Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Scalp melanoma in area with or without alopecia: clinical, dermoscopic, histopathological, molecular and dermoscopic patterns

ABSTRACT: Cross-sectional study of 48 scalp melanomas describing clinical, histopathological, molecular and dermoscopic patterns and its relation to alopecia and elastosis.

INSTRUMENT(S): Ion Torrent Proton

ORGANISM(S): Homo Sapiens

SUBMITTER: A.C.Camargo Cancer Center

PROVIDER: PRJEB53894 | EVA | 2022-06-30

REPOSITORIES: EVA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	merged.vcf.csi	Other
	merged.vcf.gz	Vcf
	merged.vcf.gz.csi	Vcf

Items per page:

1 - 3 of 3

Similar Datasets

Integration of Clinical and Molecular Biomarkers for Melanoma Survival (Berwick)

Project description:Integration of Clinical and Molecular Biomarkers for Melanoma Survival (Berwick)

| PRJNA893071 | ENA

Integration of Clinical and Molecular Biomarkers for Melanoma Survival (Berwick)

Project description:Integration of Clinical and Molecular Biomarkers for Melanoma Survival (Berwick)

| PRJNA893070 | ENA

Gene Expression Profiling-Based Identification of Molecular Subtypes in Stage IV Melanoma with Different Clinical Outcome

Project description:This SuperSeries is composed of the following subset Series: GSE22153: Gene Experssion Profiling-Based Identification of Molecular Subtypes in Stage IV Melanoma with Different Clinical Outcome (test set) GSE22154: Gene Experssion Profiling-Based Identification of Molecular Subtypes in Stage IV Melanoma with Different Clinical Outcome (validation set) Refer to individual Series

2010-06-08 | E-GEOD-22155 | biostudies-arrayexpress

Molecular Analysis of Brain Neoplasia

Project description:The study includes fresh frozen tumors representing different brain tumor types and grades that were collected, processed and analyzed in a standardized fashion to allow for large-scale cross sample analysis. The sample collection was accompanied by careful and prospective clinical data acquisition, allowing matched molecular and clinical data analyses. The aim is to integrate molecular data with clinical annotations to identify new targets for therapy.

2009-02-10 | E-MTAB-3073 | biostudies-arrayexpress

Radiomic subtyping improves disease stratification beyond key molecular, clinical and standard imaging characteristics in patients with glioblastoma.

Project description:Background: To analyze the potential of radiomics for disease stratification beyond key molecular, clinical and standard imaging features in patients with glioblastoma. Methods: Quantitative imaging features (n=1043) were extracted from the multiparametric MRI of 181 patients with newly-diagnosed glioblastoma prior to standard-of-care treatment (allocated to a discovery and validation set, 2:1 ratio). A subset of 386/1043 features were identified as reproducible (in an independent MRI-test-retest cohort) and selected for analysis. A penalized Cox-model with 10-fold cross-validation (Coxnet) was fitted on the discovery set to construct a radiomic signature for predicting progression-free and overall survival (PFS, OS). The incremental value of a radiomic signature beyond molecular (MGMT-promoter methylation, DNA-methylation subgroups), clinical (patients age, KPS, extent-of-resection, adjuvant treatment) and standard imaging parameters (tumor volumes) for stratifying PFS and OS was assessed with multivariate Cox-models (performance quantified with prediction error curves). Results: The radiomic signature (constructed from 8/386 features identified through Coxnet) increased the prediction accuracy for PFS and OS (in both discovery and validation set) beyond the assessed molecular, clinical and standard imaging parameters (p≤0.01). Prediction errors decreased by 36% for PFS and 37% for OS when adding the radiomic signature (as compared to 29% and 27% with molecular + clinical features alone). The radiomic signature was - along with MGMT-status - the only parameter with independent significance on multivariate analysis (p≤0.01). Conclusions: Our study stresses the role of integrating radiomics into a multi-layer decision framework with key molecular and clinical features to improve disease stratification and to potentially advance personalized treatment of patients with glioblastoma.

2018-01-05 | GSE103659 | GEO

Predicting Human Clinical Outcomes using Mouse Multi-Organ Transcriptome

Project description:Approximately 90% of pre-clinically validated drugs fail in clinical trials due to unanticipated clinical outcomes, costing over several hundred million US dollars per drug. Despite such critical importance, translating pre-clinical data to clinical outcomes remain a major challenge. Herein, we designed a modality-independent and unbiased approach to predict clinical outcomes of drugs. The approach exploits their multi-organ transcriptome patterns induced in mice and a unique mouse-transcriptome database “humanized” by machine learning algorithms and human clinical outcome datasets. The cross-validation with small-molecule, antibody and peptide drugs shows effective and efficient identification of the previously known outcomes of 5,519 adverse events and 11,312 therapeutic indications. In addition, the approach is adaptable to deducing potential molecular mechanisms underlying these outcomes. Furthermore, the approach identifies previously unsuspected repositioning targets. These results, together with the fact that it requires no prior structural or mechanistic information of drugs, illustrate its versatile applications to drug development process.

2020-01-09 | GSE142068 | GEO

Study of clinical VRE

Project description:Study of clinical VRE

| PRJEB13267 | ENA

Molecular patterns of resistance to immune checkpoint blockade in melanoma

Project description:Molecular patterns of resistance to immune checkpoint blockade in melanoma

| PRJNA1026564 | ENA

Gene expression Classification of Colon Cancer defines six molecular subtypes with distinct clinical, molecular and survival characteristics [CGH]

Project description:From a clinical and molecular perspective, colon cancer (CC) is a heterogeneous disease but to date no classification based on high-density transcriptome data has been established. The aim of this study was to build up a robust molecular classification of mRNA expression profiles (Affymetrix U133Plus2) of a large series of 443 CC and to validate it on an independent serie of 123 CC and 906 public dataset. We identified and validated six molecular subtypes in this large cohort as a combination of multiple molecular processes that complement current disease stratification based on clinicopathological variables and molecular markers. The biological relevance of these subtypes was consolidated by significant differences in survival. These insights open new perspectives for improving prognostic models and targeted therapies.

2013-05-22 | GSE40966 | GEO

Integrative molecular and clinical profiling of acral melanoma links focal amplification of chr22q11.21 to metastasis

Project description:Integrative molecular and clinical profiling of acral melanoma links focal amplification of chr22q11.21 to metastasis

| PRJNA782616 | ENA