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Purity Independent Subtyping of Tumors (PurIST), a clinically robust single sample classifier for tumor subtyping in pancreatic cancer (NanoString)

ABSTRACT: Purity Independent Subtyping of Tumors (PurIST), a clinically robust single sample classifier for tumor subtyping in pancreatic cancer (NanoString)

PROVIDER: PRJNA607901 | ENA |

REPOSITORIES: ENA

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Purity Independent Subtyping of Tumors (PurIST), a clinically robust single sample classifier for tumor subtyping in pancreatic cancer (NanoString)

Project description:We assess three major subtype classification schemas in the context of results from two clinical trials and by meta-analysis of publicly available expression data to assess statistical criteria of subtype robustness and overall clinical relevance. We then developed a Single Sample Classifier (SSC) using penalized logistic regression based on the most robust and replicable schema. We demonstrate that a tumor-intrinsic two subtype schema is most robust, replicable, and clinically relevant. We developed purity independent subtyping of tumors (PurIST), a SSC with robust and highly replicable performance on a wide range of platforms and sample types. We show that PurIST subtypes have meaningful associations with patient prognosis and have significant implications for treatment response to FOLIFIRNOX. Keywords: Expression profiling by array

2020-02-21 | GSE131051 | GEO

Purity Independent Subtyping of Tumors (PurIST), a clinically robust single sample classifier for tumor subtyping in pancreatic cancer

Project description:Purity Independent Subtyping of Tumors (PurIST), a clinically robust single sample classifier for tumor subtyping in pancreatic cancer

| PRJNA607898 | ENA

Purity Independent Subtyping of Tumors (PurIST), a clinically robust single-sample classifier for tumor subtyping in pancreatic cancer (RNA-Seq)

Project description:Purpose: Molecular subtyping for pancreatic cancer has made substantial progress in recent years, facilitating the optimization of existing therapeutic approaches to improve clinical outcomes in pancreatic cancer. With advances in treatment combinations and choices, it is becoming increasingly important to determine ways to place patients on the best therapies upfront. Although various molecular subtyping systems for pancreatic cancer have been proposed, consensus regarding proposed subtypes, as well as their relative clinical utility, remains largely unknown and presents a natural barrier to wider clinical adoption. Methods: We assess three major subtype classification schemas in the context of results from two clinical trials and by meta-analysis of publicly available expression data to assess statistical criteria of subtype robustness and overall clinical relevance. We then developed a single-sample classifier (SSC) using penalized logistic regression based on the most robust and replicable schema. Results: We demonstrate that a tumor-intrinsic two-subtype schema is most robust, replicable, and clinically relevant. We developed Purity Independent Subtyping of Tumors (PurIST), a SSC with robust and highly replicable performance on a wide range of platforms and sample types. We show that PurIST subtypes have meaningful associations with patient prognosis and have significant implications for treatment response to FOLIFIRNOX. Conclusions: The flexibility and utility of PurIST on low-input samples such as tumor biopsies allows it to be used at the time of diagnosis to facilitate the choice of effective therapies for patients with pancreatic ductal adenocarcinoma and should be considered in the context of future clinical trials.

2019-12-10 | GSE131050 | GEO

Purity Independent Subtyping of Tumors (PurIST), a clinically robust single-sample classifier for tumor subtyping in pancreatic cancer (RNA-Seq)

Project description:Purity Independent Subtyping of Tumors (PurIST), a clinically robust single-sample classifier for tumor subtyping in pancreatic cancer (RNA-Seq)

| PRJNA542388 | ENA

Development and validation of a NanoString BASE47 bladder cancer gene classifier

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

Concordance of PAM50 molecular subtyping between oligonucleotide microarray and NanoString nCounter assay for Taiwanese breast cancer

Project description:Five molecular subtypes have been identified according to intrinsic genes transcription. Microarrays have been adopted for molecular subtyping in addition to the original NanoString nCounter assay. This study aimed to evaluate subtyping consistency among Taiwanese breast cancers. Our study showed that fundamental discrepancy exists between distinct gene expression measuring approaches, and cross platform equivalence should not be overemphasized with too much extrapolation.

2021-09-01 | GSE162228 | GEO

Purity Independent Subtyping of Tumors (PurIST), A Clinically Robust, Single-sample Classifier for Tumor Subtyping in Pancreatic Cancer.

Project description:PurposeMolecular subtyping for pancreatic cancer has made substantial progress in recent years, facilitating the optimization of existing therapeutic approaches to improve clinical outcomes in pancreatic cancer. With advances in treatment combinations and choices, it is becoming increasingly important to determine ways to place patients on the best therapies upfront. Although various molecular subtyping systems for pancreatic cancer have been proposed, consensus regarding proposed subtypes, as well as their relative clinical utility, remains largely unknown and presents a natural barrier to wider clinical adoption.Experimental designWe assess three major subtype classification schemas in the context of results from two clinical trials and by meta-analysis of publicly available expression data to assess statistical criteria of subtype robustness and overall clinical relevance. We then developed a single-sample classifier (SSC) using penalized logistic regression based on the most robust and replicable schema.ResultsWe demonstrate that a tumor-intrinsic two-subtype schema is most robust, replicable, and clinically relevant. We developed Purity Independent Subtyping of Tumors (PurIST), a SSC with robust and highly replicable performance on a wide range of platforms and sample types. We show that PurIST subtypes have meaningful associations with patient prognosis and have significant implications for treatment response to FOLIFIRNOX.ConclusionsThe flexibility and utility of PurIST on low-input samples such as tumor biopsies allows it to be used at the time of diagnosis to facilitate the choice of effective therapies for patients with pancreatic ductal adenocarcinoma and should be considered in the context of future clinical trials.

| S-EPMC6942634 | biostudies-literature

Purity Independent Subtyping of Tumors (PurIST), a clinically robust single sample classifier for tumor subtyping in pancreatic cancer

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

2020-02-21 | GSE131049 | GEO

NanoString nCounter with the Oxford Classifier of Carcinoma of the Ovary

Project description:NanoString nCounter with the Oxford Classifier of Carcinoma of the Ovary

| PRJNA635505 | ENA

Development and validation of a NanoString BASE47 bladder cancer gene classifier

Project description:Development and validation of a NanoString BASE47 bladder cancer gene classifier

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