Expanding the Preclinical Toolbox with a Novel Patient-Derived Organoid Platform
The human epidermal growth factor receptor 2 (HER2) plays a central role in regulating cell growth, division, and survival. When overexpressed, it acts as an "on switch", driving accelerated cell growth and uncontrolled tumor spread. This biology underlies the aggressive behavior observed in HER2-positive cancers, which is associated with higher recurrence rates and poorer patient outcomes. While genomic changes in HER2 are most frequently discussed in the context of breast cancer, HER2 alterations are also found in non-small cell lung, ovarian, colorectal, and pancreatic cancers.
HER2-targeted therapies have significantly changed the treatment landscape and brought new hope and better outcomes for patients. Among them, HER2-targeted antibody-drug conjugates (ADCs) have emerged as a particularly promising approach, combining the specificity of antibodies with the potency of cytotoxic payloads. These agents enable targeted delivery directly to HER2-expressing tumor cells while limiting off-target toxicity. FDA approved drugs in this class include trastuzumab emtansine (T-DM1, Kadcyla®) and trastuzumab deruxtecan (T-DXd, Enhertu®), both of which have demonstrated meaningful clinical responses (Figure 1). A growing number of next-generation HER2 ADCs are now in development.
Figure 1. FDA approved HER2-targeted ADCs trastuzumab emtansine (T-DM1, Kadcyla®) and trastuzumab deruxtecan (T-DXd, Enhertu®). Figure adapted from Joubert et al1.
As ADCs become an increasingly important therapeutic modality, the limitations of conventional preclinical models have become magnified. Drug developers need better, more physiologically relevant and predictive preclinical models to support ADC development, ones capable of reproducing tumor architecture, heterogeneity, and treatment response.
Despite their widespread use, traditional 2D cell culture models have well-recognized drawbacks with respect to evaluating targeted therapies. They often fail to capture key biological features of tumors and inadequately replicate the complex tumor microenvironment (TME) that influences drug response in patients. Target accessibility, receptor density, internalization kinetics, and payload penetration are all influenced by three-dimensional tumor structure, features which are largely absent in 2D cultures. As a result, 2D assays may overestimate drug potency or fail to distinguish on-target activity from nonspecific cytotoxicity, reducing their predictive value for clinical translation.
In contrast, growing tumor cells as 3D organoids allows for a spatially organized structure that more closely resembles the in vivo microenvironment and architecture of patient tumors. Champions has developed a scalable 3D screening platform based on HER2-positive patient-derived xenograft organoids (PDXOs). Derived from well-characterized PDX models, our ex vivo 3D organoids maintain clinically translatable HER2 expression, providing a relevant and predictive platform for evaluating the efficacy of HER2-targeted ADCs.
HER2-positive ex vivo PDXO breast cancer models generated from our TumorGraft3D (CTG-3D) platform were evaluated for HER2 expression as well as functional responses to the clinically approved HER2-targeted ADCs, trastuzumab emtansine (T-DM1) and trastuzumab deruxtecan (T-Dxd). The workflow is shown in Figure 2.
Figure 2. Workflow overview to evaluate HER2-targeted ADCs using breast cancer PDXO models.
Immunohistochemical (IHC) analysis of PDXO tissue blocks demonstrated complete and intense membrane staining (HER2 score 3+) across HER2-positive models. Consistent with these findings, flow cytometric analysis of enzyme-dissociated organoids identified HER2-positivity in 61.8% to 90.1% of cells. In contrast, the HER2-negative control PDXO model showed no detectable HER2 staining by IHC and negligible HER2 expression by flow cytometry, confirming assay specificity and the absence of nonspecific background signal. A subset of the results is shown in Figure 3.
Figure 3. HER2 expression assessed by IHC and flow cytometry analysis. A) HER2-positive PDXO and B) HER2-negative PDXO model.
Together, the IHC staining and flow cytometry results are consistent with the clinical annotation and indicate that HER2 expression is maintained relatively homogeneously in breast cancer PDXO models.
After confirmation of HER2 expression, the PDXO models were evaluated for their response to two clinically approved HER2-targeted ADCs, trastuzumab deruxtecan (T-DXd) and trastuzumab emtansine (T-DM1). Both ADCs were tested against HER2-positive and HER2-negative breast cancer PDXOs and reference breast cancer cell lines. In addition to intact ADCs, corresponding cytotoxic payloads (Exatecan and DM1), naked antibody (trastuzumab), and—for T-DXd—an isotype-payload conjugate (IgG-DXd) were evaluated to distinguish potency, selectivity, and target-dependent activity. Drug response was measured using the CellTiter-Glo® viability assay following six days of incubation with the ADCs and control treatments.
Figure 4. Representative dose-response curves (IC₅₀). A) HER2-positive PDXO model comparing exatecan (payload) and trastuzumab deruxtecan (T-DXd) and B) control naked antibody trastuzumab and isotype-payload conjugate IgG-DXd.
Across HER2-positive breast cancer PDXO models and control cell lines, ADCs demonstrated greater efficacy and specificity compared with naked antibody and isotype-payload controls (Figure 4, and data not shown). Full datasets across additional PDXO models are presented in the accompanying AACR 2025 poster. These results highlight the utility of PDXO models derived from Champions’ CTG-3D platform for evaluating next-generation ADCs and discriminating between payload-driven cytotoxicity, nonspecific conjugate effects, and HER2-targeted ADC activity.
To further quantify differential treatment responses, normalized Area Under the Curve (∆AUC) values were calculated to capture response differences between isotype controls and ADCs across the full concentration range. HER2-positive breast cancer cell lines and PDXO models exhibited higher ∆AUC values (approximately 30–50%, data not shown), indicating stronger ADC activity relative to controls. In contrast, HER2-negative models showed substantially lower ∆AUC values (~20%), consistent with reduced ADC activity in the absence of target expression (Figure 5).
Figure 5. Normalized ∆AUC. The response difference between isotypes and ADCs is calculated as: [AUC of Isotype] – [AUC of ADC] / [AUC of Isotype ] x 100%
Collectively, these findings demonstrate that breast cancer PDXO models generated from Champions’ CTG-3D platform reproduce expected response patterns to FDA-approved HER2-targeted ADCs with known pharmacological profiles, supporting their utility as a predictive ex vivo platform for ADC development.
Champions’ CTG-3D platform is a biologically relevant ex vivo 3D platform that can support critical preclinical decisions, including payload comparison and mechanism-based screening, enabling ADC candidates to be screened and prioritized before more expensive in vivo evaluation and clinical development is undertaken.
For drug developers, this translates into greater confidence in candidate selection, improved translational relevance of preclinical data, and reduced risk of late-stage attrition of potentially viable candidates. By enabling better-informed decisions earlier in the development process, the CTG-3D platform helps accelerate the advancement of safer and more effective ADC-based therapies.