eLifeGenomicsPeer-reviewed journal article
Increased inflammatory signature in myeloid cells of non-small cell lung cancer patients with high clonal hematopoiesis burden
Hyungtai SimiD, Hyun Jung ParkiD, Geun‐Ho Park, Yeon Jeong KimiD, Woong‐Yang ParkiD, Se‐Hoon LeeiD, Murim ChoiiD
Abstract
Clonal hematopoiesis of indeterminate potential (CHIP) allows estimation of clonal dynamics and documentation of somatic mutations in the hematopoietic system. Recent studies utilizing large cohorts of the general population and patients have revealed significant associations of CHIP burden with age and disease status, including in cancer and chronic diseases. An increasing number of cancer patients are treated with immune checkpoint inhibitors (ICI), but the association of ICI response in non-small cell lung cancer (NSCLC) patients with CHIP burden remains to be determined. We collected blood samples from 100 metastatic NSCLC patients before and after ICI for high-depth sequencing of the CHIP panel and 63 samples for blood single-cell RNA sequencing(scRNA-seq). Whole exome sequencing (WES) was performed in an independent replication cohort of 180 patients. The impact of CHIP status on the immunotherapy response was not significant. However, metastatic lung cancer patients showed higher CHIP prevalence (44/100 for patients vs 5/42 for controls; P = 0.01). In addition, lung squamous cell carcinoma patients showed increased burden of larger clones compared to lung adenocarcinoma patients (8/43 for LUSC vs 2/50 for LUAD; P = 0.04). Furthermore, single cell RNA-seq analysis of the matched patients showed significant enrichment of inflammatory pathways mediated by NF-B in myeloid clusters of the severe CHIP group. Our findings suggest minimal involvement of CHIP mutation and clonal dynamics during immunotherapy but a possible role of CHIP as an indicator of immunologic response in NSCLC patients.
- organism: human
- assay: bulk RNA-seq
- assay: scRNA-seq
- assay: whole-exome sequencing
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: database / resource
- approach: statistical model
- approach: experimental assay
- Citations
- 1
- FWCI
- 0.31
- Field percentile
- top 48%
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