Abstract 3127: Development of three-dimensional cancer cell line spheroid models for the <i>in vitro</i> functional characterization of cytotoxic antibody-drug conjugates
Bibliographic record
Abstract
Abstract Background: Antibody-drug conjugates (ADCs) are an effective class of cancer therapeutics which have gained prominence for the treatment of several malignancies. A cytotoxic ADC consists of a linker-payload conjugated to a monoclonal antibody, which targets a distinct tumor-associated antigen (TAA) to enable the delivery of the cytotoxic payload to cancer cells. Presently, there is a need for in vitro models that better recapitulate in vivo tumor tissue complexity to aid in the screening and evaluation of ADCs during preclinical development. Hence, efforts have been made to develop in vitro three-dimensional (3D) models with improved translation to in vivo tumor models compared to traditional two-dimensional (2D) cancer cell line monolayer models. We aimed to generate monoculture cancer cell line spheroids in a high-throughput manner. Subsequently, we sought to evaluate the spheroid penetration capability of structurally distinct ADCs and to assess the 3D cytotoxic activity of a variety of microtubule inhibitor (MTI) and topoisomerase 1 inhibitor (TOPO1i)-bearing ADCs targeting multiple TAAs, comparing to activity in 2D models. Methods: Monoculture cancer cell spheroids were generated by seeding cells from a variety of tumor types, using an automated liquid-handling robot, into microtiter plates treated with ultra-low attachment coating, which allows for scaffold-free self-assembly of cancer cells into a 3D arrangement. Following spheroid formation by incubation for 2-3 days under standard culturing conditions, spheroids were treated with ADCs at a range of concentrations for functional evaluation: the spheroid penetration capability of ADCs of different antibody formats was assessed using high-content confocal imaging of spheroids treated with fluorescently labeled antibodies; the cytotoxic activity of ADCs was characterized using an ATP quantification luminescent reagent, live/dead cell fluorescent stains, and confocal imaging. Results: Monoculture spheroids of varying morphologies were successfully and reproducibly generated with a large panel of >50 cancer cell lines derived from >10 tumor types. Antibodies with different formats demonstrated target-mediated binding and a range of spheroid penetration depths. Additionally, MTI and TOPO1i ADCs evaluated in a panel of cancer cell spheroids demonstrated robust 3D cytotoxicity and differentiated activity when compared to the 2D monolayer assay. In a number of cases, the potency trends across multiple ADCs in 3D cytotoxicity assays were more predictive of in vivo efficacy compared to the 2D assay. Taken together, our high-throughput spheroid assays present important and straightforward in vitro tools to aid in the screening and selection of therapeutic cytotoxic ADC candidates for the treatment of solid tumors. Citation Format: Jodi Wong, Andrea Hernández Rojas, Allysha Bissessur, Lemlem T. Degefie, Araba P. Sagoe-Wagner, Samir Das, Vincent Fung, Kevin Yin, Renee Duan, Sam Lawn, Laurence Madera, Catrina Mi Jung Kim, Alex Wu, Mark E. Petersen, Raffaele Colombo, Jamie R. Rich, Stuart D. Barnscher. Development of three-dimensional cancer cell line spheroid models for the in vitro functional characterization of cytotoxic antibody-drug conjugates [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3127.
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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 | 0.001 |
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".