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• How upcycled prostate cancer sequences enabled key findings on telomeres length

  The length of telomeres, the chromosomes’ end tails, as depicted in the figure below, physiologically shorten with aging. This means that younger cells have longer telomeres compared to their aged counterparts; one could say that the length of cells’ telomeres is a measure of their replicative power. Cancer cells are known to be able to turn this into their favour, and expand their replicative immortality through telomere lengthening and maintenance. Despite being a well-studied pan-cancer hallmark, the relation of the telomeres length to specific cancer genomic features is poorly understood. Julia Livingstone and her colleagues from P.C. Boutros' team in Los Angeles focused their work on prostate cancer. To investigate the relations between telomere length and the clinico-genomics of prostate tumours, they used whole genome sequencing (WGS) of 392 published tumour–reference pairs previously deposited at the EGA under accession numbers EGAS00001000400 and EGAS00001000900. Their work was published on Nature Communications in November 2021. They determined the telomere length of the sequencing sample, from both tumour and non-tumour adjacent tissues, and subsequently ran statistical analysis to investigate possible correlation between the telomeres length with several cancer features. The team discovered that telomeres length does not correlate with any specific driver gene mutation, but it does with the overall frequency of single nucleotides variants in a sample. This finding suggests that tumours with shorter telomeres accumulate more mutations without strong selective pressures for specific ones. While proliferation rate did not correlate directly with telomere length, methylation of almost half of all genes strongly did. The team’s results are massive and reveal a complicated and fascinating relation of different clinical and genomic features of prostate cancer with telomere length. In the paper discussion, they smartly consider different possibilities. As noted above, all these amazing results have been obtained analysing previously sequenced samples. Remarkably, the researchers who generated the samples did not focus at all on telomeres length. They indeed studied specific genes mutations and the features of early and late-onset prostate cancer. This proves, once again, the power of sequencing data re-use. This data is tremendously rich in information, and can hardly be exhausted with any number of analyses. Generating sequencing data, especially Whole Genome Sequencing, even better if tumor-reference paired, is like shopping every last article at the grocery store. You can then cook several recipes, or prepare a seven-course dinner, and still have ingredients left for someone else to borrow and cook something you have not thought about, a recipe that is just not in your cookbook. Noteworthy, the data retrieved from the EGA for this paper is associated with rich and complete metadata, meaning pheno-clinical information regarding the sample donors. As Julia Livingstone and her team wrote, using rich clinical annotations enabled them to assess the relationship between telomere length and patients outcome. This highlights once again the importance of reporting and annotating metadata correctly; It might feel like extra work for the researchers depositing their data, but it really has a huge value. Our team at the EGA is currently working to improve the metadata annotation process, with the aim to maximize the benefits for the scientific community.

  Blog prostate-cancer-sequences-enabled-key-findings-on-telomeres-length

• Dataset Rdata segmentations

  This data set contains an .Rdata file for all the processed segmentation profiles from 81 lpWGS samples included used in downstream analyses from Github repository Evo_history_CACRC. Lastly, there is an .Rdata object with 50 segmentations for 50 total samples from 25 sporadic SNP adenomas used in the comparison with colitis samples.

  Dataset EGAD00010001586

• DAC for EGA study: Multi-omic analyses from a randomized phase II study of epigenetic priming followed by nivolumab in previously treated metastatic non-small cell lung cancer

  This is the Data Access Committee for the EGA study: Multi-omic analyses from a randomized phase II study of epigenetic priming followed by nivolumab in previously treated metastatic non-small cell lung cancer (temp ID 800).

  Dac EGAC50000000563

• Characterization of iPSC derived macrophages - cardiovascular pilot (2017-05-24)

  This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ This dataset contains all the data available for this study on 2017-05-24.

  Dataset EGAD00001003347

• RGB TGCT Data Access Committee

  The RGB TGCT Data Access Committee is responsible for reviewing and approving requests for access to controlled genomic data generated from testicular germ cell tumor (TGCT) patients treated at the National Cancer Institute of Mexico (INCan). The committee evaluates requests to ensure that data use complies with the informed consent provided by participants and with applicable institutional and ethical regulations.

  Dac EGAC50000000959

• UniKilinikum Wuerzburg MSNZ AGRasche/AG Riedel EMD DAC

  This is a Data access committee for the Data presented in the paper tilted "Spatially resolved transcriptomics reveals profound subclonal heterogeneity and T cell dysfunction in extramedullary myeloma"

  Dac EGAC50000000173

• Liver Tumours WGS (2020-02-20)

  The aim of this study is to define the mutational landscape of human liver tumours. . This dataset contains all the data available for this study on 2020-02-20.

  Dataset EGAD00001005993

• Res1_H23_exp1_MC_04.03.22

  1 sample is pure plasmid DNA and 8 samples are cell pellets for genomic DNA extraction. CRISPR PCR1 and PCR2 indexing - Please use standard Kozuke primers.

  Dataset EGAD00001012229

• Res1_HT29_exp1_MC_02.03.22

  1 sample is pure plasmid DNA and 10 samples are cell pellets for genomic DNA extraction. CRISPR PCR1 and PCR2 indexing - Please use standard Kozuke primers.

  Dataset EGAD00001012230

• Res1_HT29_exp2_MC_03.03.22

  1 sample is pure plasmid DNA and 10 samples are cell pellets for genomic DNA extraction. CRISPR PCR1 and PCR2 indexing - Please use standard Kozuke primers.

  Dataset EGAD00001012231