Expression Data from Leukemia Patients Supporting Personalized Functional Cancer Genomics Analyses
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE42731 | GEO | 2013/10/04
SECONDARY ACCESSION(S): PRJNA182980
REPOSITORIES: GEO
Similar Datasets
Project description:A set of expression samples used to support a personalized genomics analysis with regards to functional assays (e.g. siRNA or small molecule screens) 50 patient samples from a variety of disease types and several tissues
2013-10-04 | E-GEOD-42731 | biostudies-arrayexpress
Project description:Motivation: Sample source, procurement process, and other technical variations introduce batch effects into genomics data. Algorithms to remove these artifacts enhance differences between known biological covariates, but also carry potential concern of removing intra-group biological heterogeneity and thus any personalized genomic signatures. As a result, accurate identification of novel subtypes from batch corrected genomics data is challenging using standard algorithms designed to remove batch effects for class comparison analyses. Nor can batch effects be corrected reliably in future applications of genomics-based clinical tests, in which the biological groups are by definition unknown a priori. Results: Therefore, we introduce new algorithm, personalized-SVA (pSVA), blind to biological covariates corrected technical artifacts while retaining biological heterogeneity in genomic data. This algorithm facilitated accurate subtype identification in head and neck cancer from gene expression data in both formalin fixed and frozen samples. When applied to predict HPV status, pSVA improved cross- study validation even if the sample batches were highly confounded with HPV status in the training set. Availability: All analyses were performed using R version 2.15.0. The code and data used to generate the results of this manuscript is available from https://sourceforge.net/projects/psva.
2016-12-12 | GSE53355 | GEO
Project description:This clinical trial studies the effectiveness of a web-based cancer education tool called Helping Oncology Patients Explore Genomics (HOPE-Genomics) in improving patient knowledge of personal genomic testing results and cancer and genomics in general. HOPE-Genomics is a web-based education tool that teaches cancer/leukemia patients, and patients who may be at high-risk for developing cancer, about genomic testing and provide patients with information about their own genomic test results. The HOPE-Genomics tool may improve patient’s genomic knowledge and quality of patient-centered care. In addition, it may also improve education and care quality for future patients.
| 2378933 | ecrin-mdr-crc
Project description:An overall goal of functional genomics has been to measure the impact of variants on molecular endophenotypes (e.g. gene expression levels or the degree of TF binding) and relate this to organismal traits and disease phenotypes. However, all the experiments to date have been described relative to a generic reference genome, significantly hobbling their interpretation. Here, we describe a strategy for finding significant relationships between disease variation and genomic annotation via personal functional genomics, by performing personal genome sequencing and paired functional genomics experiments, on the same individual.
2024-05-22 | PXD022787 | Pride
Project description:This SuperSeries is composed of the following subset Series: GSE29996: Deep sequencing of gastric carcinoma reveals somatic mutations relevant to personalized medicine [Affymetrix SNP array data] GSE29998: Deep sequencing of gastric carcinoma reveals somatic mutations relevant to personalized medicine [Illumina mRNA expression array data] Refer to individual Series
2012-10-25 | E-GEOD-29999 | biostudies-arrayexpress
Project description:Macaque species share over 93% genome homology with humans and develop many disease phenotypes similar to those of humans, making them valuable animal models for the study of human diseases (e.g.,HIV and neurodegenerative diseases). However, the quality of genome assembly and annotation for several macaque species lags behind the human genome effort. To close this gap and enhance functional genomics approaches, we employed a combination of de novo linked-read assembly and scaffolding using proximity ligation assay (HiC) to assemble the pig-tailed macaque (Macaca nemestrina) genome. This combinatorial method yielded large scaffolds at chromosome-level with a scaffold N50 of 127.5 Mb; the 23 largest scaffolds covered 90% of the entire genome. This assembly revealed large-scale rearrangements between pig-tailed macaque chromosomes 7, 12, and 13 and human chromosomes 2, 14, and 15. We subsequently annotated the genome using transcriptome and proteomics data from personalized induced pluripotent stem cells (iPSCs) derived from the same animal. Reconstruction of the evolutionary tree using whole genome annotation and orthologous comparisons among three macaque species, human and mouse genomes revealed extensive homology between human and pig-tailed macaques with regards to both pluripotent stem cell genes and innate immune gene pathways. Our results confirm that rhesus and cynomolgus macaques exhibit a closer evolutionary distance to each other than either species exhibits to humans or pig-tailed macaques. These findings demonstrate that pig-tailed macaques can serve as an excellent animal model for the study of many human diseases particularly with regards to pluripotency and innate immune pathways.
2020-05-04 | PXD018943 | Pride
Project description:The accurate identification and prioritization of antigenic peptides presented by class-I and -II human leukocyte antigens (HLA-I and -II) recognized by autologous T cells is crucial for the development of cancer immunotherapies. While several clinical neoantigen prediction pipelines are now publicly available, none of them allows the direct integration of mass spectrometry immunopeptidomics data that can uncover antigenic peptides derived from various canonical and non-canonical sources. Therefore, we have developed and shared a unique ‘end-to-end’ clinical proteo-genomic pipeline, called NeoDisc. NeoDisc is a fast and modular computational pipeline that combines state-of-the-art publicly available and in-house software for genomics, transcriptomics, mass-spectrometry-based immunopeptidomics, and in silico tools for the identification, prediction, and prioritization of tumor-specific and immunogenic antigens from multiple sources. We demonstrated the application of NeoDisc for personalized antigen discovery, in the context of heterogenic antigenic landscape and defective cellular antigen presentation machineries, and we highlighted its clinical implementation.
2024-08-30 | PXD047641 | Pride
Project description:Human milk is the truest form of personalized nutrition, supporting dynamic needs of the infant with important nutritional and bioactive constituents that change throughout lactation. Additionally, human milk is individual specific and is unique for each mother-infant dyad. Proteins and endogenous peptides are 2 key classes of major human milk components making up the proteome, each with unique and synergistic functionality, working to provide protection for the healthy development of infants. Our objective was to comprehensively characterize and quantify the human milk proteome for varying early life challenges. We assessed in-depth individual variations of the human milk proteome across lactation, by mass spectrometry. Finding that the human milk proteome showed continuous and gradual changes over lactation, and that inflammatory events correlated with a strong and rapid change in the composition of human milk proteins and peptides. Personalized human milk profiling resulted in the systematic annotation of the milk proteome, and elucidated how early onset inflammatory events can lead to infant immune training from human milk.
2021-02-09 | PXD014917 | Pride
Project description:Precision Phenomics to Personalized Drug Therapy (P3DT)
| PRJNA595707 | ENA
Project description:Daily sampling reveals personalized diet-microbiome associations
| PRJEB29065 | ENA