There has been an increase in immunotherapy (IO) used for treating advanced cholangiocarcinoma (CCA). However, data are limited to the sensitivity and resistance to chemotherapy and IO in CCA, making it complex to identify predictive biomarkers to guide treatment decision-making.
Roswell Park Comprehensive Cancer Center investigators hypothesized that sensitivity to IO and chemotherapy in programmed death-ligand 1 (PD-L1)–high and PD-L1–low patients can be differentiated using transcriptional signatures.1,2
Additionally, the signatures could facilitate personalized treatment strategies and identify candidates for other novel therapeutic approaches. Riya Jayesh Patel, MD, presented results from this study at ASCO GI.
Transcriptomic signatures related to chemotherapy and IO sensitivity in the CCA cohort of The Cancer Genome Atlas (TCGA-CHOL) were explored. Transcriptional dysregulation was assessed by comparing the transcriptional profiles of the 2 groups.
Differential expression analysis, using limma (a package for analyzing microarray and RNA-sequencing data), was utilized for Gene Set Enrichment Analysis (GSEA) to assess the predicted function of transcriptional dysregulation between the 2 groups. Immune deconvolution was assessed using the Tumor Immune Estimation Resource (TIMER) deconvolution software to evaluate differences in immune infiltration.
The resulting scores from this analysis were used to determine how resistant cell population estimates correlated with immune factors like PD-L1 expression.
Of 36 patients, 33 (91.7%) were of liver and intrahepatic biliary duct origin; 1 (2.8%) was gallbladder primary; and 2 (5.5%) were labeled as “others.”
All patients were divided into 2 groups based on the PD-L1 expression levels. Patients were designated as PD-L1-high (n=11) if their PD-L1 levels had the same or more than average PD-L1 expression and PD-L1-low (n=25) if their PD-L1 levels had the same or more than average expression PD-L1 within the cohort.
Scores from this analysis were used to correlate immune cell estimates with critical immune factors. Highly expressive PD-L1 tumors demonstrated immunosuppressive infiltration.
Using TIMER deconvolution software, several significant correlations were noted between various immune cells: CD4+ and CD8+ T cells (Pearson product-moment correlation coefficient [R]=–0.35; P=.02), CD8+ and neutrophils (R=0.42; P=.009), and CD8+ and myeloid dendritic cells (mDCs; R=0.3348; P=.04).
Additionally, significant correlations were noted between PD-L1 expression and immune cells, including PD-L1 and B cells (R=0.54; P=.0005), macrophages (R=0.41; P=.01), CD8+ T cells (R=0.43; P=.008), neutrophils (R=0.56; P=.0003), and mDCs (R=0.48; P=.002).
GSEA analysis revealed that tumors with high PD-L1 expression exhibit a significant enrichment of unique transcriptional dysregulation patterns.
The genes identified in this study play a crucial role in predicting the effectiveness of chemotherapy drugs used to treat CCA. These genes have also been established as prognostic markers in various other types of cancer.
Additionally, specific metabolic patterns have been observed in tumors with low PD-L1 expression, which is also known to have prognostic value in other cancer types. GSEA also revealed alterations to immune cell pathways associated with PD-L1 expression.
Significant variations were observed in the immune deconvolution scores of CD8+ T cells, macrophages, and neutrophils when comparing PD-L1-high and PD-L1-low patients.
Furthermore, patients with low levels of PD-L1 exhibited notable changes in the genes responsible for metabolic pathways, specifically those involved in regulating cholesterol, amino acid transportation, and cellular respiration.
The PD-L1-high group showed enrichment of immune signatures, and the PD-L1-low group showed enrichment of immune signatures in the TCGA-CHOL patients.
Patients with CCA with high levels of PD-L1 displayed expected differential enrichment of immune signatures. It is reported for the first time that CCA PD-L1-low patients are uniquely enriched for differential metabolic signatures.
If validated in cohorts treated with IO, these findings could offer valuable insights and serve as evidence supporting the effectiveness of metabolism-targeted therapies in overcoming therapeutic resistance in CCA and potentially facilitating personalized treatment strategies for other novel therapeutic approaches.2
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