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Polic, I.

Publications and source records attributed to Polic, I..

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↗
bioRxiv2025

Reproducibility and Accuracy of Nanopore-Based Methylome Profiling of Streptococcus dysgalactiae subspecies equisimilis Strains from Cancer Patients

BackgroundDNA methylation influences bacterial gene regulation, virulence, and restriction-modification (RM) systems. Advances by Oxford Nanopore Technologies (ONT) now enable direct methylome profiling from nanopore sequencing using the Dorado basecaller. However, the comparative performance of ONT-only versus hybrid-assembly reference-based methylation calling, particularly regarding genomic DNA quality and inter-operator variability, remains understudied. MethodsSix operators independently prepared fifteen sequencing libraries each for nanopore (MinION R10.4.1 flow cells, Mk1D) and Illumina MiniSeq platforms for two Streptococcus dysgalactiae subsp. equisimilis strains (UT9728, 12 replicates; UT10237, 3 replicates). MicrobeMod v1.0.3 was used to identify methylation and motif profiles using Illumina-corrected hybrid reference assemblies (HRAs) and ONT-only reference assemblies (ORAs). Reproducibility and accuracy were compared using a custom genome annotation feature-enabled modular analysis that mapped and counted methylation site calls to CDS, rRNA and tRNA coordinates. ResultsStrain UT9728 predominantly exhibited N6-methyladenine (6mA) at GATC motifs, whereas strain UT10237 displayed dual methylation patterns: C5-methylcytosine (5mC) at CCWGG motifs and 6mA at GAGNNNNNTAA motifs. Both strains contained Type I and Type II RM systems; UT10237 uniquely harbored a Type IIG RM system with combined restriction and methylation activities. Motif identification concordance using HRAs and ORAs exceeded 99.9%. Reproducibility for methylation calls was high across independent replicates for both HRA (Pearsons r >0.989) and ORA (Pearsons r >0.993) methylation calls in GATC and CCWGG motifs but lower in the GAGNNNNNTAA motif (Pearsons r (HRA) = 0.80; r (ORA) = 0.78). ORA-based methylation site calls for all motifs showed excellent precision and recall compared to HRA-based calls (F1-score >99.999%). ConclusionOur findings support the accuracy, robustness, and utility of ONT-only data based methylome profiling for bacterial epigenetic characterization. Our analytical framework facilitates detailed evaluations of reproducibility and accuracy. Data Summary and AvailabilityO_LIUnprocessed_data_files: DOI: 10.5281/zenodo.15555625 O_LIRaw paired-end FastQ Files (Illumina). C_LIO_LIRaw FastQ Files (ONT; unmodified basecalls). C_LIO_LIRaw uBAM Files (ONT; 6mA_5MC_modified_basecalls). C_LI C_LIO_LIGenome_assemblies_and_annotation_files: DOI: 10.5281/zenodo.15558488 O_LIGenome_Assemblies reconstucted with ONT reads and polished with Illumina Reads (Hybrid assemblies);(strain_name_ _Hyb.gbk) C_LIO_LIGenome_Assemblies reconstructed with ONT reads and polished with ONT reads (ONT-only assemblies); (strain_name_ _ONT.fasta) C_LIO_LIGenBank flatfiles from Hybrid assemblies; (strain_name_ _Hyb.gbk) C_LIO_LIGenbank flatfiles from ONT-only assemblies. (strain_name_ _ONT.gbk) C_LI C_LIO_LIData_Tables and Python_code for modular analysis: DOI: doi.org/10.5281/zenodo.15579791 O_LI9728_10237_ORA+HRA_all_reps_microbemod_output.zip C_LIO_LIMethylation calls mapped to parsed genbank features & feature count matrix files. - [Formula] 9728_HRA_reproducibility_analysis_tables.zip - [Formula] 9728_ORA_reproducibility_analysis_tables.zip - [Formula] 10237_HRA_5mC_6mA_reproducibility_analysis_tables.zip - [Formula] 10237_ORA_reproducibility_analysis_tables.zip C_LIO_LIPython code for annotation feature-based modular analysis - [Formula] methylation_feature_hash_assert_FILTER_9728.py - [Formula] methylation_feature_hash_NOFILTER_9728.py - [Formula] methylation_feature_hash_motif_10237.py - [Formula] hyb_vs_ont_per_replicate_comparison.py - [Formula] hyb_vs_ont_combined_plot.py C_LI C_LIO_LIGenome Assembly_Code Availibility: https://github.com/TSchababerle/Bacterial-Methylation O_LIBash script for automated Hybrid Genome Assembly C_LIO_LIBash Script for automated ONT-Only Assembly C_LI C_LI Impact StatementThis study provides a focused proof-of-principle demonstrating the robustness and reproducibility of Oxford Nanopore Technologies (ONT) sequencing-based methylome profiling without short-read correction. Using independent replicates prepared by multiple operators, we show that nanopore-only methods yield consistent, accurate methylation profiles concordant with short-read corrected ONT reads. This highlights the potential of ONT-only sequencing as a broadly accessible, reliable approach for rapid bacterial epigenomic characterization across diverse clinical contexts.

genomics↗