Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non-Small Cell Lung Cancer

Abstract

Purpose: The presence of a high degree of tumor-infiltrating lymphocytes (TIL) has been proven to be associated with outcome in patients with non-small cell lung cancer (NSCLC). However, recent evidence indicates that tissue architecture is also prognostic of disease-specific survival and recurrence. We show a set of descriptors (spatial TIL, SpaTIL) that capture density, and spatial colocalization of TILs and tumor cells across digital images that can predict likelihood of recurrence in early-stage NSCLC.

Experimental design: The association between recurrence in early-stage NSCLC and SpaTIL features was explored on 301 patients across four different cohorts. Cohort D₁ (n = 70) was used to identify the most prognostic SpaTIL features and to train a classifier to predict the likelihood of recurrence. The classifier performance was evaluated in cohorts D₂ (n = 119), D₃ (n = 112), and D₄ (n = 112). Two pathologists graded each sample of D₁ and D₂; intraobserver agreement and association between manual grading and likelihood of recurrence were analyzed.

Results: SpaTIL was associated with likelihood of recurrence in all test sets (log-rank P < 0.02). A multivariate Cox proportional hazards analysis revealed an HR of 3.08 (95% confidence interval, 2.1-4.5, P = 7.3 × 10^-5). In contrast, agreement among expert pathologists using tumor grade was moderate (Kappa = 0.5), and the manual TIL grading was only prognostic for one reader in D₂ (P = 8.0 × 10^-3).

Conclusions: A set of features related to density and spatial architecture of TILs was found to be associated with a likelihood of recurrence of early-stage NSCLC. This information could potentially be used for helping in treatment planning and management of early-stage NSCLC.See related commentary by Peled et al., p. 1449.

Figure 1.

Representative TMA tissue spots of recurrent (top row) and non-recurrent (bottom row) early-stage NSCLC cases. The first column (a, d) shows the original H&E-stained images. Identification of TILs (yellow) and non-TILs (cyan) is presented in the second column (b, e). The third column (c, f) illustrates the qualitative representation of one of the SpaTIL features overlaid on the original images, specifically, the variation in the density of lymphocyte clusters. The color bars represent the density measurement (H stands for highly dense clusters while L stands for low-density or sparse clusters). Non-recurrence cases are characterized by the presence of more high-density clusters while recurrence cases were comprised of a larger number of low-density clusters.

Figure 2.

Prognostic prediction results for human readers, Q_D, and Q_S for D₁. a) Bar plot illustrating the Kappa index and correlation coefficient computed between readers 1 and 2, b) Kaplan-Meier curves for readers 1 and 2, c) ROC curve and corresponding CI for Q_S, d) Kaplan-Meier plot for Q_S classifier using recurrence free survival as endpoint, e) Kaplan-Meier plot for Q_D classifier. The number of cases in each category is indicated in the charts.

Figure 3.

Prognostic prediction results for human readers, Q_D, and Q_S for D₂. a) Bar plot illustrating the Kappa index and correlation coefficient computed between readers 1 and 2, b) Kaplan-Meier curves for readers 1 and 2, c) ROC curve and corresponding CI for Q_S, d) Kaplan-Meier plot for Q_S classifier using recurrence free survival as endpoint, e) Kaplan-Meier plot for Q_D classifier. The number of cases in each category is indicated in the charts.

Figure 4.

Prognostic prediction results for Q_D and Q_S for D₃ (top row) and D₄ (bottom row). First column (a, d) shows the ROC curve and corresponding CI for Q_S, second column (b, e) presents the Kaplan-Meier plots for Q_S classifier using recurrence free survival as endpoint, and third column (c, f) illustrates the Kaplan-Meier plot for Q_D classifier. The number of cases in each category is indicated in the charts.