A transcriptomic approach to explore the immune landscape of patient with pancreatic ductal adenocarcinoma with prognostic impact
681 Background: Pancreatic ductal adenocarcinoma (PDAC) is characterized by its immunologically cold tumor microenvironment (TME) with scarce T cell infiltration and few molecular signatures of immune activation. To date, the immunotherapies including the immune checkpoint blockade of PD-1, and CTLA...
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| Published in: | Journal of clinical oncology Vol. 42; no. 3_suppl; p. 681 |
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| Main Authors: | , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
20-01-2024
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| Online Access: | Get full text |
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| Summary: | 681
Background: Pancreatic ductal adenocarcinoma (PDAC) is characterized by its immunologically cold tumor microenvironment (TME) with scarce T cell infiltration and few molecular signatures of immune activation. To date, the immunotherapies including the immune checkpoint blockade of PD-1, and CTLA-4 either single or in combination have shown modest results in PDAC tumors. Recently, the Lymphocyte Activation Gene (LAG3) has emerged as a promising checkpoint target as indicated by preclinical and clinical data. The aim of this study is to explore the immune profile of a series of PDAC and to evaluate its prognostic impact. Methods: A 5-mm thick section of Formalin-Fixed Paraffin-Embedded (FFPE) tissue from a retrospective cohort of 28 PDAC cases were analyzed using the Precision Immunooncology panel (PIP, HTG Molecular Diagnostics) on a NextSeq 550 sequencer (Illumina). The RNA expression of 1392 immune-related genes were analyzed. Principal Components (PC) and Manhattan distance were studied for data visualization and clustering. Maxstat algorithm (maxstat v0.7-25) was used to establish optimal cut-offs for variable categorization. Log-rank and Cox regression were used for both univariate and multivariate for survival. All tests were two-tailed. The statistical analysis was performed using R studio (R version 4.0.3). Lastly, immunohistochemistry (IHC) for CD4, CD8, CD20, PD1, PD-L1, and LAG-3 proteins was performed on tissue microarrays (TMAs) to assess their correlation with RNA expression. Results: Unsupervised analysis of the gene expression identified two clusters of patients with differentially prognostic information. These two groups were defined with a logistic regression model of 14 genes ( BTLA, CXCR5, DLX6, KLHDC9, KRT13, LAG3, LGSN, MICB, SIGLEC5, SPINK5, SPN, TBX21, UPK2, ZIC5) showing for cluster1 and cluster 2 a median survival of 13 (0-97.583) and 57 (0.67-256.08) months, respectively (p-value = 0.0088). This predictive model was independently validated in the PDAC dataset of the Cancer Genome Atlas (TCGA) (p<0.0001). Interestingly, cluster 1, was characterized by the significant overexpression of LAG3 which has recently been proposed as the next immune checkpoint (IC) receptor target. Moreover, Cluster 1, exhibited the overexpression of other ICs such as PD1, PD-L1, IDO1, LIF and CTLA4, defining cluster 1 as an immunologically hot tumor. IHC for LAG3 and other ICs showed a moderate correlation with HTG results. Conclusions: In this study, a prognostic transcriptomic-based signature of 14 genes has been defined and validated for PDAC. This signature clearly identifies two prognostic groups that could constitute the basis for tailored immunotherapy with specific IC inhibitors. LAG-3 is a promising target for immunotherapy in PDAC patients. |
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| ISSN: | 0732-183X 1527-7755 |
| DOI: | 10.1200/JCO.2024.42.3_suppl.681 |