Clonal Hematopoiesis Is Associated with Changes to T Cell Contexture in Solid Tumours
Bibliographic record
Abstract
Introduction: Clonal hematopoiesis of indeterminate potential (CHIP) is an age-related, nonmalignant condition in which hematopoietic stem cells acquire somatic mutations that drive their self-renewal and clonal expansion of progeny blood cells. Despite being nonmalignant, CHIP gives rise to proinflammatory immune cells, and thus contributes to immune dysregulation in a number of diseases. Recently, it has been shown that CHIP is associated with poorer survival outcomes in patients with solid tumours - in part due to alterations in the tumour microenvironment (TME) (reviewed in PMID: 37343201) - as well as differential responses to immunotherapies. Importantly, these therapies impinge on antitumour T cells within the TME. However, while the impact of CHIP on myeloid immune compartments has been extensively studied, the effect on T cell populations is largely unknown. In this study, we explore the impact of CHIP on T cells within the TME of solid tumours. Methods: GATK-Mutect2 and ANNOVAR toolkits were used to make CHIP calls from the peripheral blood (PB) of 1550 patients (10 primary cancer types) from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Similar methods were applied to bulk tumour DNA sequencing for the detection of CHIP mutations that were also found in tumour-infiltrating cells (CHIP_Tum). DESeq2 was used to determine log fold change and adjusted p-value of differentially expressed genes, including exhaustion markers (i.e. PDCD1, CTLA4, HAVCR2 etc). The ranked list was then inputted into Gene Set Enrichment Analysis (GSEA) for the following gene sets: KEGG_antigen_processing_and_presentation, GOBP_t_cell_mediated_cytotoxicity, and a custom gene set of 11 T cell exhaustion markers. Abundance of immune cell populations was estimated with CibersortX. Results: To elucidate the effect of CHIP on T cells within the TME, we first estimated the abundance of T cell populations across all tumour types using CibersortX. Interestingly, we found that the presence of CHIP in PB alone (CHIP_PB) or in PB and tumour (CHIP_Tum) was not associated with significant alterations in CD4 or CD8 populations within the tumour. Notably, however, CHIP_Tum was associated with changes in expression of multiple genes related to T cell exhaustion (i.e. CTLA4, HAVCR2). This was particularly true in brain and colorectal cancers, where there was a trend towards increased expression of multiple exhaustion-related genes with CHIP_Tum (e.g. HAVCR2 in brain, p=0.1377, CTLA4 in colorectal, p=0.1253). GSEA analysis of these two tumour types revealed that the presence of CHIP_Tum, or CHIP_PB characterized by mutations in Tet2 (CHIP_Tet2), was associated with a significant enrichment in genes related to T cell mediated cytotoxicity, antigen processing and presentation, and T cell exhaustion. Conversely, in ovarian cancer, CHIP status was associated with a significant decrease in T cell mediated cytotoxicity and antigen processing and presentation, demonstrating that the influence of CHIP on tumour immune status varies by cancer type. To better understand the potential clinical relevance of these CHIP-associated changes in gene expression, we tested for enrichment of gene expression signatures predictive of anti-PD1 immune checkpoint blockade (ICB) response. Strikingly, CHIP_Tum and CHIP_Tet2 showed significant enrichment of positive ICB response signatures in brain and colorectal cancers, in line with the increased expression of cytotoxicity and exhaustion-related gene signatures in these tumours. However, in ovarian cancer, CHIP_Tet2 was associated with a significant decrease in ICB response signatures in ovarian cancer, again congruent with GSEA results. Conclusion: Our results demonstrate that CHIP status in patients with solid tumours influences expression of markers related to antigen processing and presentation, T cell cytotoxicity and T cell exhaustion within the TME. Further, in some tumour types, CHIP is associated with changes in predicted response to ICB. These data suggest that a combination of CHIP status and T cell-related gene expression signatures may be useful biomarkers to predict response to ICB of some tumours, which is especially relevant in the current era of immunotherapy. Future work will aim to determine whether the influence of CHIP on T cell function is driven by myeloid or lymphoid cells carrying CHIP variants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".