NUP98 fusions c omprise a family o f rare r ecurrent a lterations i n A ML, associated w ith adverse outcomes. To define the underlying biology and clinical implications of this family of fusions, we performed comprehensive transcriptome, epigenome, and immunophenotypic profiling of 2,235 children and young adults with AML and identified 160 NUP98 rearrangements (7.2%), including 108 NUP98-NSD1 (4.8%), 32 NUP98-KDM5A (1.4%) and 20 NUP98-X cases (0.9%) with 13 different fusion partners. Fusion partners defined disease characteristics and biology; patients with NUP98-NSD1 or NUP98-KDM5A had distinct immunophenotypic, transcriptomic, and epigenomic profiles. Unlike the two most prevalent NUP98 fusions, NUP98-X variants are typically not cryptic. Furthermore, NUP98-X cases are associated with WT1 mutations, and have epigenomic profiles that resemble either NUP98- NSD1 or NUP98-KDM5A. Cooperating FLT3-ITD and WT1 mutations define NUP98-NSD1, and chromosome 13 aberrations are highly enriched in NUP98-KDM5A. Importantly, we demonstrate that NUP98 fusions portend dismal overall survival, with the noteworthy exception of patients bearing abnormal chr13.

Purpose The COVID‐19 pandemic has created extensive disruptions for medical education, causing urgency to implement and develop solutions to combat this disturbance. For students to pursue learning opportunities, the education system must improve. Augmented (AR) and virtual reality (VR) provide a promising future for the enhancement of medical education. This review article aims to evaluate the benefits and efficacy of AR and VR, especially during the COVID‐19 pandemic. Methods Multiple peer‐reviewed, randomized trials were synthesized into a review study investigating the efficacy, benefit, and use of VR and AR for medical education. The reviewed studies included medical and graduate‐level students as participants and aimed to support the use of AR and VR as an educational adjunct. The results of these trials support the use of additional technology in medical education. Results The COVID‐19 pandemic propelled medical schools, educators, and students into a world of total computerized learning. This pandemic acted as a necessary push to implement novel, untested technologies into the educational sector. Previous research demonstrates that AR and VR can confront these unfamiliar challenges and allow for a safe and beneficial COVID‐19 education, consisting of socially‐distanced and low‐risk patient‐care practice. Conclusions The benefits of continuous, safe learning outweigh the potential technological challenges of implementing AR and VR into medical education. During the pandemic, the medical community is faced with teaching students many skills necessary to become adept medical practitioners. Although the COVID‐19 pandemic offers a unique opportunity for the implementation of new technologies, virtual instruction does not have to end beyond this crisis. Virtual technology can teach medical students interpersonal skills such as empathy, engaging in difficult conversations, and delivering bad news. Because students have traditionally been exposed to passive teaching styles, it is time to look toward expanding medical students’ repertoire and allow them to personalize their education.

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BACKGROUND: Cell-free methylation DNA immunoprecipitation-sequencing (cfMeDIP-seq) identifies genomic regions with DNA methylation, using a protocol adapted to work with low-input DNA samples and with cfDNA. This method allows for DNA methylation profiling of circulating tumour DNA in cancer patients' blood samples. Such epigenetic profiling of circulating tumour DNA provides information about in which tissues tumour DNA originates, a key requirement of any test for early cancer detection. In addition, DNA methylation signatures provide prognostic information and can detect relapse. For robust quantitative comparisons between samples, immunoprecipitation enrichment methods like cfMeDIP-seq require normalization against common reference controls. METHODS: To provide a simple and inexpensive reference for quantitative normalization, we developed a set of synthetic spike-in DNA controls for cfMeDIP-seq. These controls account for technical variation in enrichment efficiency due to biophysical properties of DNA fragments. Specifically, we designed 54 DNA fragments with combinations of methylation status (methylated and unmethylated), fragment length (80 bp, 160 bp, 320 bp), G+C content (35%, 50%, 65%), and fraction of CpG dinucleotides within the fragment (1/80 bp, 1/40 bp, 1/20 bp). We ensured that the spike-in synthetic DNA sequences do not align to the human genome. We integrated unique molecular indices (UMIs) into cfMeDIP-seq to control for differential amplification after enrichment. To assess enrichment bias according to distinct biophysical properties, we conducted cfMeDIP-seq solely on spike-in DNA fragments. To optimize the amount of spike-in DNA required, we added varying quantities of spike-in control DNA to sheared HCT116 colon cancer genomic DNA prior to cfMeDIP-seq. To assess batch effects, three separate labs conducted cfMeDIP-seq on peripheral blood plasma samples from AML patients. RESULTS: We show that cfMeDIP-seq enriches for highly methylated regions, capturing ≥99.99% of methylated spike-in control fragments with ≤0.01% non-specific binding and preference for both high G+C content fragments and fragments with more CpGs. The use of 0.01 ng of spike-in control DNA total provided sufficient sequencing reads to adjust for variance due to fragment length, G+C content, and CpG fraction. Using the known amount of each spiked-in fragment, we created a generalized linear model that absolutely quantifies molar amount from read counts across the genome, while adjusting for fragment length, G+C content, and CpG fraction. Employing our spike-in controls greatly mitigates batch effects, reducing batch-associated variance to ≤1% of the total variance within the data. DISCUSSION: Incorporation of spike-in controls enables absolute quantification of methylated cfDNA generated from MeDIP-seq experiments. It mitigates batch effects and corrects for biases in enrichment due to known biophysical properties of DNA fragments and other technical biases. We created an R package, spiky, to convert read counts to picomoles of DNA fragments, while adjusting for fragment properties that affect enrichment. The spiky package is available on GitHub (https://github.com/trichelab/spiky) and will soon be available on Bioconductor.