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• Genomic landscape and molecularly-informed therapy in thymic carcinoma and other advanced thymic epithelial tumors (H021, HIPO)

  The molecular landscape of malignant thymic epithelial tumors (TET) is largely unknown. To better understand and target these clinically challenging entities, we characterize 81 patients harboring 46 thymus carcinomas (TC), 8 neuroendocrine tumors of the thymus (NET) and 27 thymomas using WGS/WES, transcriptome and methylome analysis. TC harbor different molecular alterations compared to thymoma and NET and have higher tumor mutational burden than thymomas. 14/76 TET patients show (likely) pathogenic germline alterations. Composite biomarker analysis indicates homologous repair deficiency in a subset of patients. We identify immunologically hot and cold TC. Cold TC show significantly shorter overall survival. 71/81 patients receive molecularly-informed therapy recommendations, which are applied in 28/71 cases leading to a DCR of 60% and an ORR of 24% (both n=25). 8/23 patients reach PFS ratios > 1.3, seven of them having TC.

  Study EGAS00001006408

• DNA methylation-based classification of sinonasal tumors [Proteomics data]

  The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent previously misclassified adenoid cystic carcinomas. The repository includes the raw mass spectrometry-based proteomics data

  Study EGAS00001006712

• DNA methylation-based classification of sinonasal tumors [DNA sequencing]

  The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent previously misclassified adenoid cystic carcinomas. This repository includes the results from DNA sequencing and mass spectrometry-based proteomics.

  Study EGAS00001006713

• The impact of mutational clonality in predicting the response to anti-PD-L1/PD-L1 in advanced urothelial cancer

  The advent of immune checkpoint inhibitors (ICI) has radically changed the management and therapeutic treatment landscape for patients with cancer. It has been shown that the use of these inhibitors can result in long-term complete remissions even in cases with advanced stages of the disease. However, only a small fraction of patients respond to the treatment. A better understanding of which factors could drive this clinical benefit is fundamental to building more accurate predictive biomarkers and developing alternative therapeutic options for patients who are unlikely to benefit. In order to address this issue, we have generated and analyzed whole exome sequencing (WES) and RNA sequencing (RNASeq) data from a cohort of metastatic urothelial carcinoma patients (N = 27) treated with ICI such as anti-PD-(L)1 monoclonal antibodies.

  Study EGAS00001007086

• Digital tEchnology For Lung Cancer Treatment

  The aim of the DEFLeCT project (Digital tEchnology For Lung Cancer Treatment) is to generate a technological platform for precision medicine in order to set predictive personalised algorithms for patient diagnosis and therapy. All resectable patients having histologically confirmed stage IB-IIIA non-small cell lung cancer(NSCLC) will be enrolled in the PROMOLE study, a related and ongoing observational prospective clinical study, for tissue sampling. A large biobank of lung cancer samples and the corresponding healthy tissues and biological components (ie, blood, stools, etc.) with complete clinical, pathological and molecular information will be collected. The project will lay the basis of a knowledge hub and qualified technology aimed not only at answering the medical and scientific community's questions, but also meant to be useful to individual patients by predicting the response to adjuvant and second-line drugs in case of relapse of the disease.

  Study EGAS00001007219

• Genomic and transcriptomic landscape of T-cell lymphoblastic lymphoma compared to T-cell acute lymphoblastic leukemia: similar subtypes and different fusions

  Due to the lower incidence of T-LBL and difficulties in obtaining diagnostic T-LBL material, extensive research on T-LBL has been hampered whereas genetic aberrations in T-ALL are thoroughly characterized. Given the similarities and differences between T-LBL and T-ALL, the question has been raised whether T-LBL and T-ALL represent two different diseases or different manifestations of the same disease. This study aims to identify the genomic and transcriptomic landscape of T-LBL and compare the findings to what is found T-ALL. Comparison of the molecular aberrations between T-LBL and T-ALL can provide insights into the overlap and differences in malignant development between the two entities, which could lead to improved risk stratification in T-LBL in order to eventually adapt T-LBL treatment protocols based on molecular-genetic prognostic factors.

  Study EGAS00001007767

• Colorectal Microenvironment Spatial Mapping

  In colorectal cancer (CRC), interactions between tumor and immune cells within the tumor microenvironment are critical drivers of tumorigenesis, immune evasion, prognosis, and therapeutic response. Therefore, a deeper understanding of the spatial organization of cancer and immune cells within the CRC microenvironment is urgently needed. In this study, we aimed to characterize distinct cell populations and patterns of spatial gene expression associated with CRC tumorigenesis and immune infiltration. Spatial transcriptomics (ST) was performed on CRC and adjacent normal tissues to investigate the patterns of gene expression. Integrative ST data and publicly available single-cell RNA sequencing (scRNA-seq) data analysis tools were used for data integration, dimension reduction, clustering, and differential expression analysis. This study identified novel immunosuppressive cell populations and immune regulatory factors in CRC. Further functional validation of these findings will be essential to assess their potential as therapeutic targets.

  Study EGAS00001008254

• SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish population

  The SweGen project represents a comprehensive map of genetic variation in the Swedish population. This Study contains four datasets where the first one contains whole-genome sequencing data in CRAM-format for 942 individuals from the Swedish Twin Registry, the second one contains genetic variant frequencies in VCF-format for the same individuals, the third one contains whole-genome sequencing data in CRAM-format for 58 individuals from the Northern Sweden Population Health Study and the fourth one contains genetic variant frequencies in VCF-format for the same individuals. The data, serving as a reference dataset, is a valuable and national resource for genetics research and clinical diagnostics and can be used, for instance, to increase understanding of both rare and complex disorders. It also serves as an international resource for larger studies of human population genetics.

  Study EGAS50000000906

• Whole genome sequencing data from tumor and normal samples

  Whole genome sequencing FASTQ files from tumor and normal samples from 202 patients. All experiments reported in this article were performed in the Clinical Laboratory Improvement Amendments (CLIA)-certified and College of American Pathologists (CAP)-accredited laboratories at Personalis Inc., as guided by the Association for Molecular Pathology (AMP) and CAP’s joint recommendations52. Preferred input material consisting of FFPE slides (enabling H&E and macrodissection which is part of the standard NeXT Personal process) was not available for the majority (94%) of tumor samples. Most tumor samples (72%) were processed from FFPE curls, with the remaining processed from pre-extracted DNA and fresh frozen material. Where FFPE material on slide was available, tumor sections were macrodissected to increase tumor content, with a minimum allowable cellularity threshold of 20% determined by pathological review. The tumor cellularity threshold of 20% was chosen based on prior work demonstrating that no significant correlation is observed between tumor purity and assay limit of detection (LOD) at this threshold31. Genomic DNA was isolated from tumor and normal samples and WGS libraries prepared as previously described31. Genomic DNA was isolated from matched tumor and normal samples using the Qiagen AllPrep DNA/RNA FFPE Tissue Kit or the QIAamp DNA Mini Kit (QIAGEN, Germantown, MD, USA) using internally optimized workflows. WGS sequencing libraries were prepared with 120-500 ng of acoustically sheared genomic DNA (Covaris LLC, Woburn, MA, USA) using the KAPA HyperPrep Kit (Roche Sequencing Solutions, Pleasanton, CA, USA) and customized methods. Libraries were cleaned-up using AMPure XP beads and then quantified using the KAPA Library Quantification Kit (Roche Sequencing Solutions, Pleasanton, CA, USA), before being sequenced to 30X depth of coverage using a NovaSeq 6000 instrument (Illumina, San Diego, CA, USA).

  Dataset EGAD50000001909

• Somatic mutations, clonal architecture and genomic evolution in multiple myeloma

  Multiple myeloma is an incurable plasma cell malignancy whose molecular pathogenesis is incompletely understood. We used whole exome sequencing, copy number profiling and cytogenetic to analyses 84 samples from 67 patients with myeloma. In addition to known myeloma genes, we identify new candidate genes, including truncations of SP140, ROBO1 and FAT3 and clustered missense mutations in EGR1. We find oncogenic mutations in cancer genes not previously implicated in myeloma, including SF3B1, PI3KCA and PTEN. We define diverse processes contributing to the mutational repertoire, including kataegis and somatic hypermutation. Most cases have at least one cluster of subclonal variants, including subclonal driver mutations, implying on-going tumor evolution. Serial samples revealed diverse patterns of clonal evolution, including linear evolution, differential clonal response and branching evolution. Our findings reveal the myeloma genome to be heterogeneous across patients and, within individual patients, to exhibit diversity in clonal admixture and dynamics in response to therapy.

  Dataset EGAD00001000339