Publication-only abstracts (abstract number preceded by an "e"), published in conjunction with the 2019 ASCO Annual Meeting but not presented at the Meeting, can be found online only.
Immune gene expression and bayesian network analysis in advanced non small cell lung cancer (NSCLC) patients treated with immunotherapy.
Metastatic Non-Small Cell Lung Cancer
Lung Cancer—Non-Small Cell Metastatic
2019 ASCO Annual Meeting
J Clin Oncol 37, 2019 (suppl; abstr e20693)
Author(s): Sara Baglivo, Fortunato Bianconi, Francesca Romana Tofanetti, Biagio Ricciuti, Lorenza Pistola, Annamaria Siggillino, Maria Sole Reda, Clelia Mencaroni, Maria Francesca Currà, Valeria Teti, Giulio Metro, Guido Bellezza, Vincenzo Minotti, Fausto Roila, Vienna Ludovini; Medical Oncology, S. Maria della Misericordia Hospital, Perugia, Italy; Independent Researcher, Perugia, Italy; Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Boston, MA; Department of Clinical Pathology, S.Maria della Misericordia Hospital, Perugia, Perugia, Italy; Department of Experimental Medicine - Section of Anatomic Pathology and Histology - Medical School - University of Perugia, Perugia, Italy; Medical Oncology, S.Maria della Misericordia Hospital, Peruiga, Italy
Background: Immune checkpoint inhibitors (ICIs) have revoluzionized the therapeutic paradigm for different types of cancer including NSCLC. Clinical benefit, however, is limited to a minority of patients. The only adopted predictive biomarker, PD-L1 IHC testing, suffers from some limitations. A better understanding of biomarkers associated with response to ICIs is needed. Here, we studied immune gene expression profile and association with clinical response to immunotherapy in advanced NSCLC patients (pts) treated with ICI. Methods: A total of 37 Formalin-fixed, paraffin-embedded (FFPE) samples from advanced NSCLCs were analyzed by RNA-Seq using the Oncomine Immuno Response Assay (OIRRA) (ThermoFisher Scientific) to measure the expression level of 395 genes associated with 36 functional groups including checkpoint pathways, lymphocyte regulation and cytokine interactions, using the Ion Chef and Ion Torrent PGM. Gene level differential expression analysis were performed with the Torrent Suite and Transcriptome Analysis Console (TAC) 4.0 Software. Gene network analysis based on Bayesian algorithm was performed by GeneMANIA database querying with the genes selected through mRNA expression analysis. Results: Among 37 FFPE samples only 18 showed more than 300 OIRRA detectable target genes. In this subgroup, gene expression analysis revealed 7 genes (CCR2, CRTAM, FASLG, SELL, TIGIT, TNFRSF4, and TP63) up-regulated and one gene (CXCL8) down-regulated (p-value < 0.05) in ICI-responders compare to ICI-no responders. Bayesian enrichment computational analysis of the eight gene expression signature showed a more complex network which involves other 10 genes (SIRPG, GZMK, XCL2, CD8A, CD2, IFNG, SIT1, TAGAP, PTPRC and GZMH), correlated with different functional groups. Three main immune-pathways were identified (p < 0.01) (T cell activation, leucocyte activation and migration) involving TIGIT, TNFRSF4, CCR2 and CXCL8 genes among the gene expression signature identified. Conclusions: Our results revealed an immune response gene expression signature of 8 genes differentially expressed between ICI and ICI-no responders. Cancer systems biology analysis approach strengthen our findings identifying an immune molecular network and confirm the correlation of the gene expression signature with relevant immune regulatory functions. If validated, our results may have an important role for the development of a robust test to select patients properly and predict immune response to enable precision immunotherapy.