Search

BackgroundNeuroblastoma (NB) places a substantial health burden on families worldwide. This study aimed to develop an immune checkpoint-based signature (ICS) based on the expression of immune checkpoints to better assess patient survival risk and potentially guide patient selection for immunotherapy of NB.MethodsImmunohistochemistry integrated with digital pathology was used to determine the expression levels of 9 immune checkpoints in 212 tumor tissues used as the discovery set. The GSE85047 dataset (n=272) was used as a validation set in this study. In the discovery set, the ICS was constructed using a random forest algorithm and confirmed in the validation set to predict overall survival (OS) and event-free survival (EFS). Kaplan-Meier curves with a log-rank test were drawn to compare the survival differences. A receiver operating characteristic (ROC) curve was applied to calculate the area under the curve (AUC).ResultsSeven immune checkpoints, including PD-L1, B7-H3, IDO1, VISTA, T-cell immunoglobulin and mucin domain containing-3 (TIM-3), inducible costimulatory molecule (ICOS) and costimulatory molecule 40 (OX40), were identified as abnormally expressed in NB in the discovery set. OX40, B7-H3, ICOS and TIM-3 were eventually selected for the ICS model in the discovery set, and 89 patients with high risk had an inferior OS (HR 15.91, 95% CI 8.87 to 28.55, pConclusionsWe propose an ICS that significantly differentiates between low-risk and high-risk patients, which might add prognostic value to age and provide clues for immunotherapy in NB.

BackgroundInfiltrating immune cells have been reported as prognostic markers in many cancer types. We aimed to evaluate the prognostic role of tumor-infiltrating lymphocytes, namely CD3+ T cells, CD8+ cytotoxic T cells and memory T cells (CD45RO+), in neuroblastoma.Patients and MethodsImmunohistochemistry was used to determine the expression of CD3, CD8 and CD45RO in the tumor samples of 244 neuroblastoma patients. We then used digital pathology to calculate the densities of these markers and derived an immunoscore based on such densities.ResultsDensities of CD3+ and CD8+ T cells in tumor were positively associated with the overall survival (OS) and event-free survival (EFS), whereas density of CD45RO+ T cells in tumor was negatively associated with OS but not EFS. An immunoscore with low density of CD3 and CD8 (CD3-CD8-) was indictive of a greater risk of death (hazard ratio 6.39, 95% confidence interval 3.09-13.20) and any event (i.e., relapse at any site, progressive disease, second malignancy, or death) (hazard ratio 4.65, 95% confidence interval 2.73-7.93). Multivariable analysis revealed that the CD3-CD8- immunoscore was an independent prognostic indicator for OS, even after adjusting for other known prognostic indicators.ConclusionsThe new immunoscore based on digital pathology evaluated densities of tumor-infiltrating CD3+ and CD8+ T cells contributes to the prediction of prognosis in neuroblastoma patients.

We aimed to develop a deep learning (DL) model to predict DNA mismatch repair (MMR) status in colorectal cancers (CRC) based on hematoxylin and eosin-stained whole-slide images (WSIs) and assess its clinical applicability.The DL model was developed and validated through three-fold cross validation using 441 WSIs from the Cancer Genome Atlas (TCGA) and externally validated using 78 WSIs from the Pathology AI Platform (PAIP), and 355 WSIs from surgical specimens and 341 WSIs from biopsy specimens of the Sun Yet-sun University Cancer Center (SYSUCC). Domain adaption and multiple instance learning (MIL) techniques were adopted for model development. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUROC). A dual-threshold strategy was also built from the surgical cohorts and validated in the biopsy cohort. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-score, and the percentage of patients avoiding IHC testing were evaluated.The MIL model achieved an AUROC of 0·8888±0·0357 in the TCGA-validation cohort, 0·8806±0·0232 in the PAIP cohort, 0·8457±0·0233 in the SYSUCC-surgical cohort, and 0·7679±0·0342 in the SYSUCC-biopsy cohort. A dual-threshold triage strategy was used to rule-in and rule-out dMMR patients with remaining uncertain patients recommended for further IHC testing, which kept sensitivity higher than 90% and specificity higher than 95% on deficient MMR patient triage from both the surgical and biopsy specimens, result in more than half of patients avoiding IHC based MMR testing.A DL-based method that could directly predict CRC MMR status from WSIs was successfully developed, and a dual-threshold triage strategy was established to minimize the number of patients for further IHC testing.The study was funded by the National Natural Science Foundation of China (82073159, 81871971 and 81700576), the Natural Science Foundation of Guangdong Province (No. 2021A1515011792 and No.2022A1515012403) and Medical Scientific Research Foundation of Guangdong Province of China (No. A2020392).

Additional file 2: Table S1. Clinicopathological characteristics of the patients in the training and validation cohorts stratified according to the ICS. Table S2. Immune checkpoint co-expression by tumour cells (TCs) in the training cohort. Table S3. Five-year OS, DFS and DMFS estimates for different groups. Table S4. Number of events for different groups. Table S5. Univariate analysis of factors associated with overall survival in the training and validation cohorts. Table S6. Univariate analysis of factors associated with disease-free survival in the training and validation cohorts. Table S7. Univariate analysis of factors associated with distant metastasis-free survival in the training and validation cohorts. Table S8. Multivariable Cox regression analysis of factors associated with survival in the training and validation cohorts. Table S9. Summary of the multivariable analyses of prognostic factors for OS, DFS, DMFS and corresponding risk score in the training set of 208 nasopharyngeal carcinoma patients.

Additional file 1: Figure S1. Representative images of immune checkpoint expression in TCs and TAICs from nasopharyngeal carcinoma determined by immunohistochemistry. Figure S2. Kaplan-Meier curves for disease-free survival according to 13 immune checkpoint features. Figure S3. Kaplan-Meier curves for distant metastasis-free survival according to 13 immune checkpoint features. Figure S4. Construction of the ICS, a classifier comprising 5 immune checkpoint features. Figure S5. Determination of the optimum cutoff value for the ICS risk score.