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Cullin-RING (really intersting new gene) E3 ubiquitin ligases (CRLs) are the largest E3 family and direct numerous protein substrates for proteasomal degradation, thereby impacting a myriad of physiological and pathological processes including cancer. To date, there are no reported small-molecule inhibitors of the catalytic activity of CRLs. Here, we describe high-throughput screening and medicinal chemistry optimization efforts that led to the identification of two compounds, 33-11 and KH-4-43, which inhibit E3 CRL4 and exhibit antitumor potential. These compounds bind to CRL4's core catalytic complex, inhibit CRL4-mediated ubiquitination, and cause stabilization of CRL4's substrate CDT1 in cells. Treatment with 33-11 or KH-4-43 in a panel of 36 tumor cell lines revealed cytotoxicity. The antitumor activity was validated by the ability of the compounds to suppress the growth of human tumor xenografts in mice. Mechanistically, the compounds' cytotoxicity was linked to aberrant accumulation of CDT1 that is known to trigger apoptosis. Moreover, a subset of tumor cells was found to express cullin4 proteins at levels as much as 70-fold lower than those in other tumor lines. The low-cullin4-expressing tumor cells appeared to exhibit increased sensitivity to 33-11/KH-4-43, raising a provocative hypothesis for the role of low E3 abundance as a cancer vulnerability.

Phosphoinositide 3-kinase (PI3K) activity is stimulated by diverse oncogenes and growth factor receptors, and elevated PI3K signaling is considered a hallmark of cancer. Many PI3K pathway-targeted therapies have been tested in oncology trials, resulting in regulatory approval of one isoform-selective inhibitor (idelalisib) for treatment of certain blood cancers and a variety of other agents at different stages of development. In parallel to PI3K research by cancer biologists, investigations in other fields have uncovered exciting and often unpredicted roles for PI3K catalytic and regulatory subunits in normal cell function and in disease. Many of these functions impinge upon oncology by influencing the efficacy and toxicity of PI3K-targeted therapies. Here we provide a perspective on the roles of class I PI3Ks in the regulation of cellular metabolism and in immune system functions, two topics closely intertwined with cancer biology. We also discuss recent progress developing PI3K-targeted therapies for treatment of cancer and other diseases.

Objective: Venous thromboembolic event (VTE) after spine surgery is a rare but potentially devastating complication. With the advent of machine learning, an opportunity exists for more accurate prediction of such events to aid in prevention and treatment. Methods: Seven models were screened using 108 database variables and 62 preoperative variables. These models included deep neural network (DNN), DNN with synthetic minority oversampling technique (SMOTE), logistic regression, ridge regression, lasso regression, simple linear regression, and gradient boosting classifier. Relevant metrics were compared between each model. The top four models were selected based on area under the receiver operator curve; these models included DNN with SMOTE, linear regression, lasso regression, and ridge regression. Separate random sampling of each model was performed 1000 additional independent times using a randomly generated training/ testing distribution. Variable weights and magnitudes were analyzed after sampling. Results: Using all patient‑related variables, DNN using SMOTE was the top‑performing model in predicting postoperative VTE after spinal surgery (area under the curve [AUC] =0.904), followed by lasso regression (AUC = 0.894), ridge regression (AUC = 0.873), and linear regression (AUC = 0.864). When analyzing a subset of only preoperative variables, the top‑performing models were lasso regression (AUC = 0.865) and DNN with SMOTE (AUC = 0.864), both of which outperform any currently published models. Main model contributions relied heavily on variables associated with history of thromboembolic events, length of surgical/anesthetic time, and use of postoperative chemoprophylaxis. Conclusions: The current study provides promise toward machine learning methods geared toward predicting postoperative complications after spine surgery. Further study is needed in order to best quantify and model real‑world risk for such events. [ABSTRACT FROM AUTHOR]

Background: Traumatic vertebral artery dissections (tVADs) occur in up to 20% of patients with head trauma, yet data on their presentation and associated sequelae are limited. Aims and Objectives: To characterize the tVAD population and identify factors associated with clinical outcomes. Materials and Methods: We retrospectively analyzed all cases of tVAD at our institution from January 2004 to December 2018 with respect to mechanism of injury, clinical presentation, anatomic factors, associated pathologies, and relevant outcomes. Results: Of the 123 patients with tVAD, the most common presenting symptoms were neck pain (n=76, 67.3%), headache (57.5%), and visual changes (29.6%). 101 cases (82.1%) were unilateral, and 22 cases (17.9%) were bilateral. V2 was the most involved anatomic segment (83 cases, 70.3). 30 cases (25.4%) led to stroke, and 39 cases (31.7%) had a concomitant cervical fracture. The anatomic segment and number of segments involved, and baseline clinical and demographic characteristics were not associated with risk of stroke. Patients with associated fractures were older (50.3 years v. 36.4 years, p=0.0233), had a higher comorbid disease burden (CCI 1 vs. CCI 1, p<0.0007), were more likely to smoke (OR 3.0 [1.2178, 7.4028], p=0.0202), be male (OR 7.125 [3.0181, 16.8236], p<0.0001), and have mRS≥3 at discharge (OR 3.0545 [1.0937, 8.5752], p=0.0449). On multivariable regression, only fracture independently predicted mRS≥3 at discharge (OR 5.6898 [1.5067, 21.4876], p=0.010). Conclusion: tVADs may be associated with stroke and/or cervical fracture. Presenting symptoms predict stroke, but baseline demographic and clinical characteristics do not. Comorbid cervical fractures, not stroke, drive negative outcomes [ABSTRACT FROM AUTHOR]