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Igor Kovalchuk



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    MA04 - Models and Biomarkers (ID 122)

    • Event: WCLC 2019
    • Type: Mini Oral Session
    • Track: Biology
    • Presentations: 1
    • Now Available
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      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): Igor Kovalchuk

      • Abstract
      • Presentation
      • Slides

      Background

      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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