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

ABSTRACT: This SuperSeries is composed of the SubSeries listed below.

ORGANISM(S): Homo sapiens

PROVIDER: GSE131049 | GEO | 2020/02/21

REPOSITORIES: GEO

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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.

| S-EPMC6942634 | biostudies-literature

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 (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

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

| PRJNA542386 | ENA

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 (NanoString)

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

| PRJNA542387 | ENA

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)

| PRJNA607901 | ENA

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

A robust classifier to distinguish noise from fMRI independent components.

Project description:Analyzing Functional Magnetic Resonance Imaging (fMRI) of resting brains to determine the spatial location and activity of intrinsic brain networks--a novel and burgeoning research field--is limited by the lack of ground truth and the tendency of analyses to overfit the data. Independent Component Analysis (ICA) is commonly used to separate the data into signal and Gaussian noise components, and then map these components on to spatial networks. Identifying noise from this data, however, is a tedious process that has proven hard to automate, particularly when data from different institutions, subjects, and scanners is used. Here we present an automated method to delineate noisy independent components in ICA using a data-driven infrastructure that queries a database of 246 spatial and temporal features to discover a computational signature of different types of noise. We evaluated the performance of our method to detect noisy components from healthy control fMRI (sensitivity?=?0.91, specificity?=?0.82, cross validation accuracy (CVA)?=?0.87, area under the curve (AUC)?=?0.93), and demonstrate its generalizability by showing equivalent performance on (1) an age- and scanner-matched cohort of schizophrenia patients from the same institution (sensitivity?=?0.89, specificity?=?0.83, CVA?=?0.86), (2) an age-matched cohort on an equivalent scanner from a different institution (sensitivity?=?0.88, specificity?=?0.88, CVA?=?0.88), and (3) an age-matched cohort on a different scanner from a different institution (sensitivity?=?0.72, specificity?=?0.92, CVA?=?0.79). We additionally compare our approach with a recently published method. Our results suggest that our method is robust to noise variations due to population as well as scanner differences, thereby making it well suited to the goal of automatically distinguishing noise from functional networks to enable investigation of human brain function.

| S-EPMC3991682 | biostudies-literature

Putative biomarkers for predicting tumor sample purity based on gene expression data.

Project description:BACKGROUND:Tumor purity is the percent of cancer cells present in a sample of tumor tissue. The non-cancerous cells (immune cells, fibroblasts, etc.) have an important role in tumor biology. The ability to determine tumor purity is important to understand the roles of cancerous and non-cancerous cells in a tumor. METHODS:We applied a supervised machine learning method, XGBoost, to data from 33 TCGA tumor types to predict tumor purity using RNA-seq gene expression data. RESULTS:Across the 33 tumor types, the median correlation between observed and predicted tumor-purity ranged from 0.75 to 0.87 with small root mean square errors, suggesting that tumor purity can be accurately predicted ????? expression data. We further confirmed that expression levels of a ten-gene set (CSF2RB, RHOH, C1S, CCDC69, CCL22, CYTIP, POU2AF1, FGR, CCL21, and IL7R) were predictive of tumor purity regardless of tumor type. We tested whether our set of ten genes could accurately predict tumor purity of a TCGA-independent data set. We showed that expression levels from our set of ten genes were highly correlated (??=?0.88) with the actual observed tumor purity. CONCLUSIONS:Our analyses suggested that the ten-gene set may serve as a biomarker for tumor purity prediction using gene expression data.

| S-EPMC6933652 | biostudies-literature

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