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Peer-reviewedCC BY 4.0ACS Omega

Integrated Whole-Cell Phage Display and Machine Learning Identify Candidate Targeting Ligands in KRAS-Defined Lung Cancer Contexts

  • Guido Uguzzoni1
  • Dimas Carolina Belisario2,3
  • Andrea Pagnani4,5
  • Serena Marchiò2,3
5 affiliations
  1. 1Université Grenoble Alpes, CEA, INSERM, IRIG, UA13 BGE , ,
  2. 2University of Turin , , ,
  3. 3INOC - Istituto Nazionale Oncologico Candiolo: IRCCS , ,
  4. 4Polytechnic University of Turin , , ,
  5. 5Italian Institute for Genomic Medicine (IIGM) , ,
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Abstract

Phage display can identify ligands that target distinctive cell surface phenotypes, but selection outputs are often complex and difficult to interpret. Here, we developed an integrated workflow combining phage display biopanning, next-generation sequencing (NGS), machine learning (ML), and flow cytometry to discover short peptide binders associated with genotype-defined cancer contexts. Phage display was performed on lung cancer cell lines representing KRAS wild-type (WT), G12C, G12D, and G12V backgrounds, and experimental outputs were used to train a multimodal probabilistic model. From the resulting model-generated candidates, 98 nonredundant heptapeptides were selected for synthesis, and 88 were successfully solubilized and tested experimentally. Flow cytometry validation revealed heterogeneous binding behaviors, including broad pan-cellular binders and candidates with mutant-vs-WT binding preference. Extended dose–response profiling and normal-cell counter-screening enabled prioritization based on binding magnitude, curve behavior, normal-cell reactivity, and sequence developability. Overall, this study shows how whole-cell selection data sets can be analyzed computationally and experimentally to identify and prioritize peptide candidates for targeting strategies.

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Cite

BibTeXRIS
BibTeX
@article{uguzzoni2026integrated,
  title = {{Integrated Whole-Cell Phage Display and Machine Learning Identify Candidate Targeting Ligands in KRAS-Defined Lung Cancer Contexts}},
  author = {Guido Uguzzoni and Dimas Carolina Belisario and Andrea Pagnani and Serena Marchiò},
  journal = {ACS Omega},
  year = {2026},
  month = nov,
  doi = {10.1021/acsomega.6c10403},
  url = {https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c10403/5464026/Integrated-Whole-Cell-Phage-Display-and-Machine},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Integrated Whole-Cell Phage Display and Machine Learning Identify Candidate Targeting Ligands in KRAS-Defined Lung Cancer Contexts
AU  - Guido Uguzzoni
AU  - Dimas Carolina Belisario
AU  - Andrea Pagnani
AU  - Serena Marchiò
PY  - 2026
DA  - 2026/11/04
JO  - ACS Omega
DO  - 10.1021/acsomega.6c10403
UR  - https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c10403/5464026/Integrated-Whole-Cell-Phage-Display-and-Machine
AB  - Phage display can identify ligands that target distinctive cell surface phenotypes, but selection outputs are often complex and difficult to interpret. Here, we developed an integrated workflow combining phage display biopanning, next-generation sequencing (NGS), machine learning (ML), and flow cytometry to discover short peptide binders associated with genotype-defined cancer contexts. Phage display was performed on lung cancer cell lines representing KRAS wild-type (WT), G12C, G12D, and G12V backgrounds, and experimental outputs were used to train a multimodal probabilistic model. From the resulting model-generated candidates, 98 nonredundant heptapeptides were selected for synthesis, and 88 were successfully solubilized and tested experimentally. Flow cytometry validation revealed heterogeneous binding behaviors, including broad pan-cellular binders and candidates with mutant-vs-WT binding preference. Extended dose–response profiling and normal-cell counter-screening enabled prioritization based on binding magnitude, curve behavior, normal-cell reactivity, and sequence developability. Overall, this study shows how whole-cell selection data sets can be analyzed computationally and experimentally to identify and prioritize peptide candidates for targeting strategies.
SN  - 2470-1343
ER  -