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• Lactate metabolism in cancer stem cell fate regulation

  Tumors arise as a result of uncontrolled cell proliferation driven by mutations in the signaling pathways that regulate the balance between cell division and differentiation. Despite of that, in the tumor, cancer stem cells seem to retain their capability to differentiate, which can lead to differential chemotherapy response. We previously showed that intestinal stem cells and differentiated cells display specific metabolic features and that they interact through metabolites. Moreover, we found that altering mitochondrial metabolism is sufficient to lead and define stem cell differentiation. In cancer, cellular metabolism is highly derailed. However, how metabolic changes in the cancer cell contribute to cell fate decisions remains unknown. Recently, the crosstalk between metabolism and epigenetics has emerged as one of the players in tumor development. Here we aim to address the impact of the altered metabolism of cancer cells on their epigenome as a regulatory axis of cell identity and fate. We use human-derived tumor organoids where we introduce genetic encoded fluorescent-based reporters to visualize cell identity and metabolism at single cell level. We combine 4D live imaging with a recently developed machine learning method to re-construct cell-lineages, trace stem cell state and metabolic changes during tumor organoid development. We find that, mimicking the tumor metabolic microenvironment (Warburg effect-driven low-glucose high-lactate) leads to changes cellular metabolism and histone acetylation. Strikingly, these alterations provoke profound changes in tumor development by increasing the population of cancer stem cells and their proliferation and differentiation dynamics.

  Study EGAS50000000063

• Whole-genome and transcriptome versus panel sequencing in precision oncology: A translational-clinical comparison

  Precision oncology offers new cancer treatment options. While the use of whole-genome (WGS) and transcriptome (TS) sequencing remains often confined to specific clinical programs, molecular analysis with gene panels is more common clinical practice. Methods: Twenty patients with rare or advanced tumors sequenced by WGS/TS within the DKFZ/NCT/DKTK-MASTER program from 2015 to 2020 (MASTER 1) were additionally sequenced with the TSO500 DNA and TST170 RNA Assay by Illumina. Original Molecular Tumor Board (MTB) therapy recommendations (TR) were juxtaposed with TR from NGS panel data analysis (comparison 1). To mitigate temporal differences between the two sequencings, WGS/TS was bioinformatically reanalyzed (MASTER 2), and new TR were again compared (comparison 2). Results: In comparison 1, 47.1% of the MASTER 1’s TR and 62.8% of the panel’s TR were identical, as were 45.9% of the TR of MASTER 2 and 54.9% of the panel in comparison 2. In both MASTER 1 and MASTER 2, 26.5% and 36.1% of TR, respectively, relied on Biomarkers (BM) which were not covered by the panel. Eight of the ten MTB-derived therapy implementations were also supported in the panel, the two remaining ones being based on BM absent from the panel. Conclusions: About half of the TR in WGS/TS and panel sequencing were identical. Around a third of TR issued by WGS/TS were based on BM which are not covered by the panel, leading to therapy implementations. The presented data underscores the clinical value of comprehensive molecular analyses in individuals with advanced cancers.

  Study EGAS50000000431

• Array data for oesophageal and related samples – sj_paper_methyl_normal_release

  Array data for oesophageal and related samples – sj_paper_methyl_normal_release

  Dataset EGAD00010001834

• Young-Boost Whole Exome Sequencing (WES)

  Datasets associated with Young Boost Trial for Breast Cancer patients. Whole Exome Sequencing (WES) performed on 109 Samples (75 Tumor and 34 Normal). WES was performed to identify potential predictive biomarkers for treatment response.

  Dataset EGAD50000001171

• Clinical and ctDNA data for IMpassion031

  Clinical and ctDNA data for IMpassion031, including survival, response, and ctDNA data from baseline through post-surgery time points. 222 samples run on Signatera assay. File type is csv.

  Dataset EGAD50000001420

• WGS dataset for two NUP98-Rearranged Acute Myeloid Leukemia PDX samples

  This dataset includes whole genome sequencing of two patient-derived xenograft (PDX) samples with NUP98-Rearranged Acute Myeloid Leukemia. Bam files are provided for each sample.

  Dataset EGAD50000001563

• Chromatin accessibility profiling of primary human hepatocytes

  The chromatin accessibility profiling of primary human hepatocytes using Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq).

  Study EGAS50000001230

• Indonesian Genome Diversity Project 2, genotyping data

  This study reports Illumina Omni 2.5M genotyping chip data for 48 individuals from Aru, Mentawai, New Guinea and Sumba islands.

  Study EGAS00001003670

• scATAC sequence runs of 29 samples of RRMM (multiple myeloma) tumors

  scATAC sequencing was performed of 29 samples of RRMM tumors using 10xGenomics for the preparation and NovaSeq6000 for sequencing.

  Dataset EGAD00001009683

• A95626A

  Whole genome sequencing for single cells for library A95626A 1040 cells; filetype=bam

  Dataset EGAD00001007103