A validated integrated clinical and molecular glioblastoma long-term survival-predictive nomogram.

Background: Glioblastoma (GBM) is the most common primary malignant brain tumor in adulthood. Despite multimodality treatments, including maximal safe resection followed by irradiation and chemotherapy, the median overall survival times range from 14 to 16 months. However, a small subset of GBM patients live beyond 5 years and are thus considered long-term survivors. Methods: A retrospective analysis of the clinical, radiographic, and molecular features of patients with newly diagnosed primary GBM who underwent treatment at The University of Texas MD Anderson Cancer Center was conducted. Eighty patients had sufficient quantity and quality of tissue available for next-generation sequencing and immunohistochemical analysis. Factors associated with survival time were identified using proportional odds ordinal regression. We constructed a survival-predictive nomogram using a forward stepwise model that we subsequently validated using The Cancer Genome Atlas. Results: Univariate analysis revealed 3 pivotal genetic alterations associated with GBM survival: both high tumor mutational burden ( Conclusions: Our newly devised long-term surviva

Single Center Review of Femoral Arteriovenous Grafts for Hemodialysis

It is unclear how to manage high risk hemodialysis patients who present with an indwelling catheter. The National Kidney Foundation Practice Guidelines urge prompt removal of the catheter, but the guidelines do not specifically address the problem of patients whose only option is a femoral arteriovenous (AV) graft.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41306/1/268_2005_Article_62.pd

A road map for the treatment of pediatric diffuse midline glioma

Objectives:Traditional journal clubs have been shown to be insufficient in improving residents\u27 scholarly productivity, often due to the inability to sustain residents\u27 interest and participation. Additionally, the 2019 novel coronavirus (COVID-19) pandemic restrictions caused a decline in academic scholarly productivity across residency programs. We evaluated the impact of a resident-led research club called \u27journal café\u27 on residents\u27 scholarly productivity by comparing scholarly output between the journal café members and non-members during the COVID-19 pandemic. Methods: The journal café was established in the 2012/2013 academic year by internal medicine residents of a university residency program in Atlanta, Georgia, to foster self-directed collaboration among residents based on shared interests in academic research. The journal café runs independently of the residency program\u27s journal club. We categorized IM residents at our institution into journal café members and non-members and collected data on their research productivity during residency training and the COVID-19 pandemic. The survey was conducted between April and June 2021 and analyzed data presented using frequencies, tables, and appropriate charts. Results: Sixty-eight residents (29 journal café members and 39 non-members) completed the survey (response rate of 85%). A significantly higher number of journal café members reported having five or more research publications (55.1% vs 7.1%, P \u3c .001) and scientific presentations (48.3% vs 2.6%, P \u3c .001) compared with non-members. Additionally, more journal café members published COVID-19-related research in peer-reviewed journals compared with non-members (68% vs 32%, n = 19). Finally, most of the residents cited the opportunity of a platform to share and brainstorm on research ideas as the reason for joining the journal café. Conclusion: We found an association between journal café participation and increased scholarly activity, particularly during the COVID-19 pandemic. Independent resident-led research clubs supported by the residency program may complement the traditional journal clubs and enhance residents\u27 participation in research

More is not always better: a case postrenal transplant large volume diuresis, hyponatremia, and postoperative seizure

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74084/1/j.1432-2277.2005.00221.x.pd

A validated integrated clinical and molecular glioblastoma long-term survival-predictive nomogram

BackgroundGlioblastoma (GBM) is the most common primary malignant brain tumor in adulthood. Despite multimodality treatments, including maximal safe resection followed by irradiation and chemotherapy, the median overall survival times range from 14 to 16 months. However, a small subset of GBM patients live beyond 5 years and are thus considered long-term survivors.MethodsA retrospective analysis of the clinical, radiographic, and molecular features of patients with newly diagnosed primary GBM who underwent treatment at The University of Texas MD Anderson Cancer Center was conducted. Eighty patients had sufficient quantity and quality of tissue available for next-generation sequencing and immunohistochemical analysis. Factors associated with survival time were identified using proportional odds ordinal regression. We constructed a survival-predictive nomogram using a forward stepwise model that we subsequently validated using The Cancer Genome Atlas.ResultsUnivariate analysis revealed 3 pivotal genetic alterations associated with GBM survival: both high tumor mutational burden (P = .0055) and PTEN mutations (P = .0235) negatively impacted survival, whereas IDH1 mutations positively impacted survival (P < .0001). Clinical factors significantly associated with GBM survival included age (P < .0001), preoperative Karnofsky Performance Scale score (P = .0001), sex (P = .0164), and clinical trial participation (P < .0001). Higher preoperative T1-enhancing volume (P = .0497) was associated with shorter survival. The ratio of TI-enhancing to nonenhancing disease (T1/T2 ratio) also significantly impacted survival (P = .0022).ConclusionsOur newly devised long-term survival-predictive nomogram based on clinical and genomic data can be used to advise patients regarding their potential outcomes and account for confounding factors in nonrandomized clinical trials

Uncovering Spatiotemporal Heterogeneity of High-Grade Gliomas: From Disease Biology to Therapeutic Implications

Glioblastomas (GBM) are the most common and aggressive tumors of the central nervous system. Rapid tumor growth and diffuse infiltration into healthy brain tissue, along with high intratumoral heterogeneity, challenge therapeutic efficacy and prognosis. A better understanding of spatiotemporal tumor heterogeneity at the histological, cellular, molecular, and dynamic levels would accelerate the development of novel treatments for this devastating brain cancer. Histologically, GBM is characterized by nuclear atypia, cellular pleomorphism, necrosis, microvascular proliferation, and pseudopalisades. At the cellular level, the glioma microenvironment comprises a heterogeneous landscape of cell populations, including tumor cells, non-transformed/reactive glial and neural cells, immune cells, mesenchymal cells, and stem cells, which support tumor growth and invasion through complex network crosstalk. Genomic and transcriptomic analyses of gliomas have revealed significant inter and intratumoral heterogeneity and insights into their molecular pathogenesis. Moreover, recent evidence suggests that diverse dynamics of collective motion patterns exist in glioma tumors, which correlate with histological features. We hypothesize that glioma heterogeneity is not stochastic, but rather arises from organized and dynamic attributes, which favor glioma malignancy and influences treatment regimens. This review highlights the importance of an integrative approach of glioma histopathological features, single-cell and spatially resolved transcriptomic and cellular dynamics to understand tumor heterogeneity and maximize therapeutic effects

MRI-Guided Stereotactic Biopsy of Murine GBM for Spatiotemporal Molecular Genomic Assessment

Brain tumor biopsies that are routinely performed in clinical settings significantly aid in diagnosis and staging. The aim of this study is to develop and evaluate a methodological image-guided approach that would allow for routine sampling of glioma tissue from orthotopic mouse brain tumor models. A magnetic resonance imaging-guided biopsy method is presented to allow for spatially precise stereotaxic sampling of a murine glioma coupled with genome-scale technology to provide unbiased characterization of intra- and intertumoral clonal heterogeneity. Longitudinal and multiregional sampling of intracranial tumors allows for successful collection of tumor biopsy samples, thus allowing for a pathway-enrichment analysis and a transcriptional profiling of RNA sequencing data. Spatiotemporal gene expression pattern variations revealing genomic heterogeneity were found