Abstract 1230: High throughput quantitative molecular characterization of cytotoxic antibody-drug conjugates in spheroid models for improved functional characterization, screening and candidate selection
Bibliographic record
Abstract
Abstract Background: Antibody-drug conjugates (ADCs) are a class of cancer therapeutics comprised of a linker-payload conjugated to a monoclonal antibody targeting a tumor-associated antigen (TAA), to enable the delivery of the cytotoxic payload to cancer cells. Presently, there is a need for improved in vitro models that better recapitulate in vivo tumor tissue complexity to aid in the screening and evaluation of novel ADCs during preclinical development. Specifically, we have developed in vitro 3D models from cancer cell lines yielding spheroids in a rapid, robust and uniform manner. Using these spheroid models, we have developed cell-based assays to functionally evaluate the cytotoxic activity of ADCs in vitro. For further comprehensive characterization of ADC activity in 3D cell line models, we utilized the nCounter® ADC Development Panel, a specialized gene expression tool for molecular characterization of biological function, with customizable gene content to address complex questions important for the success of ADCs throughout discovery, pre-clinical and clinical development. Methods: Spheroids were generated by seeding ovarian cancer cell lines into microtiter plates treated with ultra-low attachment coating, using automated liquid-handling robots, followed by incubation with ADCs under standard culturing conditions, prior to functional transcriptomic evaluation. The nCounter ADC Development Panel was analytically validated and included performance specification of accuracy, precision, analytical specificity, and RNA input range of the panel to directly profile 770 genes addressing essential biological questions relevant to ADC activity, including tumor targeting and antigen expression; ADC internalization; payload release; drug mechanisms of action; target cell death; immunogenic cell death; and mechanisms of resistance. Results: The combination of high-throughput spheroid assays and comprehensive transcriptomic analysis presented herein highlight an important and streamlined workflow to aid in the screening and selection of therapeutic cytotoxic ADC candidates for the treatment of solid tumors. Citation Format: Meghan Hogan, Andrea Hernández Rojas, Araba Sagoe-Wagner, Jodi Wong, Sarah Church, Brigitte Lovell, Sergio Hernandez, Lakshmi Chandramohan, Anna Juncker-Jensen, Kirsteen Maclean. High throughput quantitative molecular characterization of cytotoxic antibody-drug conjugates in spheroid models for improved functional characterization, screening and candidate selection [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 1230.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".