Pal, Sourav

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. Citation Format: 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.

Abstract Aptamers are short single-stranded DNA or RNA ligands that fold into defined three-dimensional structures and bind targets with antibody-like affinity and specificity, yet retain key advantages for central nervous system (CNS) applications including chemical synthesis, low immunogenicity, thermal stability, and facile modular engineering. This review summarizes recent advances positioning aptamers as “theranostic” platforms for CNS disorders, spanning discovery, optimization, delivery, and integrated sensing. We highlight the evolution of selection technologies from classical protein-‘Systematic Evolution of Ligands by Exponential Enrichment’ (SELEX) to Cell-SELEX and in vivo SELEX, now increasingly coupled with high-throughput sequencing, bioinformatics, molecular docking/molecular dynamics, and emerging artificial intelligence/machine learning (AI/ML) approaches that learn sequence–structure–function relationships and accelerate candidate prioritization and de novo design. We discuss chemical strategies that address nuclease degradation and rapid renal clearance, while noting trade-offs between stability and folding-dependent affinity. Since blood–brain barrier (BBB) transport remains the dominant translational barrier, we evaluate non-invasive intranasal delivery, receptor-mediated transcytosis via endothelial shuttles, intrathecal administration, and vector-enabled intracellular expression for sustained neuroimmune modulation. Finally, we discuss multifunctional conjugates and carriers and CNS-focused aptamer-based sensors for neurotransmitters and misfolded proteins, alongside multiplexed aptamer proteomics for biomarker discovery and monitoring. Collectively, converging advances in selection, computation, chemistry, and delivery are enabling aptamer-based closed-loop theranostics tailored to CNS disease biology.