Abstract 6748: Comparison of activities and identification of predictive genomic biomarkers of response to trastuzumab and the trastuzumab deruxtecan ADC using the OncoPanel® platform
Bibliographic record
Abstract
Abstract Antibody-drug conjugates (ADCs) represent a novel class of therapeutic agents that allow the targeted delivery of cytotoxic payloads to tumor cells via the selectivity afforded by monoclonal antibodies (mAbs) directed against tumors expressing particular antigens. When the mAb component of the ADC binds to its cell surface antigen, the ADC-antigen complex is internalized by the cancer cell. Once inside the cell, the drug payload is released from the mAb either through a chemical reaction or enzymatic digestion within lysosomes, ultimately allowing the exertion of its cytotoxic effects. In 2013, the first ADC approved for treating solid tumors was the Her2-targeted ado-trastuzumab emtansine, which was developed for metastatic breast cancer. As of June 2024, the US Food and Drug Administration has approved thirteen ADCs, with many more currently undergoing preclinical development and clinical trials. The anti-Her2-targeted ADC trastuzumab deruxtecan (TDxd) features a cleavable enzymatic linker, unlike trastuzumab emtansine, which utilizes a non-cleavable thioether linker. This cleavable linker enables deruxtecan, a topoisomerase I inhibitor, to diffuse out of cells following cleavage, thereby inducing cytotoxic effects even in tumors with low Her2 expression levels. Solid tumors overexpressing Her2—including breast, colorectal, and gastric cancers, as well as Her2-mutated lung cancers—have shown significant responses to TDxd. We evaluated the activity and selectivity of TDxd, along with the unmodified antibody and free payload when administered separately, against a panel of 300 human tumor cell lines. Potencies were assessed from ten-point dose response curves using a four-parameter log-logistic model with custom curve-fitting software to determine IC50 and EC50 values. In this study, we compare and contrast the potency and efficacy of each tested agent and explore the genomic biomarkers associated with sensitivity and resistance to each of these treatments. Additionally, we have also evaluated the effect of these agents from a broader tissue biology perspective, including use of the BioMAP® cellular phenotypic platform. Citation Format: Luciano Galdieri, Steven Garner, Brogan Epkins, Emily Schultz, Daria Clucas, Kaitlyne Powers, Justin Lipner, Elsa Liu, ChunYao Lee, Alastair J. King. Comparison of activities and identification of predictive genomic biomarkers of response to trastuzumab and the trastuzumab deruxtecan ADC using the OncoPanel® platform [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 6748.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".