Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors
Year: 2022
Type: article
Study design: Observational Study
Abstract: 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.
Source: Science Advances
Institutions: Case Western Reserve University, Weill Cornell Medicine, University of Pennsylvania, University of Colorado Anschutz, Yale University, Magee-Womens Research Institute, Magee-Womens Hospital, University of Pittsburgh Medical Center, NYU Langone Health, New York University, Louis Stokes Cleveland VA Medical Center
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $4,500
Awards: G1044130170, RadxTools for assessing tumor treatment response on imaging, G1266336578, G1478154455, G1505200503, G1596146291, Lung cancer in East Africa and the relationship to HIV-1 infection: epidemiology, molecular characterization and imaging, Pathology Image Informatics Platform for visualization, analysis and management, G2228475719, G2244966566, G2931214990, G338730684, G3564444776, HistoTools: A suite of digital pathology tools for quality control, annotation and dataset identification, G86714149, Clinical and Translational Science Collaborative of Cleveland, Novel Radiomics for Predicting Response to Immunotherapy for Lung Cancer, Yale Clinical and Translational Science Award
OpenAlex ID: W4281767664