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Gene Expression Signatures for the Accurate Diagnosis of Peripheral T-Cell Lymphoma Entities in the Routine Clinical Practice [training]

ABSTRACT: Gene Expression Signatures for the Accurate Diagnosis of Peripheral T-Cell Lymphoma Entities in the Routine Clinical Practice [training]

PROVIDER: PRJNA937291 | ENA |

REPOSITORIES: ENA

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Gene Expression Signatures for the Accurate Diagnosis of Peripheral T-Cell Lymphoma Entities in the Routine Clinical Practice [training]

Project description:Peripheral T-cell lymphoma (PTCL) includes heterogeneous clinicopathologic entities with numerous diagnostic and treatment challenges. We previously defined robust transcriptomic signatures that distinguish common PTCL entities and identified two novel biologic and prognostic PTCL-not otherwise specified subtypes (PTCL-TBX21 and PTCL-GATA3). We aimed to consolidate a gene expression–based subclassification using formalin-fixed, paraffin-embedded (FFPE) tissues to improve the accuracy and precision in PTCL diagnosis. We assembled a well-characterized PTCL training cohort (n = 105) with gene expression profiling data to derive a diagnostic signature using fresh-frozen tissue on the HG-U133plus2.0 platform (Affymetrix, Inc, Santa Clara, CA) subsequently validated using matched FFPE tissues in a digital gene expression profiling platform (nCounter, NanoString Technologies, Inc, Seattle, WA). Statistical filtering approaches were applied to refine the transcriptomic signatures and then validated in another PTCL cohort (n = 140) with rigorous pathology review and ancillary assays.

2023-02-23 | GSE225757 | GEO

Gene Expression Signatures for the Accurate Diagnosis of Peripheral T-Cell Lymphoma Entities in the Routine Clinical Practice

Project description:Gene Expression Signatures for the Accurate Diagnosis of Peripheral T-Cell Lymphoma Entities in the Routine Clinical Practice

| PRJNA937288 | ENA

Gene Expression Signatures for the Accurate Diagnosis of Peripheral T-Cell Lymphoma Entities in the Routine Clinical Practice [validation]

Project description:Gene Expression Signatures for the Accurate Diagnosis of Peripheral T-Cell Lymphoma Entities in the Routine Clinical Practice [validation]

| PRJNA937290 | ENA

Gene Expression Signatures for the Accurate Diagnosis of Peripheral T-Cell Lymphoma Entities in the Routine Clinical Practice [validation]

2023-02-23 | GSE225758 | GEO

Routine use of microarray-based gene expression profiling to identify patients with low cytogenetic risk acute myeloid leukemia: accurate results can be obtained even with suboptimal samples

Project description:This SuperSeries is composed of the following subset Series: GSE34577: Routine use of microarray-based gene expression profiling to identify patients with low cytogenetic risk acute myeloid leukemia: accurate results can be obtained even with suboptimal samples (training samples) GSE34714: Routine use of microarray-based gene expression profiling to identify patients with low cytogenetic risk acute myeloid leukemia: accurate results can be obtained even with suboptimal samples. (test samples) Refer to individual Series

2012-01-04 | E-GEOD-34823 | biostudies-arrayexpress

Gene Expression Signatures for the Accurate Diagnosis of Peripheral T-Cell Lymphoma Entities in the Routine Clinical Practice

Project description:This SuperSeries is composed of the SubSeries listed below.

2023-02-23 | GSE225759 | GEO

Genomic Grade Index (GGI): Feasibility in routine practice and impact on treatment decisions in early breast cancer

Project description:Genomic Grade Index (GGI) is a 97-gene signature that improves histologic grade (HG) classification in invasive breast carcinoma. In this prospective study we sought to evaluate the feasibility of performing GGI in routine clinical practice and its impact on treatment recommendations. Patients with pT1pT2 or operable pT3, N0-3 invasive breast carcinoma were recruited from 8 centers in Belgium. Fresh surgical samples were sent at room temperature in the MapQuant Dx™ PathKit for centralized genomic analysis. Genomic profiles were determined using Affymetrix U133 Plus 2.0 and GGI calculated using the MapQuant Dx™ protocol, which defines tumors as low or high Genomic Grade (GG-1 and GG-3 respectively). 180 pts were recruited and 155 were eligible. The MapQuant test was performed in 142 cases and GGI was obtained in 78% of cases (n=111). Reasons for failures were 15 samples with <30% of invasive tumor cells (11%), 15 with insufficient RNA quality (10%), and 1 failed hybridization (<1%). For tumors with an available representative sample (≥ 30% inv. tumor cells) (n=127), the success rate was 87.5 %. GGI reclassified 69% of the 54 HG2 tumors as GG-1 (54%) or GG-3 (46%). Changes in treatment recommendations occurred mainly in the subset of HG2 tumors reclassified into GG-3, with increased use of chemotherapy in this subset. The use of GGI is feasible in routine clinical practice and impacts treatment decisions in early-stage breast cancer.

2013-06-03 | GSE43365 | GEO

Genomic Grade Index (GGI): Feasibility in routine practice and impact on treatment decisions in early breast cancer

Project description:Genomic Grade Index (GGI) is a 97-gene signature that improves histologic grade (HG) classification in invasive breast carcinoma. In this prospective study we sought to evaluate the feasibility of performing GGI in routine clinical practice and its impact on treatment recommendations. Patients with pT1pT2 or operable pT3, N0-3 invasive breast carcinoma were recruited from 8 centers in Belgium. Fresh surgical samples were sent at room temperature in the MapQuant DxM-bM-^DM-" PathKit for centralized genomic analysis. Genomic profiles were determined using Affymetrix U133 Plus 2.0 and GGI calculated using the MapQuant DxM-bM-^DM-" protocol, which defines tumors as low or high Genomic Grade (GG-1 and GG-3 respectively). 180 pts were recruited and 155 were eligible. The MapQuant test was performed in 142 cases and GGI was obtained in 78% of cases (n=111). Reasons for failures were 15 samples with <30% of invasive tumor cells (11%), 15 with insufficient RNA quality (10%), and 1 failed hybridization (<1%). For tumors with an available representative sample (M-bM-^IM-% 30% inv. tumor cells) (n=127), the success rate was 87.5 %. GGI reclassified 69% of the 54 HG2 tumors as GG-1 (54%) or GG-3 (46%). Changes in treatment recommendations occurred mainly in the subset of HG2 tumors reclassified into GG-3, with increased use of chemotherapy in this subset. The use of GGI is feasible in routine clinical practice and impacts treatment decisions in early-stage breast cancer. Total RNA was extracted from fresh tumor tissues comprising M-bM-^IM-%30% invasive tumor cells and hybridized on Affymetrix microarrays if the RIN was M-bM-^IM-% 7.

2013-06-03 | E-GEOD-43365 | biostudies-arrayexpress

Diagnostic performance of transcriptomic signatures for the evaluation of suspected tuberculosis in routine clinical practice

Project description:Diagnostic performance of transcriptomic signatures for the evaluation of suspected tuberculosis in routine clinical practice

| PRJNA602856 | ENA

Detection of mutant NPM1 mRNA in acute myeloid leukemia (AML) using custom gene expression arrays [Training cohort]

Project description:Abstract Mutations in the gene encoding nucleophosmin (NPM1) carry prognostic value for patients with acute myeloid leukemia (AML). Various techniques are currently being used to detect these mutations in routine molecular diagnostics. Incorporation of accurate NPM1 mutation detection on a gene expression platform would enable simultaneous detection with various other expression biomarkers. Here we present an array based mutation detection using custom probes for NPM1 WT mRNA and NPM1 type A, B, and D mutant mRNA. This method was 100% accurate on a training cohort of 505 newly diagnosed unselected AML cases. Validation on an independent cohort of 143 normal karyotype AML cases revealed no false negative results, and one false positive (sensitivity 100.0%, and specificity 98.7%). Based on this, we conclude that this method provides a reliable method for NPM1 mutation detection. The method can be applied to other genes/mutations as long as the mutant alleles are sufficiently high expressed. Training cohort of 505 AML cases analyzed using the AMLprofiler

2013-01-01 | E-GEOD-42194 | biostudies-arrayexpress

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