Dataset Information

Pulmonary enteric adenocarcinoma: a literature review.

ABSTRACT: Pulmonary enteric adenocarcinoma (PEAC) is an extremely rare type of non-small cell lung cancer (NSCLC) with a histologic pattern that mimics metastatic colorectal cancer (MCC). The main clinical symptoms in PEAC patients are dyspnoea, coughing, hemoptysis, and chest and back pain. The first article about PEAC appeared in 1991 in the form of a case report. As a variant of invasive lung carcinoma, only a small number of case reports and clinical research studies have been carried out, and the only one guidance on diagnosis and treatment is the WHO Tumor Classification book. It is important for doctors to distinguish PEAC from MCC to extend survival time and improve the quality of life. We reviewed the existing literature regarding the diagnosis, treatment, and prognosis of PEAC to provide some valuable clinical references.

SUBMITTER: Li H

PROVIDER: S-EPMC7330779 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Pulmonary enteric adenocarcinoma: a literature review.

Li Haiyan H Cao Wei W

Journal of thoracic disease 20200601 6

Pulmonary enteric adenocarcinoma (PEAC) is an extremely rare type of non-small cell lung cancer (NSCLC) with a histologic pattern that mimics metastatic colorectal cancer (MCC). The main clinical symptoms in PEAC patients are dyspnoea, coughing, hemoptysis, and chest and back pain. The first article about PEAC appeared in 1991 in the form of a case report. As a variant of invasive lung carcinoma, only a small number of case reports and clinical research studies have been carried out, and the onl ...[more]

PMID: 32642243

Similar Datasets

Project description:Pulmonary enteric adenocarcinoma (PEAD) is a rare non-small cell lung cancer subtype. It is poorly characterized and cannot be distinguished from metastatic colorectal or upper gastrointestinal adenocarcinomas (ADCs) by means of routine pathological methods. As DNA methylation patterns are known to be highly tissue specific, we aimed to develop a methylation-based algorithm to differentiate these entities. To this end, genome wide methylation profiles of 600 primary pulmonary, colorectal and upper gastrointestinal ADCs obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database were used as a reference cohort to train a machine learning algorithm. The resulting classifier correctly classified all samples from a validation cohort consisting of 680 primary pulmonary, colorectal and upper gastrointestinal ADCs from TCGA and the GEO database, demonstrating the ability of the algorithm to reliably distinguish these three entities. We then analyzed DNA methylation data of 15 PEADs as well as four pulmonary metastases and four primary colorectal ADCs with the algorithm. All 15 PEADs were reliably classified as primary pulmonary tumors and all four metastases as well as all four primary colorectal ADC samples were identified as primary colorectal ADCs. In a t-distributed stochastic neighbor embedding analysis, the PEAD samples did not form a separate methylation subclass but rather diffusely intermixed with other pulmonary ADCs. Additional characterization of the PEAD series using fluorescence in-situ hybridization, next generation sequencing and copy number analysis revealed KRAS mutations in nine of 15 samples (60%) and a high number of structural chromosomal changes. Except for an unusually high rate of chromosome 20 gain (66.7%) the molecular data was mostly reminiscent of standard pulmonary ADCs. In conclusion, we provide sound evidence of the pulmonary origin of PEAD and in addition provide a publicly available machine learning based algorithm to reliably distinguish PEAD from metastatic colorectal cancer and upper gastrointestinal adenocarcinomas.

Dataset Information

Pulmonary enteric adenocarcinoma: a literature review.

Publications

Pulmonary enteric adenocarcinoma: a literature review.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets