8432 results for "canc*"

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• Shot-gun stool metagenomics and colorectal cancer risk.

  This study aims to identify metagenomics biomarkers in colorectal cancer (CRC). The study includes 156 samples of shot-gun stool metagenomics, corresponding to 51 patients with CRC, 54 patients with high-risk adenoma, and 51 healthy controls. The sequencing libraries were prepared with 2 µg of total DNA using the Nextera XT DN Sample Prep Kit (Illumina, San Diego, CA, USA). Sequencing was performed at 150 nucleotides, paired-end, using an Illumina HiSeq 4000 platform.

  Study EGAS00001007025

• HER2_positive_Breast_Cancer_

  We propose to definitively characterise the somatic genetics of HER2+ve breast cancer through generation of comprehensive catalogues of somatic mutations in a number of cases by high coverage genome sequencing coupled with integrated transcriptomic and methylation analyses.

  Study EGAS00001000042

• Towards Objective Anal Cancer Screening: From Swab-Based Genome-Wide Methylation Marker Discovery to Novel Test Development

  Anal high-grade squamous intraepithelial lesions (HSIL-AIN2/3) are precursors to anal cancer, and early detection and treatment through screening is crucial for effective cancer prevention. Current guidelines recommend anal swab-based screening and highlight the need for objective biomarkers to improve risk stratification. While DNA methylation markers have shown strong potential in anal biopsies, their performance in anal swabs is reduced, likely due to sample-type dependency. This study aimed to identify a DNA methylation classifier specifically derived from anal swabs of men who have sex with men living with HIV (MSMLWH) to enhance diagnostic accuracy. Genome-wide methylation profiling was performed on anal swabs from 36 HSIL-AIN3 cases, 34 controls (<low-grade squamous intraepithelial lesions (<LSIL)), and 7 anal cancer patients using the 850K Infinium MethylationEPIC BeadChip (Illumina). Candidate CpG sites were selected via logistic regression with adaptive multi-group ridge penalties using co-data and an empirical Bayes approach (ecpc). The top candidate markers were validated using quantitative methylation-specific PCR (qMSP) on an independent series of 204 swabs to identify an optimal methylation classifier. Ecpc analysis identified 50 CpG sites with a combined area-under-the-curve (AUC) of 0.80. All ten candidate markers evaluated by qMSP showed significant increases in methylation levels associated with disease severity (p<0.001). From these, a 3-gene methylation classifier was identified, demonstrating excellent performance with an AUC of 0.80 (95% confidence interval:0.72-0.88) in distinguishing HSIL-AIN3+ from ≤LSIL. This study identified a robust 3-gene methylation classifier with strong potential for detecting HSIL-AIN3 and anal cancer when applied to anal swabs from MSMLWH.

  Study EGAS00001008433

• High clinical utility of comprehensive multi-omic molecular profiling of rare and hard-to-diagnose pediatric tumors

  The role of comprehensive genomic profiling for therapeutic decision-making is established in high-risk pediatric cancers, but its utility in rare and diagnostically challenging tumors is unclear. Here we report 123 non-high-risk patients enrolled in the Australian ZERO Childhood Cancer Program for diagnostic uncertainty, clinician request to address a specific molecular query, or other rare tumors. Comprehensive multi-omic profiling led to a change in diagnosis in 17.9% (22/123) of patients, with overall diagnostic utility in 35% (43/123). Molecular queries were resolved in 97.6% (40/41). Multi-omic results informed conventional management in 20.3% (25/123). Precision-guided therapy was recommended in 67.5% (83/123), and administered in 36.1% (30/83), with an objective response or prolonged (>6 months) stable disease in 88.9% of evaluable cases (16/18). Findings were confirmed in an independent cohort from the Canadian KiCS program (n=41). In conclusion, in rare and diagnostically challenging pediatric tumors, multi-omic profiling improved diagnostic accuracy and informed clinical management, supporting its integration into routine care.

  Study EGAS00001008471

• Expanding the utility of transcriptome analysis for mutation detection in high-risk childhood precision oncology

  In precision oncology, rapid and comprehensive mutation detection is essential for diagnosis, prognosis and treatment. Whole transcriptome sequencing (RNA-seq) offers unique advantages by capturing functional consequences of genomic alterations and has the potential to detect not only oncogenic fusions, but also a broad spectrum of clinically relevant mutations traditionally identified from DNA-based approaches. We evaluated the utility of RNA-seq in a cohort of 477 high-risk paediatric tumours enrolled in the ZERO childhood cancer precision medicine programme with matched WGS data. A combination of reference-guided and reference-free RNA-seq algorithms were used to detect and interpret fusions and intragenic structural variants (SVs), resolving events from complex rearrangements and paralogous loci. RNA-seq detected 95% of gene fusions, including 33 missed by WGS. All intragenic SVs (100%) detected by WGS were also identified by RNA-seq and RNA-seq was essential for identification and/or interpretation of 30% of SVs. Additional functional insight such as aberrant splice-junctions and transcript abundance were critical for resolving pathogenicity. For single nucleotide variants and small insertions/deletions (SNVs/Indels), RNA-seq confirmed 92% and was essential for refining pathogenicity in 22% through allele-specific expression and splice assessment. These findings highlight RNA-seq as a critical complement to WGS, that transforms interpretation by adding functional context, thereby elevating diagnostic confidence and clinical decision-making.

  Study EGAS00001008472

• Inference of transcription factor binding from cell-free DNA enables tumor subtype prediction and early detection

  Deregulation of transcription factors (TFs) is an important driver of tumorigenesis, but non-invasive assays for assessing transcription factor activity are lacking. We Here we developed and validated a minimally invasive method for assessing TF activity based on cell-free DNA sequencing and nucleosome footprint analysis. We analyzed whole genome sequencing data for >1,000 cell-free DNA samples from cancer patients and healthy controls using a newly developed bioinformatics pipeline developed by us that infers accessibility of TF binding sites from cell-free DNA fragmentation patterns. We observe patient-specific as well as tumor-specific patterns, including accurate prediction of tumor subtypes in prostate cancer, with important clinical implications for the management of patients. Furthermore, we show that cell-free DNA TF profiling is capable of detection of early-stage colorectal carcinomas. Our approach for mapping tumor-specific transcription factor binding in vivo based on blood samples makes a key part of the noncoding genome amenable to clinical analysis.

  Study EGAS00001003206

• Use_of_Deep_Sequencing_to_Dectect_Clonal_Mutations_In_Sun_Exposed_Skin_Epidermis_PART2

  This study aims to define the landscape of somatic mutations in sun exposed human skin by deep sequencing, analyse their frequency and use the data to infer the effect of mutations on proliferating cell behaviour. The frequency of each mutation will reflect the size of the clone of cells in the tissue sample. By analyzing small samples, clones with as few as 100 cells will be detectable. Allele frequency distributions for each mutation will be used to infer cell fate using published methods (Klein et al. 2010). This study will shed unprecedented light on the early clonal events that lead to the emergence of cancer.

  Study EGAS00001000603

• Yemen_and_Chad_Genotyping

  HumanOmni2.5-8 data from Chad and Yemen.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/

  Study EGAS00001001231

• A Genomics-Based Classification of Human Lung Tumors

  We characterized genome alterations in 1255 clinically annotated lung tumors of all histological subgroups to identify genetically defined and clinically relevant subtypes. More than 55% of all cases had at least one oncogenic genome alteration potentially amenable to specific therapeutic intervention, including several personalized treatment approaches that are already in clinical evaluation. Marked differences in the pattern of genomic alterations existed between and within histological subtypes, thus challenging the original histomorphological diagnosis. Immunohistochemical studies confirmed many of these reassigned subtypes. The reassignment eliminated almost all cases of large cell carcinomas, some of which had therapeutically relevant alterations. Prospective testing of our genomics-based diagnostic algorithm in 5145 lung cancer patients enabled a genome-based diagnosis in 3863 (75%) patients, confirmed the feasibility of rational reassignments of large cell lung cancer, and led to improvement in overall survival in patients with EGFR-mutant or ALK-rearranged cancers. Thus, our findings provide support for broad implementation of genome-based diagnosis of lung cancer.

  Study EGAS00001000647

• Chordoma_Extension_Study

  Extension analysis to pursue candidate genes of interest in chordoma

  Study EGAS00001000892