Abstract 6259: Spatial transcriptomics-driven in silico modeling of therapeutic effects of antibody-drug conjugates and their association with treatment response in lung adenocarcinoma

Abstract Background: Antibody-drug conjugates (ADCs) are targeted therapies that deliver cytotoxic payloads to cells expressing specific antigens. Despite their increasing use in clinical trials for advanced non-small cell lung cancer (NSCLC), many patients experience suboptimal outcomes. While ADC efficacy depends on factors such as antibodies, linkers and payloads, the impact of the tumor microenvironment (TME) remains unclear. This study uses spatial transcriptomics (ST) to investigate the spatial dynamics influencing ADC efficacy within the TME of NSCLC. Methods: Visium ST datasets from 36 lung adenocarcinoma (LUAD) tissues were analyzed to identify factors influencing ADC efficacy. Key indices included Target Specificity Score (TSS), which reflects the ratio of target expression in malignant versus normal epithelial cells; Target-Linker Match Score (TLM), which measures overlap between target regions and linker enzyme activity; Therapeutic Index (TI), calculated as TSS × TLM; and Intratumoral Target Expression (ITE), which quantifies average target expression. To evaluate the bystander effect, Moran's I index assessed spatial clustering patterns of target expression, and Minimum Target-High Distance (MTHD) assessed the average of the minimum distance from low to high target expression regions. Fifteen ADCs were selected from NSCLC clinical trial data, including targets, linkers, and overall response rates (ORRs). Correlations between spatial indices and ORRs were analyzed to validate the utility of the indices. Results: Of the 36 tissues analyzed, 3 were excluded due to insufficient or excessive tumor regions. Among the six indices evaluated, only MTHD showed a strong negative correlation with the reported ORRs (Spearman's R: -0.715, p = 0.003). This suggests that shorter distances between low and high target expression regions are associated with improved ADC efficacy, highlighting the critical role of spatial homogeneity in the bystander effect. To further evaluate the potential of the indices in identifying ADCs with poor therapeutic efficacy, we compared the indices between ADCs with ORR values below the 10th percentile (ORR Low) and those above (ORR High); only the TSS showed a significant difference (p < 0.05). ROC analysis identified a TSS cut-off value of 4.744, yielding an AUC of 0.962 for discriminating between ORR Low and High groups. Conclusion: Spatial indices derived from spatial transcriptomics, particularly those reflecting target specificity and bystander effect, were found to significantly influence treatment response and predict ADC failure, respectively. This in silico modeling approach offers a promising screening method to identify ADCs with the highest potential for therapeutic efficacy. Citation Format: Sungwoo Bae, Minki Choi, Hongyoon Choi, Dongjoo Lee, Kwon Joong Na, Daeseung Lee. Spatial transcriptomics-driven in silico modeling of therapeutic effects of antibody-drug conjugates and their association with treatment response in lung adenocarcinoma [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 6259.