Ignacio Ivan Wistuba

This study compares spatial immune architectures in tumor and tumor-adjacent tissues of early-stage NSCLC, finding distinct immune cell densities, spatial patterns, and associations with genomic features and patient outcomes.

This study compares imaging-based spatial transcriptomics platforms using FFPE tumor samples, finding differences in performance and highlighting factors affecting data quality.

The paper shows that the integrated stress response (ISR) coordinates the translation of PD-L1 and CD155, two immune checkpoint proteins, in lung cancer, and suggests combining ISR inhibition with PD-1 blockade as a therapeutic strategy.

The paper introduces ICeITH, a Bayesian model to assess intratumor heterogeneity of tumor-infiltrating leukocytes using multi-region gene expression data, showing improved accuracy over existing methods.

The paper investigates how anti-cancer immunity evolves from preneoplasia to invasive lung adenocarcinomas, identifying changes in immune response and molecular features that may facilitate immune evasion.

This study examines DNA methylation changes and intra-tumor heterogeneity in lung adenocarcinoma progression, finding increased methylation aberrations and immunosuppression in later-stage lesions.

This study shows that transcriptomic features, not genomic characteristics, drive the histologic phenotype of lung cancers.

This study examines genomic and TCR repertoire heterogeneity in SCLC, finding that lower TCR heterogeneity and mutational burden correlate with better survival.

The paper analyzes single-cell RNA-seq data to identify ACE2 expression patterns in human lung epithelial cells, finding high ACE2 expression in alveolar type II cells and co-expression with pathogen receptors.

The paper develops a deep learning tool to analyze pathology images and identify TME features predictive of survival in lung cancer.

DeMixT improves transcriptomic deconvolution by estimating component proportions and expression profiles in heterogeneous samples, showing high accuracy in validation and TCGA data.