Abstract 2587: Evaluating the potential of radiomics features to predict outcome to an antibody-drug conjugate therapy in non-small cell lung cancer patients

Abstract This study evaluated radiomics, a non-invasive biomarker, for assessment of tumor heterogeneity in non-small cell lung cancer (NSCLC) patients treated with an AXL-targeting antibody-drug conjugate (ADC), enapotamab vedotin (EnaV), through analyzing baseline and post-treatment radiomic features from CT images for predicting outcome. Clinical development of EnaV was discontinued in 2020 due to low response rates unimproved by dose optimization and/or predictive biomarkers. Study data included CT images, target expression and clinical data from 77 advanced NSCLC patients treated at 1.0 mg/kg (3Q4W), 1.8 mg/kg (Q3W), or 2.2 mg/kg EnaV (1Q3W) in the first in human phase I/II study assessing safety and preliminary efficacy of EnaV (NCT02988817). Radiologist-segmented tumors were examined using the cancer imaging phenomics tooklit CaPTk1, deriving 225 radiomic features from 77 baseline and 50 Cycle 2 post-treatment CT images. Batch effects were balanced using nested ComBat2, and radiomic feature dimensionality was reduced in two stages: clustering features with ≥80% similarity followed by principal component analysis (PCA). Radiomic PC1 was utilized in statistical models to assess overall and progression-free survival (OS, PFS), overall response rate (ORR), and disease control rate (DCR). Both baseline and delta radiomic PC1 were evaluated for correlation with biomarker and clinical activity, independently and with soluble or tumoral AXL expression. Baseline radiomic PC1 significantly predicted DCR: confirmed (p=0.03), and unconfirmed (p=0.03). Baseline tumor membrane AXL predicted ORR for this NSCLC subset: confirmed (p=0.03) or unconfirmed (p=0.02). Delta analysis showed delta PC1 predicted unconfirmed (p=0.03) and confirmed (p=0.03) DCR and remained predictive for DCR with soluble AXL (p=0.04). No association was found between baseline and delta radiomics with OS or PFS. Baseline and delta radiomic signatures showed potential in predicting NSCLC response. Preliminary findings suggest that radiomics combined with target expression predicted DCR, an early clinical activity indicator, although they did not predict OS or PFS. These outcomes pertain to a NSCLC subset and might not apply to other tumor types. Tumoral AXL expression did not linearly correlate with EnaV activity in a patient-derived soft tissue sarcoma model3, possibly due to AXL expression in both tumor cells and myeloid-derived suppressor cells, or the presence of tumor-intrinsic MMAE resistance4, implying the AXL expression and EnaV activity relationship is contingent on the tumor microenvironment5. Further validation in larger cohorts is planned. 1 Davatzikos C, et al. J Med. Imaging 2018 2 Horng H, et al. Sci. Reports 2022 3 Van Renterghem B, et al. Int J Mol Sci. 2022 4 Holtzhausen A, et al. Cancer Immunol Res 2019 5 Boshuizen J, et al. Cancer Res. 2021 Citation Format: Andrew W. Chen, Lauren K. Brady, Eric A. Cohen, Walter C. Mankowski, Leonid Roshkovan, Mohammed Qutaish, Sharyn Katz, Patricia G. Castro, Nora Pencheva, Maria Jure-Kunkel, Brandon W. Higgs, Despina Kontos. Evaluating the potential of radiomics features to predict outcome to an antibody-drug conjugate therapy in non-small cell lung cancer patients [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 2587.