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Browsing by Subject "Tumor mutation burden"
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Item Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations(BMC, 2020) Huang, Zhi; Johnson, Travis S.; Han, Zhi; Helm, Bryan; Cao, Sha; Zhang, Chi; Salama, Paul; Rizkalla, Maher; Yu, Christina Y.; Cheng, Jun; Xiang, Shunian; Zhan, Xiaohui; Zhang, Jie; Huang, Kun; Medicine, School of MedicineBackground: Recent advances in kernel-based Deep Learning models have introduced a new era in medical research. Originally designed for pattern recognition and image processing, Deep Learning models are now applied to survival prognosis of cancer patients. Specifically, Deep Learning versions of the Cox proportional hazards models are trained with transcriptomic data to predict survival outcomes in cancer patients. Methods: In this study, a broad analysis was performed on TCGA cancers using a variety of Deep Learning-based models, including Cox-nnet, DeepSurv, and a method proposed by our group named AECOX (AutoEncoder with Cox regression network). Concordance index and p-value of the log-rank test are used to evaluate the model performances. Results: All models show competitive results across 12 cancer types. The last hidden layers of the Deep Learning approaches are lower dimensional representations of the input data that can be used for feature reduction and visualization. Furthermore, the prognosis performances reveal a negative correlation between model accuracy, overall survival time statistics, and tumor mutation burden (TMB), suggesting an association among overall survival time, TMB, and prognosis prediction accuracy. Conclusions: Deep Learning based algorithms demonstrate superior performances than traditional machine learning based models. The cancer prognosis results measured in concordance index are indistinguishable across models while are highly variable across cancers. These findings shedding some light into the relationships between patient characteristics and survival learnability on a pan-cancer level.Item Immune Checkpoint Inhibitors for the Treatment of Bladder Cancer(MDPI, 2021-01-03) Lopez-Beltran, Antonio; Cimadamore, Alessia; Blanca, Ana; Massari, Francesco; Vau, Nuno; Scarpelli, Marina; Cheng, Liang; Montironi, Rodolfo; Pathology and Laboratory Medicine, School of MedicineA number of immune checkpoint inhibitors (ICIs) have been approved as first-line therapy in case of cisplatin-ineligible patients or as second-line therapy for patients with metastatic urothelial carcinoma (mUC) of the bladder. About 30% of patients with mUC will respond to ICIs immunotherapy. Programmed death-ligand 1 (PD-L1) expression detected by immunohistochemistry seems to predict response to immune checkpoint inhibitors in patients with mUC as supported by the objective response rate (ORR) and overall survival (OS) associated with the response observed in most clinical trials. Pembrolizumab, an anti-PD-1 antibody, demonstrated better OS respective to chemotherapy in a randomized phase 3 study for second-line treatment of mUC. Nivolumab, a PD-1 antibody, also demonstrated an OS benefit when compared to controls. Atezolizumab, Durvalumab, and Avelumab antibodies targeting PD-L1 have also received approval as second-line treatments for mUC with durable response for more than 1 year in selected patients. Atezolizumab and Pembrolizumab also received approval for first-line treatment of patients that are ineligible for cisplatin. A focus on the utility of ICIs in the adjuvant or neoadjuvant setting, or as combination with chemotherapy, is the basis of some ongoing trials. The identification of a clinically useful biomarker, single or in association, to determine the optimal ICIs treatment for patients with mUC is very much needed as emphasized by the current literature. In this review, we examined relevant clinical trial results with ICIs in patients with mUC alone or as part of drug combinations; emphasis is also placed on the adjuvant and neoadjuvant setting. The current landscape of selected biomarkers of response to ICIs including anti-PD-L1 immunohistochemistry is also briefly reviewed.