12312 results for "cancer rna-seq"

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• Atypical teratoid/rhabdoid tumoroids reveal subgroup-specific drug vulnerabilities - RNA-Seq

  We demonstrate that ATRT tumoroids retain subgroup-specific epigenetic and gene expression profiles

  Dataset EGAD00001009794

• Clonal Evolution and Heterogeneity of Osimertinib Acquired Resistance Mechanisms in EGFR Mutant Lung Cancer

  Current understanding of acquired resistance to osimertinib in EGFR-mutant lung cancer is largely based on the use of targeted sequencing platforms and cell-free DNA. In this study we examined the clonal evolution and heterogeneity of acquired resistance mechanisms to osimertinib through whole exome and RNA-sequencing of prospectively collected pre-and post-osimertinib resistant tumors and metastases at rapid autopsy. We enrolled 34 patients with histologically confirmed EGFR mutant advanced lung adenocarcinoma in a prospective Phase II clinical trial to evaluate osimertinib treatment and the use of local ablative therapy (LAT) for oligoprogressive disease (NCT02759835) in EGFR mutant NSCLC from April 2016 until data cutoff in September 2019. Patients were not selected based on oligometastatic status, rather 32/34 patients had multiple organ involvement, including lung, liver, adrenal, brain and bone. Biopsies or surgeries were performed pre-osimertinib treatment and at first progression on osimertinib. A subset of patients also underwent biopsies at second progression on osimertinib, and following end of life in-patient hospice and rapid autopsy upon death. 63% (n=15/24) of first-line osimertinib and 50% (n=5/10) of second-line osimertinib treated patients had RECIST defined partial response. Overall, 21 patients had RECIST defined first progression on osimertinib. Two patients on this trial discontinued treatment and were lost to follow up. Of the 21 patients who had RECIST defined progression, 14 patients underwent LAT (8 first-line and 6 second-line). Twelve patients had paired pre- and post-osimertinib resistant tumors and three had post-osimertinib resistant tumors available for analysis. Four patients (LAT002, LAT006, LAT014 and LAT021) consented for a rapid autopsy protocol and underwent rapid autopsy after deatH. To elucidate acquired osimertinib resistance mechanisms, we performed whole-exome (WES), targeted sequencing and RNA-seq on 66 tumors and matched germline DNA from tumor tissue obtained from a total of 15 patients.

  Study phs002001

• Single cell RNA sequencing of 33 primary colorectal cancer

  Study EGAS00001003779

• RNA-Seq analysis of cocaine use disorder in Brodmann Area 9

  We aimed to identify cocaine use disorder associated changes in gene expression in the Brodmann Area 9 subregion of the human prefrontal cortex. RNA-sequencing was performed in N=13 individuals with and N=12 individuals without CUD. The individuals are originating from a cohort of N=42 individuals where DNA methylation (DNAm) levels were previously analyzed using the EPIC array.

  Study EGAS50000000150

• RNA-Seq of human longitudinal whole blood samples from PCR-positive and PCR-negative adults following controlled inoculation with Influenza A H3N2 virus.

  Understanding of the transcriptomic profile of individuals in early influenza infection is limited. To investigate this, longitudinal whole-blood samples (n=178) were taken from adult participants following controlled inoculation with Influenza A H3N2 virus (sampling at baseline (0) and days 1, 2, 3, 7, 10 and 14 post-challenge). Most participants became influenza PCR-positive; a minority remained PCR-negative. Total RNA was extracted from PAXgene tubes before undergoing globin and rRNA depletion. DNA libraries were constructed using the NEBNext® Ultra™ II Directional RNA Library Prep Kit for Illumina. All samples were then sequenced using Illumina NovaSeq 6000.

  Dataset EGAD50000000956

• Paired-end RNA-Seq Dataset of 72 Brain Organoid Samples: Sequencing and Gene Expression Analysis

  72 fastq sequencing files of 72 brain-organoid paired-end sequenced samples (R1/R2). RNA sequencing libraries were prepared using the NEBNext Ultra II RNA Library Prep Kit for Illumina following the manufacturer’s instructions (New England Biolabs, USA). mRNAs were first enriched with Oligo(dT) beads. The samples were sequenced using a 2x150 Pair-End configuration (ver. 1.5) on an Illumina NovaSeq 6000 The raw sequence reads were aligned to the human reference genome GRCh38 using the STAR aligner (Dobin et al., 2013). Gene expression levels were quantified using the FeatureCounts tool (Liao et al., 2014) (file: "All_Counts_final.xlsx") For more information, see "Study Description"

  Dataset EGAD50000000935

• Ribosomal profiling on Epstein-Barr virus transformed B-lymphoblastoid cell lines.

  This dataset contains Ribo-seq BAM files of Epstein-Barr virus-transformed B-lymphoblastoid cell lines from six individuals (HG00114, HG00282, NA12005, NA12044, NA12717 and NA12751) of the GEUVADIS Project. For each EBV-LCL, 20 million cells were treated with 2 μg/ml harringtonine and 100 μg/ml cycloheximide. After lysing cells in lysis buffer supplemented with 100 μg/ml cycloheximide, ribosome complexes were purified by density purification. Small RNA molecules were isolated using the NucleoSpin miRNA Kit (Bioke, Leiden, Netherlands), followed by RNA PAGE gel separation. Universal linkers were added after dephosphorylation. After reverse transcription, the cDNA was circularized and, after ribosomal RNA depletion, barcoded and sequenced on Illumina NextSeq 500.

  Dataset EGAD50000000465

• CMML Collection of WES, WGS, RNA-Seq and ERRBS data

  We first performed a comprehensive analysis of the genome of 66 chronic myelomonocytic leukemia cases using whole exome (N=49) or whole genome (N=17) sequencing for paired leukemic-control DNA. Then, we realized serial analysis of whole exome sequence in 17 cases, completed with serial RNA sequencing and DNA methylation analysis in 9 cases, including patients treated or not with hypomethylating agents.

  Study EGAS00001001264

• Cross-tissue transcriptomic analysis of human secondary lymphoid organ residing ILC3 reveals a default quiescent state in the absence of inflammation

  A cross tissue transcriptional comparison of human ILC3 isolated from non-inflamed lymph nodes (LN) and spleens, inflamed tonsils and the peripheral blood (PB). Using uniform gating and high-purity cell-sorting, ILC3 were sorted as Lineage(CD3,CD19,CD34,CD14,CD94)-CD45intCD127+CRTH2-CD117+. From LN, spleen, tonsils and PB, NKp44- ILC3 were isolated and, in addition, NKp44+ ILC3 were sorted from spleen and tonsils. RNA quality was measured on a RNA 6000 Pico Kit (Agilent Technologies) using a 2100 Bioanalyzer (Agilent Technologies). 100 pg high-quality RNA was isolated and cDNA was generated using the SMART-Seq (v3) Ultra Low Input RNA Kit (Clontech) with 15 cycles of amplification. Amplified and Covaris-fragmented cDNA was quality-checked on a High Sensitivity DNA chip (Agilent Technologies) and further processed according the TruSeq Nano DNA Library Preparation Kit (Illumina). Data was processed as follows: SMARTer adapters were trimmed with cutadapt and the resulting sequences were aligned to the human RefSeq transcriptome using 14 TopHat2 (Kim et al., 2013). Normalization and quantification was performed using Cufflinks (Trapnell et al., 2012). FPKM counts were determined per gene with HTSeq-count.

  Study EGAS00001002636

• Upcycling and merging data to challenge the dogma and identify new therapeutic targets for Glioblastoma

  Glioblastoma is the most common brain tumour. Characterised by a poor prognosis and its recurrence after multimodal treatments, the search for preventable risk factors has been mainly inconclusive up to date. Recently, the data merge from datasetsdeposited at the EGA allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. Challenges in glioblastoma research As for virtually all cancer types, many efforts have been made to unveil the molecular features responsible of the disease, and several cellular pathways have indeed been identified as being frequently mutated in glioblastoma. Nonetheless, targeted therapies based on identified genes have so far failed to improve outcome, thus survival mostly relies on a standard treatment unchanged since 2005. This is a frustrating situation for both the scientific and medical communities, and above all for the patients, still facing a dreadful path. Some researchers hypothesised that this may be due to the inability to efficiently target cancer stem cells, the originators of the other cell types, thus inducing cancer relapse. In 2019 Charles P. Couturier and colleagues sequenced RNA from single cells of freshly excised glioblastomas of 16 patients, and demonstrated that glioblastoma cells replicate normal brain cell development with a conserved neural cancer cell hierarchy centered around glial progenitor-like cells. In this way, they helped identify the possible target cells to improve efficacy and durability of treatment. Data upcycling at the EGA: the glioblastoma case study Single cell RNA sequencing is generated with a laborious and expensive protocol. The quality of the starting material is crucial (cells sample freshly extracted from the patients) and often several attempts are needed before producing reliable quality sequencing results. Collecting big numbers of patients is also challenging. The sequencing data produced by Kevin Petrecca’s group in Montreal, Canada and deposited at the EGA (EGAS00001004422) was recently upcycled by Lin Wang in San Francisco, California and pooled with their freshly produced ones, and then deposited at the EGA as Dataset EGAS00001004909 The data merge allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. They profiled 86 primary-recurrent patient-matched paired glioblastoma samples with single-nucleus RNA, among other techniques. These very comprehensive results lead the team to challenge the findings from several other cancer fields where chemotherapy standard chemo-radiation therapy selection pressure at the level of genomic alterations; this is indeed not the case for glioblastoma, where the pressure results in phenotypic transition between cellular states. Several technical controls were made to ensure that the merge of the data was not introducing a bias in the results, and an Inter-table analysis demonstrated nearly equal contribution to overall variance from each of the studies included, indicating that the findings were not due to inter-laboratory technical effect. A novel principal-component analysis showed that the largest contribution to variation in primary glioblastoma neoplastic cells was an axis between MES (mesenchymal) and proneural expression programs. In summary, treating glioblastoma often makes a MES, as commented by Lucy Stead about this remarkable work. Check Aaron Diaz’s team Nature Cancer paper, with strong technical tools, state of the art analysis and data from different sources converging to the same outcomes that made possible a significant step toward a better handling of a frightful cancer. References Couturier, C.P., Ayyadhury, S., Le, P.U. et al. Single-cell RNA-seq reveals that glioblastoma recapitulates a normal neurodevelopmental hierarchy. Nat Commun 11, 3406 (2020). Stead, L.F. Treating glioblastoma often makes a MES. Nat Cancer 3, 1446–1448 (2022). Wang, L., Jung, J., Babikir, H. et al. A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets. Nat Cancer 3, 1534–1552 (2022). Related links: Kevin Petrecca’s group dataset deposited at the EGA: EGAS00001004422 Lin Wang dataset deposited at the EGA: EGAS00001004909

  Blog upcycling-and-merging-data-to-challenge-glioblastoma