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

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

PROVIDER: PRJNA607898 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR11124500.fastq.gz	Fastqsanger.gz
	SRR11124501.fastq.gz	Fastqsanger.gz
	SRR11124502.fastq.gz	Fastqsanger.gz
	SRR11124503.fastq.gz	Fastqsanger.gz
	SRR11124504.fastq.gz	Fastqsanger.gz

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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 (NanoString)

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

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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)

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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.

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

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Verification of Molecular Subtyping of Bladder Cancer in the GUSTO Clinical Trial

Project description:The GUSTO clinical trial (Gene expression subtypes of Urothelial carcinoma: Stratified Treatment and Oncological outcomes) uses molecular subtypes to guide neoadjuvant therapies in participants with muscle-invasive bladder cancer (MIBC). Before commencing the GUSTO trial, we needed to determine the reliability of a commercial subtyping platform (Decipher Bladder; Veracyte) when performed in an external trial laboratory as this has not been done previously. Here we report our pre-trial verification of the TCGA molecular subtyping model using gene expression profiling. Formalin fixed paraffin embedded tissue blocks of MIBC were used for gene expression subtyping by gene expression microarrays. Intra- and inter-laboratory technical reproducibility, together with quality control of laboratory and bioinformatics processes were assessed. Eighteen samples underwent analysis. RNA of sufficient quality and quantity was successfully extracted from all samples. All subtypes were represented in the cohort. Each sample was subtyped twice in our laboratory and once in a separate reference laboratory. No clinically significant discordance in subtype occurred between intra- or inter-laboratory replicates. Examination of sample histopathology showed variability of morphological appearances within and between subtypes. Overall, these results show that molecular subtyping by gene expression profiling is reproducible, robust, and suitable for use in the GUSTO clinical trial.

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