Purity Independent Subtyping of Tumors (PurIST), A Clinically Robust, Single-sample Classifier for Tumor Subtyping in Pancreatic Cancer.
Clin Cancer Res
; 26(1): 82-92, 2020 01 01.
Article
em En
| MEDLINE
| ID: mdl-31754050
ABSTRACT
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. EXPERIMENTALDESIGN:
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias Pancreáticas
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Biomarcadores Tumorais
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Regulação Neoplásica da Expressão Gênica
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Biologia Computacional
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Perfilação da Expressão Gênica
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Tipagem Molecular
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Clin Cancer Res
Assunto da revista:
NEOPLASIAS
Ano de publicação:
2020
Tipo de documento:
Article