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• Cell-Free DNA Genomic and Fragmentomic Features for Early Outcome Prediction in Diffuse Large B-Cell Lymphoma

  Purpose: Diffuse large B-cell lymphoma (DLBCL) patients need an accurate and early risk stratification strategy as prompt therapy escalation may improve outcome. Patients and methods: We evaluated cell free DNA (cfDNA) genomic and fragmentomic features in 190 patients with large B-cell lymphoma from more than 40 hospitals in the Benelux using Whole Genome Sequencing (WGS). We defined a new metric called ACT score (Aberrations, Contribution of short fragments, Terminal motif analyses) by combining four cfDNA signals and evaluated its performance to predict end-of-treatment (EOT) response and survival. Results: Individual cfDNA features and ACT score were altered per EOT response after one cycle of treatment (T1). The ACT score at T1 outperformed all individual cfDNA features in predicting EOT response (AUC = 0.74). Patients with a positive ACT score had an inferior outcome compared to ACT score negative patients [progression-free survival (PFS): HR 5.0 (95% CI 3.1-8.2), log-rank test, p < 0.0001) and overall survival (OS): HR 6.5 (95% CI 3.5-12.2), log-rank test, p < 0.0001]. The 2-year PFS in ACT score positive and negative patients were 29.8% and 78.1%, respectively. The prognostic value of the ACT score is independent of the International Prognostic Index and interim imaging. Conclusions: ACT score computed from a single plasma sample collected after one cycle of treatment can predict clinical outcome. This low-cost and easy-to-interpret test does not require tissue biopsies or a priori knowledge of mutations and has the potential to aid patient selection in interventional clinical trials and risk-adapted treatment strategies.

  Study EGAS50000000412

• EstMB cohort participants sequenced with MGISEQ-2000 platform

  Participants collected a fresh stool sample immediately after defecation using a sterile Pasteur pipette and placed it in a 15 mL conical polypropylene tube. They were instructed to collect the sample as close as possible to their study center visit. Samples were transported and stored at −80 °C until DNA extraction. Microbial DNA was extracted from 200 mg of stool using a QIAamp DNA Stool Mini Kit (Qiagen), following manufacturer's instructions, after all samples were collected. DNA was quantified using a Qubit 2.0 Fluorometer with a dsDNA Assay Kit (Thermo Fisher Scientific). Library preparation and circularization of equimolarly pooled libraries were done with MGIEasy FS DNA Library Prep Set (MGI Tech Co, Ltd, Shenzhen, China) according to the standard protocol. The sequencing was performed with MGISEQ-2000 High-throughput Sequencing Set (FCL PE150) according to the manufacturer’s instructions (MGI Tech, Shenzhen, China)

  Dataset EGAD50000002213

• Center for Common Disease Genomics [CCDG] - Cardiovascular: SWISS-AF/SWISS-AF-PVI/BEAT-AF

  This study is a part of NHGRI's Centers for Common Disease Genomics (CCDG), which is a collaborative large-scale genome sequencing effort to comprehensively identify rare risk and protective variants contributing to multiple common disease phenotypes. Current estimates anticipate that the CCDG program will sequence approximately 140K whole genomes and 225K whole exomes during the life of the project. The Cardiovascular Disease working group of the CCDG considered five diseases: early-onset coronary artery disease (EOCAD), stroke, atrial fibrillation (AF), congestive heart failure and type 2 diabetes. AF will affect between 6-12 million individuals in the US by 2050. AF is also associated with increased risks of stroke, dementia, heart failure, death, and high health care costs. Many risk factors for AF have been identified, including advancing age, cardiovascular disease (CVD), and CVD risk factors. However, there is little knowledge how to prevent AF. Furthermore, therapies for AF are only partially effective, and are themselves associated with substantial morbidity. Previously, heritable forms of AF have been considered rare; yet in the last decade, it has been established that AF, and in particular early-onset forms of AF, are heritable. Genome-wide association studies (GWAS) provide a powerful tool to identify common variants underlying disease risk. The AFGen Consortium currently consists of investigators from more than 25 studies with &gt;20,000 individuals with AF and &gt;100,000 without AF. In the latest analyses, 14 loci have been identified for AF1. Broadly, the loci implicate genes related to cardiopulmonary development, cardiac-expressed ion channels, and cell signaling molecules. Source: https://ccdg.rutgers.edu/sites/default/files/CCDG_CVD_EOAF_FINAL_w_link.pdf Three collections were brought together to form Swiss atrial fibrillation cases: SWISS-AF, SWISS-AF-PVI, and BEAT-AF. SWISS-AF is a prospective observational, multicentric cohort study in Switzerland to increase our knowledge on structural brain damage and its changes over time in patients with atrial fibrillation. Overall, 2400 patients with documented atrial fibrillation aged &gt;65 years will be included and followed on a yearly basis. SWISS-AF-PVI is a registry of patients with atrial fibrillation undergoing catheter-based ablation of atrial fibrillation (pulmonary vein isolation) to determine long-term success rates of catheter-based ablation of atrial fibrillation (pulmonary vein isolation) and to evaluate factors associated with long-term success of catheter-based ablation of atrial fibrillation (pulmonary vein isolation). BEAT-AF is a prospective observational, multicenter cohort study in Switzerland. Overall, 1550 patients with documented atrial fibrillation were enrolled in seven study centers. The main aim of this study is to investigate the therapy and the medical consequences of patients with atrial fibrillation. Samples from all three collections were sent to The Broad Institute of MIT and Harvard to generate genetic data using whole exome sequencing and whole genome genotyping (Infinium Global Screening Array); all cases with atrial fibrillation.

  Study phs002242

• Rapid brain tumor classification from sparse epigenomic data

  Although the intraoperative, molecular differential diagnosis of the approximately one hundred different brain tumor entities described to date has been a goal of neuropathology in the last decade, this has not yet been achieved in a clinically relevant time frame of less than one hour after biopsy collection. Recent advances in third-generation sequencing technologies have brought this once-elusive goal within reach. However, established machine learning techniques rely on concepts and methods, impractical for live diagnostic workflows in clinical applications. Here, we present MethyLYZR, a Naïve Bayesian framework enabling fully tractable live classification of cancer epigenomes. MethyLYZR can be run in parallel with an ongoing Nanopore experiment with negligible computational cost and provides clinically relevant and accurate cancer classification results within 15 minutes of sequencing. Therefore, only the time required for DNA extraction and the Nanopore sequencer's maximum parallel throughput remain limiting factors for even faster time-to-results. We demonstrate the potential utility of the MethyLYZR framework not only for the neurosurgical intraoperative use case but also for other oncologic indications and cell-free DNA from liquid biopsies.

  Study EGAS50000000559

• Clinical data

  Clinical data for KATHERINE: Clinical data include Treatment Arm, Invasive Disease Free Survival (IDFS), Clinical Stage at Presentation, Hormone Receptor Status, Preoperative HER2 Directed Therapy, Pathological Node.

  Dataset EGAD00001008785

• WGS & RNAseq of paired tumor-normal patients presenting a BRCA2 alteration

  Raw data FASTQ files of RNAseq (Stranded mRNA prep Ligation-Illumina on NovaSeq 6000, 100bp paired-end) & WGS (PCR Free Roche KAPA Hyper Prep Library on Illumina Novaseq 6000, 150bp paired-end) for 40 samples corresponding to the matched tumor/normal of 10 patients. Raw data FASTQ files of WGS (PCR Free Roche KAPA Hyper Prep Library on Illumina Novaseq 6000, 150bp paired-end) of the tumor of two additional patients.

  Dataset EGAD50000000871

• Repertoire and clinical hierarchy of AR locus alterations in castration-resistant prostate cancer

  Somatic alterations to the androgen receptor (AR) gene are pivotal drivers of treatment resistance in metastatic castration-resistant prostate cancer (mCRPC), but their prevalence, clinical impact, and etiology remain incompletely understood. Here, we analyzed 3399 plasma cell-free DNA samples and 1988 leukocyte DNA samples from 1995 metastatic prostate cancer patients with matched clinical data and extensive AR locus coverage.

  Study EGAS50000001101

• The tissue origin of highly elevated cell-free DNA in patients with and without cancer

  Forty years ago, the first measurements of cell free DNA (cfDNA) from cancer patients revealed that its total concentration was often elevated compared to healthy controls. The basis for this increase, and in particular the source of the extra cfDNA in such patients, has never been determined. To address this issue, we assessed methylation patterns of cfDNA from 57 individuals, 34 of whom had unequivocally elevated cfDNA concentrations (i.e., &gt;15 ng/mL of plasma). We found that the excess cfDNA in cancer patients did not come from either neoplastic cells or surrounding cells from the tumor's tissue of origin. Instead, 80% of the cfDNA was derived from neutrophils, B cells or T cells, with neutrophils accounting for ~2/3. B cells contributed more than T cells despite the great excess of T cells over B cells in the blood. To see if this result was generalizable to other clinical situations in which elevated cfDNA concentrations have been described, we evaluated nine patients before and one day following surgery. After surgical resection, cfDNA concentration increased by an average of 16.1-fold, as expected. The majority of this increase (55%) was contributed by leukocytes, though damaged hepatocytes contributed a substantial minority (31% of increase). These data suggest that the elevated concentrations of cfDNA in cancer patients, as with patients after surgery, arises largely from dying leukocytes—not from the neoplastic cells or the tissue of origin of the cancer.

  Study EGAS00001005400

• Genome-wide cell-free DNA termini in patients with cancer

  The structure, fragmentation pattern, length and terminal sequence of cell-free DNA (cfDNA) is under the influence of nucleases present in the blood. We hypothesized that differences in the diversity of bases at the end of cfDNA fragments can be leveraged on a genome-wide scale to enhance the sensitivity for detecting the presence of tumor signals in plasma. We surveyed the cfDNA termini in 72 plasma samples from 319 patients with 18 different cancer types using low-coverage whole genome sequencing. The fragment-end sequence and diversity were altered in all cancer types in comparison to 76 healthy controls. We converted the fragment end sequences into a quantitative metric and observed that this correlates with circulating tumor DNA tumor fraction (R = 0.58, p &lt; 0.001, Spearman). Using these metrics, we were able to classify cancer samples from control at a low tumor content (AUROC of 91% at 1% tumor fraction) and shallow sequencing coverage (mean AUROC = 0.99 at &gt;1M fragments). Combining fragment-end sequences and diversity using machine learning, we classified cancer from healthy controls (mean AUROC = 0.99, SD = 0.01). Using unsupervised clustering we showed that early-stage lung cancer can be classified from control or later stages based on fragment-end sequences. We observed that fragment-end sequences can be used for prognostication (hazard ratio: 0.49) and residual disease detection inresectable esophageal adenocarcinoma patients, moving fragmentomics toward a greater clinical implementation.

  Study EGAS00001005738

• Genome-wide cell-free DNA termini in patients with cancer

  The structure, fragmentation pattern, length and terminal sequence of cell-free DNA (cfDNA) is under the influence of nucleases present in the blood. We hypothesized that differences in the diversity of bases at the end of cfDNA fragments can be leveraged on a genome-wide scale to enhance the sensitivity for detecting the presence of tumor signals in plasma. We surveyed the cfDNA termini in 72 plasma samples from 319 patients with 18 different cancer types using low-coverage whole genome sequencing. The fragment-end sequence and diversity were altered in all cancer types in comparison to 76 healthy controls. We converted the fragment end sequences into a quantitative metric and observed that this correlates with circulating tumor DNA tumor fraction (R = 0.58, p &lt; 0.001, Spearman). Using these metrics, we were able to classify cancer samples from control at a low tumor content (AUROC of 91% at 1% tumor fraction) and shallow sequencing coverage (mean AUROC = 0.99 at &gt;1M fragments). Combining fragment-end sequences and diversity using machine learning, we classified cancer from healthy controls (mean AUROC = 0.99, SD = 0.01). Using unsupervised clustering we showed that early-stage lung cancer can be classified from control or later stages based on fragment-end sequences. We observed that fragment-end sequences can be used for prognostication (hazard ratio: 0.49) and residual disease detection inresectable esophageal adenocarcinoma patients, moving fragmentomics toward a greater clinical implementation.

  Study EGAS00001005747