Virtual Library

Start Your Search

S. Jiang

Author of

  • +

    MINI 02 - Immunotherapy (ID 92)

    • Event: WCLC 2015
    • Type: Mini Oral
    • Track: Biology, Pathology, and Molecular Testing
    • Presentations: 1
    • +

      MINI02.11 - Immunological Markers Predict the Prognosis of Patients with Squamous Non-Small Cell Lung Cancer (ID 605)

      10:45 - 12:15  |  Author(s): S. Jiang

      • Abstract
      • Presentation
      • Slides

      As one of the novel therapy strategies, PD-L1 has been shown the function of down-regulating T-cell activation through receptor PD-1. Moreover, prognosis of cancer patients are based not only on tumor-related factors but also on host-related factors, particularly systemic inflammatory response. As significant indicators of patients’ inflammation status, circulating monocyte count, neutrophil ratio and lymphocyte ratio were proved as predictors of prognosis in various cancers. Squamous non-small cell lung cancer (NSCLC) revealed to be divergent clinical and molecular phenotypes compared with non-squamous NSCLC. Significantly, combining application of appropriate biomarkers in prognosis prediction is emerging its high importance in cancer research.

      Chart review was performed on 1286 consecutive patients, 156 of these patients were enrolled in the final analysis. Patients with squamous NCSLC were randomly assigned (2:1) centrally by computer into training group and validation group. Monocyte ratio, Neutrophils to Lymphocytes Ratio, PD-L1 immunostaining score and PD-1-positive stained tumor-infiltrating lymphocytes counts were assessed by Fisher’s linear discriminant analysis to discriminate if OS would exceeding 5 years. The final model was used to calculate the discriminant score in each study participant. And this prediction model was validated in a second set of squamous NCSLC patients. We internally validated the model using a cross-validation procedure.

      4 independent predictors of OS were identified by using FLDA with stepwise variant-selection. The clinical classifying model was described by the following equation: Y = −1.212 + 0.211 × NLR ratio + 0.437 × monocyte ratio - 0.390 × PD-L1 + 0.035 × PD-1 (eigenvalue 0.673, canonical correlation 0.634, P < 0.001). In this equation, PD-L1 represented PD-L1 immunostaining score; and PD-1 represented PD-1 positive TILs counts. For the training set of 104 leave-one-out-cross-validated cases, 27 of 29 OS > 5 years (93.1% sensitivity) and 61 of 75 OS <= 5 years (81.3% specificity) were correctly classified with an overall accuracy of 84.6% (88 of 104) and an AUC of 0.938 [P < 0.001, 95% confidence interval (CI) 0.864–1] Next, the predicting model consisting of the 4 predictors (NLR ratio, monocyte ratio, PD-L1 and PD-1) were applied to the validation set of 52 patients (14 OS > 5 years and 38 OS <= 5 years). A survival prediction for 38 of the 52 patients (73.1%) with an AUC of 0.908 (P < 0.001, 95% CI 0.806–1) was achieved. Also, 12 of 14 OS > 5 years (85.7% sensitivity) and 26 of 38 OS <= 5 years (68.4% specificity) were correctly identified.

      The analysis of a set of immunological markers could effectively and reproducibly classify patients with squamous NCSLC according to their overall survival. Further prospective validation in larger independent cohorts of patients with similar or different regimens is warranted to fully assess its predictive power. The 4-immunological-marker model offers a novel tool for survival prediction and could have important clinical implications for the consideration of differential treatment strategies in patients with squamous NCSLC, thus providing a framework for future individualized therapy.

      Only Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login, select "Add to Cart" and proceed to checkout. If you would like to become a member of IASLC, please click here.

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.