Biology subjects

Wistuba, I.

Publications and source records attributed to Wistuba, I..

6 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↗

bioRxiv2024

Comparison of imaging-based single-cell resolution spatial transcriptomics profiling platforms using formalin-fixed, paraffin-embedded tumor samples

Imaging-based spatial transcriptomics (ST) is evolving rapidly as a pivotal technology in studying the biology of tumors and their associated microenvironments. However, the strengths of the commercially available ST platforms in studying spatial biology have not been systematically evaluated using rigorously controlled experiments. In this study, we used serial 5-m sections of formalin-fixed, paraffin-embedded surgically resected lung adenocarcinoma and pleural mesothelioma tumor samples in tissue microarrays to compare the performance of the single cell ST platforms CosMx, MERFISH, and Xenium (uni/multi-modal) platforms in reference to bulk RNA sequencing, multiplex immunofluorescence, GeoMx Digital Spatial Profiler, and hematoxylin and eosin staining data for the same samples. In addition to objective assessment of automatic cell segmentation and phenotyping, we performed pixel-resolution manual evaluation of phenotyping to carry out pathologically meaningful comparison between ST platforms. Our study detailed the intricate differences between the ST platforms, revealed the importance of parameters such as tissue age and probe design in determining the data quality, and suggested reliable workflows for accurate spatial profiling and molecular discovery.

bioinformatics↗

bioRxiv2024

Coordinated translational control of multiple immune checkpoints by the integrated stress response pathway in lung cancer

The integrated stress response (ISR) is an adaptive pathway hijacked by cancer cells to survive cellular stresses in the tumor microenvironment. ISR activation potently induces Programmed Death Ligand 1 (PD-L1), leading to suppression of anti-tumor immunity. Here we sought to uncover additional immune checkpoint proteins regulated by the ISR to elucidate mechanisms of tumor immune escape. We show that CD155 and PD-L1 are coordinately induced by the ISR, enhancing translation of both immune checkpoint proteins through bypass of inhibitory upstream open reading frames (uORFs) in their 5' UTRs. Analysis of primary human lung tumors identifies a significant correlation between PD-L1 and CD155 expression. ISR activation accelerates tumorigenesis and inhibits T cell function, effects that can be overcome by combining PD-1 blockade with the ISR inhibitor ISRIB. These studies uncover a novel mechanism by which two immune checkpoint proteins are coordinately regulated and suggest a new therapeutic strategy for lung cancer patients. Statement of SignificanceThis study uncovers a novel mechanism for the coordinated translational regulation of the PD- L1/PD1 and CD155/TIGIT immune checkpoint pathways and highlights the ISR as a therapeutic vulnerability for lung cancer. Inhibition of the ISR pathway bolsters PD-1 blockade, potentially unveiling a new therapeutic strategy for lung cancer patients.

cancer biology↗

bioRxiv2023

A novel Bayesian model for assessing intratumor heterogeneity of tumor infiltrating leukocytes with multi-region gene expression sequencing

Intratumor heterogeneity (ITH) of tumor-infiltrated leukocytes (TILs) is an important phenomenon of cancer biology with potentially profound clinical impacts. Multiregion gene expression sequencing data provide a promising opportunity that allows for explorations of TILs and their intratumor heterogeneity for each subject. Although several existing methods are available to infer the proportions of TILs, considerable methodological gaps exist for evaluating intratumor heterogeneity of TILs with multi-region gene expression data. Here, we develop ICeITH, immune cell estimation reveals intratumor heterogeneity, a Bayesian hierarchical model that borrows cell type profiles as prior knowledge to decompose mixed bulk data while accounting for the within-subject correlations among tumor samples. ICeITH quantifies intratumor heterogeneity by the variability of targeted cellular compositions. Through extensive simulation studies, we demonstrate that ICeITH is more accurate in measuring relative cellular abundance and evaluating intratumor heterogeneity compared with existing methods. We also assess the ability of ICeITH to stratify patients by their intratumor heterogeneity score and associate the estimations with the survival outcomes. Finally, we apply ICeITH to two multi-region gene expression datasets from lung cancer studies to classify patients into different risk groups according to the ITH estimations of targeted TILs that shape either pro- or anti-tumor processes. In conclusion, ICeITH is a useful tool to evaluate intratumor heterogeneity of TILs from multi-region gene expression data.

bioinformatics↗

bioRxiv2020

Genomic and TCR Repertoire Intratumor Heterogeneity of Small-cell Lung Cancer and its Impact on Survival

Small-cell lung cancer (SCLC) is speculated to harbor complex genomic intratumor heterogeneity (ITH) associated with high recurrence rate and suboptimal response to immunotherapy. Here, we revealed a rather homogeneous mutational landscape but extremely suppressed and heterogeneous T cell receptor (TCR) repertoire in SCLCs. Higher mutational burden, lower chromosomal copy number aberration (CNA) burden, less CNA ITH and less TCR ITH were associated with longer overall survival of SCLC patients. Compared to non-small cell lung cancers (NSCLCs), SCLCs had similar predicted neoantigen burden and mutational ITH, but significantly more suppressed and heterogeneous TCR repertoire that may be associated with higher CNA burden and CNA ITH in SCLC. Novel therapeutic strategies targeting CNA could potentially improve the tumor immune microenvironment and response to immunotherapy in SCLC.

genomics↗

bioRxiv2018

Lung Cancer Explorer (LCE): an open web portal to explore gene expression and clinical associations in lung cancer

We constructed a lung cancer-specific database housing expression data and clinical data from over 6,700 patients in 56 studies. Expression data from 23 \"whole-genome\" based platforms were carefully processed and quality controlled, whereas clinical data were standardized and rigorously curated. Empowered by this lung cancer database, we created an open access web resource - the Lung Cancer Explorer (LCE), which enables researchers and clinicians to explore these data and perform analyses. Users can perform meta-analyses on LCE to gain a quick overview of the results on tumor vs normal differential gene expression and expression-survival association. Individual dataset-based survival analysis, comparative analysis, and correlation analysis are also provided with flexible options to allow for customized analyses from the user.

cancer biology↗