Microsatellite instability (MSI) has been approved as a pan-cancer biomarker for immune checkpoint blockade (ICB) therapy. However, current MSI identification methods are not available for all patients. We proposed an ensemble multiple instance deep learning model to predict microsatellite status based on histopathology images, and interpreted the pathomics-based model with multi-omics correlation. Methods: Two cohorts of patients were collected, including 429 from The Cancer Genome Atlas (TCGA-COAD) and 785 from an Asian colorectal cancer (CRC) cohort (Asian-CRC). We established the pathomics model, named Ensembled Patch Likelihood Aggregation (EPLA), based on two consecutive stages: patch-level prediction and WSI-level prediction. The initial model was developed and validated in TCGA-COAD, and then generalized in Asian-CRC through transfer learning. The pathological signatures extracted from the model were analyzed with genomic and transcriptomic profiles for model interpretation. Results: The EPLA model achieved an area-under-the-curve (AUC) of 0.8848 (95% CI: 0.8185-0.9512) in the TCGA-COAD test set and an AUC of 0.8504 (95% CI: 0.7591-0.9323) in the external validation set Asian-CRC after transfer learning. Notably, EPLA captured the relationship between pathological phenotype of poor differentiation and MSI ( P < 0.001). Furthermore, the five pathological imaging signatures identified from the EPLA model were associated with mutation burden and DNA damage repair related genotype in the genomic profiles, and antitumor immunity activated pathway in the transcriptomic profiles. Conclusions: Our pathomics-based deep learning model can effectively predict MSI from histopathology images and is transferable to a new patient cohort. The interpretability of our model by association with pathological, genomic and transcriptomic phenotypes lays the foundation for prospective clinical trials of the application of this artificial intelligence (AI) platform in ICB therapy.
Objectives: Coronavirus disease 2019 (COVID-19) is sweeping the globe and has resulted in infections in millions of people. Patients with COVID-19 face a high fatality risk once symptoms worsen; therefore, early identification of severely ill patients can enable early intervention, prevent disease progression, and help reduce mortality. This study aims to develop an artificial intelligence-assisted tool using computed tomography (CT) imaging to predict disease severity and further estimate the risk of developing severe disease in patients suffering from COVID-19. Materials and Methods: Initial CT images of 408 confirmed COVID-19 patients were retrospectively collected between January 1, 2020 and March 18, 2020 from hospitals in Honghu and Nanchang. The data of 303 patients in the People's Hospital of Honghu were assigned as the training data, and those of 105 patients in The First Affiliated Hospital of Nanchang University were assigned as the test dataset. A deep learning based-model using multiple instance learning and residual convolutional neural network (ResNet34) was developed and validated. The discrimination ability and prediction accuracy of the model were evaluated using the receiver operating characteristic curve and confusion matrix, respectively. Results: The deep learning-based model had an area under the curve (AUC) of 0.987 (95% confidence interval [CI]: 0.968-1.00) and an accuracy of 97.4% in the training set, whereas it had an AUC of 0.892 (0.828-0.955) and an accuracy of 81.9% in the test set. In the subgroup analysis of patients who had non-severe COVID-19 on admission, the
BackgroundGenetic variations of some driver genes in non-small cell lung cancer (NSCLC) had shown potential impact on immune microenvironment and associated with response or resistance to programmed cell death protein 1 (PD-1) blockade immunotherapy. We therefore undertook an exploratory analysis to develop a genomic mutation signature (GMS) and predict the response to anti-PD-(L)1 therapy.MethodsIn this multicohort analysis, 316 patients with non-squamous NSCLC treated with anti-PD-(L)1 from three independent cohorts were included in our study. Tumor samples from the patients were molecularly profiled by MSK-IMPACT or whole exome sequencing. We developed a risk model named GMS based on the MSK training cohort (n=123). The predictive model was first validated in the separate internal MSK cohort (n=82) and then validated in an external cohort containing 111 patients from previously published clinical trials.ResultsA GMS risk model consisting of eight genes (TP53, KRAS, STK11, EGFR, PTPRD, KMT2C, SMAD4, and HGF) was generated to classify patients into high and low GMS groups in the training cohort. Patients with high GMS in the training cohort had longer progression-free survival (hazard ratio (HR) 0.41, 0.28–0.61, p<0.0001) and overall survival (HR 0.53, 0.32–0.89, p=0.0275) compared with low GMS. We noted equivalent findings in the internal validation cohort and in the external validation cohort. The GMS was demonstrated as an independent predictive factor for anti-PD-(L)1 therapy comparing with tumor mutational burden. Meanwhile, GMS showed undifferentiated predictive value in patients with different clinicopathological features. Notably, both GMS and PD-L1 were independent predictors and demonstrated poorly correlated; inclusion of PD-L1 with GMS further improved the predictive capacity for PD-1 blockade immunotherapy.ConclusionsOur study highlights the potential predictive value of GMS for immunotherapeutic benefit in non-squamous NSCLC. Besides, the combination of GMS and PD-L1 may serve as an optimal partner in guiding treatment decisions for anti-PD-(L)1 based therapy.
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