A digitised tumour-infiltrating lymphocyte scoring in non-small cell lung cancer helps to predict response to immune checkpoint inhibitors
Tumour infiltrating lymphocyte (TIL) scoring based on a machine-learning model has superior classification accuracy for an immune checkpoint inhibitor (ICI) response in patients with advanced non-small cell lung cancer (NSCLC) according to a retrospective analysis by an international research group.
Immunotherapy with immune-checkpoint inhibitors (ICI) has revolutionised the field of oncology for many patients. Nevertheless, not all patients with non-small cell lung cancer benefit from these agents with studies suggesting that in advanced disease, at 1 year, the overall survival rate with, for example, nivolumab, was only 51%. A more favourable response to ICI therapy occurs in those with high programmed cell death ligand-1 expression and a high tumour mutation burden (TMB). A further prognostic factor associated with an improved prognosis in NSCLC patients is high tumour-infiltrating lymphocyte (TIL) levels and which are visually assessed on routine haematoxylin and eosin-stained slides. However, with the increasing use of machine-based learning algorithms in healthcare, some preliminary data highlights the potential for such assessment of haematoxylin and eosin-stained slide sections.
Given the prognostic value of TIL levels, for the present study, researchers developed a machine-learning TIL scoring model to evaluate its association with clinical outcomes in patients with advanced NSCLC. The researchers undertook a retrospective analysis of patient cohorts prescribed PD-(L)1 inhibitors initially for a discovery cohort within a French hospital, followed by an independent validation cohort from hospitals in the UK and the Netherlands. The machine learning model counted tumour, stroma and tumour infiltrating lymphocyte cells whereas values for TMB and PD-L1 expression were determined separately.
Tumour infiltrating lymphocyte cells and ICI response
A total of 685 patients with advanced-stage NSCLCL treated with first or second-line ICI monotherapy were included within the two independent cohorts. The median age in both groups was 66.
Among patients in the discovery cohort, those with a higher TIL cell count had a significantly longer median progression-free survival (Hazard ratio, HR = 0.74, 95% CI 0.61 – 0.90, p = 0.003) and a significantly longer overall survival (HR = 0.76, p = 0.02). Moreover, similar findings of an association between higher tumour infiltrating lymphocyte cell count and both progression-free and overall survival were also observed in the validation cohort.
When using PD-L1 levels as a biomarker, the area under the curve (AUC) was 0.68 and for tumour infiltrating lymphocyte cell levels, only 0.55 and 0.59 for TMB. But when combined, both the PD-L1/TIL and TMB/PD-L1 had higher AUC values (0.68 and 0.70 respectively).
The authors concluded that TIL levels were robustly and independently associated with the response to ICI treatment and could be easily incorporated into the workflow of pathology laboratories at minimal additional cost and might even enhance precision therapy.
Rakaee M et al. Association of Machine Learning-Based Assessment of Tumor-Infiltrating Lymphocytes on Standard Histologic Images With Outcomes of Immunotherapy in Patients With NSCLC. JAMA Oncol 2022