Sirpαfc Treatment Targets Human Acute Myeloid Leukemia Stem Cells
Bibliographic record
Abstract
Acute myeloid leukemia (AML) is an aggressive hematologic malignancy and is the most common type of acute leukemia in adults. Although a majority of patients achieve remission following cytotoxic chemotherapy, most will relapse and ultimately die. Therapy resistance and relapse are driven by leukemia stem cells (LSC). Evidence of genetic and functional heterogeneity in the LSC compartment underscores the importance of developing therapeutic strategies that will target all subclones effectively. We previously showed that LSCs in AML depend on CD47-SIRPα interaction to evade immune surveillance (Theocharides et al, JEM 2012). CD47 acts as a "do not eat me" signal that binds to the inhibitory receptor SIRPα on macrophages and masks cancer cells from macrophage-mediated phagocytosis. TTI-621 (Trillium Therapeutics Inc., Ontario, Canada) is a human SIRPαFc protein formed by fusing the IgV doman of human SIRPα to a human IgG1-Fc moiety; it is designed to bind CD47 on leukemia cells and disrupt its interaction with SIRPα on host macrophages. Our previous studies in AML cell lines and a small number of primary AML samples demonstrated increased phagocytosis in vitro and decreased engraftment in xenotransplant models following SIRPαFc treatment (Theocharides et al, JEM 2012, Petrova et al, Clin Cancer Res 2017). Here, we tested the efficacy of TTI-621 against a broad panel of primary AML samples in xenotransplantation models to determine efficacy and response rates in this heterogeneous disease. Bulk cells obtained from the peripheral blood of 30 AML patients representing a broad range of cytogenetic and molecular subtypes were transplanted intrafemorally into sublethally-irradiated NSG mice. After a 2-week engraftment period, mice were treated with either SIRPαFc or control IgG by intraperitoneal injection 3×/week for 4 weeks, following which leukemic engraftment was determined by flow cytometry. In all but 1 sample, a significant reduction in AML engraftment was seen in SIRPαFc-treated mice compared to controls. For 23 samples defined as good responders, SIRPαFc treatment resulted in 91% (range 53-100%, p<0.0001) and 98% (range 88-100%, p<0.0001) reduction of leukemic engraftment in the injected femur and in non-injected bones, respectively, compared to controls. Six samples demonstrated a lesser response that was largely observed in the non-injected bones, with relative reduction of 69% (range 43-93%, p=0.03); these samples were defined as partial responders. The majority of samples from patients with unfavorable features such as age >60, adverse cytogenetic risk, and secondary AML, as well as samples obtained from relapsed/resistant patients, were classified as good responders. Notably, 20 of 23 good responders had a high LSC17 score, which we have shown is associated with poor initial therapy response and short survival following standard treatments (Ng et al, Nature 2016). To determine whether SIRPαFc treatment killed LSCs, we transplanted leukemia cells harvested from primary treated mice into untreated secondary recipients at limiting dilution. For four independent samples, including three partial responders and the one non-responder, we observed a significantly lower LSC frequency (3.9-10.3 fold, p=0.002-0.024) in mice transplanted with SIRPαFc-treated cells compared to controls, indicating that SIRPαFc treatment reduced LSC numbers in primary mice, despite partial or no reduction of bulk disease. Our data demonstrate that SIRPαFc effectively targets LSCs in a human AML xenotransplantion model with high response rates across a heterogeneous cohort of primary AML samples, including samples with unfavorable risk features. SIRPαFc may be most effective in the remission setting as maintenance therapy for patients with detectable residual disease, to eradicate residual LSCs and prevent relapse. Disclosures Jin: Trillium Therapeutics: Other: licensing agreement. Wong:Trillium Therapetuics: Employment. Uger:Trillium Therapetuics: Employment. Minden:Trillium Therapetuics: Other: licensing agreement. Danska:Trillium Therapeutics: Other: licensing agreement, Research Funding. Wang:Pfizer AG Switzerland: Honoraria, Other: Travel and accommodation; Pfizer International: Honoraria, Other: Travel and accommodation; Trilium therapeutics: Other: licensing agreement, Research Funding; NanoString: Other: Travel and accommodation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".