Huibo Zhang

BACKGROUND: Low-grade endometrial stromal sarcoma (LG-ESS) is a rare tumor that lacks a prognostic prediction model. Our study aimed to develop a nomogram to predict overall survival of LG-ESS patients. METHODS: A total of 1172 patients confirmed to have LG-ESS between 1988 and 2015 were selected from the Surveillance, Epidemiology and End Results (SEER) database. They were further divided into a training cohort and a validation cohort. The Akaike information criterion was used to select variables for the nomogram. The discrimination and calibration of the nomogram were evaluated using concordance index (C-index), area under time-dependent receiver operating characteristic curve (time-dependent AUC), and calibration plots. The net benefits of the nomogram at different threshold probabilities were quantified and compared with those of the International Federation of Gynecology and Obstetrics (FIGO) criteria-based tumor staging using decision curve analysis (DCA). Net reclassification index (NRI) and integrated discrimination improvement (IDI) were also used to compare the nomogram's clinical utility with that of the FIGO criteria-based tumor staging. The risk stratifications of the nomogram and the FIGO criteria-based tumor staging were compared. RESULTS: Seven variables were selected to establish the nomogram for LG-ESS. The C-index (0.814 for the training cohort and 0.837 for the validation cohort) and the time-dependent AUC (> 0.7) indicated satisfactory discriminative ability of the nomogram. The calibration plots showed favorable consistency between the prediction of the nomogram and actual observations in both the training and validation cohorts. The NRI values (training cohort: 0.271 for 5-year and 0.433 for 10-year OS prediction; validation cohort: 0.310 for 5-year and 0.383 for 10-year OS prediction) and IDI (training cohort: 0.146 for 5-year and 0.185 for 10-year OS prediction; validation cohort: 0.177 for 5-year and 0.191 for 10-year OS prediction) indicated that the established nomogram performed significantly better than the FIGO criteria-based tumor staging alone (P < 0.05). Furthermore, DCA showed that the nomogram was clinically useful and had better discriminative ability to recognize patients at high risk than the FIGO criteria-based tumor staging. CONCLUSIONS: A prognostic nomogram was developed and validated to assist clinicians in evaluating prognosis of LG-ESS patients.

Immunotherapy has moved to the forefront of modern oncologic treatment in the past few decades. Various forms of immunotherapy currently are emerging, including oncolytic viruses. In this therapy, viruses are engineered to selectively propagate in tumor cells and reduce toxicity for non-neoplastic tissues. Adenovirus is one of the most frequently employed oncolytic viruses because of its capacity in tumor cell lysis and immune response stimulation. Upregulation of immunostimulatory signals induced by oncolytic adenoviruses (OAds) might significantly remove local immune suppression and amplify antitumor immune responses. Existing genetic engineering technology allows us to design OAds with increasingly better tumor tropism, selectivity, and antitumor efficacy. Several promising strategies to modify the genome of OAds have been applied: capsid modifications, small deletions in the pivotal viral genes, insertion of tumor-specific promoters, and addition of immunostimulatory transgenes. OAds armed with tumor-associated antigen (TAA) transgenes as cancer vaccines provide additional therapeutic strategies to trigger tumor-specific immunity. Furthermore, the combination of OAds and immune checkpoint inhibitors (ICIs) increases clinical benefit as evidence shown in completed and ongoing clinical trials, especially in the combination of OAds with antiprogrammed death 1/programed death ligand 1 (PD-1/PD-L1) therapy. Despite remarkable antitumor potency, oncolytic adenovirus immunotherapy is confronted with tough challenges such as antiviral immune response and obstruction of tumor microenvironment (TME). In this review, we focus on genomic modification strategies of oncolytic adenoviruses and applications of OAds in cancer immunotherapy.