11750 results for "cancer rna-seq"

in 17.90 milliseconds.

• Accurate detection and classification of pediatric sarcomas based on cell-free DNA fragmentation patterns

  Sequencing of cell-free DNA in the blood of cancer patients (“liquid biopsy”) provides attractive opportunities not only for early diagnosis, but also for minimally invasive monitoring of treatment response and disease courses. To unlock liquid biopsy analysis for pediatric tumors with few genetic aberrations, we developed an integrated genetic/epigenetic analysis method and applied it to 241 deep whole-genome sequencing profiles of 95 patients with Ewing sarcoma and 31 patients with other pediatric sarcomas. We achieved sensitive detection and classification of circulating tumor DNA in peripheral blood independent of any genetic alterations. We evaluated different metrics for cell-free DNA fragmentation analysis and developed LIQUORICE, a bioinformatic tool for detecting circulating tumor DNA based on tumor-specific chromatin structure. Using machine learning methods, we combined several fragmentation-based metrics into an integrated approach for liquid biopsy analysis tailored to cancers with low mutation rates but widespread epigenetic deregulation. Clinical associations highlighted the potential value of cfDNA fragmentation patterns as prognostic biomarkers in Ewing sarcoma. Additionally, we performed low coverage whole-genome-sequencing on 43 tumor biopsy samples from patients with Ewing sarcoma, in order to compare copy number aberrations detected in cell-free DNA and biopsy samples of the same patients. For validation of the epigenetic signatures inferred from cell-free DNA, we further performed reduced representation bisulfite sequencing (RRBS) on 38 matched biopsy samples from patients with Ewing sarcoma. In summary, our study provides a comprehensive analysis of circulating tumor DNA beyond recurrent somatic mutations, and it renders the benefits of liquid biopsy more readily accessible for childhood cancers.

  Study EGAS00001005127

• Molecular evolution of metastatic clear cell renal cell carcinoma progressing under tyrosine kinase inhibitor therapy (HIPO-045)

  Background:Acquired resistance to targeted drugs is largely due to intra-tumor molecular heterogeneity and clonal selection. Objective:The objective of this study was to determine molecular alterations and mediating resistant mechanisms and to suggest additional treatment options.Design, Setting, and Participants:Four patients were enrolled into the MORE (Molecular Renal Cancer Evolution) trial. We investigated the mutational spectra of metastatic clear cell renal cell carcinoma (ccRCC) at baseline and compared them to those upon clonal evolution at progression on systemic therapy with tyrosine kinase inhibitors.Outcome Measurements:DNA was isolated from tumor tissues after surgery, metastasis biopsies after progression on sunitinib or axitinib, and subjected to whole-exome sequencing (WES). In addition, the exomes of circulating tumor DNA (ctDNA) from two patients were sequenced at baseline and at progression.Results and Limitations:Our data provide evidence for clonal evolution and various acquired resistance mechanisms of ccRCC with subclonal mutations in FLT4, MTOR, ITGA3/5, SETD2, and VHL on metastatic progression. The limited number of shared mutations in tissue and plasma biopsies indicates that both sources provide complementary information, possibly due to mutations present in metastatic lesions which were not amenable to tissue sampling but are represented in the ctDNA. Conclusion:Acquired resistance to targeted therapies in metastatic ccRCC is due to intra-tumor heterogeneity and clonal evolution. Larger patient cohorts have to be studied to estimate the benefit of WES from biopsies and ctDNA for therapy decision in metastatic ccRCC.Patient Summary:We analyzed the molecular profiles of ccRCC patients in primary tissues and metastatic tissues upon progression on tyrosine kinase inhibitor therapy. Clinically relevant mutations upon progression were detected in the metastatic sites of all of four patients, suggesting that it might be possible to derive therapies based on sequencing the metastases.?HIPO-045

  Study EGAS00001001861

• Ultra-Fast Patient-Derived Xenografts Identify Functional and Spatial Tumour Heterogeneities that Drive Therapeutic Resistance

  Treatment of patients with advanced cancers increasingly relies on agents targeting specific molecular or cellular aberrations. These expensive therapies often fail because of pre-existing drug resistance. Intra-tumoural molecular and micro-environmental heterogeneity resistance can create resistance in one or more subclones of a tumour, leading to spatio-functional heterogeneity. We created a rapid approach for quantifying tumour functional phenotypes based on tumour-implantation into the chorioallantoic membrane of chick embryos. We apply this technique to 43 separate biopsies from 6 patients with renal cell carcinoma (RCC) with 36 replicates per biopsy, achieving a 93.6% engraftment rate (1449/1548). Challenging these models with sunitinib, an anti-angiogenic therapy frequently used in RCC allowed us to quantify spatio-functional heterogeneity in individual patients. Prediction of de novo drug resistance in advanced cancer patients would facilitate precision medicine by tailoring systemic therapies and improve clinical trial design outcome measures. We describe a patient-derived xenograft platform where fresh patient tumour samples are implanted into the chorioallantoic membrane (CAM) of chick embryos at high engraftment efficiencies, permitting large scale “tumour avatar” studies to guide the selection of drugs and to predict clinical drug resistance outcomes in renal cell carcinoma (RCC) patients. Applying this “tumour avatar” model in a co-clinical trial on RCC patients, we observe intra-tumoural functional heterogeneity with sunitinib treatment, revealing the presence of drug-resistant clones prior to the initiation of therapy. Multi-regional exome Matched DNA sequencing of the primary tumour and metastases revealed DNA mutational signatures associated with sunitinib resistance at baseline and after treatment. These These studies demonstrate a rapid and affordable method of findings confirm the existence of genetic and surveying functional tumour heterogeneities heterogeneity.

  Study EGAS00001002627

• Exome sequencing from cfDNA blood samples. 159 samples at 2x101bp Illumina reads in Fastq format.

  Differential presence of exons (DPE) by next generation sequencing (NGS) is a method of interpretation of whole exome sequencing. This method has been proposed to design a predictive and diagnostic algorithm with clinical value in plasma from patients bearing colorectal cancer (CRC). The aim of the present study was to determine a common exonic signature to discriminate between different clinical pictures, such as non-metastatic, metastatic and non-disease (healthy), using a sustainable and novel technology in liquid biopsy. Through DPE analysis, we determined the differences in DNA exon levels circulating in plasma between patients bearing CRC vs. healthy, patients bearing CRC metastasis vs. non-metastatic and patients bearing CRC metastasis vs. healthy comparisons. We identified a set of 510 exons (469 up and 41 down) whose differential presence in plasma allowed us to group and classify between the three cohorts. Random forest classification (machine learning) was performed and an estimated out-of-bag (OOB) error rate of 35.9% was obtained and the predictive model had an accuracy of 75% with a confidence interval (CI) of 56.6-88.5. In conclusion, the DPE analysis allowed us to discriminate between different patho-physiological status such as metastatic, non-metastatic and healthy donors. In addition, this analysis allowed us to obtain very significant values with respect to previous published results, since we increased the number of samples in our study. These results suggest that circulating DNA in patient’s plasma may be actively released by cells and may be involved in intercellular communication and, therefore, may play a pivotal role in malignant transformation (genometastasis).

  Study EGAS00001006656

• Analysis of error profiles in deep next-generation sequencing data

  BackgroundSequencing errors are key confounding factors for detecting low-frequency genetic variants that are important for cancer molecular diagnosis, treatment, and surveillance using deep next-generation sequencing (NGS). However, there is a lack of comprehensive understanding of errors introduced at various steps of a conventional NGS workflow, such as sample handling, library preparation, PCR enrichment, and sequencing. In this study, we use current NGS technology to systematically investigate these questions.ResultsBy evaluating read-specific error distributions, we discover that the substitution error rate can be computationally suppressed to 10-5 to 10-4, which is 10- to 100-fold lower than generally considered achievable (10-3) in the current literature. We then quantify substitution errors attributable to sample handling, library preparation, enrichment PCR, and sequencing by using multiple deep sequencing datasets. We find that error rates differ by nucleotide substitution types, ranging from 10-5 for A>C/T>G, C>A/G>T, and C>G/G>C changes to 10-4 for A>G/T>C changes. Furthermore, C>T/G>A errors exhibit strong sequence context dependency, sample-specific effects dominate elevated C>A/G>T errors, and target enrichment PCR led to aprox 6-fold increase of overall error rate. We also find that more than 70% of hotspot variants can be detected at 0.1% - 0.01% frequency with the current NGS technology by applying in-silico error suppression.ConclusionsWe present the first comprehensive analysis of sequencing error sources in conventional NGS workflows. The error profiles revealed by our study highlight new directions for further improving NGS analysis accuracy both experimentally and computationally, ultimately enhancing the precision of deep sequencing.

  Study EGAS00001003444

• ExomeSeq Neoantigen Immunogenicity Landscapes

  Immune checkpoint blockade (ICB) therapy is a cornerstone of oncologic treatment for patients with advanced stage non-small cell lung cancer (NSCLC) and other malignancies. Neoantigen immunoediting drives ICB efficacy, yet the fundamental physiochemical characteristics of neoantigens and how neoantigen immunogenicity shapes treatment response remains poorly understood. To help address these questions, a prospective clinical trial of NSCLC patients treated with nivolumab was conducted. We assessed genomic alterations in tumors from 58 patients and performed large-scale neoepitope immunogenicity analyses, before and during treatment (CheckMate153, CA209-153). Tumors were analyzed by whole-exome and transcriptome sequencing. In responding patients, loss of mutation and neoantigen burden early during therapy associated with clinical benefit. We evaluated the immunogenicity of 1,453 candidate neoantigens and identified 502 neopeptides that bound to MHC I and 196 neopeptides that were immunogenically recognized by T cells in the setting of nivolumab treatment. These T cell reactive neoantigens were differentially present in clonal populations that underwent distinctive evolutionary trajectories across responders and nonresponders. Mapping these neoantigens to tumor clonal dynamics and clinical response revealed strong selection against immunogenic neoantigen harboring clones compared to non-immunogenic clones. Using this large collection of neoantigens, we identified position specific amino acid features related to immunogenicity, which we used to develop and validate an immunogenicity score. Changes in the genomic and neoantigen immunogenicity landscapes were accompanied by temporal changes in the tumor microenvironment. Nivolumab-induced microenvironmental evolution in NSCLC shared some similarities with that in melanoma, yet critical differences in immunologic programs were apparent from comparative network analysis between tumor types. This study provides unprecedented molecular portraits of the neoantigen landscapes underlying nivolumab’s mechanism of action.

  Study EGAS00001007508

• RNASeq Neoantigen Immunogenicity Landscape

  Immune checkpoint blockade (ICB) therapy is a cornerstone of oncologic treatment for patients with advanced stage non-small cell lung cancer (NSCLC) and other malignancies. Neoantigen immunoediting drives ICB efficacy, yet the fundamental physiochemical characteristics of neoantigens and how neoantigen immunogenicity shapes treatment response remains poorly understood. To help address these questions, a prospective clinical trial of NSCLC patients treated with nivolumab was conducted. We assessed genomic alterations in tumors from 58 patients and performed large-scale neoepitope immunogenicity analyses, before and during treatment (CheckMate153, CA209-153). Tumors were analyzed by whole-exome and transcriptome sequencing. In responding patients, loss of mutation and neoantigen burden early during therapy associated with clinical benefit. We evaluated the immunogenicity of 1,453 candidate neoantigens and identified 502 neopeptides that bound to MHC I and 196 neopeptides that were immunogenically recognized by T cells in the setting of nivolumab treatment. These T cell reactive neoantigens were differentially present in clonal populations that underwent distinctive evolutionary trajectories across responders and nonresponders. Mapping these neoantigens to tumor clonal dynamics and clinical response revealed strong selection against immunogenic neoantigen harboring clones compared to non-immunogenic clones. Using this large collection of neoantigens, we identified position specific amino acid features related to immunogenicity, which we used to develop and validate an immunogenicity score. Changes in the genomic and neoantigen immunogenicity landscapes were accompanied by temporal changes in the tumor microenvironment. Nivolumab-induced microenvironmental evolution in NSCLC shared some similarities with that in melanoma, yet critical differences in immunologic programs were apparent from comparative network analysis between tumor types. This study provides unprecedented molecular portraits of the neoantigen landscapes underlying nivolumab’s mechanism of action.

  Study EGAS00001007509

• Multi-Layered Molecular Profiling Informs the Diagnosis and Targeted Therapy of Desmoplastic Small Round Cell Tumor

  Desmoplastic small round cell tumor (DSRCT) is an ultra-rare soft tissue sarcoma with dismal prognosis and limited therapeutic options. Here, we identified individual therapeutic targets in a cohort of 30 DSRCT patients by use of multilayered molecular profiling (genomic, transcriptomic, and (phospho-)proteomic analyses) within the DKFZ NCT DKTK MASTER program. 28 of the 30 patients received at least one personalized treatment recommendation. In line with the observation that DSRCTs are an entity with a low mutational load, most of the treatment recommendations were based on target gene or -protein expression. 13 patients received a recommended therapy, resulting in four partial responses, four stable diseases, and five progressive diseases. Eight of 13 patients received the multi tyrosine kinase inhibitor pazopanib, with mixed response. Based on high ERBB2 expression levels, two patients received the antibody drug conjugate trastuzumab deruxtecan (T-DXd), showed exceptional, long-lasting responses and are currently (8/2024) still on treatment and progression-free. We detected high ERBB2 protein expression in all DSRCT samples with proteome data (n = 9), which suggests that ERBB2 may be a particularly useful therapeutic target for DSRCT patients. For one of the two exceptional responders we did not detect a substantial ERBB2/HER2 overexpression, which may indicate that T-DXd could also be efficacious in DSRCT patients with low levels of HER2, as also described for breast cancer patients. In conclusion, multi-omic molecular profiling revealed personalized therapeutic targets in patients with DSRCT with HER2 targeting, with T-DXd as a promising therapeutic option for this ultra-rare and difficult-to-treat tumor entity.

  Study EGAS00001007934

• Transcriptome and Epigenome of TIL Infusion for Cancer Immunotherapy

  Adoptive T cell therapy (ACT) using ex vivo–expanded autologous tumor-infiltrating lymphocytes (TILs) can mediate complete regression of certain human cancers. The impact of TIL phenotypes on clinical success of TIL-ACT is currently unclear. Samples were analyzed from TIL infusion products from metastatic melanoma human patients who responded to TIL-ACT (responders) and patients who did not (non-responders). Single cell transcriptome of TIL infusion products were analyzed for CD8 TIL phenotype and function. In patients with available neoantigen reactive TIL, single cell TCR clonotyping was performed to study anti-tumor TIL states between responders and non-responders. Epigenetic studies on TIL infusion products were performed using ATAC sequencing between paired CD39- C69- and CD39+ CD69+ CD8+ TILs.

  Study phs002436

• Gliomas, glioneuronal and neuronal tumors

  The dataset represents a total of 85 DNA samples from 22 male and 20 female pediatric patients affected with gliomas, glioneuronal, and neuronal tumors. The samples were subject to whole genome sequencing, WGS, [71 samples, (representing 18 male and 17 female individuals)] and whole exome sequencing, WES, [14 samples, (representing 4 males and 3 female individuals)]. One tumor tissue sample and one peripheral blood sample were analyzed from each of 84 patients, whereas two tumor tissue samples and one peripheral blood sample were analyzed from one patient. The WGS samples were sequenced 2x150 bp paired-end on an Illumina HiSeqX v2.5 instrument, and the WES samples were sequenced 2x100 bp paired-end on an Illumina HiSeq 2500 instrument. The FASTQ files generated were aligned to the human reference genome sequence GRCh38/hg38 using bwa-mem, with the ALT-aware option turned on. Sorting of reads and marking of PCR duplicates was performed with GATK. Base quality score recalibration and joint realignment of reads around insertions and deletions (indels) were conducted using GATK tools. The dataset consists of 85 files in the CRAM format (lossless compression) with a total file size of ~13,3 TB. Additional genomic and molecular data (FASTQ, BAM, IDAT, and VCF files) and limited clinical data can be requested by ethically approved projects conducting research in the field of pediatric cancer.

  Dataset EGAD50000000300