Abstract A033: Integrating cell-SELEX and deep learning method for discovery of high-affinity aptamers targeting cholangiocarcinoma

Abstract

Cholangiocarcinoma (CCA) is an aggressive biliary malignancy with poor prognosis and limited treatment options, underscoring the need for targeted RNA-based therapeutic strategies. Though DNA- aptamers could be one of the promising targeted ligands, conventional enrichment-driven selection often fails to reliably identify high-affinity binders due to PCR bias, stochastic drift, and sequencing errors. In this study, we combined cell-SELEX with a data-driven artificial intelligence framework to identify DNA aptamers targeting CCA cells. Eleven rounds of selection were performed on a library exceeding 6×1014 sequences, with iterative negative selection against normal hepatocytes and cholangiocytes to enhance tumor specificity. Given the limitations of enrichment-based ranking in prioritizing functional binders, we developed and evaluated four deep learning architectures—MLP, CNN, BiLSTM, and a hybrid CNN–BiLSTM—using aptamer pools derived from the final SELEX round. These models were built by integrating one-hot sequential features and DNA structural descriptors of the dataset along with their enrichment patterns across SELEX rounds. Among them, the hybrid CNN–BiLSTM model demonstrated superior performance by capturing both local sequence motifs and long-range dependencies. Model performance was evaluated using AUC-ROC and complementary classification metrics on an independent test set. The hybrid CNN–BiLSTM model achieved the best overall performance with an AUC-ROC of 0.97 and a precision of 68%, indicating strong discriminative power under class imbalance (strong: non-binder=1:68). The model exhibited high specificity (99.5%; 29,855/30,005 true negatives) and moderate sensitivity (71.7%; 312/435 true positives), reflecting its ability to reliably filter non-binding sequences while retaining true functional binders. Model-prioritized candidates were experimentally validated using qPCR-based aptamer–cell binding assays. Notably, two low-abundance/high-prediction aptamers (Read counts: 4) exhibited 2–3-fold higher binding than the reference aptamer (Positive Control) derived from traditional SELEX enrichment. A high-read/high-prediction candidate showed ∼2-fold greater binding than the positive control, highlighting the model’s ability to recover overlooked functional binders. To assess translational relevance, selected aptamers were used to functionalize the lactosome-based nanovesicles for siRNA delivery targeting PD-L1, resulting in measurable gene silencing in HuCCT1 cells after 48 hours. Collectively, this integrative framework enables accurate identification and prioritization of high-affinity, functionally relevant aptamers beyond enrichment alone. This approach enhances SELEX efficiency, supports rational ligand design, and provides a scalable strategy for developing aptamer-guided RNA therapeutics for any target. Sourav Pal, Julia Driscoll, Brandon Wilbanks, Ayano Kabashima, Rory Smoot, Tushar Patel. Integrating cell-SELEX and deep learning method for discovery of high-affinity aptamers targeting cholangiocarcinoma [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr A033.