Biology subjects

Kavraki, L.

Publications and source records attributed to Kavraki, L..

3 recordsLinked to original sources

bioRxiv2026

Distinct Spatial Immune Architectures in Tumor and Tumor-Adjacent Tissues of Early-Stage Non-Small Cell Lung Cancer

Background: Lung cancer remains the leading cause of cancer-related deaths in the United States with over 124,000 estimated deaths for 2026. Previous studies have found that tumor-adjacent lung tissues may provide additional insight into the immune microenvironment of early-stage NSCLC. Methods: Multiplex immunofluorescence (mIF) imaging was performed on 192 tissues from 101 early-stage non-small cell lung cancer patients including 91 matched tumor-adjacent pairs, using three mIF panels. Spatial analyses were performed to identify distinctions between tissues and identify associations with clinical and genomic features. Results: Tumor tissue showed significantly higher densities of T cells, macrophages, B cells, and memory/regulatory populations than adjacent tissue (p<0.001). Tumors exhibited greater spatial heterogeneity, with higher prevalence of spatial patterning (47.7% vs. 31.2%) and more consistently localized organization, whereas adjacent tissue showed stronger individual-cell clustering. Pairwise colocalization (Ripley's L-cross) revealed selective spatial segregation in tumors (including B-cells from memory/regulatory cells and among cytotoxic T-cell subsets) and reduced immune proximity to malignant cells relative to the strong immune-epithelial association in adjacent tissue. Spatial features were linked to genomic features or patient outcomes: tumor neoantigen burden exhibited a positive association with CD3+ T cells in the tumor, while colocalization between CD45RO+CD57+GZMB+ cells and CD57+GZMB+ cells was negatively associated with overall survival outside the tumor. Conclusion: Tumor and adjacent tissues harbor distinct spatial immune architectures, with spatial features displaying associations with genomic features or patient outcomes. These findings highlight immune microenvironment reorganization and the importance of incorporating spatial context from both compartments into risk assessment in NSCLC.

immunology↗

bioRxiv2026

HER2 mutation-derived neoantigens in NSCLC as actionable targets for TCR therapy

HER2 mutations are oncogenic drivers in 1-6% of non-small cell lung cancers (NSCLC), but therapeutic resistance limits the durability of current HER2-targeted treatments. Here, we identify T-cell receptors (TCRs) targeting recurrent HER2 hotspot mutations as a potential immunotherapeutic strategy for HER2-mutant NSCLC. Using neoepitope prediction and antigen-specific T-cell enrichment, we isolated HLA-A*02:01restricted TCRs recognizing HER2 A775insYVMA, S310F, and G776delinsVC mutations, collectively covering approximately 60% of HER2-mutant NSCLC. These TCRs selectively recognized mutant HER2 epitopes without detectable wild-type reactivity and some displayed cross-recognition of related hotspot variants, expanding the spectrum of targetable tumors. The G776delinsVC-specific TCR also exhibited co-receptorindependent activity showcased by its ability to activate CD4+ T cells. Importantly, timelapse single-cell flow cytometry analyses demonstrated that TCR-engineered T cells repeatedly reacquired activated polyfunctional states following serial antigen stimulation, while serial tumor rechallenge assays confirmed sustained cytotoxic activity across multiple rounds of tumor killing. These findings identify recurrent HER2 mutations as shared immunotherapeutic targets and provide a foundation for the development of TCR-based therapies for HER2-mutant NSCLC.

immunology↗

bioRxiv2023

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles

Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing protein conformational ensembles. In this work we: (1) provide an overview of existing methods and tools for protein structural ensemble generation and analysis; (2) unify existing approaches in an open-source Python package, and a portable Docker image, providing interactive visualizations within a Jupyter Notebook pipeline; (3) test our pipeline on a few canonical examples found in the literature. Representative ensembles produced by EnGens can be used for many downstream tasks such as protein-ligand ensemble docking, Markov state modeling of protein dynamics and analysis of the effect of single-point mutations.

bioinformatics↗