A novel diagnostic approach for the classification of small B-cell lymphoid neoplasms based on the NanoString platform
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PROVIDER: PRJNA758893 | ENA |
REPOSITORIES: ENA
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Project description:Small B-cell lymphoid neoplasms (SBCLNs) are a heterogeneous group of diseases characterized by malignant clonal proliferation of mature B-cells. However, the classification of SBCLNs remains a challenge, especially in cases where histopathological analysis is unavailable or those with atypical laboratory findings or equivocal pathologic data. In this study, gene expression profiling of 1,039 samples from 27 GEO datasets was first investigated to select highly and differentially expressed genes among SBCLNs. Samples from 57 SBCLN cases and 102 nonmalignant control samples were used to train a classifier using the NanoString platform. The classifier was built by employing a cascade binary classification method based on the random forest algorithm with 35 refined gene signatures. Cases were successively classified as chronic lymphocytic leukemia/small lymphocytic lymphoma, conventional mantle cell lymphoma, follicular lymphoma, leukemic non-nodal mantle cell lymphoma, marginal zone lymphoma, lymphoplasmacytic lymphoma/Waldenström’s macroglobulinemia, and other undetermined. The classifier algorithm was then validated using an independent cohort of 197 patients with SBCLNs. Under the distribution of our validation cohort, the overall sensitivity and specificity of proposed algorithm model were >95% respectively for all the cases with tumor cell content greater than 0.72. Combined with additional genetic aberrations including IGH-BCL2 translocation, MYD88 L265P mutation, and BRAF V600E mutation, the optimal sensitivity and specificity were respectively found at 0.88 and 0.98. In conclusion, the established algorithm demonstrated to be an effective and valuable ancillary diagnostic approach for the sub-classification and pathologic investigation of SBCLN in daily practice.
2021-11-18 | GSE183030 | GEO
Project description:Small B-cell lymphoid neoplasms (SBCLNs) are a heterogeneous group of diseases characterized by malignant clonal proliferation of mature B-cells. However, the classification of SBCLNs remains a challenge, especially in cases where histopathological analysis is unavailable or those with atypical laboratory findings or equivocal pathologic data. In this study, gene expression profiling of 1039 samples from 27 gene expression omnibus (GEO) datasets was first investigated to select highly and differentially expressed genes among SBCLNs. Samples from 57 SBCLN cases and 102 nonmalignant control samples were used to train a classifier using the NanoString platform. The classifier was built by employing a cascade binary classification method based on the random forest algorithm with 35 refined gene signatures. Cases were successively classified as chronic lymphocytic leukemia/small lymphocytic lymphoma, conventional mantle cell lymphoma, follicular lymphoma, leukemic non-nodal mantle cell lymphoma, marginal zone lymphoma, lymphoplasmacytic lymphoma/Waldenström's macroglobulinemia, and other undetermined. The classifier algorithm was then validated using an independent cohort of 197 patients with SBCLNs. Under the distribution of our validation cohort, the overall sensitivity and specificity of proposed algorithm model were >95%, respectively, for all the cases with tumor cell content greater than 0.72. Combined with additional genetic aberrations including IGH-BCL2 translocation, MYD88 L265P mutation, and BRAF V600E mutation, the optimal sensitivity and specificity were respectively found at 0.88 and 0.98. In conclusion, the established algorithm demonstrated to be an effective and valuable ancillary diagnostic approach for the sub-classification and pathologic investigation of SBCLN in daily practice.
| S-EPMC9042706 | biostudies-literature
Project description:Accurate diagnostic discrimination of benign renal oncocytoma (OC) and malignant renal cell carcinomas (RCC) is not only useful for planning appropriate treatment strategies of patients with renal masses but also for estimating prognosis. Classification of renal neoplasms solely by histopathology can often be challenging for a variety of reasons. The aim of this study was to develop and validate a genomic algorithm for molecular classification of renal cortical neoplasms that could be implemented in a routine clinical diagnostic setting. Using TCGA (The Cancer Genome Atlas) copy number profiles of over 600 RCC specimens, prior FISH studies and published literature, a classification algorithm was developed consisting of 15 genomic markers: loss of VHL, 3p21, 8p, and chromosomes 1, 2, 6, 10 and 17, and gain of 5qter, 16p, 17q, 20q, and chromosomes 3, 7, and 12. Criteria for scoring specimens for the presence of each genomic marker were established. As validation, 191 surgically resected formalin-fixed paraffin-embedded renal neoplasms were blindly submitted to targeted array-CGH and were classified according to the algorithm. Upon histologic re-review leading to exclusion of three specimens and using histology as the gold standard, the algorithm correctly classified 58 of 62 (93%) clear cell renal cell carcinoma, 51 of 56 (91%) papillary RCC, and 33 of 34 (97%) chromophobe RCC. Of the 36 OC specimens, 17 were classified as OC, two as a malignant subtype, 14 as benign, and three exhibited alterations not associated with a specific subtype. In ten of the latter two groups, CCND1-rearrangement was detected by fluorescence in situ hybridization, affording a classification as OC. Together, 33 of 36 (92%) OC were classified as OC or benign. For the entire validation cohort, an overall diagnostic sensitivity of 93% and above 97% specificity was achieved, suggesting that the implementation of genome-based molecular classification in a clinical diagnostic setting could impact the overall management and outcome of patients with renal tumors. A total of 191 RCC FFPE samples are analyzed including 63 clear cell RCC (ccRCC), 57 papillary RCC (pRCC), 35 chromophobe RCC (chrRCC) and 36 oncocytoma (OC). Two-color array-comparative genomic hybdrization on custom designed using RCC DNA as test and normal sex-matched DNA as reference.
2015-04-29 | E-GEOD-57162 | biostudies-arrayexpress
Project description:Accurate diagnostic discrimination of benign renal oncocytoma (OC) and malignant renal cell carcinomas (RCC) is not only useful for planning appropriate treatment strategies of patients with renal masses but also for estimating prognosis. Classification of renal neoplasms solely by histopathology can often be challenging for a variety of reasons. The aim of this study was to develop and validate a genomic algorithm for molecular classification of renal cortical neoplasms that could be implemented in a routine clinical diagnostic setting. Using TCGA (The Cancer Genome Atlas) copy number profiles of over 600 RCC specimens, prior FISH studies and published literature, a classification algorithm was developed consisting of 15 genomic markers: loss of VHL, 3p21, 8p, and chromosomes 1, 2, 6, 10 and 17, and gain of 5qter, 16p, 17q, 20q, and chromosomes 3, 7, and 12. Criteria for scoring specimens for the presence of each genomic marker were established. As validation, 191 surgically resected formalin-fixed paraffin-embedded renal neoplasms were blindly submitted to targeted array-CGH and were classified according to the algorithm. Upon histologic re-review leading to exclusion of three specimens and using histology as the gold standard, the algorithm correctly classified 58 of 62 (93%) clear cell renal cell carcinoma, 51 of 56 (91%) papillary RCC, and 33 of 34 (97%) chromophobe RCC. Of the 36 OC specimens, 17 were classified as OC, two as a malignant subtype, 14 as benign, and three exhibited alterations not associated with a specific subtype. In ten of the latter two groups, CCND1-rearrangement was detected by fluorescence in situ hybridization, affording a classification as OC. Together, 33 of 36 (92%) OC were classified as OC or benign. For the entire validation cohort, an overall diagnostic sensitivity of 93% and above 97% specificity was achieved, suggesting that the implementation of genome-based molecular classification in a clinical diagnostic setting could impact the overall management and outcome of patients with renal tumors.
2015-04-29 | GSE57162 | GEO
Project description:Purpose: We sought to develop and evaluate a diagnostic classifier of UC subtype with the goal of accurate classification from clinically available specimens. Methods: Tumor samples from 52 patients with high-grade UC were profiled for BASE47 genes concurrently by RNAseq as well as NanoString. After design and technical validation of a BASE47 NanoString probeset, results from the RNAseq and NanoString were used to translate diagnostic criteria to the Nanostring platform. Evaluation of repeatability and accuracy was performed to derive a final Nanostring based classifier. Diagnostic classification resulting from the NanoString BASE47 classifier was validated on an independent dataset (n=63). The training and validation datasets accurately classified 87% and 93% of samples, respectively. Results: We have derived a NanoString-platform BASE47 classifier that accurately predicts basal-like and luminal-like subtypes in high grade urothelial cancer. We have further validated our new NanoString BASE47 classifier on an independent dataset and confirmed high accuracy when compared with our original Transcriptome BASE47 classifier. Conclusions: The NanoString BASE47 classifier provides a faster turnaround time, a lower cost per sample to process, and maintains the accuracy of the original subtype classifier for better clinical implementation.
2020-11-03 | GSE160693 | GEO
Project description:BackgroundDespite genomic simplicity, recent studies have reported at least 3 major atypical teratoid rhabdoid tumor (ATRT) subgroups with distinct molecular and clinical features. Reliable ATRT subgrouping in clinical settings remains challenging due to a lack of suitable biological markers, sample rarity, and the relatively high cost of conventional subgrouping methods. This study aimed to develop a reliable ATRT molecular stratification method to implement in clinical settings.MethodsWe have developed an ATRT subgroup predictor assay using a custom genes panel for the NanoString nCounter System and a flexible machine learning classifier package. Seventy-one ATRT primary tumors with matching gene expression array and NanoString data were used to construct a multi-algorithms ensemble classifier. Additional validation was performed using an independent gene expression array against the independently generated dataset. We also analyzed 11 extra-cranial rhabdoid tumors with our classifier and compared our approach against DNA methylation classification to evaluate the result consistency with existing methods.ResultsWe have demonstrated that our novel ensemble classifier has an overall average of 93.6% accuracy in the validation dataset, and a striking 98.9% accuracy was achieved with the high-prediction score samples. Using our classifier, all analyzed extra-cranial rhabdoid tumors are classified as MYC subgroups. Compared with the DNA methylation classification, the results show high agreement, with 84.5% concordance and up to 95.8% concordance for high-confidence predictions.ConclusionsHere we present a rapid, cost-effective, and accurate ATRT subgrouping assay applicable for clinical use.
| S-EPMC10825849 | biostudies-literature
Project description:The newest WHO classification suggests eliminating cases with BRAF and NRAS mutations from the categories of Spitz tumors (ST) and Spitz melanoma (SM). We aimed to better characterize the genomics of Spitz neoplasms and assess whether integrating genomic data with morphologic diagnosis improves classification and prognostication. We performed DNA and RNA sequencing on 80 STs, 26 SMs, and 22 melanomas with Spitzoid features (MSF). NGS data was used to reclassify tumors by moving BRAF/NRAS-mutated cases to MSF. Eighty-one percent of STs harbored kinase fusions/truncations. Of SMs, 77% had fusions/truncations, 8 involving MAP3K8. Novel fusions identified were MYO5A-FGFR1, MYO5A-ERBB4, and PRKDC-CTNNB1. The majority of MSFs (84%) had BRAF, NRAS, or NF1 mutations, and 62% had TERT promoter mutations. Only after reclassification, the following was observed: 1) mRNA expression showed distinct clustering of MSF; 2) 6/7 cases with recurrence and all distant metastases were MSFs; 3) RFS was worse in MSF than ST and SM groups (p=0.0073); 4) classification incorporating genomic data was highly predictive of recurrence (OR 13.20, p=0.0197). The majority of STs and SMs have kinase fusions as primary initiating genomic events. Eliminating BRAF/NRAS-mutated neoplasms from these categories results in improved classification and prognostication of melanocytic neoplasms with Spitzoid cytomorphology.
2020-02-26 | GSE142441 | GEO
Project description:The newest WHO classification suggests eliminating cases with BRAF and NRAS mutations from the categories of Spitz tumors (ST) and Spitz melanoma (SM). We aimed to better characterize the genomics of Spitz neoplasms and assess whether integrating genomic data with morphologic diagnosis improves classification and prognostication. We performed DNA and RNA sequencing on 80 STs, 26 SMs, and 22 melanomas with Spitzoid features (MSF). NGS data was used to reclassify tumors by moving BRAF/NRAS-mutated cases to MSF. Eighty-one percent of STs harbored kinase fusions/truncations. Of SMs, 77% had fusions/truncations, 8 involving MAP3K8. Novel fusions identified were MYO5A-FGFR1, MYO5A-ERBB4, and PRKDC-CTNNB1. The majority of MSFs (84%) had BRAF, NRAS, or NF1 mutations, and 62% had TERT promoter mutations. Only after reclassification, the following was observed: 1) mRNA expression showed distinct clustering of MSF; 2) 6/7 cases with recurrence and all distant metastases were MSFs; 3) RFS was worse in MSF than ST and SM groups (p=0.0073); 4) classification incorporating genomic data was highly predictive of recurrence (OR 13.20, p=0.0197). The majority of STs and SMs have kinase fusions as primary initiating genomic events. Eliminating BRAF/NRAS-mutated neoplasms from these categories results in improved classification and prognostication of melanocytic neoplasms with Spitzoid cytomorphology.
2020-08-27 | GSE142442 | GEO
Project description:Pancreatic neuroendocrine neoplasms (PNENs) are biologically and clinically heterogeneous neoplasms. We used quantitative global proteomic analysis on 40 PNENs to compliment paired transcriptome data.
2021-11-30 | PXD024175 | Pride
Project description:Integrating Next Generation Sequencing with Morphology Improves Prognostic and Biologic Classification of Spitz Neoplasms
| PRJNA608792 | ENA