Alkylating chemotherapy is a central component of the management of glioblastoma (GBM). Among the factors that regulate the response to alkylation damage, NF-κB acts to both promote and block cytotoxicity. In this study, we used genome-wide expression analysis in U87 GBM to identify NF-κB–dependent factors altered in response to temozolomide and found the long noncoding RNA (lncRNA) MALAT1 as one of the most significantly upregulated. In addition, we demonstrated that MALAT1 expression was coregulated by p50 (p105) and p53 via novel κB- and p53-binding sites in the proximal MALAT1 coding region. Temozolomide treatment inhibited p50 recruitment to its cognate element as a function of Ser329 phosphorylation while concomitantly increasing p53 recruitment. Moreover, luciferase reporter studies demonstrated that both κB and p53 cis-elements were required for efficient transactivation in response to temozolomide. Depletion of MALAT1 sensitized patient-derived GBM cells to temozolomide cytotoxicity, and in vivo delivery of nanoparticle-encapsulated anti-MALAT1 siRNA increased the efficacy of temozolomide in mice bearing intracranial GBM xenografts. Despite these observations, in situ hybridization of GBM specimens and analysis of publicly available datasets revealed that MALAT1 expression within GBM tissue was not prognostic of overall survival. Together, these findings support MALAT1 as a target for chemosensitization of GBM and identify p50 and p52 as primary regulators of this ncRNA. Significance: These findings identify NF-κB and p53 as regulators of the lncRNA MALAT1 and suggest MALAT1 as a potential target for the chemosensitization of GBM.

PURPOSE: The COVID-19 pandemic has resulted in significant changes in health care delivery, including the rapid adoption of telemedicine across multiple specialties and practice environments. This includes postoperative visits (POV), despite limited data on outcomes following these telemedicine POV. We sought to determine whether these types of visits successfully identify and address postoperative complications when compared with in-person POV. METHODS: This was a retrospective cohort study of patients undergoing elective inpatient cancer-related surgery from March 2020 through December 2020. The exposure variable was type of POV (telemedicine v in-person). The primary outcome was unplanned hospital readmission within 90 days, and secondary outcomes included 30-day readmission, length of stay of first readmission, and mortality. RESULTS: Five-hundred thirty-five patients underwent elective inpatient operations and met our inclusion criteria. Of these, 98 (18.5%) had an initial telemedicine POV. There was no difference in 90-day readmission on the basis of POV type (16.3% telemedicine v 16.5% in-person, P = .99). Reasons for readmission did not differ between patients who underwent a telemedicine POV compared with in-person POV (all P > .05). After adjustment for patients' demographic and clinical factors, telemedicine POV was not associated with 90-day readmission (odds ratio, 0.89; 95% CI, 0.43 to 1.70; P = .77). CONCLUSION: Telemedicine POV use adopted during the COVID-19 pandemic did not increase risk of readmission when compared with in-person visits following inpatient oncologic surgery. These data can help inform policy on the continued use and application of telemedicine after the pandemic.

Total neoadjuvant therapy (TNT) is a novel strategy for rectal cancer that administers both (chemo)radiotherapy and systemic chemotherapy before surgery. TNT is expected to improve treatment compliance, tumor regression, organ preservation, and oncologic outcomes. Multiple TNT regimens are currently available with various combinations of the treatments including induction or consolidation chemotherapy, triplet or doublet chemotherapy, and long-course chemoradiotherapy or short-course radiotherapy. Evidence on TNT is rapidly evolving with new data on clinical trials, and no definitive consensus has been established on which regimens to use for improving outcomes. Clinicians need to understand the advantages and limitations of the available regimens for multidisciplinary decision making. This article reviews currently available evidence on TNT for rectal cancer. A decision making flow chart is provided for tailor-made use of TNT regimens based on tumor location and local and systemic risk.

This narrative review explores the utilization of machine learning (ML) and artificial intelligence (AI) models to enhance perioperative cancer care. ML and AI models offer significant potential to improve perioperative cancer care by predicting outcomes and supporting clinical decision-making. Tailored for perioperative professionals including anesthesiologists, surgeons, critical care physicians, nurse anesthetists, and perioperative nurses, this review provides a comprehensive framework for the integration of ML and AI models to enhance patient care delivery throughout the perioperative continuum.