Transcriptomic Analysis of Clonal Hematopoiesis in Non-Small Cell Lung Carcinoma Unveils a Tumorigenic and Dysregulated Immune Profile

Introduction:

Present in 30% of individuals with solid tumors, clonal hematopoiesis of indeterminate potential (CHIP) is increasingly recognized for its role in modifying the tumor immune microenvironment (TIME) and modulating immune responses. Given the significance of inflammation in lung cancer development, CHIP could influence prognosis of non-small cell lung cancer (NSCLC), and its subtypes, lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). This study explores the molecular and immunological changes associated with CHIP, aiming to provide insights into how CHIP influences tumor progression and immune response in NSCLC.

Methods:

Somatic mutations associated with CHIP were identified in peripheral blood whole exome sequences of NSCLC, LUSC and LUAD patients from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), using established methods (PMID: 36652671). CHIP in tumor tissue (CHIP-Tum) was determined by detecting the same peripheral blood driver mutations in tumor DNA. RNA sequencing (RNA-seq) analysis of tumor samples was performed with kallisto for pseudo-alignment and quantification, and DESeq2 for normalization. Differentially expressed genes underwent gene set enrichment analysis (GSEA) of gene ontology (GO) terms. T cell exhaustion markers such as CTLA-4, TIGIT, and BTLA were examined. The MFeaST package was used for feature selection to distinguish CHIP status in lung cancer, using 5-fold cross-validation and the consensus of multiple algorithms. Selected genes underwent over-representation analysis using g:Profiler, and classifiers were trained to predict CHIP status. CIBERSORTx was used to assess immune cell composition, and HALO software was used to analyze cellular densities and ratios in H&E stained images.

Results:

CHIP prevalence was 18% (54/298) in NSCLC, 15% (16/108) in LUSC, and 20% (38/190) in LUAD. The total number of CHIP variants were 70 in NSCLC, 19 in LUSC and 51 in LUAD, with DNMT3A being the most common mutation, followed by TET2. RNA-seq analysis identified 101 genes in NSCLC, 59 genes in LUSC, and 38 genes in LUAD, that were differentially expressed by CHIP status (p_adj<0.05). While CHIP+ NSCLC and LUAD demonstrated significant pathway enrichments linked to tumor growth, CHIP+ LUSC patients revealed significant pathway enrichments involved in both positive and negative regulation of innate and adaptive immune responses. This suggests heterogenous and multidirectional immune dysregulation rather than solely hyperinflammation in the TIME. Similarly, selected discriminative genes by CHIP status in LUSC demonstrated an overrepresentation of immune processes, including antigen processing and presentation, interleukin and interferon signaling, T cell response, and response to bacterial and viral infection. Furthermore, CHIP-Tum+ LUSC demonstrated trending decreased expression of T cell exhaustion markers. Using selected discriminative genes in NSCLC and LUSC by CHIP status, trained classifiers demonstrated moderate accuracy (NSCLC: 71.4%, LUSC: 91.3%), moderate specificity (NSCLC: 76.5%, LUSC: 97.8%), low sensitivity (NSCLC: 48.1%, LUSC: 50.0%), and moderate area under the curve values (NSCLC: 0.677, LUSC: 0.856). In silico immunophenotyping of LUSC revealed enrichment of dendritic cells in DNMT3A-driven CHIP (p<0.05), and trending enrichment of CD8+ T cells seen with CHIP and DNMT3A-driven CHIP. In LUAD, there was an enrichment of regulatory T cells seen with CHIP (p<0.05), CHIP-Tum (p<0.05), and DNMT3A-driven CHIP (p<0.01). Preliminary HALO software analysis confirmed increased lymphocyte infiltration in CHIP+ or CHIP-Tum+ NSCLC.

Conclusions:

The role of CHIP in tumor progression and immune response appears to differ across lung cancer subtypes and CHIP drivers. In LUSC, patients with CHIP exhibited altered immune responses and decreased markers of T cell exhaustion, indicating potential sensitivity to immune checkpoint inhibitors. While optimizations are necessary to address class imbalances in classifier training, identifying genes that discriminate CHIP status could enhance detection methods and reveal new targets for immunotherapy. Future studies will focus on exploring the TIME of NSCLC and its subtypes using in situ histological imaging. CHIP is emerging as a key biomarker for understanding tumor progression and preparing for the next era of personalized cancer management.