CK2 genes are overexpressed in many human cancers, and most often overexpression is associated with worse prognosis. Site-specific expression in mice leads to cancer development (e.g., breast, lymphoma) indicating the oncogenic nature of CK2. CK2 is involved in many key aspects of cancer including inhibition of apoptosis, modulation of signaling pathways, DNA damage response, and cell cycle regulation. A number of CK2 inhibitors are now available and have been shown to have activity against various cancers in vitro and in pre-clinical models. Some of these inhibitors are now undergoing exploration in clinical trials as well. In this review, we will examine some of the major cancers in which CK2 inhibition has promise based on in vitro and pre-clinical studies, the proposed cellular and signaling mechanisms of anti-cancer activity by CK2 inhibitors, and the current or recent clinical trials using CK2 inhibitors.

Oncolytic virotherapy (OVT) is a promising approach in which WT or engineered viruses selectively replicate and destroy tumor cells while sparing normal ones. In the last two decades, different oncolytic viruses (OVs) have been modified and tested in a number of preclinical studies, some of which have led to clinical trials in cancer patients. These clinical trials have revealed several critical limitations with regard to viral delivery, spread, resistance, and antiviral immunity. Here, we focus on promising research strategies that have been developed to overcome the aforementioned obstacles. Such strategies include engineering OVs to target a broad spectrum of tumor cells while evading the immune system, developing unique delivery mechanisms, combining other immunotherapeutic agents with OVT, and using clinically translatable mouse tumor models to potentially translate OVT more readily into clinical settings.

A multitude of proteins are aberrantly expressed in cancer cells, including the oncogenic serine-threonine kinase CK2. In a previous report, we found increases in CK2 transcript expression that could explain the increased CK2 protein levels found in tumors from lung and bronchus, prostate, breast, colon and rectum, ovarian and pancreatic cancers. We also found that, contrary to the current notions about CK2, some CK2 transcripts were downregulated in several cancers. Here, we investigate all other cancers using Oncomine to determine whether they also display significant CK2 transcript dysregulation. As anticipated from our previous analysis, we found cancers with all CK2 transcripts upregulated (e.g. cervical), and cancers where there was a combination of upregulation and/or downregulation of the CK2 transcripts (e.g. sarcoma). Unexpectedly, we found some cancers with significant downregulation of all CK2 transcripts (e.g. testicular cancer). We also found that, in some cases, CK2 transcript levels were already dysregulated in benign lesions (e.g. Barrett’s esophagus). We also found that CK2 transcript upregulation correlated with lower patient survival in most cases where data was significant. However, there were two cancer types, glioblastoma and renal cell carcinoma, where CK2 transcript upregulation correlated with higher survival. Overall, these data show that the expression levels of CK2 genes is highly variable in cancers and can lead to different patient outcomes.

OBJECTIVE: Nonroutine discharge after elective spine surgery increases healthcare costs, negatively impacts patient satisfaction, and exposes patients to additional hospital-acquired complications. Therefore, prediction of nonroutine discharge in this population may improve clinical management. The authors previously developed a machine learning algorithm from national data that predicts risk of nonhome discharge for patients undergoing surgery for lumbar disc disorders. In this paper the authors externally validate their algorithm in an independent institutional population of neurosurgical spine patients.METHODS: Medical records from elective inpatient surgery for lumbar disc herniation or degeneration in the Transitional Care Program at Brigham and Women’s Hospital (2013–2015) were retrospectively reviewed. Variables included age, sex, BMI, American Society of Anesthesiologists (ASA) class, preoperative functional status, number of fusion levels, comorbidities, preoperative laboratory values, and discharge disposition. Nonroutine discharge was defined as postoperative discharge to any setting other than home. The discrimination (c-statistic), calibration, and positive and negative predictive values (PPVs and NPVs) of the algorithm were assessed in the institutional sample.RESULTS: Overall, 144 patients underwent elective inpatient surgery for lumbar disc disorders with a nonroutine discharge rate of 6.9% (n = 10). The median patient age was 50 years and 45.1% of patients were female. Most patients were ASA class II (66.0%), had 1 or 2 levels fused (80.6%), and had no diabetes (91.7%). The median hematocrit level was 41.2%. The neural network algorithm generalized well to the institutional data, with a c-statistic (area under the receiver operating characteristic curve) of 0.89, calibration slope of 1.09, and calibration intercept of −0.08. At a threshold of 0.25, the PPV was 0.50 and the NPV was 0.97.CONCLUSIONS: This institutional external validation of a previously developed machine learning algorithm suggests a reliable method for identifying patients with lumbar disc disorder at risk for nonroutine discharge. Performance in the institutional cohort was comparable to performance in the derivation cohort and represents an improved predictive value over clinician intuition. This finding substantiates initial use of this algorithm in clinical practice. This tool may be used by multidisciplinary teams of case managers and spine surgeons to strategically invest additional time and resources into postoperative plans for this population.