Infiltrating lymphocyte score of digital tumors predicts benefit of ICI in lung cancer

A scanned tumor-infiltrating lymphocyte score in non-small cell lung cancer helps predict response to immune checkpoint inhibitors

Tumor 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 with a retrospective analysis of an international research group.

Immunotherapy with immune checkpoint inhibitors (ICI) has revolutionized the field of oncology for many patients. However, not all NSCLC patients benefit from these agents, with studies suggesting that in advanced disease, the overall 1-year survival rate with, for example, nivolumab was only 51%. A more favorable response to ICI therapy occurs in those with high expression of programmed cell death ligand 1 and high tumor mutation burden (TMB). Another prognostic factor associated with an improved prognosis in patients with NSCLC is elevated levels of tumor-infiltrating lymphocytes (TIL), which are visually assessed on slides stained with routine hematoxylin and eosin. However, with the increasing use of machine learning algorithms in healthcare, some preliminary data highlight the potential for this evaluation of slide sections stained with hematoxylin and eosin.

Given the prognostic value of TIL levels, for the present study, researchers developed a machine learning TIL scoring model to assess its association with clinical outcomes in patients with advanced NSCLC. The researchers performed a retrospective analysis of cohorts of patients prescribed PD-(L)1 inhibitors initially for a discovery cohort in a French hospital, followed by an independent validation cohort of hospitals in the UK and the Netherlands. The machine learning model counted tumor cells, stromal lymphocytes and tumor infiltration, while values ​​for TMB and PD-L1 expression were determined separately.

Tumor infiltrating lymphocyte cells and the ICI response

A total of 685 patients with advanced-stage NSCLCL treated with monotherapy with either first-line or second-line ICI were enrolled in the two independent cohorts. The median age in both groups was 66 years.

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 significantly longer overall survival (HR = 0.76, p = 0.02). Furthermore, similar findings of an association between higher tumor-infiltrating lymphocyte counts and progression-free survival 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 tumor infiltrating lymphocyte cell levels, only 0.55 and 0.59 for TMB. But when combined, both 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 response to ICI treatment and can be easily incorporated into the workflow of pathology laboratories at minimal additional cost and may even improve precision therapy.

Rakaee M et al. Association of machine learning-based assessment of tumor-infiltrating lymphocytes on standard histological images with immunotherapy outcomes in patients with NSCLC. JAMA Oncol 2022

Infiltrating lymphocyte score of digital tumors predicts benefit of ICI in lung cancer

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