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• Human genome-wide variations in the Massim region

  The Massim, a cultural region encompassing the southeastern tip of mainland Papua New Guinea (PNG) and nearby islands, is famous for the Kula trading network, in which different valuables circulate in different directions among the islands. To explore Massim genetic history, we generated genome-wide data from across the region and found variable levels of Papuan-related (indigenous) ancestry, including a novel ancestry associated with Rossel Island, and Austronesian-related ancestry that arrived later. We find genetic evidence for different patterns of contact across PNG, the Massim, and the Bismarck and Solomon Archipelagoes for Austronesian-related vs. Papuan-related ancestry, with more geographic restriction for the latter. Moreover, Kula-practicing groups share more genetic similarity than do other groups, and this likely predates the origin of Kula, suggesting that high between-group contact facilitated the formation of Kula. Our study provides the first comprehensive genome-wide assessment of Massim inhabitants and new insights into the fascinating Kula system.

  Study EGAS00001006010

• Serial TERT rearrangement breakpoint quantification in circulating tumor DNA enables minimal residual disease monitoring in patients with neuroblastoma

  Approximately 30% of high-risk neuroblastomas harbor genomic TERT rearrangements at diagnosis, resulting in high TERT transcript levels and active telomerase. Combined with mutations in the RAS/MAPK/ALK signaling transduction network, these patients have a dismal prognosis. Our objective was to establish a liquid biopsy-based monitoring strategy for this particularly vulnerable patient subgroup, for whom no in-time molecular diagnostic tools exist to date. Detection and quantification of TERT rearrangement breakpoints in cell-free tumor DNA circulating in peripheral blood and bone marrow plasma from patients with TERT-driven neuroblastoma improved minimal residual disease monitoring and early relapse detection in individual patients. TERT rearrangement breakpoints, as a single marker or combined with mutations in the RAS/MAPK/ALK signaling transduction network, can be reliably analyzed from 1ng DNA using droplet digital PCR assays fit for clinical routine use. TERT rearrangement breakpoints are reflected in cell-free tumor DNA and can serve as robust biomarkers for disease activity.

  Study EGAS00001007365

• Identification of gene mutations and fusion genes in patients with Sézary Syndrome

  Sezary syndrome (SS) is a leukemic form of cutaneous T-cell lymphoma (CTCL) with an aggressive clinical course. The goal of our study is to understand the genetic basis of the disease by looking for driver gene mutations and fusion genes in 15 erythrodermic patients with circulating Sezary cels, fourteen of them fulfilling the diagnostic criteria of SS. Peripheral blood samples were collected for each patient and CD4+ T-lymphocytes and Granulocytes were isolated. DNA and RNA was extracted for matched normal and tumor materials. Whole exome sequencing was performed on matched normal and tumor tissues of 12 patients and RNA sequencing was performed on 10 patients. For 7 of these patients, we have both exome as well as RNA sequencing data. In this study, we present a complex genomic landscape of Sezary syndrome with several point mutations, copy number variations and fusion events which could contribute to the pathogenesis of SS.

  Study EGAS00001001706

• Sequencing data from MethylScan assay on plasma

  Machine learning models in biomedical research are often hindered by demographic imbalances in clinical datasets, leading to biased predictions that disadvantage minority populations. Existing bias correction methods face limitations in handling the heterogeneity of biomedical data and the complexity of demographic influences. Here, we present DeBias, a computational framework for mitigating demographic biases in high-dimensional biomedical datasets. DeBias eliminates bias-associated subspaces within the entire feature space using control samples and enables global bias correction across all features, effectively preserving disease-specific signals while minimizing demographic distortions. To demonstrate its effectiveness, we applied DeBias to cfDNA methylation data for cancer detection. It achieved a >6-fold reduction in the number of features containing biases and outperformed existing methods in improving the cancer detection performance for minority populations. Our method represented a promising step toward developing machine learning models that are more accurate, reliable, and equitable for minority populations.

  Study EGAS00001008127

• PASCAL-MID Data Access Committee

  DAC for the center of Personalized Approach to Genome Editing of Stem Cells for Autotransplantation of Monogenic Immunodeficiencies (PASCAL-MID) at Aarhus University and Aarhus University Hospital

  Dac EGAC50000000680

• DATA FILES FOR SJPHALL

  DATA FILES FOR SJPHALL

  Dataset EGAD00001000163

• V4_Colorectal_panel_test

  Targeted gene screen of cell line tumours for testing the new V4 Colorectal gene panel.

  Study EGAS00001001807

• DATA FILES FOR GRUBER SJAMLM7 EXOME

  DATA FILES FOR GRUBER SJAMLM7 EXOME

  Dataset EGAD00001003134

• PACA-CA RNASeq fastq files

  Fastq files for PACA-CA RNA Seq analysis, for DCC release 27

  Dataset EGAD00001003972

• DATA FILES FOR SJMEL-WGS

  DATA FILES FOR SJMEL-WGS

  Dataset EGAD00001001032