von Bonin, Michael Krämer, Philipp Ensel, Nadja Holtschke, Christoph Röllig, Martin Bornhäuser, Veit Buecklein, Marion Subklewe, Stefan W. Krause, Simon Voelkl, Tobias Berg, Michael A. Rieger, Cornelia Brendel, Jörg Hoffmann, Nicole Hofmeister-Mielke, Richard F. Schlenk, Sylvie Freeman, Uta Oelschlägel

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsImmunophenotypingCD33Minimal residual diseaseMyeloid leukemiaMyeloidMedicinePopulationOncologyFlow cytometryLeukemiaCD117CD34ImmunologyInternal medicineBiologyGeneticsStem cell

Abstract

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Aims In AML, several risk factors obtained at first diagnosis (FD) have been reported to be associated with shorter RFS and OS. The primary prognostic relevance of multicolour flow cytometry (MFC) has been a matter of debate for years. During follow-up (FU), the prognostic relevance of MRD as detected by MFC is less controversial and MFC is recommended in particular (but not exclusive) for those patients (pts) with no reliable genetic marker. We thought to evaluate the prognostic value at FD of a recently established antigen panel and a corresponding analysis strategy, which had been originally developed for MRD-detection. Methods Based on an 8-colour antibody panel (CD45, CD34, CD117, HLA-DR, CD13, CD33, CD7, CD56), we have developed a hierarchical gating strategy with mainly fixed gates. That allows to detect MRD with a high level of standardization and inter-observer reliability (Röhnert M., et al. 25th EHA 2020). Four distinct categories of aberrations (deficiency of CD13 or CD33, cross-lineage expression of CD7 or CD56) detectable on at least 10% of the myeloid blast population were used to define aberrant phenotypes termed leukemia associated immunophenotypes (LAIP) at FD. These categories were also chosen to define MRD during FU. MRDpos by LAIP was defined as the (re-)occurrence of an aberrant category already detectable at FD, while MRDpos by DfN (different from normal) was defined by the de-novo detection of an aberrant category at FU. The prognostic value of the aberrant phenotypes at FD was examined in a cohort of 528 pts. In 122 pts, we further analysed MRD (LAIP/DfN) after completion of intensive induction chemotherapy (IT). Consolidation therapy consisted of allogeneic hematopoietic stem cell transplantation (n=77) or chemotherapy (n=45). The bone marrow samples were measured centrally and analysed independently by three different investigators. Results The probability to achieve a complete remission (CR) varied between the different aberrant phenotypes (LAIP) at FD. Compared to pts without aberrant phenotype (CR rate=68%, n/N=100/148), pts with CD56only (the sole aberrant category was a cross-lineage expression of CD56=only) had a significantly lower CR rate (46%, n/N=15/33, p=0.019). The other exclusive aberrant categories did not significantly influence CR rates compared to pts without LAIP: CD13only (75%, n/N=53/71, p=0.286), CD33only (64%, n/N=59/97, p=0.28) and CD7only (62%, n/N=31/50, p=0.472). In pts with possibly co-occurring aberrant categories (compound aberrant phenotype=comp), the CR rate was significantly higher in CD13comp compared to all other patients (75% vs. 64%, 107/143 vs. 246/385, p=0.018). The other compound aberrancies did not significantly influence CR rates: CD33comp (63% vs. 68%, 90/143 vs. 263/385, p=0.244), CD7comp (66% vs. 67%, 72/109 vs. 281/419, p=0.842) and CD56comp (68% vs. 66%, 84/123 vs. 269/405, p=0.699). Regarding overall survival (OS), just CD56only retained its statistical significance (HR 2.5, CI 1.4-4.7, p=0.004). CD13comp was associated with favourable outcome but without reaching statistical significance (HR 0.7, CI 0.4-1.0, p=0.059). In the cohort of pts with MRD assessment at the end of IT, 67% were classified as responders (CR n=62, CRi n=19) and 33% as non-responders (PR n=14, refractory n=26) by cytomorphology. By MFC, 71% of these pts were classified as MRDpos (n=51/36 responders/non-responders) and 29% as MRDneg (n=30/4). MRDpos was defined by LAIP only (23%), DfN only (44%) or concordantly by LAIP+DfN (33%). OS of MRDneg pts was significantly longer compared to MRDpos patients (HR 4.3, CI 1.0-18.1, p=0.033). Conclusions Using our analysis approach originally developed for MRD monitoring, MFC could provide additional information for initial risk stratification. The presence of an isolated cross-lineage expression of CD56 (CD56only) was associated with a lower CR rate and significant shorter OS. In contrast, CD13comp (CD13 deficiency ± other aberrant categories) was associated with a higher CR rate and prolonged OS. Furthermore, MRDpos as defined by the combined LAIP/DfN strategy provided significant prognostic information. The presented results are currently refined and validated using genetically defined subcategories. The approach has to be confirmed in an independent cohort of pts. Disclosures Rollig: Amgen, Astellas, BMS, Daiichi Sankyo, Janssen, Roche: Consultancy; Abbvie, Novartis, Pfizer: Consultancy, Research Funding. Buecklein:Pfizer: Consultancy; Novartis: Research Funding; Celgene: Research Funding; Amgen: Consultancy; Gilead: Consultancy, Research Funding. Subklewe:Novartis: Consultancy, Research Funding; Janssen: Consultancy; Roche AG: Consultancy, Research Funding; AMGEN: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria; Morphosys: Research Funding; Seattle Genetics: Research Funding; Pfizer: Consultancy, Honoraria; Gilead Sciences: Consultancy, Honoraria, Research Funding. Krause:Pfizer: Honoraria; MSD: Honoraria; Takeda: Honoraria; Gilead: Other: Travel Support; Celgene: Other: Travel Support; Siemens: Research Funding. Schlenk:Roche: Research Funding; AstraZeneca: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; PharmaMar: Research Funding; Daiichi Sankyo: Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accomodations, Expenses, Research Funding, Speakers Bureau; Novartis: Speakers Bureau.

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)0.001
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.245
Teacher spread0.230 · 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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Citations2
Published2020
Admission routes1
Has abstractyes

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