Will, E. | Search bioRxiv

bioRxiv2025

Novel Predictive Spatial Biomarker in Non-Small Cell Lung Carcinoma: The Diversity of Niches Unlocking Treatment Sensitivity (DONUTS)

Probabilistic spatial modelling techniques developed on large-scale tumor-immune Atlases ([~]35M individually mapped cells; 50,000 high power fields) were used to characterize predictive features of treatment-responsive lung cancer. We identified CD8+FoxP3+ cell density as a robust pre-treatment biomarker for outcomes across disease stages and therapy types. In parallel, single-cell RNAseq studies of CD8+FoxP3+ T-cells revealed an activated, early effector phenotype, substantiating an anti-tumor role, and contrasting with CD4+FoxP3+ T-regulatory cells. A spatial biomarker was developed using an empirical probabilistic model to define the immediate cell neighbors or niche surrounding CD8+FoxP3+ cells and proximity to the tumor-stromal boundary. The resultant Diversity of Niches Unlocking Treatment Sensitivity (DONUTS) are more prevalent than the CD8+FoxP3+ cells themselves, mitigating sampling error in small biopsies. Further, the DONUTS only require four markers, are additive to PD-L1, and associate with tertiary lymphoid structure counts. Taken together, the DONUTS represent a next-generation predictive biomarker poised for clinical implementation. HIGHLIGHTSO_LILarge-scale tumor-immune Atlases drive robust computational biomarker development C_LIO_LICD8+FoxP3+ cells are anti-tumor T-cells and predict response to therapy C_LIO_LIThe niches or spatial donuts around CD8+FoxP3+ cells boost biomarker performance C_LIO_LICD8+FoxP3+ donuts are hallmarks of a larger immune organization that includes TLS C_LI

pathology↗

bioRxiv2024

Screening macrocyclic peptide libraries by yeast display allows control of selection process and affinity ranking

Macrocyclic peptides provide an attractive modality for drug development due to their ability to bind challenging targes, their small size, and amenability to powerful in vitro evolution techniques such as phage or mRNA display. While these technologies proved capable of generating and screening extremely large libraries and yielded ligands to already many targets, they often do not identify the best binders within a library due to the difficulty of monitoring performance and controlling selection pressure. Furthermore, only a small number of enriched ligands can typically be characterised due to the need of chemical peptide synthesis and purification prior to characterisation. In this work, we address these limitations by developing a yeast display-based strategy for the generation, screening and characterisation of structurally highly diverse disulfide-cyclised peptides. Analysis and sorting by quantitative flow cytometry enabled monitoring the performance of millions of individual macrocyclic peptides during the screening process and allowed us identifying macrocyclic peptide ligands with affinities in the low micromolar to high picomolar range against five highly diverse protein targets. X-ray analysis of a selected ligand in complex with its target revealed optimal shape complementarity, large interaction surface, constrained peptide backbones and multiple inter- and intra-molecular interactions, rationalising the high affinity and exquisite selectivity. The novel technology described here offers a facile, quantitative and cost-effective alternative to rapidly and efficiently generate and characterise fully genetically encoded macrocycle peptide ligands with sufficiently good binding properties to even therapeutically relevant targets.

biochemistry↗