Abstract 1705: Identification of an immune gene expression signature for progression of breast ductal carcinoma in situ (dcis)

e14731 Background: Ductal carcinoma in situ (DCIS) display favorable outcome while little is known about the factors associated with invasive recurrence. To identify better prognostic biomarkers we performed gene expression analysis followed by immunohistochemistry (IHC) staining validation. Methods: Differential gene expression analysis of 29 pure DCIS patients was performed using nanostring platform. RNA was extracted from paraffin blocks from age/size matched 11 recurrence-free and 18 invasive-recurrence cases (disease free interval > 5 years). Gene annotation enrichment analysis was done for differentially expressed genes (DEG) using DAVID. Eighty-two pure DCIS cases were selected for external validation by IHC staining. Allred score cutoff 1 was used for survival analysis. Results: Ninety-nine differentially expressed genes were found statistically significant (p-value < 0.05). Androgen receptor (AR) gene, which encodes a transcription factor AR, has recently been highlighted as a favorable prognostic marker and a therapeutic target in invasive tumor (fold change = - 1.35, p < 0.001). AR protein expression was externally validated by IHC staining of 82 pure DCIS cases (24 invasive-recurrence versus 58 recurrence-free). Similar to gene expression analysis result, patients with invasive recurrence showed lower AR staining score than recurrence-free patients (p = 0.007). Cox regression analysis showed lower AR level as an independent risk factor of long-term invasive recurrence (HR 7.43, 95%CI 1.50 – 36.62). Gene enrichment analysis revealed enrichment of kinase pathway and cell cycle pathway in recurred cases (Enrichment Score = 2.43, 2.41 respectively). Conclusions: DEG pattern was observed among pure DCIS cases. AR may serve as a prognostic biomarker and targeting kinase, cell proliferation may be effective for higher risk DCIS patients.

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Immune gene expression and bayesian network analysis in advanced non small cell lung cancer (NSCLC) patients treated with immunotherapy.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e20693 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e20693-e20693

Author(s):

Sara Baglivo ◽

Fortunato Bianconi ◽

Francesca Romana Tofanetti ◽

Biagio Ricciuti ◽

Lorenza Pistola ◽

...

Keyword(s):

Gene Expression ◽

Immune Response ◽

Network Analysis ◽

Expression Analysis ◽

Gene Expression Signature ◽

Immune Gene ◽

Expression Signature ◽

Immune Gene Expression ◽

Ffpe Samples ◽

Nsclc Patients

e20693 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.

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