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MA04 - Models and Biomarkers (ID 122)
- Event: WCLC 2019
- Type: Mini Oral Session
- Track: Biology
- Presentations: 1
- Now Available
- Moderators:Montse Sanchez-Cespedes, Kwok-kin Wong
- Coordinates: 9/08/2019, 13:30 - 15:00, Interlaken (1988)
MA04.10 - Development and Validation of a Gene Expression-Based Prognostic Signature in Early-Stage Squamous Cell Carcinoma of the Lung (Now Available) (ID 2643)
13:30 - 15:00 | Author(s): Olga Kovalchuk
Squamous cell carcinoma of the lung (SqCCL) accounts for about 30% of all lung cancers and is usually associated with smoking. The clinical outcomes of early stage SqCCL are heterogeneous; while 60% of Stage I and II SqCCL patients never present with recurrence after surgery, the remaining will ultimately succumb to the disease. Therefore, a robust prognostication tool is an unmet clinical need. Here, we describe the development and validation of a gene expression-based prognostic signature in Stage I and II SqCCL patients.Method
A total of 673 primary tumour samples obtained from surgically resected Stage I and II SqCCL patients were included in this study. The Cancer Genome Atlas (TCGA) cohort contained 365 patients with gene expression data generated using RNA sequencing (RNAseq). Five data sets (GSE30219, GSE37745, GSE50081, GSE4573, GSE14814) containing 308 patients profiled using Affymetrix microarrays were obtained from the Gene Expression Omnibus (GEO) database; batch effect mitigation of gene expression data was performed using ComBat. An additional cohort of consecutive Stage I and Stage II SqCLC patients was assembled at the Tom Baker Cancer Centre (TBCC), University of Calgary and gene expression was profiled using RNAseq. We performed a two-stage development of the gene signature by performing penalized elastic net Cox regression analysis in the TCGA training cohort followed by refinement of the gene list in the compiled GEO database patients. Final validation was performed using the in-house TBCC cohort. Progression-free survival (PFS) and overall survival (OS) were the primary and secondary outcomes of interest, respectively.Result
All datasets used in this study were found to consist of patients with comparable clinical characteristics. A gene expression signature associated with PFS was developed in TCGA cohort that significantly stratified patients into high and low risk groups. The signature was refined in the complied GEO database cohort and validated in the U of C cohort. The signature also effectively stratified patients into high and low risk groups based on OS. We are currently performing multivariable analysis of the refined gene signature, adjusting for covariates of known prognostic value.Conclusion
Our signature, if prospectively validated, will guide clinical decision making in SqCCL. Effective risk stratification using our signature may identify Stage I patients that will benefit from adjuvant therapy and stage II patients that could be spared adjuvant treatment following surgical resection.
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P2.04 - Immuno-oncology (ID 167)
- Event: WCLC 2019
- Type: Poster Viewing in the Exhibit Hall
- Track: Immuno-oncology
- Presentations: 1
- Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
P2.04-30 - Seq-ing a Better Way to Detect PD-L1 in NSCLC (ID 2534)
10:15 - 18:15 | Author(s): Olga Kovalchuk
Immunotherapies targeted against PD-L1 and PD-1 have caused a paradigm shift in the treatment of NSCLC, and PD-L1 protein expression has emerged as a standard diagnostic biomarker that predicts which patients are more likely to respond to immunotherapy. However, the use of PD-L1 protein expression as a biomarker is complicated by differences in PD-L1 antibodies, immunohistochemistry methods and platforms, pathologist scoring, and positivity cut-points. We propose that using RNA-sequencing (RNA-seq) methodologies will be an equally reliable approach to determine PD-L1 expression within a tumour and will yield a greater depth of biomarker information than PD-L1 IHC alone.Method
We performed quantitative immunohistochemistry (qIHC) on 262 resected stage I-III formalin-fixed paraffin-embedded (FFPE) NSCLC patient samples registered in the Glans-Look Lung Cancer Research (GLR) database using the commercially available E1L3N PD-L1 antibody (Cell Signalling Technologies). Staining intensity was quantified and used to establish a positivity threshold that was subsequently used to define positivity cut-points of <1%, 1%-49%, and >50% (as used for determining treatment eligibility for Pembrolizumab). We performed single-end RNA-sequencing on FFPE samples from the same GLR patient cohort. Raw counts were normalized to counts-per-million for use in our analyses.Result
We compared the PD-L1 mRNA expression to the PD-L1 protein staining intensity across the tissue core and found a significant correlation (p<0.001, Spearman’s rho=0.538). We also found significant correlation between PD-L1 mRNA expression and the percent-positivity score determined by qIHC (p<0.001, Spearman’s rho=0.605), which was particularly apparent when comparing PD-L1 mRNA expression between cut-point groups where expression was significantly higher in the 1%-49% and >50% groups. Interestingly, we also found moderate, yet significant correlation between PD-1 mRNA expression and both PD-L1 protein staining intensity and percent positivity (p<0.001, Spearman’s rho=0.437 and 0.415 respectively), and we were able to identify several differentially expressed genes between the PD-L1 positive and negative groups.Conclusion
Given the high degree of correlation between PD-L1 mRNA expression and PD-L1 protein staining and positivity, RNA-seq can be a viable option for assessing candidacy for immunotherapy. In addition to the wealth of supplementary data on important biomarkers, RNA-seq offers the possibility for using non-invasive procedures such as liquid biopsy to measure PD-L1 levels in a sequential, objective fashion.