Accelerating selective cancer ligand discovery through an accessible phage display analysis suite

Identifying novel druggable targets remains a major challenge in cancer research. Of the approximately 5,000 potentially druggable cancer genes found through various sequencing techniques, 94% are well characterized, yet only 14.3% have approved drugs. This gap highlights the limitations of indirect, sequence-driven approaches, which infer targets rather than directly measuring functional binding. Direct discovery platforms like phage display help overcome this barrier by screening ligands in biologically relevant contexts. Phage display is a high-throughput screening technology that presents vast libraries of randomized peptides or proteins on bacteriophage surfaces to identify ligands that selectively bind cancer associated targets. Broader use of this technique is limited by restricted access to high-diversity libraries and the absence of standardized, user-friendly pipelines for analyzing next-generation sequencing (NGS) phage screen data. These barriers make it difficult to distinguish true binders from artifacts caused by non-specific binding, amplification bias, or sequencing noise. These challenges are especially pronounced in whole-cell and tissue-based screens, where identifying true binders is critical for discovering targets in authentic cancer environments. To address these limitations, we developed a high-diversity (>10 8 ), disulfide-constrained, variable-loop-length peptide library optimized for cancer target discovery, along with a unified phage screen NGS analysis pipeline. The library design supports high-affinity binders and enables analysis of sequence and loop-size biases across targets. The software pipeline provides a standardized processing workflow which directly influences downstream analysis. Machine-learning-based denoising and robust motif discovery further improve identification of enriched sequences and true binders. The platform has been validated with established protein-target screens and successfully used to identify ligands selective for known protein targets in multiple myeloma. These results provide a strong foundation for ongoing whole-cell panning on drug-resistant multiple myeloma lines to discover ligands with therapeutic and diagnostic potential. Our long-term aim is to build a community-driven database of phage screens processed with this unified pipeline, analogous to TCGA for RNA-seq, allowing cross-study comparisons and in-silico identification of ligands or binding motifs selective for cancer instead of surrounding healthy tissue. This standardized platform lowers barriers to actionable ligand discovery in cancer research and accelerates the development of selective agents for cancer therapeutics.