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Brady McKee



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    MA15 - Usage of Computer and Molecular Analysis in Treatment Selection and Disease Prognostication (ID 141)

    • Event: WCLC 2019
    • Type: Mini Oral Session
    • Track: Pathology
    • Presentations: 1
    • Now Available
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      MA15.06 - Stage I Lung Adenocarcinoma Gene Expression Associated with Aggressive Histologic Features for Guiding Precision Surgery and Therapy (Now Available) (ID 1124)

      15:45 - 17:15  |  Author(s): Brady McKee

      • Abstract
      • Presentation
      • Slides

      Background

      Stage I lung adenocarcinomas (LUADs) show heterogeneity in histologic patterns which correlate with malignant behavior. Solid, micropapillary and cribriform patterns are associated with worse survival whereas lepidic (in situ) predominance has the best prognosis. In this study, we sought to characterize histologic pattern specific gene expression in resected clinical stage I LUADs. We also aimed to train and validate a genomic biomarker predictive of histologic aggressive patterns with the ultimate goal of being able to impact surgical and therapeutic decision making for post-biopsy management.

      Method

      A training cohort of 56 tumors from patients meeting NCCN high-risk screening criteria with stage I LUAD was included for pathologic annotation and whole exome RNA sequencing. Histologic pattern subtyping in 5% increments including all diagnostic slides was performed. A single representative FFPE block was chosen for RNA library-prep with Illumina TruSeq Access Kit and sequencing. Negative binomial models were used to identify gene expression differences associated with percent solid, cribriform, or micropapillary histology, and EnrichR was used for gene pathway enrichment analysis. Ss-GSEA was used to predict tumor infiltration of 20 immune cell types. A random-forest classifier for predicting aggressive histologic patterns was trained using 5-fold cross validation. A set of tumors from 16 independent patients with ≤2.0 cm clinical stage I LUAD was macro-dissected into 32 paired components (lepidic + non-lepidic regions) and subjected to RNAseq. Six tumors were defined as non-aggressive (lepidic + acinar/papillary) and ten tumors were defined as aggressive (lepidic + solid/micropapillary/cribriform). Four aggressive tumors were upstaged after surgical resection.

      Result

      In the training cohort, we identified 1322 genes associated with tumor histologic composition(FDR q <0.05 and fold-change > 2). Genes whose expression differs with solid histology% are enriched for involvement in DNA replication, cell cycle regulation and inflammation (FDR q<0.001). Genes whose expression is associated with micropapillary% are enriched for involvement in tRNA-aminoacylation and decrease of T-cell activity (FDR q<0.001). The functional enrichment of genes whose expression is associated with cribiform% was less informative. LUADs with micropapillary patterns exhibited gene expression consistent with decreased antigen presentation and low T-cell infiltration, and solid patterns exhibited gene expression consistent with increased infiltration of T-regulatory and Th2 cells (FDR q<0.05).

      A gene expression classifier was trained to predict the presence of aggressive histologic patterns. We validated this classifier on a set of 16 tumor specimens from which we macro-dissected and analyzed tissue from the most aggressive histologic pattern (AUC = 1.00). We also found that this classifier could differentiate lepidic regions isolated from aggressive tumors from lepidic regions isolated from non-aggressive tumors (AUC = 0.74).

      Conclusion

      We identified solid-, micropapillary- and cribriform-specific gene expression and associated immune response among clinical stage I LUADs, and developed a classifier predictive of aggressive histologic features using either lepidic (in situ) or non-lepidic components. As such, this biomarker has the potential to predict histologic aggressiveness even from pre-surgical tumor biopsies where all histologic patterns may not be represented. Such a biomarker may be useful in guiding clinical decision making including extent of surgical resection.

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