Immune checkpoint inhibitors (ICI), such as anti-PD-1 agents, have become part of the standard of care treatment of advanced non-small cell lung cancer (NSCLC). Predictive biomarkers are needed to identify patients that benefit from anti-PD-1 treatments. Tumor infiltrating lymphocytes (TILs) and PD-L1 are major players in the ICI mechanism of action. In this study, we assess the impact of real-world clinicopathological variables, including TILs and PD-L1, on anti-PD-1 efficacy. Methods: We performed a monocenter retrospective study in advanced NSCLC treated with nivolumab or pembrolizumab between January 2015 and February 2019. The impact of baseline clinical and pathological variables was assessed by univariate and multivariate models. TILs, defined as CD8+T-cells, and PD-L1 were scored in tumor and stroma, and correlated with progression free survival (PFS) and overall survival (OS). Results: We included 366 patients of whom 141 were assessed for tumor and stromal TILs. The median follow-up time was 487 days. In the whole cohort, PFS was associated with high tumor PD-L1, high albumin and good performance. OS was associated with low LDH, high albumin, good performance and 'first-line treatment'. In the TILs subcohort, stromal TILs had the strongest impact on PFS and OS. Stromal TILs were a stronger marker for PFS and OS than tumoral TILs, tumoral PD-L1 or stromal PD-L1. Remaining factors for PFS and OS were albumin and albumin with LDH, respectively. Conclusions: This real-world study on clinicopathological features shows that stromal CD8 + TILs were the strongest predictor for PFS and OS in patients with advanced NSCLC on anti-PD-1 therapy. Other predictors for PFS and OS included albumin and albumin together with LDH, respectively. This study highlights the pivotal role of the stromal compartment in the mechanisms of action of ICI, and the need for further studies aiming to overcome this stromal firewall.
ImportanceCurrently, 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.ObjectiveTo 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 ParticipantsThis 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.ExposuresAll patients received anti–PD-(L)1 monotherapy.Main Outcomes and MeasuresObjective 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.ResultsOverall, 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 RelevanceIn 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.
Background: Tumor size and metastatic extent may influence tumor response to immunotherapy in non-small cell lung cancer (NSCLC). The aim of this study was to examine the relationship between both baseline sum of longest diameters (bSLD) and number of metastatic organs (NMO) and the tumor response to pembrolizumab. Secondly, we aimed to analyze the association of baseline SLD and NMO with progression-free survival (PFS) and overall survival (OS). Methods: This retrospective study included patients with high PD-L1 expressing tumors (≥50%) and a good performance score (ECOG ≤ 2) that received first-line pembrolizumab monotherapy. Tumor response was calculated as the 'SLD-change score' and 'early treatment discontinuation' within 3 months on therapy (ETD). The relationship of both bSLD (based on RECIST v1.1) and NMO with tumor response and survival outcome (PFS, OS) was evaluated. Results: No significant differences in SLD-change score could be found using bSLD (OR = 1.010, 95%CI = 0.999-1.021), or using NMO at baseline (OR = 1.608, 95%CI = 0.943-2.743). A bSLD cut-off value of 90 mm was found to be most distinctive for ETD. This cut-off value showed a significant difference for PFS (HR = 2.28, 95%CI = 1.12-4.64, p = 0.023) and OS (HR = 2.99, 95%CI = 1.41-6.34, p = 0.004). NMO also showed a difference for PFS and OS, however, not statistically significant. Conclusions: Tumor size and metastatic extent could not discriminate for tumor response, however, a bSLD of 90 mm could differentiate for PFS and OS.
9065 Background: The presence of Tertiary Lymphoid Structures (TLS) in multiple cancer types has been recognized as a potential predictive biomarker for response to immune-checkpoint blockade. However, there is no standardized method to quantify their presence. In this context, Artificial Intelligence (AI)-based assessment of histology images may well contribute to improve reproducibility, accuracy and speed of TLS quantification. Methods: We developed an automated workflow for quantification of TLS on digitized H&E slides through A) pixel-level classification of tissue using supervised artificial neural networks model, B) object-level cell classification of candidate TLS regions, C) merging the two approaches for curation and validation of TLS versus non-TLS regions. 433 advanced stage non-small cell lung cancer (NSCLC) patients treated with first or subsequent line of anti-PD-(L)1 single agent at DFCI were included in this study. Results: TLS were detected in 37% (n = 161) of the patients H&E slides, with the highest score of 4.7 TLS per mm2 (interquartile range: Q1 = 0, Q2 = 0, Q3 = 0.03 TLS/mm2). TLS density (per mm2) was significantly higher in surgically resected (n = 246; TLSPOS= 49%) compared to bioptic samples (n = 187; TLSPOS= 21%). No association was observed between TLS and tumor mutational burden (TMB) or PD-L1 protein expression as continuous variables. Among clinically actionable mutations, EGFR (all subtypes) mutated patients (n = 38) had a significantly lower number of TLS compared to patients without EGFR mutations. Patients with ≥ 0.01 TLS/mm2 had a significantly higher objective response rate (32% vs 22%, p = 0.03), a significantly longer median progression-free survival (PFS, 4.8 vs 2.7 months, HR: 0.73, 95% CI: 0.59-0.90, p = 0.004), and a significantly improved median overall survival (OS, 16.5 vs 12.5 months, HR: 0.72, 95% CI: 0.57-0.92, p = 0.008). In multivariable analysis, after adjusting for PD-L1 (≥ vs < 50%), TMB (≥ vs < 10 mu/Mb), sex, age, ECOG score, smoking and line of treatment, TLS/mm2 (≥ vs < 0.01) levels were found to be an independent positive predictive factor for both PFS (HR:0.69, 95% CI: 0.54-0.88, p = 0.003) and OS (HR: 0.70, 95% CI: 0.52-0.93, p = 0.01). Conclusions: These findings suggest that TLS status is an independent predictor of immunotherapy effectiveness in NSCLC, with predictive value similar to that of PD-L1 expression and TMB. This novel AI system has potential for automated identification and quantification of the TLS on digital histopathological slides, and could be utilized in a standard pathology workflow with relative ease. These findings are currently being validated in other solid tumors and cohorts.
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