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Record W2741056151 · doi:10.1158/1538-7445.am2017-2653

Abstract 2653: The anti-myeloma activity of TTI-621 (SIRPαFc), a CD47-blocking immunotherapeutic, is enhanced when combined with a proteasome inhibitor

2017· article· en· W2741056151 on OpenAlexaff
Emma Linderoth, Simone Helke, Vivian Lee, Tapfuma Mutukura, Mark Wong, Gloria H. Y. Lin, Lisa D. Johnson, Xinli Pang, Jeff Winston, Penka S. Petrova, Robert A. Uger, Natasja Nielsen Viller

Bibliographic record

VenueCancer Research · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPhagocytosis and Immune Regulation
Canadian institutionsTrillium Therapeutics (Canada)
Fundersnot available
KeywordsCD47Cancer researchBortezomibPhagocytosisMacrophageProteasome inhibitorImmune systemImmunologyMultiple myelomaMedicineBiologyIn vitroBiochemistry

Abstract

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Abstract CD47 is transmembrane glycoprotein that delivers an anti-phagocytic (“do not eat”) signal by binding to its receptor, signal-regulatory protein α (SIRPα), on the surface of macrophages. Many tumors, including multiple myeloma (MM), express high levels of CD47 as a means to exploit the CD47-SIRPα pathway and escape macrophage-mediated immune surveillance. TTI-621 (SIRPαFc) is a soluble recombinant fusion protein consisting of the CD47-binding domain of human SIRPα linked to the Fc region of human IgG1. It is designed to promote anti-tumor responses by blocking the CD47 “do not eat” signal and engaging activating Fcγ receptors on macrophages. We have previously shown that TTI-621 enhances phagocytosis of malignant cells in vitro and exhibits anti-tumor activity in acute myeloid leukemia and B lymphoma xenograft models. In this study we investigated the anti-myeloma activity of TTI-621 as both a single agent and in combination with bortezomib or carfilzomib, two proteasome inhibitors that are approved for use in MM patients. The ability of TTI-621 to trigger macrophage phagocytosis of MM cells was assessed using a flow cytometry-based phagocytosis assay. Blockade of CD47 using TTI-621 effectively triggered macrophage-mediated phagocytosis of MM cells, and this anti-tumor effect was significantly enhanced by pre-treatment of MM cells with bortezomib or carfilzomib. In order to understand the molecular mechanism behind this enhanced phagocytic effect, we performed immunophenotyping of the tumor cells and observed an upregulation of pro-phagocytic “eat” signals on the tumor cell surface that potentially augment the phagocytic response. To investigate whether CD47 blockade in the context of proteasome inhibition translates to enhanced anti-tumor activity in vivo, we employed a MM xenograft model. NOD.SCID mice were subcutaneously engrafted with human MM cells followed by treatment with TTI-621, in the absence or presence of concurrent administration of a proteasome inhibitor. As monotherapies, both TTI-621 and the proteasome inhibitor reduced tumor burden relative to vehicle controls. Moreover, the combination of CD47 blockade and proteasome inhibition resulted in a greater reduction in tumor growth as compared to the two drugs alone. In conclusion, these data demonstrate that TTI-621 exhibits anti-myeloma activity that is further enhanced by combination with bortezomib or carfilzomib. TTI-621 monotherapy is currently being evaluated in a Phase 1b study in patients with MM and other hematological malignancies (NCT02663518). These data provide a rationale to evaluate a combination study of TTI-621 and a proteasome inhibitor in MM patients. Citation Format: Emma Linderoth, Simone Helke, Vivian Lee, Tapfuma Mutukura, Mark Wong, Gloria H. Lin, Lisa D. Johnson, Xinli Pang, Jeff Winston, Penka S. Petrova, Robert A. Uger, Natasja N. Viller. The anti-myeloma activity of TTI-621 (SIRPαFc), a CD47-blocking immunotherapeutic, is enhanced when combined with a proteasome inhibitor [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2653. doi:10.1158/1538-7445.AM2017-2653

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.066
GPT teacher head0.365
Teacher spread0.299 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

Quick stats

Citations6
Published2017
Admission routes1
Has abstractyes

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