Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors

Abstract

Immune checkpoint inhibitors (ICIs) show prominent clinical activity across multiple advanced tumors. However, less than half of patients respond even after molecule-based selection. Thus, improved biomarkers are required. In this study, we use an image analysis to capture morphologic attributes relating to the spatial interaction and architecture of tumor cells and tumor-infiltrating lymphocytes (TILs) from digitized H&E images. We evaluate the association of image features with progression-free (PFS) and overall survival in non-small cell lung cancer (NSCLC) (N = 187) and gynecological cancer (N = 39) patients treated with ICIs. We demonstrated that the classifier trained with NSCLC alone was associated with PFS in independent NSCLC cohorts and also in gynecological cancer. The classifier was also associated with clinical outcome independent of clinical factors. Moreover, the classifier was associated with PFS even with low PD-L1 expression. These findings suggest that image analysis can be used to predict clinical end points in patients receiving ICI.

Fig. 1.. Consort flow diagram of patient selection.

Patient selection from four different NSCLC cohorts (D_1–4) and a gynecological cancer cohort (D₅).

Fig. 2.. Flowchart of high-level workflow.

Entire workflow comprises (A) tissue preparation and digitization, (B) tile processing from annotation, (C) region and cell segmentation, (D) image analysis and feature extraction, (E) predictive model construction and validation, and (F) cohort overview.

Fig. 3.. Visualization of features relating to TIL–non-TIL interactions.

A high-risk example is found on the left panel. On the right panel is a low-risk sample. (A and B) The whole-slide image (WSI) is depicted with a green-marked line surrounding the tissue that was indicated as sufficient for processing. (C and D) Two small panels indicate the grouping behavior of the lymphocyte and nonlymphocyte cells by means of a two-dimensional plot with both cell groups intertwined, where the numbers in red indicate the main panel it is coming from. (E and F) A zoomed-in region of interest. The groups of cells form distinctive clusters. The clusters are connected through colored lines, which indicate the distance between lymphocytes (green line), nonlymphocytes (orange line), cluster of lymphocytes (cyan line), and cluster of nonlymphocytes (yellow line). Descriptors are calculated that capture general architectural structure and immune-cell interplay. (G and H) Texture-based feature related to the heatmap of the nuclei and surrounding tissue for the low- and high-risk cases. Zernike polynomials and Haralick descriptors are calculated from the shape and texture of the cell.

Fig. 4.. Forest plot for individual cohort.

Survival analysis with OS (A) and PFS (B) between HistoTIL-defined low- and high-risk groups.

Fig. 5.. Survival analysis of PD-L1 expression.

The Kaplan-Meier survival analysis of OS in high–PD-L1 (A) and low–PD-L1 (B) expression group and PFS in high–PD-L1 (C) and low–PD-L1 (D) expression group on D₃.

Fig. 6.. Survival analysis of manual TIL grading and comparing with HistoTIL.

Kaplan-Meier survival analysis of (A) manual TIL grading with OS, (B) manual TIL grading with PFS, and (C) manual TIL grading with OS, three stratified groups. (D) Correlation between manual grading and HistoTIL prediction.

Fig. 7.. Prediction of clinical response by HistoTIL.

Receiver operating characteristic (ROC) analysis for HistoTIL in predicting clinical response by RECIST among five independent cohorts.

Fig. 8.. Survival analysis of NSCLC among ICI agents.

Kaplan-Meier OS assessment among ICI agents (nivolumab, pembrolizumab, and combination therapy) for patients with NSCLC with OS (A to C) and PFS (D to F).