Project description:Chronic lymphocytic leukemia (CLL) is a heterogeneous malignancy, characterized by a variable clinical course. While clinical and laboratory parameters are increasingly being used to refine prognosis, they do not accurately predict response to commonly used therapy. We used gene expression profiling to generate and further refine prognostic and predictive markers. Genomic signatures that reflect progressive disease and responses to chemotherapy or chemo-immunotherapy were created using cancer cell lines and patient leukemia samples. We validated these signatures using independent clinical data from four separate cohorts representing a total of 301 CLL patients. A prognostic genomic signature created from patient leukemic cell gene expression data coupled with clinical parameters could statistically differentiate patients with stable or progressive disease in the training dataset. The progression signature was then validated in two independent datasets, demonstrating a capacity to accurately identify patients at risk for progressive disease. In addition, two distinct genomic signatures that predict response to chlorambucil or pentostatin, cyclophosphamide, and rituximab were also generated and were shown to accurately distinguish responding and non-responding CLL patients. Microarray analysis of CLL patientsâ lymphocytes can be used to refine prognosis and predict response to different therapies. These results have direct implications for standard and investigational therapeutics in CLL patients. Experiment Overall Design: For the predictive genomic signature or response to pentostatin, cyclophosphamide, and rituximab, 20 CLL leukemia samples were used in the training set, and 20 CLL leukemia samples were used in the validation set

2009-11-11 | E-GEOD-10137 | biostudies-arrayexpress

CAF variants control the tumor-immune microenvironment and predict skin cancer malignancy

Project description:CAF variants control the tumor-immune microenvironment and predict skin cancer malignancy

| PRJNA1072630 | ENA

lgG4 lesion of EoE patient biopsy by AutoSTOMP

Project description:To test the ability of the Automated Spatially Targeted Optical Micro Proteomics (AutoSTOMP) protocol for clinical research. We collected from a patient diagnosed with active EoE (≥15 eosinophils/hpf), according to consensus guidelines, using standard endoscopy procedures, then we try to selectively obtain the proteome from the discrete lgG4+ lesions of the biopsy sections by AutoSTOMP, and try to link the lesion structure with its specially skewed constituents comparing to the rest of the tissue.

2021-08-10 | PXD026819 | Pride

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data