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To describe demographic, clinical, and radiographic features of tumefactive demyelination (TD) and identify factors associated with severe attacks and poor outcomes. Retrospective review of TD cases seen at Mayo Clinic, 1990-2021. Of 257 patients with TD, 183/257 (71%) fulfilled the 2017 multiple sclerosis (MS) McDonald criteria at the last follow-up, 12/257 (5%) had myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), 0 had aquaporin-4-IgG seropositive neuromyelitis optic spectrum disorders (AQP4+ NMOSD), and 62/257 (24%) were cryptogenic. Onset before age 18 was present in 18/257 (7%). Female to male ratio was 1.3:1. Cerebrospinal fluid oligoclonal (CSF) bands were present in 95/153 (62%). TD was the first demyelinating attack in 176/257 (69%). At presentation, 59/126 (47%) fulfilled Barkhof criteria for dissemination in space, 59/100 (59%) had apparent diffusion coefficient (ADC) restriction, and 57/126 (45%) had mass effect. Despite aggressive clinical presentation at onset, 181/257 (70%) of patients remained fully ambulatory (Expanded Disability Status Scale [EDSS] ≤4) after a 3.0-year median follow-up duration. Severe initial attack-related disability (EDSS ≥4) was more common in patients with motor symptoms (81/143 vs. 35/106, p < 0.0001), encephalopathy (20/143 vs. 2/106, p < 0.0001) and ADC restriction on initial MRI (42/63 vs. 15/33, p = 0.04). Poor long-term outcome (EDSS ≥4) was more common in patients with older onset age (41.9 ± 15 vs. 36.8 ± 15.6, p = 0.02) and motor symptoms at onset (49/76 vs. 66/171, p < 0.0001). Most TD patients should be considered part of the MS spectrum after excluding MOGAD and NMOSD. Motor symptoms and older age at presentation portend a poor outcome.

Objectives Previous studies noted discordance of programmed death‐1 (PD‐1) and one of its ligands (PD‐L1) across patient‐matched primary and metastatic clear cell renal cell carcinoma (ccRCC). There are inconsistencies if the primary or metastatic tumor has higher expression, and whether metastatic tumor expression is associated with patient outcome. Thus, we examined PD‐1 and PD‐L1 in patient‐matched tumors using a large number of ccRCC patients with long follow‐up. Materials and Methods We analyzed PD‐1 and PD‐L1 using immunohistochemistry in patient‐matched primary and metastatic tumors from 110 ccRCC patients. Concordance was assessed among longitudinal metastatic tumors, as well as across patient‐matched primary and metastatic tumors. Cox proportional hazards regression was used to evaluate the associations of metastatic tumor expression with cancer‐specific survival. Results We observed inter‐metastatic tumor heterogeneity of PD‐1 in 25 (69%) of the 36 patients and of PD‐L1 in seven (19%) patients. Concordance between patient‐matched primary and metastatic tumors was 73% (Kappa = 0.16, 95% CI: −0.003‐0.32). Similarly, concordance of PD‐L1 between metastatic and patient‐matched primary tumors was 78% (Kappa = 0.27, 95% CI: 0.09‐0.46). Both markers demonstrated higher expression in primary vs metastatic tumors. Metastatic tumor expression of PD‐1 was significantly associated with metastatic location (P < .0001) and ccRCC‐specific survival (HR = 2.15, 95% CI: 1.06‐4.36, P = .035). Conclusions The expression of PD‐1 and PD‐L1 is discordant across patient‐matched ccRCC tumors, with higher expression in primary tumors. Higher PD‐1 expression was associated with metastatic location and lower cancer‐specific survival. If validated, these results highlight the importance of evaluating these biomarkers in metastatic tissue specifically. We examined PD‐1 and PD‐L1 in patient‐matched tumors in a large cohort of ccRCC with long‐term follow‐up. The expression of PD‐1 and PD‐L1 was discordant across patient‐matched ccRCC tumors, with higher expression in primary tumors. Higher PD‐1 expression was associated with metastatic location and lower cancer‐specific survival.

Introduction As the pandemic continues with new variants emerging, faculty and students require support with education's rapid shift to the virtual space. The Mayo Clinic Center for Clinical and Translational Science curriculum team works closely with faculty to support a smooth transition to offering graduate courses in a virtual learning environment. The aim of the present project was to explore faculty and student perceptions of these remote learning strategies to gain an understanding of the innovations required to improve future educational offerings. Methods All faculty and learners involved in nine Clinical and Translational Science courses in spring 2020 were invited to participate in a web-based questionnaire. Quantitative analysis was performed on closed-ended items, including 5-point Likert-scale questions used to assess the range of views. Qualitative free-text responses were independently analyzed for repetitive themes and summarized. Additionally, comparisons of faculty and course evaluations and student grade point averages (GPAs) from the in-person courses and their subsequent virtual course offerings were considered. Results Survey results indicated several positive impacts with moving courses into the virtual environment, including increased accessibility as well as more student-centered education. Learners joining from sites outside of the originating campus were especially grateful for the virtual classroom because they felt newly integrated within classes. Faculty and course evaluations, as well as student GPAs, remained consistent. Conclusion New COVID-19 variants continue to shift education online, and innovative ideas are required to further improve future virtual course offerings. Increased engagement is warranted, both from faculty to incorporate activities designed specifically for a virtual classroom, and from students to increase participation by activating their microphones and webcams. Greater opportunities for global involvement and connectedness arise. Finally, this project advocates for adequate eLearning staffing to support quality online education as the need for pedagogical and technical provision continues.

Background Stored biological samples with pathology information and medical records are invaluable resources for translational medical research. However, RNAs extracted from the archived clinical tissues are often substantially degraded. RNA degradation distorts the RNA-seq read coverage in a gene-specific manner, and has profound influences on whole-genome gene expression profiling. Result We developed the transcript integrity number (TIN) to measure RNA degradation. When applied to 3 independent RNA-seq datasets, we demonstrated TIN is a reliable and sensitive measure of the RNA degradation at both transcript and sample level. Through comparing 10 prostate cancer clinical samples with lower RNA integrity to 10 samples with higher RNA quality, we demonstrated that calibrating gene expression counts with TIN scores could effectively neutralize RNA degradation effects by reducing false positives and recovering biologically meaningful pathways. When further evaluating the performance of TIN correction using spike-in transcripts in RNA-seq data generated from the Sequencing Quality Control consortium, we found TIN adjustment had better control of false positives and false negatives (sensitivity = 0.89, specificity = 0.91, accuracy = 0.90), as compared to gene expression analysis results without TIN correction (sensitivity = 0.98, specificity = 0.50, accuracy = 0.86). Conclusion TIN is a reliable measurement of RNA integrity and a valuable approach used to neutralize in vitro RNA degradation effect and improve differential gene expression analysis.

Glioma is a highly fatal and heterogeneous brain tumor with few known risk factors. Our study examines genetically predicted variability in blood cell indices in relation to glioma risk and survival in 3418 cases and 8156 controls. We find that increased platelet to lymphocyte ratio (PLR) confers an increased risk of glioma (odds ratio (OR) = 1.25, p  = 0.005), especially tumors with isocitrate dehydrogenase (IDH) mutations (OR = 1.38, p  = 0.007) and IDH mut 1p/19q intact (IDH mut-intact OR = 1.53, p  = 0.004) tumors. Genetically inferred increased counts of lymphocytes (IDH mut-intact OR = 0.70, p  = 0.004) and neutrophils (IDH mut OR = 0.69, p  = 0.019; IDH mut-intact OR = 0.60, p  = 0.009) show inverse associations with risk, which may reflect enhanced immune-surveillance. Considering survival, we observe higher mortality risk in patients with IDH mut 1p/19q with genetically predicted increased counts of lymphocytes (hazard ratio (HR) = 1.65, 95% CI: 1.24–2.20), neutrophils (HR = 1.49, 1.13–1.97), and eosinophils (HR = 1.59, 1.18–2.14). Polygenic scores for blood cell traits are also differentially associated with 17 tumor immune microenvironment features in a subtype-specific manner, including signatures related to interferon signaling, PD-1 expression, and T-cell/Cytotoxic responses. Our findings highlight immune-mediated susceptibility mechanisms with potential disease management implications. Glioma is an aggressive brain tumor subtype with few known risk factors. Here, the authors utilise Mendelian Randomisation to investigate correlation of immune cell counts with subtype-specific risk and mortality in glioma patients.

We used deep sequencing technology to profile the transcriptome, gene copy number, and CpG island methylation status simultaneously in eight commonly used breast cell lines to develop a model for how these genomic features are integrated in estrogen receptor positive (ER+) and negative breast cancer. Total mRNA sequence, gene copy number, and genomic CpG island methylation were carried out using the Illumina Genome Analyzer. Sequences were mapped to the human genome to obtain digitized gene expression data, DNA copy number in reference to the non-tumor cell line (MCF10A), and methylation status of 21,570 CpG islands to identify differentially expressed genes that were correlated with methylation or copy number changes. These were evaluated in a dataset from 129 primary breast tumors. Gene expression in cell lines was dominated by ER-associated genes. ER+ and ER- cell lines formed two distinct, stable clusters, and 1,873 genes were differentially expressed in the two groups. Part of chromosome 8 was deleted in all ER- cells and part of chromosome 17 amplified in all ER+ cells. These loci encoded 30 genes that were overexpressed in ER+ cells; 9 of these genes were overexpressed in ER+ tumors. We identified 149 differentially expressed genes that exhibited differential methylation of one or more CpG islands within 5 kb of the 5' end of the gene and for which mRNA abundance was inversely correlated with CpG island methylation status. In primary tumors we identified 84 genes that appear to be robust components of the methylation signature that we identified in ER+ cell lines. Our analyses reveal a global pattern of differential CpG island methylation that contributes to the transcriptome landscape of ER+ and ER- breast cancer cells and tumors. The role of gene amplification/deletion appears to more modest, although several potentially significant genes appear to be regulated by copy number aberrations.

Repeated measures studies are frequently performed in patient-derived xenograft (PDX) models to evaluate drug activity or compare effectiveness of cancer treatment regimens. Linear mixed effects regression models were used to perform statistical modeling of tumor growth data. Biologically plausible structures for the covariation between repeated tumor burden measurements are explained. Graphical, tabular, and information criteria tools useful for choosing the mean model functional form and covariation structure are demonstrated in a Case Study of five PDX models comparing cancer treatments. Power calculations were performed via simulation. Linear mixed effects regression models applied to the natural log scale were shown to describe the observed data well. A straight growth function fit well for two PDX models. Three PDX models required quadratic or cubic polynomial (time squared or cubed) terms to describe delayed tumor regression or initial tumor growth followed by regression. Spatial(power), spatial(power) + RE, and RE covariance structures were found to be reasonable. Statistical power is shown as a function of sample size for different levels of variation. Linear mixed effects regression models provide a unified and flexible framework for analysis of PDX repeated measures data, use all available data, and allow estimation of tumor doubling time.

Background An inverse relationship between allergies with glioma risk has been reported in several but not all epidemiological observational studies. We performed an analysis of genetic variants associated with atopy to assess the relationship with glioma risk using Mendelian randomisation (MR), an approach unaffected by biases from temporal variability and reverse causation that might have affected earlier investigations. Methods Two-sample MR was undertaken using genome-wide association study data. We used single nucleotide polymorphisms (SNPs) associated with atopic dermatitis, asthma and hay fever, IgE levels, and self-reported allergy as instrumental variables. We calculated MR estimates for the odds ratio (OR) for each risk factor with glioma using SNP-glioma estimates from 12,488 cases and 18,169 controls, using inverse-variance weighting (IVW), maximum likelihood estimation (MLE), weighted median estimate (WME) and mode-based estimate (MBE) methods. Violation of MR assumptions due to directional pleiotropy were sought using MR-Egger regression and HEIDI-outlier analysis. Results Under IVW, MLE, WME and MBE methods, associations between glioma risk with asthma and hay fever, self-reported allergy and IgE levels were non-significant. An inverse relationship between atopic dermatitis and glioma risk was found by IVW (OR 0.96, 95% confidence interval (CI) 0.93–1.00, P = 0.041) and MLE (OR 0.96, 95% CI 0.94–0.99, P = 0.003), but not by WME (OR 0.96, 95% CI 0.91–1.01, P = 0.114) or MBE (OR 0.97, 95% CI 0.92–1.02, P = 0.194). Conclusions Our investigation does not provide strong evidence for relationship between atopy and the risk of developing glioma, but findings do not preclude a small effect in relation to atopic dermatitis. Our analysis also serves to illustrate the value of using several MR methods to derive robust conclusions.

Developing patient derived models from individual tumors that capture the biological heterogeneity and mutation landscape in advanced prostate cancer is challenging, but essential for understanding tumor progression and delivery of personalized therapy in metastatic castrate resistant prostate cancer stage. To demonstrate the feasibility of developing patient derived xenograft models in this stage, we present a case study wherein xenografts were derived from cancer metastases in a patient progressing on androgen deprivation therapy and prior to initiating pre-chemotherapy enzalutamide treatment. Tissue biopsies from a metastatic rib lesion were obtained for sequencing before and after initiating enzalutamide treatment over a twelve-week period and also implanted subcutaneously as well as under the renal capsule in immuno-deficient mice. The genome and transcriptome landscapes of xenografts and the original patient tumor tissues were compared by performing whole exome and transcriptome sequencing of the metastatic tumor tissues and the xenografts at both time points. After comparing the somatic mutations, copy number variations, gene fusions and gene expression we found that the patient's genomic and transcriptomic alterations were preserved in the patient derived xenografts with high fidelity. These xenograft models provide an opportunity for predicting efficacy of existing and potentially novel drugs that is based on individual metastatic tumor expression signature and molecular pharmacology for delivery of precision medicine.