DataSheet_1_Artificial Intelligence-Assisted Score Analysis for Predicting the Expression of the Immunotherapy Biomarker PD-L1 in Lung Cancer.docx

Programmed cell death ligand 1 (PD-L1) is a critical biomarker for predicting the response to immunotherapy. However, traditional quantitative evaluation of PD-L1 expression using immunohistochemistry staining remains challenging for pathologists. Here we developed a deep learning (DL)-based artificial intelligence (AI) model to automatically analyze the immunohistochemical expression of PD-L1 in lung cancer patients. A total of 1,288 patients with lung cancer were included in the study. The diagnostic ability of three different AI models (M1, M2, and M3) was assessed in both PD-L1 (22C3) and PD-L1 (SP263) assays. M2 and M3 showed improved performance in the evaluation of PD-L1 expression in the PD-L1 (22C3) assay, especially at 1% cutoff. Highly accurate performance in the PD-L1 (SP263) was also achieved, with accuracy and specificity of 96.4 and 96.8% in both M2 and M3, respectively. Moreover, the diagnostic results of these three AI-assisted models were highly consistent with those from the pathologist. Similar performances of M1, M2, and M3 in the 22C3 dataset were also obtained in lung adenocarcinoma and lung squamous cell carcinoma in both sampling methods. In conclusion, these results suggest that AI-assisted diagnostic models in PD-L1 expression are a promising tool for improving the efficiency of clinical pathologists.</p

Additional file 3 of Open chromatin interaction maps reveal functional regulatory elements and chromatin architecture variations during wheat evolution

Additional file 3: Table S4 The intrachromosomal open chromatin loops

Additional file 2 of Open chromatin interaction maps reveal functional regulatory elements and chromatin architecture variations during wheat evolution

Additional file 2: Supplementary Figure S1-S6

Additional file 5 of Open chromatin interaction maps reveal functional regulatory elements and chromatin architecture variations during wheat evolution

Additional file 5: Table S6 The interchromosomal open chromatin loops

Additional file 6 of Open chromatin interaction maps reveal functional regulatory elements and chromatin architecture variations during wheat evolution

Additional file 6: Table S8 The differentially interacted intrachromosomal loops between bread wheat and T. durum

Additional file 4 of Open chromatin interaction maps reveal functional regulatory elements and chromatin architecture variations during wheat evolution

Additional file 4: Table S5 The triads with chromatin interaction bias

Additional file 8 of Open chromatin interaction maps reveal functional regulatory elements and chromatin architecture variations during wheat evolution

Additional file 8. Review history

Additional file 7 of Open chromatin interaction maps reveal functional regulatory elements and chromatin architecture variations during wheat evolution

Additional file 7: Table S9 The differentially interacted intrachromosomal loops between bread wheat and Ae. tauschii

Additional file 1 of Open chromatin interaction maps reveal functional regulatory elements and chromatin architecture variations during wheat evolution

Additional file 1: Supplementary tables S1-S3 and S7