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• Whole genome sequencing data of paediatric KMT2A-rearranged acute lymphoblastic leukemia

  Pediatric KMT2A-rearranged leukemia is an aggressive subtype of leukemia commonly seen in infants (less tha 1 year old) and young children. It involves rearrangements of the KMT2A gene (formerly MLL) on chromosome 11q23, resulting in fusion genes that drive uncontrolled cell growth. This subtype is most often found in acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) and is associated with a poor prognosis. In infants, the 5-year event-free and overall survival rates are around 35–40%. Despite intensive chemotherapy, outcomes remain challenging, prompting ongoing research into targeted therapies and novel treatment strategies.

  Study EGAS50000001079

• RNAseq profile of monomorphic epitheliotropic intestinal T-cell lymphoma and enteropathy associated T-cell lymphoma

  Background : Enteropathy-associated T-cell lymphoma (EATL) and monomorphic epitheliotropic intestinal T-cell lymphoma (MEITL) are aggressive malignancies with limited treatment options and poor prognosis. Our research on MEITL has uncovered its genetic portrait including hallmark SEDT2 mutations, expanded its pathological spectrum, and identified molecular biomarkers influencing patients’ outcome. In this study, we aimed to establish and clarify the similar and diverging features of EATL. Methods : A large combined cohort of patients with EATL (n=30) and MEITL (n=52) was assembled and tumor samples were used for whole-exome and RNA sequencing profiling.

  Study EGAS50000001125

• Whole Exome Sequencing of monomorphic epitheliotropic intestinal T-cell lymphoma and enteropathy associated T-cell lymphoma

  Background : Enteropathy-associated T-cell lymphoma (EATL) and monomorphic epitheliotropic intestinal T-cell lymphoma (MEITL) are aggressive malignancies with limited treatment options and poor prognosis. Our research on MEITL has uncovered its genetic portrait including hallmark SEDT2 mutations, expanded its pathological spectrum, and identified molecular biomarkers influencing patients’ outcome. In this study, we aimed to establish and clarify the similar and diverging features of EATL. Methods : A large combined cohort of patients with EATL (n=30) and MEITL (n=52) was assembled and tumor samples were used for whole-exome and RNA sequencing profiling.

  Study EGAS50000001126

• RNAseq of MCL cell lines

  This study profiles the transcriptomic response of the Mino-VEN-R and Jeko-BTK-R mantle cell lymphoma (MCL) cell lines to pirtobrutinib (PBN) treatment using bulk RNA sequencing. Mino-VEN-R models drug-tolerant persister (DTP) cells that arise during exposure to non-covalent BTK inhibitors. Building on our recent findings that DTP cells undergo metabolic and phenotypic state transitions governed by TCA cycle reprogramming, this dataset aims to capture gene expression changes associated with treatment-induced plasticity. The results contribute to a broader multi-omic framework exploring mechanisms of resistance and therapeutic vulnerability in MCL.

  Study EGAS50000001087

• Neuroblastoma Smart-Seq2 Single Nuclei Sequencing Data

  Heterogeneity of tumors, including the presence of resistant clones, represents a great challenge for high-risk neuroblastoma (NB). Currently, there are no treatments that address cancer heterogeneity. Classification of bulk tumors and corresponding predictions are poor and too general. Understanding heterogeneity will provide critical insight for the development of targeted therapeutic strategies. In this study, we collected 10 human neuroblastoma tumor samples and performed whole-transcriptome profiling at single-nucleus resolution using SMART-Seq2 technology. This approach enables comprehensive characterization of transcriptional diversity within individual tumors, allowing us to identify distinct cellular subpopulations.

  Study EGAS50000001103

• Whole-Genome Shotgun Metagenomic Analysis of Rectal Mucus for Colorectal Cancer Detection

  This study evaluates the use of rectal mucus as a minimally invasive biospecimen for colorectal cancer (CRC) detection through whole-genome shotgun metagenomic sequencing. 408 rectal mucus samples were collected from patients suspected to have colorectal cancer and sequenced. These samples were analyzed to characterize microbial community composition and its association with CRC stage and anatomical site. These data provide insights into tumor-proximal microbiome signatures and demonstrate the potential of rectal mucus sampling for early and accurate CRC diagnosis. This is 1 of 4 sequencing experiments on the same sample type.

  Study EGAS50000001310

• Full AfricanNeo ModernDNA Study

  With the largest genomic dataset to date of Bantu-speaking populations, including newly generated data of modern-day and ancient DNA from previously unsampled regions in Africa, we shed fresh light on the expansion of peoples speaking Bantu languages that started ~4000 years ago in western Africa. We have genotyped 1,763 participants, including 1,487 Bantu speakers from 143 populations across 14 African countries. Our comprehensive genetic analysis of the expansion of people speaking Bantu languages reveals a complex history of serial founder events, variable levels of contact with local groups, and spread-over-spread events.

  Study EGAS50000000006

• Non-invasive prenatal diagnosis by genome-wide haplotyping of maternal cell-free plasma DNA

  Whereas non-invasive prenatal testing for aneuploidies (NIPT-A) is widely implemented, non-invasive prenatal testing for monogenic diseases (NIPT-M) is lagging. By capturing and targeted sequencing of 250000 polymorphic SNP loci from maternal plasma circulating cell-free DNA (cfDNA) and DNA from relatives, the fetal haplotype and chromosomal copy numbers are deduced. In all families tested, the cfDNA derived haplotypes are on average 97% concordant with the neonatal and embryo haplotype. This generic non-invasive prenatal diagnostic approach allows cost efficient scrutinizing the fetal genome for the presence of any inherited monogenic disease or trait.

  Study EGAS00001003634

• WXS of 147 lung cancer patients treated with immunotherapy

  We first attempted to predict MHC-binding neoantigens at high accuracy with convolutional neural networks. This prediction model outperformed previous methods in > 70% of test cases. Importantly, our method remarkably increased the predictive value of neoantigen load especially in combination with known resistance parameters. We then developed a classifier that can predict resistance from point mutations that are deleterious to protein function. Notably, genes involved in the adaptive immune response, cytokine signaling,and EGFR signaling held high explanatory power. Furthermore, when integrated with our neoantigen profiling, these anti-immunogenic mutations revealed significantly higher predictive power than known resistance factors.

  Study EGAS00001003781

• Lethal malformation syndrome

  Identification of the genetic basis of rare monogenic diseases can provide insights into disease mechanisms, biological pathways and potential therapeutic targets. Exome sequencing has proven to be a powerful, efficient strategy for the identification of the genes underlying rare Mendelian disorders. The genetic defect behind the lethal malformation syndrome under study is known in some cases and has proven useful in revealing the primary underlying defect. We have six typical unrelated families with no known mutation. The aim of this study is to identify the locus associated with this lethal malformation syndrome in these families using exome sequencing.

  Study EGAS00001000061