Search Results - EGA European Genome-phenome Archive

T-cell receptor sequencing of tumor-infiltrating lymphocytes (TIL) in breast cancer

The spatial distribution of tumor-infiltrating lymphocytes (TIL) predicts breast cancer outcome and response to systemic therapy, highlighting the importance of an intact tissue structure for characterizing patients’ tumors. Here, we present ST-FFPE, a spatial transcriptomics method for the analysis of formalin-fixed paraffin-embedded samples, which opens the possibility of interrogating widely available archival tissue. The method involves extraction, exome capture and sequencing of RNA from different tumor compartments microdissected by laser-capture, and can be used to study the cellular composition of various tumor environments. Focusing on triple-negative breast cancer (TNBC), we characterized T cells, B cells, dendritic cells, fibroblasts and endothelial cells in both stromal and intra-epithelial compartments. We found a highly variable spatial distribution of immune cell subsets among tumors. This analysis revealed that the immune repertoires of intra-epithelial T and B cells were consistently less diverse than those of stromal T and B cells. T-cell receptor (TCR) sequencing confirmed a reduced diversity and revealed higher clonality of intra-epithelial T cells relative to the corresponding stromal T cells. Analysis of the top ten dominant clonotypes in the two compartments showed a majority of shared but also some unique clonotypes both in stromal and intra-epithelial T cells. Hyperexpanded clonotypes were more abundant among intra-epithelial than stromal T cells. These findings validate the ST-FFPE method and suggest an accumulation of antigen-specific T cells within tumor core. Because ST-FFPE is readily applicable for analysis of previously collected tissue samples, it could be useful for rapid assessment of intratumoral cellular heterogeneity in multiple disease and treatment settings.

Study EGAS00001007125

Spatial and temporal intra-tumoural heterogeneity in advanced High-Grade Serous Ovarian Cancer: implications for surgical and clinical outcomes

Limited evidence exists on the extent and impact of spatial and temporal heterogeneity of high grade serous ovarian cancer (HGSOC) on tumour evolution and patients surgical and clinical outcome. We investigated this through systematic mapping of multi-site tumours at initial presentation and matched relapse from 49 chemo-naïve HGSOC patients with high tumour load, operated upfront. Our data provides a unique insight into the tumour evolution and metastatic pathways of HGSOC across time and space, how this complexity relates to surgical and clinical outcome and its consequences on clinical decision-making.

Study EGAS00001007164

Evaluation of somatic mutations in urine samples as a non-invasive method for the detection and molecular classification of endometrial cancer

Purpose: Current diagnostic methods for endometrial cancer lack specificity, leading to many women undergoing invasive procedures. The aim of this study was to evaluate somatic mutations in urine to accurately discriminate endometrial cancer patients from controls. Experimental Design: Overall, 72 samples were analyzed using next-generation sequencing with molecular identifiers targeting 47 genes. We evaluated urine supernatant samples from women with endometrial cancer (n=19) and age-matched controls (n=20). Cell pellets from urine and plasma samples from seven cases were sequenced; further, we also evaluated paired tumor samples from all cases. Finally, immunohistochemical markers for molecular profiling were evaluated in all tumor samples. Results: Overall, we were able to identify mutations in DNA from urine supernatant samples in 100% of endometrial cancers. In contrast, only one control (5%) showed variants at a variant allele frequency (VAF)≥2% in the urine supernatant samples. The molecular classification obtained by using tumor samples and urine samples showed good agreement. Analyses in paired samples revealed a higher number of mutations and VAFs in urine supernatants than in urine cell pellets and blood samples. Conclusions: Evaluation of somatic mutations using urine samples may offer a user-friendly and reliable tool for endometrial cancer detection and molecular classification. The diagnostic performance for endometrial cancer detection was very high, and cases could be molecularly classified using these noninvasive and self-collected samples. Additional multi-center evaluations using larger sample sizes are needed to validate the results and understand the potential of urine samples for the early detection and prognosis of endometrial cancer.

Study EGAS00001007396

SYNTHETIC: EOSC4Cancer Study Synthetic Colorectal Cancer Genomic data

This dataset contains 10 tumor and normal pairs synthetic WGS data of colorectal cancer that were simulated in a standard format of Illumina paired-end reads. NEAT read simulator (version 3.0, https://github.com/zstephens/neat-genreads) is utilized to synthetize these 10 pairs tumor and normal WGS data. In the procedure of data generation, simulated parameters (i.e., sequencing error statistics, read fragment length distribution and GC% coverage bias) are learned from data models provided by NEAT. The average sequencing depth for tumor and normal samples aims to reach around 110X and 60X, respectively. For generation of synthetic normal WGS data per each sample, a germline variant profile from a real patient is down-sampled randomly, which includes 50% germline variants of such a patient. It is then mixed together with an in silico germline variant profile that is modelled randomly using an average mutation rate (0.001), finally constituting a full germline profile for normal synthetic WGS data. For generation of synthetic tumor WGS data per each sample, a pre-defined somatic short variant profile (SNVs+Indels) learn from a real CRC patient is added to the germline variant profile used for creating normal synthetic WGS data of the same patient, which is utilized to produce simulated sequences. Neither copy number profile nor structural variation profile is introduced into the tumor synthetic WGS data. Tumor content and ploidy are assumed to be 100% and 2.

Study EGAS50000000386

Genomic and Transcriptomic Profiling of Colorectal Cancer in Swedish Patients

My descriptionThis study investigates the genomic and transcriptomic landscape of colorectal cancer (CRC) in Swedish patients to identify molecular biomarkers associated with disease progression, treatment response, and survival outcomes. A total of 350 patients diagnosed with CRC between 2018 and 2023 were recruited from three Swedish hospitals: Lund University Hospital, Karolinska University Hospital, and Sahlgrenska University Hospital. Matched tumor tissue and blood samples were collected from all participants after obtaining informed consent. Whole-genome sequencing (WGS) was performed on blood-derived DNA to identify germline variants, while RNA-seq analysis of tumor tissues provided transcriptomic profiles. In addition, targeted panel sequencing was carried out for selected cancer-related genes to confirm findings from the WGS data.

Study EGAS50000000158

EGAD00000000013

WTCCC1 project Breast cancer (BC) samples

Dataset EGAD00000000013

EGAD00000000048

Sequencing data from oestrogen-receptor-alpha-positive metastatic lobular breast cancer sample

Dataset EGAD00000000048

EGAD00000000049

RNA-SEQ data from oestrogen-receptor-alpha-positive metastatic lobular breast cancer sample

Dataset EGAD00000000049

EGAD00000000053

Sequencing data from Breast Cancer samples

Dataset EGAD00000000053

EGAD00000000054

NCI-H209 is an immortal cell line derived from a bone marrow metastasis of a patient with small cell lung cancer, taken before chemotherapy. The specimen showed histologically typical small cells with classic neuroendocrine features. NCI-BL209 is an EBV-transformed B-cell line derived from the same patient as the small cell lung cancer cell line, NCI-H209

Dataset EGAD00000000054