Author of

• 25953

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  P2.16 - Treatment of Early Stage/Localized Disease (Not CME Accredited Session) (ID 965)

  • Event: WCLC 2018
  • Type: Poster Viewing in the Exhibit Hall
  • Track:
  • Presentations: 1
  • Moderators:
  • Coordinates: 9/25/2018, 16:45 - 18:00, Exhibit Hall

  • 27261

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    P2.16-06 - Development and Validation of a Gene Expression-Based Nomogram to Predict Relapse in Stage I NSCLC: A Retrospective, Multi-Cohort Study (ID 13267)

    16:45 - 18:00  |  Presenting Author(s): Qixing Mao
    • Abstract

    Background
    

    Increasing patients were diagnosed with stage I non-small cell lung cancer (NSCLC), and in 30% of diagnosed patients, recurrence will develop within 5 years. Optimal postoperative adjuvant therapy is still ambiguous for stage I NSCLC. Here, we aimed to develop and validate a feasible tool for recurrent risk assessment of stage I NSCLC.

    a9ded1e5ce5d75814730bb4caaf49419 Method
    

    This retrospective study incorporated the gene expression profiles from 14 public NSCLC cohorts, including 13 microarray data sets and 1 RNA-Seq data set for The Cancer Genome Atlas (TCGA) lung adenocarcinoma cohort. In discovery phase, multiple eligible microarray data sets were used to select statistically significant genes thought to be predictive by two algorithms, Least Absolute Shrinkage and Selector Operation and Support Vector Machine-Recursive Feature Elimination. In the training phase, candidate genes were used to generate a recurrence related signature in TCGA cohort by penalized Cox regression. Recurrence related signature and other clinical variables were selected to develop a nomogram by back stepwise variable selection. In validation phase, the performance of nomogram was further tested via two independent cohorts.

    4c3880bb027f159e801041b1021e88e8 Result
    

    In this retrospective study, 14 eligible datasets and 7 published signatures were included. In discovery phase, 42 significant genes were highlighted as candidate predictors by two algorithms. A 13-gene based signature related to recurrence were generated by penalized cox regression and categorized training cohort into high-risk and low risk subgroups with significantly different recurrence free survival (HR = 8.873, 95% CI:4.228-18.480 P<0.001). The performance of signature was tested in two external cohorts (HR=3.556, 95%CI:1.587-7.996, P=0.001; HR=2.586, 95%CI:1.226-5.286, P=0.007). Furthermore, a nomogram integrating recurrence related signature, age, and histology was developed to predict the recurrence-free survival in the training cohort, and it performed well in the two external validation cohorts (concordance index: 0.737, 95%CI:0.732-0.742, P<0.001; 0.666, 95%CI: 0.650-0.682, P<0.001; 0.651, 95%CI:0.637-0.665, P<0.001 respectively).

    8eea62084ca7e541d918e823422bd82e Conclusion
    

    The proposed nomogram is a promising tool for estimating recurrence free survival in stage I NSCLC, which might have tremendous value in guiding adjuvant therapy. Prospective studies are needed to test the clinical utility of the nomogram in individualized management of stage I NSCLC.

    6f8b794f3246b0c1e1780bb4d4d5dc53

• 25957

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  P3.03 - Biology (Not CME Accredited Session) (ID 969)

  • Event: WCLC 2018
  • Type: Poster Viewing in the Exhibit Hall
  • Track:
  • Presentations: 1
  • Moderators:
  • Coordinates: 9/26/2018, 12:00 - 13:30, Exhibit Hall

  • 27478

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    P3.03-03 - Differential Microbiota Features in Lung Tumor and Adjacent Normal Tissues in Lung Cancer Patients (ID 13547)

    12:00 - 13:30  |  Presenting Author(s): Qixing Mao
    • Abstract

    Background
    

    Emerging evidence has demonstrated the link between the host microbiota and varied malignancies, including colorectal, gastric, hepatocellular, and pancreatic cancers. However, for lung cancer, one of the leading cause of the cancer-related morbidity and mortality worldwide, the interplay between the lung cancer and the lung microbiome has yet not been well investigated. In this study, we surveyed and compared the microbiota composition and diversity in paired tumor and adjacent normal tissues to test whether any tumor specific microbial features can be identified in tumor tissues.

    a9ded1e5ce5d75814730bb4caaf49419 Method
    

    We surveyed the microbiota composition of 55 lung tumor and 55 paired adjacent normal tissue samples using bacterial 16S sequencing protocol. The microbial diversity was further analyzed using QIIME pipeline. The differential taxa feature by tumor status was selected using LefSe method.

    4c3880bb027f159e801041b1021e88e8 Result
    

    We observed diversified microbiota in lung tissue samples. The dominant phyla include Proteobacteria, Firmicutes, Bacteroidetes and Actinobacteria. The overall microbiota similarity in paired tumor and adjacent tissues was significantly higher than unpaired (p<0.01). Compared to adjacent normal tissues, the lung tumors showed significantly lower alpha diversity (p=0.05) but no difference in beta diversity (PERMANOVA test, p-value=0.27). At taxa level, tumor samples showed increased Modestobacter and decreased Propionibacterium, and Enterobacteriaceae.

    8eea62084ca7e541d918e823422bd82e Conclusion
    

    In conclusion, our study demonstrated and compared the lung microbiota diversity and composition in paired tumor and adjacent normal tissues. Compared to non-tumor tissues, the reduction of potential pro-inflammatory microbial families/genera in tumor tissues suggest the possible link between the tumor microbiota to the immunosuppression tumor microenvironment.

    6f8b794f3246b0c1e1780bb4d4d5dc53