Abstract 1792: A novel lipid nanoparticle formulation transfects acute myeloid leukemia cells and primary AML samples with high efficiency and low toxicity
Bibliographic record
Abstract
Abstract RNA interference is a powerful research tool for studying gene function in cancer cells. However, functional genomic studies in Acute Myeloid Leukemia (AML) remain challenging. Classic lipid-based transfection reagents are ineffective in delivering siRNA to AML cells and lentiviral delivery of shRNA is complex and time-consuming. Lipid Nanoparticles (LNPs) are a novel approach to deliver genetic material to cells that are difficult to transfect. However, a formulation of LNP capable of transfecting AML cells and primary AML samples with high efficiency and low toxicity is lacking. Here, we developed an LNP formulation that delivers siRNA to AML cell lines and primary samples without impacting their growth and viability. To identify LNPs able to transfect AML cell lines, we generated a library of LNPs that differed in lipid compositions, aqueous phase pH, and RNA-to-ionizable lipid weight ratio through the Design of Experiment (DOE). We synthesized 54 unique LNPs formulations encapsulating siRNA targeting luciferase. We then screened this library of LNPs against OCI-AML2 leukemia cells over-expressing luciferase. From this screen and subsequent validation studies, we identified a novel formulation, LNP 1-28, as the top hit based on transfection efficiency and safety. LNP1-28 also suppressed luciferase expression in 3 AML cell lines (OCI-AML2, OCI-AML3, MOLM13) with > 80% target knockdown with 100nM of siRNA 3 days post-transfection. Next, we asked if LNP 1-28 could deliver siRNA to knockdown an endogenous protein, CD45. AML cell lines (OCI-AML2, OCI-AML3, MOLM13) were treated with LNP 1-28 carrying siRNA targeting CD45 or control sequences. Knockdown of CD45 protein was observed by flow cytometry in all tested AML cells with knockdown being time and concentration dependent. CD45 knockdown was observed as early as 1-day post-transfection and persisted for 7 days before gradually returning towards baseline. Greater than 82.5% knockdown was observed 4 days after 100nM siRNA-LNP treatment in all tested cell lines. LNP 1-28 had no effect on cell growth and viability. Using the low passage primary AML culture model 8227 cells, we demonstrated that LNP 1-28 could deliver siRNA to both bulk and CD34+/CD38- stem populations of AML. Finally, LNP 1-28 efficiently transfected primary AML patient samples and delivered siRNA to knock down CD45. In summary, we developed a novel LNP formulation capable of transfecting AML cells and primary AML samples with siRNA at high efficiency and without toxicity. Thus, we have developed a nanocarrier system for in vitro transfection that will aid in functional genomics studies of AML. Citation Format: Lan Xin Zhang, Yongran Yan, Jingan Chen, Rose Hurren, Jiachuan Bu, Juan Chen, Dakai Ling, Nathan Duong, Andrea Arruda, Hansen He, Mark D. Minden, Rod Bremner, Gang Zheng, Bowen Li, Aaron D. Schimmer. A novel lipid nanoparticle formulation transfects acute myeloid leukemia cells and primary AML samples with high efficiency and low toxicity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1792.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".