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• Precise reconstruction of the tumor microenvironment using bulk RNA-seq and a unique machine learning-based algorithm trained on artificial transcriptomes

  Cellular deconvolution algorithms virtually reconstruct tissue composition by analyzing the gene expression of complex tissues. Here, we present the decision tree machine learning algorithm, Kassandra, trained on a broad collection of > 9,400 tissue and blood sorted cell RNA profiles to accurately reconstruct the tumor microenvironment (TME). Bioinformatic correction for technical and biological variability, aberrant cancer cell expression inclusion, and the accurate quantification and normalization of transcript expression increased the stability and robustness of Kassandra. Performance was validated on over 4,000 H&E tissue slides and more than 1,000 normal and tumor tissues by comparison with cytometric, immunohistochemical or single-cell RNA-seq measurements. Kassandra accurately deconvolved stromal and immune elements of blood and tumors, revealing the role of the TME in tumor pathogenesis. Digital TME reconstruction revealed that the presence of PD1-positive CD8+ T cells strongly correlated with immunotherapy response and increased the predictive potential of established biomarkers, indicating that Kassandra could potentially be utilized in future clinical applications.

  Dataset EGAD00001008776

• Dataset for bone_cancer-EXON

  Rare cancer sequencing data of 87 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008865

• Dataset for hematopoietic_malignancy-WHOLE_GENOME

  Rare cancer sequencing data of 49 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008881

• Dataset for leiomyosarcoma-EXON

  Rare cancer sequencing data of 96 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008885

• Dataset for leiomyosarcoma-WHOLE_GENOME

  Rare cancer sequencing data of 66 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008887

• Dataset for other_cancer-WHOLE_GENOME

  Rare cancer sequencing data of 49 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008895

• The polarity and specificity of antiviral T lymphocyte responses determine susceptibility to SARS-CoV-2 infection in cancer patients and healthy individuals

  We monitored patient's anti SARS-CoV-2 immune responses using an in vitro cross presentation assay. The goal of this study was to identify immune correlates of clinical protection against SARS-CoV-2 infection. Briefly, peripheral blood mononuclear cells of patient were divided into a monocyte and lymphocyte. Monocyte were differentiated into monocyte derived dendritic (mo-DC)cells using GM-CSF and Interferon alpha. Mo-DC were then loaded with SARS-CoV-2 culture lysates , or VeroE86 lysates. SARS-CoV-2 loaded mo-DC were then used to stimulates their autologous lymphocytes and T cell cytokine secretion was monitored in the supernatant. We discriminated patients producing IL-2 and patients producing IL-5. RNA sequencing was performed for 18 patients, to identify gene profile associated with IL-2 or IL-5 production.

  Dataset EGAD00001008538

• Whole exome sequencing of non-small cell lung cancer patient-derived xenografts

  Non-small cell lung cancer (NSCLC) is the leading cause of cancer deaths worldwide. Only a fraction of NSCLC harbour actionable driver mutations and there is an urgent need for patient-derived model systems that will enable the development of new targeted therapies. We generated NSCLC patient-derived xenografts (PDXs) that recapitulate the histology and molecular features of primary NSCLC. Here, we completed whole exome sequencing on 122 NSCLC PDXs.

  Dataset EGAD00001008601

• 20210708_EGA_Melanoma_mk2 WGS 570 donors

  Illumina platform whole genome sequencing data for matched tumour-normal DNA samples from 570 melanoma patients

  Dataset EGAD00001008798

• WGS of tumor and blood control samples of neuroblastoma

  high coverage whole genome sequencing of 38 samples was done on a patterned flowcell v.2.5 (150 bp paired end, HiSeq X Ten) with coverage of about 60x for the tumor and whole blood control samples. All tumors had a tumor cell content of ≥60%. Sequencing libraries were prepared using the Truseq DNA Nano kit (Illumina) according to the manufacturers’ instructions and size selected using SPRI beads (Beckman Coulter Genomics).

  Dataset EGAD00001009624