2022
DOI: 10.1126/sciadv.abn3966
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Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors

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… Show more

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Cited by 51 publications
(26 citation statements)
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“…Digitalization of image data promises to accelerate our understanding of complex biological systems [9,[34][35][36][37]. However, each imaging technique presents unique challenges in the quantification of image data.…”
Section: Spatial Analysis Of Lymph Node Cellsmentioning
confidence: 99%
“…Digitalization of image data promises to accelerate our understanding of complex biological systems [9,[34][35][36][37]. However, each imaging technique presents unique challenges in the quantification of image data.…”
Section: Spatial Analysis Of Lymph Node Cellsmentioning
confidence: 99%
“…Previous work has focused on developing prognostic models from routine clinical data, standard of care features, histopathological assessment, molecular profiling, and more recently, morphological features acquired via DL [35][36][37][38][39] . There have also been attempts to use ML approaches and image-based features to predict clinical endpoints in different cancers, such as melanoma and non-small cell lung cancer 40,41 . In our study, we explored the end-to-end predictability of prognostic outcomes directly from histology across multiple cancer types by treating the clinical outcome endpoints such as overall survival (OS), disease-specific-survival (DSS), disease-free-interval (DFI), and progression-free interval (PFI) as potential prognostic biomarkers 42 .…”
Section: Feasibility Of Inferring Clinical Outcomes and Treatment Res...mentioning
confidence: 99%
“…A recent computational image analysis of H&E slides of non-small cell lung and gynecologic cancers has shown that the spatial architecture and the interaction of cancer cells and tumorinfiltrating lymphocytes can predict clinical benefit in patients receiving immune checkpoint inhibitors. Importantly, the computational image classifier was associated with clinical outcome independent of clinical factors and PD-L1 expression levels (69). In addition to identifying new biomarkers by highlighting specific structures and regions, computational tissue imaging could assist with the identification of new therapeutic targets.…”
Section: Biomarker Discoverymentioning
confidence: 99%