Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors
Menée à partir d'échantillons tumoraux prélevés sur 187 patients atteints d'un cancer du poumon non à petites cellules et sur 39 patientes atteintes d'un cancer gynécologique, cette étude met en évidence, à l'aide d'une analyse d'images numériques de lames histologiques avec coloration à l'hématoxyline et à l'éosine, l'association entre des caractéristiques tissulaires, liées à l'architecture et aux interactions spatiales des cellules cancéreuses et des lymphocytes intratumoraux, et le bénéfice en termes de survie des inhibiteurs de points de contrôle immunitaires
Immune checkpoint inhibitors (ICIs) show prominent clinical activity across multiple advanced tumors. However, less than half of patients respond even after molecule-based selection. Thus, improved biomarkers are required. In this study, we use an image analysis to capture morphologic attributes relating to the spatial interaction and architecture of tumor cells and tumor-infiltrating lymphocytes (TILs) from digitized H&E images. We evaluate the association of image features with progression-free (PFS) and overall survival in non–small cell lung cancer (NSCLC) (N = 187) and gynecological cancer (N = 39) patients treated with ICIs. We demonstrated that the classifier trained with NSCLC alone was associated with PFS in independent NSCLC cohorts and also in gynecological cancer. The classifier was also associated with clinical outcome independent of clinical factors. Moreover, the classifier was associated with PFS even with low PD-L1 expression. These findings suggest that image analysis can be used to predict clinical end points in patients receiving ICI.
Science Advances , article en libre accès, 2021