Identification of TMB, CD8 T‐cell abundance, and homologous repair pathway mutation frequency as predictors of the benefit–toxicity ratio of anti‐PD‐1/PD‐L1 therapy

Vitiligo is a common immune-related depigmentation condition, and its pathogenesis remains unclear. This study used a combination of bioinformatics methods and expression analysis techniques to explore the relationship between immune cell infiltration and gene expression in vitiligo. Previously reported gene expression microarray data from the skin (GSE53146 and GSE75819) and peripheral blood (GSE80009 and GSE90880) of vitiligo patients and healthy controls was used in the analysis. R software was used to filter the differentially expressed genes (DEGs) in each dataset, and the KOBAS 2.0 server was used to perform functional enrichment analysis. Compared with healthy controls, the upregulated genes in skin lesions and peripheral blood leukocytes of vitiligo patents were highly enriched in immune response pathways and inflammatory response signaling pathways. Immunedeconv software and the EPIC method were used to analyze the expression levels of marker genes to obtain the immune cell population in the samples. In the lesional skin of vitiligo patients, the proportions of macrophages, B cells and NK cells were increased compared with healthy controls. In the peripheral blood of vitiligo patients, CD8+ T cells and macrophages were significantly increased. A coexpression analysis of the cell populations and DEGs showed that differentially expressed immune and inflammation response genes had a strong positive correlation with macrophages. The TLR4 receptor pathway, interferon gamma-mediated signaling pathway and lipopolysaccharide-related pathway were positively correlated with CD4+ T cells. Regarding immune response-related genes, the overexpression of IFITM2, TNFSF10, GZMA, ADAMDEC1, NCF2, ADAR, SIGLEC16, and WIPF2 were related to macrophage abundance, while the overexpression of ICOS, GPR183, RGS1, ILF2 and CD28 were related to CD4+ T cell abundance. GZMA and CXCL10 expression were associated with CD8+ T cell abundance. Regarding inflammatory response-related genes, the overexpression of CEBPB, ADAM8, CXCR3, and TNIP3 promoted macrophage infiltration. Only ADORA1 expression was associated with CD4+ T cell infiltration. ADAM8 and CXCL10 expression were associated with CD8+ T cell abundance. The overexpression of CCL18, CXCL10, FOS, NLRC4, LY96, HCK, MYD88, and KLRG1, which are related to inflammation and immune responses, were associated with macrophage abundance. We also found that immune cells infiltration in vitiligo was associated with antigen presentation-related genes expression. The genes and pathways identified in this study may point to new directions for vitiligo treatment.

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Abstract LB-017: Combining tumor mutational burden, CD8+ T-cell abundance and PD1 mRNA expression accurately predicts response to anti-PD1/PDL1 therapy across cancers

10.1158/1538-7445.am2019-lb-017 ◽

2019 ◽

Author(s):

Joo Sang Lee ◽

Eytan Ruppin

Keyword(s):

T Cell ◽

Mrna Expression ◽

Cd8 T Cell ◽

Cell Abundance ◽

Mutational Burden ◽

Tumor Mutational Burden

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Abstract LB-017: Combining tumor mutational burden, CD8+ T-cell abundance and PD1 mRNA expression accurately predicts response to anti-PD1/PDL1 therapy across cancers

10.1158/1538-7445.sabcs18-lb-017 ◽

2019 ◽

Author(s):

Joo Sang Lee ◽

Eytan Ruppin

Keyword(s):

T Cell ◽

Mrna Expression ◽

Cd8 T Cell ◽

Cell Abundance ◽

Mutational Burden ◽

Tumor Mutational Burden

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Large scale multiomic analysis suggests mechanisms of resistance to immunotherapy in leiomyosarcoma.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.11512 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. 11512-11512

Author(s):

Galina Lagos ◽

Roman Groisberg ◽

Don S. Dizon ◽

Andrew Elliott ◽

Tabitha Copeland ◽

...

Keyword(s):

T Cell ◽

Predictive Biomarkers ◽

Fold Increase ◽

Cd8 T Cell ◽

Multiple Hypothesis Testing ◽

Gene Set Enrichment Analysis ◽

Cell Infiltration ◽

Tumor Type ◽

Cell Abundance ◽

T Cell Infiltration

11512 Background: Leiomyosarcomas (LMS) have been reported to have immunohistochemical (IHC) and gene expression signatures suggestive of an immune-responsive tumor microenvironment. Despite this, immune checkpoint inhibitors have demonstrated minimal activity in LMS. We examined molecular profiles of LMS specimens from multiple institutions to explore mechanisms of immunotherapy (IO) resistance. Methods: LMS specimens (n = 1115), including 701 uterine (uLMS) and 414 soft tissue site (stLMS) samples, underwent next-generation sequencing (NGS) of DNA (592-gene panel or whole exome) and RNA (whole transcriptome, n = 537) at Caris Life Sciences (Phoenix, AZ). A threshold of 10 mut/Mb was used to identify high tumor mutational burden (TMB-H). IHC was performed for PD-L1 (SP142; 2+|5% positive). Deficient mismatch repair (dMMR)/high microsatellite instability (MSI-H) was tested by IHC and NGS, respectively. RNA expression was analyzed using Gene Set Enrichment Analysis and Microenvironment Cell Populations-counter, with results compared to melanoma (n = 1255) as a representative immunogenic tumor type. P-values were adjusted for multiple hypothesis testing. Results: TMB-H was observed in 3.8% (n = 41) of LMS specimens, with a median of 5 mut/Mb (IQR 3.3-6.7). dMMR/MSI-H was rarely detected (1.5%, n = 17), whereas 8.2% (n = 88) were positive for PD-L1 expression. uLMS and stLMS did not differ in TMB-H (3.4 vs 4.5%, p = 0.277), PD-L1 expression (8.6 vs 7.4%, p = 0.322), or dMMR/MSI-H (2.0 vs 0.7% p = 0.207). stLMS demonstrated upregulation of immune-related gene sets, including interferon γ (p = 0.035) and α (p = 0.033) response, inflammatory response (p = 0.038), interleukin-6/STAT3 signaling (p = 0.030), and TNFα signaling (p = 0.026) compared to uLMS. Immune cell infiltration was increased in stLMS over uLMS, most notably for CD8 T-cell and B-cell abundance ( > 2-fold increase, p < 0.0001). Compared to melanoma, all LMS had lower abundance of CD8 T cells, cytotoxic lymphocytes, and B-cells ( > 2-fold decrease, p < 0.0001). Fibroblasts were more prevalent in LMS relative to melanoma (3.2-fold increase, p < 0.0001). Interestingly, while higher CD8 T-cell infiltration was positively associated with dMMR/MSI-H among LMS specimens (p = 0.032), TMB-H and PD-L1 expression were associated with lower CD8 T-cell infiltration (p < 0.01). Conclusions: Only a small proportion of LMS are TMB-H or MSI-H, suggesting that the neoantigen burden in LMS may be insufficient to promote a robust anti-tumor response, even in the presence of PD-L1 positive tumor cells. Traditional predictive biomarkers of response to IO are unlikely to be useful in LMS. Furthermore, both uLMS and stLMS have an immune microenvironment characterized by a high fibroblast and low T cell abundance relative to melanoma. Future IO trials in LMS should focus on combination therapies that may reverse the observed T-cell exclusion/desmoplastic phenotype.

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