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• RCC_HTA2.0_Reustle2020

  GeneChip HTA 2.0 data of primary renal cell carcinoma (RCC) related to Reustle et al, Genome Med 12:2020 32. Preprocessing of microarray data was performed using Robust Multi-array Average (RMA).

  Dataset EGAD00010002323

• ChIP-seq and Hodgkin lymphoma

  ChIP-seq data (H3K4Me3, H3K27Ac histone modifications) of Hodgkin lymphoma cell line L-428. Samples were processed as previously described (Sud et al., 2018). The files are in bam format, aligned to build 37 of the human genome.

  Dataset EGAD00001004322

• Ewing's Sarcoma Whole Genome Sequencing

  54 WGS Ewing's sarcoma samples sequenced at The Hospital for Sick Children Toronto (Adam Shlien's lab) and published on Science 2018. Reference Anderson et al. "Rearrangement bursts generate canonical gene fusions in bone and soft tissue tumors"

  Dataset EGAD00001004589

• McGill EMC Release 4 for assay "ATAC-seq"

  McGill EMC Release 4 for assay "ATAC-seq": Sequencing of transposase-accessible chromatin as described by Buenrostro et al. (Nature Methods 10, 1213?1218 (2013) doi:10.1038/nmeth.2688)

  Dataset EGAD00001001300

• Development of New Diagnostics, Therapeutics, and Prevention Methods for Personalized Medicine Based on Comprehensive Cancer-Related Gene Exome Analysis and Information Analysis Using Cancer Specimens Stored in TMDU Biobank.

  Biobanked tumor samples are a valuable resource in real-world solid tumor research. Panel-based genomic profiling enables the identification of clinically significant alterations that inform personalized diagnostic and treatment decisions. This study aimed to characterize the genomic landscape of solid tumors collected through the TMDU Biobank by summarizing mutational, copy number, and gene fusion alterations in a large and diverse patient cohort. This dataset contains summary-level genomic information from 1,996 tumor samples analyzed by ACT Genomics and ACT med using the ACTOnco Comprehensive 440-Gene Cancer Panel. Data provided includes (1) gene-level mutation counts, (2) a summary of copy number alterations, and (3) a summary of detected fusion events. Clinical attributes such as ICD-10 codes, age group, sex, tumor mutational burden (TMB), and microsatellite instability (MSI) status were included at the aggregate level. Summary statistics were generated from three source tables (gene, sample, and copy number data) using standardized processing rules. Due to contractual and ethical considerations, individual-level mutation locations and raw sequence data are not included; only summary-level information is provided. This dataset will support future research in tumor-type-specific genomic patterns, molecular characterization of solid malignancies, and precision oncology.

  Study JGAS000863

• Allele Balance Bias Identifies Systematic Genotyping Errors and False Disease Associations

  In recent years, Next Generation Sequencing (NGS) has become a cornerstone of clinical genetics and diagnostics. Many clinical applications require high precision, especially if rare events such as somatic mutations in cancer or genetic variants causing rare diseases need to be identified. Although random sequencing errors can be modeled statistically and deep sequencing minimizes their impact, systematic errors remain a problem even at high depth of coverage. Understanding their source is crucial to increase precision of clinical NGS applications. In this work, we studied the relation between recurrent biases in allele balance (AB), systematic errors and false positive variant calls across a large cohort of human samples analyzed by whole exome sequencing (WES). We have modeled the allele balance distribution for biallelic genotypes in 987 WES samples in order to identify positions recurrently deviating significantly from the expectation, a phenomenon we termed allele balance bias (ABB). Furthermore, we have developed a genotype callability score based on ABB for all positions of the human exome, which detects false positive variant calls that passed state-of-the-art filters. Finally, we demonstrate the use of ABB for detection of false associations proposed by rare variant association studies (RVAS).

  Study EGAS00001003027

• bulk ATACseq: A Cultured Leukemia Stem Cell Model Enables Validation of CDK6 as a Stemness Target Against Acute Myeloid Leukemia

  Acute myeloid leukemia (AML) represents a group of aggressive hematological malignancies, the clinical management of which is made challenging due to the persistence of rare and therapy resistant leukemia stem cells (LSCs) which serve as a source of disease relapse and poor outcomes. There are currently a paucity of methods to reliably enrich and study LSCs, hindering the development of therapies that specifically target LSCs. In this study, we deeply characterize the OCI-AML8227 culture model, which maintains a functional stemness hierarchy originating from its highly primitive CD34⁺CD38⁻ cells, to elucidate LSC biology and uncover LSC-specific therapeutic vulnerabilities. We analyzed both bulk and single-cell proteomics, transcriptomics, and epigenomics to generate a LSC protein-protein interaction network, which was then integrated with an LSC-focused small molecule screen using this model. From these findings, CDK6 was discovered as a therapeutic vulnerability specific to LSCs, which was validated in findings from the BEAT-AML cohort and a patient-derived xenograft (PDX) panel of AML samples through palbociclib treatment. Taken together, our studies validate CDK6 as a druggable vulnerability in LSCs, and authenticate OCI-AML8227 cells as a LSC target discovery engine.

  Study EGAS50000000787

• scATAC: A Cultured Leukemia Stem Cell Model Enables Validation of CDK6 as a Stemness Target Against Acute Myeloid Leukemia

  Acute myeloid leukemia (AML) represents a group of aggressive hematological malignancies, the clinical management of which is made challenging due to the persistence of rare and therapy resistant leukemia stem cells (LSCs) which serve as a source of disease relapse and poor outcomes. There are currently a paucity of methods to reliably enrich and study LSCs, hindering the development of therapies that specifically target LSCs. In this study, we deeply characterize the OCI-AML8227 culture model, which maintains a functional stemness hierarchy originating from its highly primitive CD34⁺CD38⁻ cells, to elucidate LSC biology and uncover LSC-specific therapeutic vulnerabilities. We analyzed both bulk and single-cell proteomics, transcriptomics, and epigenomics to generate a LSC protein-protein interaction network, which was then integrated with an LSC-focused small molecule screen using this model. From these findings, CDK6 was discovered as a therapeutic vulnerability specific to LSCs, which was validated in findings from the BEAT-AML cohort and a patient-derived xenograft (PDX) panel of AML samples through palbociclib treatment. Taken together, our studies validate CDK6 as a druggable vulnerability in LSCs, and authenticate OCI-AML8227 cells as a LSC target discovery engine.

  Study EGAS50000000788

• bulk RNA-seq: A Cultured Leukemia Stem Cell Model Enables Validation of CDK6 as a Stemness Target Against Acute Myeloid Leukemia

  Acute myeloid leukemia (AML) represents a group of aggressive hematological malignancies, the clinical management of which is made challenging due to the persistence of rare and therapy resistant leukemia stem cells (LSCs) which serve as a source of disease relapse and poor outcomes. There are currently a paucity of methods to reliably enrich and study LSCs, hindering the development of therapies that specifically target LSCs. In this study, we deeply characterize the OCI-AML8227 culture model, which maintains a functional stemness hierarchy originating from its highly primitive CD34⁺CD38⁻ cells, to elucidate LSC biology and uncover LSC-specific therapeutic vulnerabilities. We analyzed both bulk and single-cell proteomics, transcriptomics, and epigenomics to generate a LSC protein-protein interaction network, which was then integrated with an LSC-focused small molecule screen using this model. From these findings, CDK6 was discovered as a therapeutic vulnerability specific to LSCs, which was validated in findings from the BEAT-AML cohort and a patient-derived xenograft (PDX) panel of AML samples through palbociclib treatment. Taken together, our studies validate CDK6 as a druggable vulnerability in LSCs, and authenticate OCI-AML8227 cells as a LSC target discovery engine.

  Study EGAS50000000789

• Ewing and Ewing-like-sarcoma tumoroid biobank reveals distinct drug sensitivities: translocation makes the difference

  Ewing sarcoma (ES) and Ewing-like sarcomas (ELS) are highly aggressive tumors found in soft tissues and bone of mostly children and young adults. Despite being different in many aspects, including genetics, possible cell-of-origin, and pathology, patients with any of these entities all receive the same therapeutic regimen. Currently, there is a lack of preclinical cell models that can predict drug response of these entities in vitro. Here, we describe a pediatric ES and ELS tumor organoid (tumoroid) biobank containing tumoroids with different translocations, including EWSR1::FLI1, EWSR1::ERG, CIC::DUX4, and BCOR-rearrangements. Using histology, whole genome sequencing and RNA sequencing, we demonstrate that these tumoroids retain key properties of their matching patient tumors. In addition, we traced tumor subclones in the tumoroids across patient-matched longitudinal samples, which showed that cellular heterogeneity was maintained. We subsequently performed drug screening on the tumoroid models, which unveiled entity-specific drug sensitivity to various cytotoxic compounds and targeted compounds, including MCL-1 inhibitors for CIC::DUX4 sarcomas. Taken together, this newly established ES and ELS patient-derived tumoroid biobank generates a unique source of material for future basic cancer research and drug-screening.

  Study EGAS00001007941