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Record W2983330076 · doi:10.1182/blood-2019-121870

Immune Landscapes Predict Chemotherapy Resistance and Anti-Leukemic Activity of Flotetuzumab, an Investigational CD123×CD3 Bispecific Dart® Molecule, in Patients with Relapsed/Refractory Acute Myeloid Leukemia

2019· article· en· W2983330076 on OpenAlexaff
Jayakumar Vadakekolathu, Mark D. Minden, Tressa Hood, S. Church, Stephen Reeder, Heidi Altmann, Amy Sullivan, Elena Viboch, Tasleema Patel, Narmin Ibrahimova, Sarah Warren, Andrea Arruda, Marc Schmitz, Yan Liang, Alessandra Cesano, A. Graham Pockley, Peter J.M. Valk, Bob Löwenberg, Martin Bornhäuser, Sarah K. Tasian, Michael P. Rettig, Jan Davidson‐Moncada, John F. DiPersio, Sergio Rutella

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsImmune systemMedicineInterleukin-3 receptorImmunotherapyChemotherapy regimenMyeloid leukemiaOncologyLeukemiaImmunologyMyeloidInternal medicineChemotherapyCancer research

Abstract

fetched live from OpenAlex

Background Acute myeloid leukemia (AML) is a molecularly and clinically heterogeneous hematological malignancy. Chemotherapy resistance is common, and relapse is the major cause of treatment failure. Although immunotherapy may be an attractive modality to exploit in patients with AML, the ability to predict the groups of patients and the types of cancer that will respond to immune targeting remains limited. Methods Immune gene expression profiling for the high-dimensional analysis of the immunological landscape of bone marrow (BM) samples from patients with newly-diagnosed (de novo) non-promyelocytic AML (n=387) was employed to analyze the tumor microenvironment (TME). We derived immune scores from mRNA expression levels and devised an RNA-based, quantitative metric of immune infiltration, as previously published (Danaher P, et al. JITC 2017 and 2018). The PanCancer IO360™ gene expression assay was used to profile BM samples collected prior to and during flotetuzumab (FLZ) treatment from 30 AML patients treated at the RP2D (500 ng/kg/day) in the CP-MGD006-01 clinical trial (NCT#02152956), primary refractory, n=23 or relapsed, n=7. IO360 score comparisons are presented as mean ± SD and significance was assessed by the Mann Whitney U test. Results Analysis of pre-treatment BM samples from de novo AML revealed distinct immune signature modules, reflecting the co-expression of genes associated with 1) an IFNγ-dominant TME, 2) adaptive immune responses, and 3) myeloid cell abundance. When considered in aggregate, the relative intensity of gene expression in the immune modules stratified BM samples into two subgroups, which will be herein termed immune-infiltrated and immune-depleted. When AML patients were dichotomized based on median immune scores, high versus low, a higher percentage of primary refractory patients was observed within the IFNγ high module (65.4% versus 34.6%; p=0.0022). In multivariate logistic regression, an IFNγ high profile (derived from the IFNγ-dominant module) was predictive of therapeutic resistance to induction chemotherapy, even more than ELN risk categories (AUROC = 0.815 versus 0.702 with ELN risk only). In a validation cohort, Beat AML series, the IFNγ high profile when compared to other clinically established prognostic indicators in AML, i.e. disease type (primary versus secondary), WBC count, patient age at diagnosis, and ELN risk categories was significantly more predictive of therapeutic resistance to induction chemotherapy (AUROC=0.921 versus 0.709 with ELN cytogenetic risk alone; two-tailed p value=0.002673). Similarly, a higher percentage of patients with an IFNγ high profile AML in the HOVON series failed to achieve CR in response to induction chemotherapy when compared to AML cases with an IFNγ low profile (27.2% versus 15.2%; p=0.0004). We hypothesized that higher expression of IFNγ inducible genes, while underpinning chemotherapy resistance, might identify AML patients who derive benefit from immunotherapy with FLZ. BM samples from 92% of patients with evidence of FLZ anti-leukemic activity (ALA), which was defined as either CR, CRh, CRi, PR or overall benefit (>30% reduction in BM blasts), had an immune infiltrated TME relative to non-responders (SD or PD). Interestingly, the IFNγ-signaling score was significantly higher in patients with chemotherapy-refractory AML compared with relapsed AML at time of FLZ treatment, and in individuals with evidence of ALA compared to non-responders (p<0.0001). Additionally, another IFNγ-related score, the tumor inflammation signature (TIS), had strong predictive power of anti-leukemic activity to FLZ, with an AUROC value of 0.847 (p=0.001). FLZ also modified the TME, and on-treatment BM samples (available in 19 patients at the end of cycle 1) displayed increased expression of antigen presentation and immune activation genes relative to baseline, and had higher TIS scores (6.47±0.22 versus 5.93±0.15, p=0.0006), antigen processing machinery scores (5.67±0.16 versus 5.31±0.12, p=0.002), IFNγ signaling scores (3.58±0.27 versus 2.81±0.24, p=0.0004) and PD-L1 expression (3.43±0.28 versus 2.73±0.21, p=0.0062). Conclusions Our findings to date suggest that microenvironmental immune gene profiles could be used to inform the delivery of personalized immunotherapies to patients with IFNγ-dominant AML subtypes, and identify patients less likely to respond to cytotoxic chemotherapy. Disclosures Minden: Trillium Therapetuics: Other: licensing agreement. Hood:NanoString Technologies, Inc.: Employment. Church:NanoString Technologies, Inc.: Employment, Equity Ownership. Sullivan:NanoString Technologies, Inc.: Employment. Viboch:NanoString Technologies, Inc.: Employment. Warren:NanoString Technologies, Inc.: Employment. Liang:NanoString Technologies, Inc.: Employment. Cesano:NanoString Technologies, Inc.: Employment. Löwenberg:Chairman, Leukemia Cooperative Trial Group HOVON (Netherlands: Membership on an entity's Board of Directors or advisory committees; Agios Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Astellas: Membership on an entity's Board of Directors or advisory committees; Astex: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; Up-to-Date", section editor leukemia: Membership on an entity's Board of Directors or advisory committees; CELYAD: Membership on an entity's Board of Directors or advisory committees; Chairman Scientific Committee and Member Executive Committee, European School of Hematology (ESH, Paris, France): Membership on an entity's Board of Directors or advisory committees; Elected member, Royal Academy of Sciences and Arts, The Netherlands: Membership on an entity's Board of Directors or advisory committees; Editorial Board "European Oncology & Haematology": Membership on an entity's Board of Directors or advisory committees; Clear Creek Bio Ltd: Consultancy, Honoraria; Hoffman-La Roche Ltd: Membership on an entity's Board of Directors or advisory committees; Frame Pharmaceuticals: Equity Ownership; Royal Academy of Sciences and Arts, The Netherlands: Membership on an entity's Board of Directors or advisory committees; Supervisory Board, National Comprehensive Cancer Center (IKNL), Netherland: Membership on an entity's Board of Directors or advisory committees. Tasian:Incyte Corportation: Research Funding; Gilead Sciences: Research Funding; Aleta Biotherapeutics: Membership on an entity's Board of Directors or advisory committees. Rettig:WashU: Patents & Royalties: Patent Application 16/401,950. Davidson-Moncada:MacroGenics, Inc.: Employment, Equity Ownership. DiPersio:Celgene: Consultancy; NeoImmune Tech: Research Funding; Macrogenics: Research Funding, Speakers Bureau; Incyte: Consultancy, Research Funding; Karyopharm Therapeutics: Consultancy; RiverVest Venture Partners Arch Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellworks Group, Inc.: Membership on an entity's Board of Directors or advisory committees; Amphivena Therapeutics: Consultancy, Research Funding; Magenta Therapeutics: Equity Ownership; WUGEN: Equity Ownership, Patents & Royalties, Research Funding; Bioline Rx: Research Funding, Speakers Bureau. Rutella:NanoString Technologies, Inc.: Research Funding; MacroGenics, Inc.: 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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.225
Teacher spread0.219 · 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 designNon-randomized trial
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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Citations3
Published2019
Admission routes1
Has abstractyes

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