Artificial intelligence-guided analysis of the tumor microenvironment predicts response to pembrolizumab in rare...

Pathologic tumor response and changes in the tumor microenvironment (TME) predict outcomes to immune checkpoint inhibitors, but are understudied in rare tumors. We investigated whether artificial intelligence (AI)-powered analyses of pretreatment and on-treatment biopsies may inform treatment outcomes to pembrolizumab.

We evaluated 256 baseline and 248 on-treatment biopsies from 84 patients with rare tumors (10 cohorts) in a phase II pembrolizumab trial. Intratumoral tumor-infiltrating lymphocyte (iTIL) density and tumor content (TC) were assessed on H&E-stained slides using a deep learning-based analyzer (Lunit SCOPE IO). Baseline iTIL and changes in iTIL and TC were correlated with progression-free survival (PFS) and overall survival (OS). Multiplex immunofluorescence was performed in 27 paired samples to assess TME changes.

In the high-iTIL tumor group, a baseline iTIL of ≥60 cells/mm² was associated with favorable PFS (HR 0.49, 95% CI 0.25 to 0.99, p=0.046) and higher CD8⁺ and CD8⁺PD-1⁺ and lower FoxP3⁺CD8⁺PD-1⁺ T-cell density. However, this association with PFS was not seen in the overall cohort (HR 0.62, 95% CI 0.37 to 1.06, p=0.082). In paired biopsies, on-treatment increase in iTIL showed a trend toward improved PFS (HR 0.64, 95% CI 0.40 to 1.06, p=0.084) and was significantly associated with improved OS (HR 0.55, 95% CI 0.35 to 1.01, p=0.037). This increase was also associated with reduced spatial distance between CD8⁺ immune and tumor cells. Decreased TC during treatment was significantly associated with prolonged PFS and OS (PFS: HR 0.51, p=0.019; OS: HR 0.54, p=0.042). The combination of increased iTIL and decreased TC was significantly associated with better PFS (HR 0.36, p=0.009) and OS (HR 0.36, p=0.029).

AI-powered assessment of the TME before and during treatment may help inform treatment outcomes to pembrolizumab in patients with rare tumors.

NCT02721732.