Jianjun J. Zhang

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 shows DLL3 expression level predicts response and long-term survival in SCLC patients treated with tarlatamab.

The paper identifies TCRs targeting HER2 mutations in NSCLC, showing they recognize mutant epitopes and maintain cytotoxic activity, offering a potential immunotherapy strategy.

This study develops gene expression signatures from LUSC tumors to predict progression risk of precancerous lesions, finding they outperform genomic aberrations in prognosis and correlate with immune features.

This study benchmarks five DNA foundation models across genomic tasks, finding that mean token embeddings improve classification performance and specialized models outperform general ones in gene expression and QTL prediction.

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

The paper compares systemic immune parameters between Black and White lung cancer patients, finding significant differences in cytokine levels and immune cell populations.

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 develops TimiGP-Response to analyze the tumor immune microenvironment's association with immunotherapy response across multiple cancer types and identifies key immune cell interactions linked to treatment outcomes.

This paper develops pMTnet-omni, an AI system predicting TCR-pMHC binding across species and MHC classes, achieving high accuracy and showing clinical utility in predicting immune-related adverse events.

TimiGP infers functional interactions between immune cells in the tumor microenvironment and identifies their prognostic value using bulk gene expression and survival data.

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.

The paper quantifies tumor-specific total mRNA expression from bulk data and shows its association with clinical outcomes across 15 cancer types.

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