Association of Machine Learning-Based Assessment of Tumor-Infiltrating Lymphocytes on Standard Histologic Images With Outcomes of Immunotherapy in Patients With NSCLC

Abstract

Importance: Currently, predictive biomarkers for response to immune checkpoint inhibitor (ICI) therapy in lung cancer are limited. Identifying such biomarkers would be useful to refine patient selection and guide precision therapy.

Objective: To develop a machine-learning (ML)-based tumor-infiltrating lymphocytes (TILs) scoring approach, and to evaluate TIL association with clinical outcomes in patients with advanced non-small cell lung cancer (NSCLC).

Design, setting, and participants: This multicenter retrospective discovery-validation cohort study included 685 ICI-treated patients with NSCLC with median follow-up of 38.1 and 43.3 months for the discovery (n = 446) and validation (n = 239) cohorts, respectively. Patients were treated between February 2014 and September 2021. We developed an ML automated method to count tumor, stroma, and TIL cells in whole-slide hematoxylin-eosin-stained images of NSCLC tumors. Tumor mutational burden (TMB) and programmed death ligand-1 (PD-L1) expression were assessed separately, and clinical response to ICI therapy was determined by medical record review. Data analysis was performed from June 2021 to April 2022.

Exposures: All patients received anti-PD-(L)1 monotherapy.

Main outcomes and measures: Objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) were determined by blinded medical record review. The area under curve (AUC) of TIL levels, TMB, and PD-L1 in predicting ICI response were calculated using ORR.

Results: Overall, there were 248 (56%) women in the discovery cohort and 97 (41%) in the validation cohort. In a multivariable analysis, high TIL level (≥250 cells/mm2) was independently associated with ICI response in both the discovery (PFS: HR, 0.71; P = .006; OS: HR, 0.74; P = .03) and validation (PFS: HR = 0.80; P = .01; OS: HR = 0.75; P = .001) cohorts. Survival benefit was seen in both first- and subsequent-line ICI treatments in patients with NSCLC. In the discovery cohort, the combined models of TILs/PD-L1 or TMB/PD-L1 had additional specificity in differentiating ICI responders compared with PD-L1 alone. In the PD-L1 negative (<1%) subgroup, TIL levels had superior classification accuracy for ICI response (AUC = 0.77) compared with TMB (AUC = 0.65).

Conclusions and relevance: In these cohorts, TIL levels were robustly and independently associated with response to ICI treatment. Patient TIL assessment is relatively easily incorporated into the workflow of pathology laboratories at minimal additional cost, and may enhance precision therapy.

Figure 1.. Tumor-Infiltrating Lymphocytes (TILs) Distribution and Clinical Outcome

A, Distribution of TIL levels (count per mm²) across different tissue sample sites (Kruskal-Wallis test, P < .001) in both discovery and validation cohorts. B, TIL levels according to PD-L1 expression (P = .01, Kruskal-Wallis test) in the entire cohort. C, Progression-free survival (PFS) following treatment with immune checkpoint inhibitors for patients with <250 vs ≥250 TILs per mm² in the discovery (C) and (D) validation cohorts. HR indicates hazard ratio; mPFS, median PFS in months; ORR, objective response rate; PD-L1, programmed death ligand-1; TPS, tumor proportion score. ^aORR was available for only 101 out of 239 patients in the validation cohort.

Figure 2.. Forest Plots for Progression-Free Survival (PFS)

Forest plot of hazard ratio (HR) and 95% CI for PFS according to covariates in the (A) discovery (n= 446) and (B) validation (n=239) cohorts. In addition to TIL density (<250 vs ≥250 cells/mm2), clinicopathologic variables with P < .25 from univariate analyses were included. ECOG, Eastern Cooperative Oncology Group performance status; ICI, immune checkpoint inhibitors; NA, not assessed; NE, not entered; PD-L1, programmed death ligand-1; TMB, tumor mutational burden; TPS, tumor proportion score.

Figure 3.. Combined Models

A, Power of individual biomarkers (programmed death ligand-1 [PD-L1], tumor mutational burden [TMB], tumor-infiltrating lymphocytes [TILs]) or pairs with PD-L1 to predict immune checkpoint inhibitor response rate computing area under the receiver operating characteristic curve. B, Progression-free survival according to the combination of PD-L1 (<50 vs ≥50% ) expression and hematoxylin-eosin TIL levels (<250 vs ≥250 cells/mm²) scores. C, Progression-free survival according to the combination of PD-L1 (<50 vs ≥50%) expression and TMB (<10 vs ≥10 mu/Mb). mPFS indicates median PFS in months; ORR, objective response rate.

Figure 4.. Tumor-Infiltrating Lymphocytes (TILs) and Tumor Mutational Burden (TMB) in Programmed Death Ligand-1 (PD-L1)-Negative Subgroup

A, Model performance of TIL vs TMB to predict ICI response in the PD-L1–negative (<1%) subgroup (n = 50) of the DFCI cohort. B, Progression-free survival (PFS) to ICIs for TILs/mm². C, TMB/Mb in PD-L1 negative subgroup of the DFCI cohort. D, PFS to ICIs for TILs/mm² in the PD-L1–negative subgroup (n = 66) of the validation cohort. AUC indicates area under the curve; DFCI, Dana-Farber Cancer Institute; HR hazard ratio; ICI, immune checkpoint inhibitors; mPFS, median PFS in months; ORR, objective response rate.