Pulmonary enteric adenocarcinoma: a literature review

Li, Haiyan; Cao, Wei

doi:10.21037/jtd-19-4171

Cited by 23 publications

(24 citation statements)

References 32 publications

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“…GEP was uninformative in these cases as one was classified as colorectal (0.9-confidence probability), and two were predicted SCC, a non-specific classification but potentially in keeping with lung-squamous cancer (Figure 3E, Supplementary Table 2). These CDX2+ Lung-CUPs were favored to be enteric-like lung adenocarcinomas 37 .…”

Section: Resultsmentioning

confidence: 99%

“…Integrated molecular profiling of CUP can help identify rare disease subtypes and our data supports their being some recurrent CUP entities. We identified examples of pulmonary enteric adenocarcinomas that lack TTF1 expression but can express the gastrointestinal marker CDX2 37 . GEP or IHC alone could not resolve such cases, given their atypical profile; however, they still retained DNA features highly suggestive of NSCLC, including a tobacco mutational signature and somatic mutations in KRAS , STK11 , and SMARCA4 .…”

Section: Discussionmentioning

confidence: 99%

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DNA sequencing and gene-expression profiling assists in making a tissue of origin diagnosis in cancer of unknown primary

Posner

Prall

Sivakumaran

et al. 2022

Preprint

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Cancer of unknown primary (CUP) constitutes a group of metastatic cancers in which standardized clinical investigations fail to identify a tissue of origin (TOO). Gene-expression profiling (GEP) has been used to resolve TOO, and DNA sequencing to identify potential targeted treatments; however, these methods have not been widely applied together in CUP patients. To assess the diagnostic utility of DNA and RNA tests for TOO classification, we applied GEP classification and/or gene-panel DNA sequencing in 215 CUP patients. Based on a retrospective review of pathology reports and clinical data, 77% of the cohort were deemed True-CUPs (T-CUP). Among the remaining cases, a latent primary diagnosis (10%) (LP-CUP) or TOO was highly suspected based on combined clinicopathological data (13%) (histology-resolved CUP, HR-CUP). High-medium confidence GEP classifications were made for 80% of LP/HR-CUPs, and these classifications were consistent with a pathologist-assigned diagnosis in 94% of cases, while only 56% of T-CUPs had high-medium confidence predictions. The frequency of somatic mutations in cancer genes was similar to 2,785 CUPs from AACR GENIE Project. DNA features, GEP classification, and oncovirus detection assisted making a TOO diagnosis in 37% of T-CUPs. Gene mutations and mutational signatures of diagnostic utility were found in 31% T-CUPs. GEP classification was useful in 13% of cases and viral detection in 4%. Among resolved T-CUPs, lung and biliary were the most frequently identified cancer types, while kidney cancer represented another minor subset. Multivariate survival analysis showed that unresolved T-CUPs had poorer overall survival than LP/HR-CUPs (Hazard ratio=1.9, 95% CI 1.1 − 3.2, p=0.016), while the risk of death was similar in genomically-resolved T-CUPs and LP/HR-CUPs. In conclusion, combining DNA and RNA profiling with clinicopathological data supported a putative TOO diagnosis in over a third of T-CUPs. DNA sequencing benefited T-CUP tumors with atypical transcriptional patterns that hindered reliable GEP classification.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

DNA sequencing and gene-expression profiling assists in making a tissue of origin diagnosis in cancer of unknown primary

Posner

Prall

Sivakumaran

et al. 2022

Preprint

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“…The de nition of growth patterns and tumor grading was based on the classi cation of lung adenocarcinomas published by the IASLC/ATS/ERS in 2011 [24]. Growth pattern risk strati cation was done by de ning micropapillary (MMP), solid predominant (SPA) and variants of invasive (VIA) lung adenocarcinomas as high-risk and all other growth patterns as low-risk lesions following the literature on the prognostic subgroups of lung adenocarcinomas [4][5][6][7]. All inconclusive histologic samples were subsumed in others.…”

Section: Methodsmentioning

confidence: 99%

“…NSCLCs most common subtype is lung adenocarcinoma (LUAD), making LUAD the most prevalent histologic type of all primary lung cancers in both sexes [3]. The prognosis of patients with LUAD and equal TNM stages differed signi cantly depending on their progressive features and predominant growth patterns [4][5][6][7], which was considered in the 2015 published world health organization (WHO) classi cation of lung tumors [8]. However, histopathologic workup for diagnosis is challenging in inoperable patients, and it is frequently and solely based on small tumor samples from biopsy or cytology [9].…”

Section: Declarations Introductionmentioning

confidence: 99%

PET/CT radiomics and machine learning enable non-invasive survival stratification and histologic tumor risk profiling in patients with lung adenocarcinoma

Zhao

Kluge

Papp

et al. 2021

Preprint

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Purpose Risk stratification in patients with lung adenocarcinoma (LUAD) is mandatory for treatment guiding and outcome prediction. Amongst clinical parameters including histological analyses, imaging procedures provide important information. The present study aimed to investigate the ability of machine learning models trained on clinical and 2-deoxy-2-[¹⁸F]fluoro-D-glucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) derived radiomic data to predict overall survival (OS), tumor grade (TG), and histologic growth pattern risk (GPR) in treatment naïve LUAD patients. Methods 421 treatment naïve patients with histologically diagnosed lung adenocarcinoma and available [18F]FDG PET/CT imaging were retrospectively analyzed. Four patient cohorts were generated based on the available data for predicting 4-year OS (n = 276), 3-year OS (n = 280), TG (n = 298) and GPR (n = 265). [18F]FDG-positive lesions were delineated semiautomatically, from which 2082 radiomic features were extracted and combined with endpoint-specific clinical and demographic parameters. Machine learning models were built for the prediction of 4-year OS (M4OS), 3-year OS (M3OS), tumor grading (MTG) and histologic growth pattern risk (MGPR), respectively. Monte Carlo (MC) cross-validation with 100-folds and 80:20 training to validation split was employed as a performance evaluation for all models. Kaplan-Meier survival analysis was performed to assess the association between the M4OS and M3OS predictions with OS. Results Area under the receiver operator characteristics curve (AUC) was highest for M4OS (AUC 0.88), followed by M3OS (AUC 0.84), while MTG and MGPR performed equally well (AUC 0.76). Predictions of M4OS (HR 0.128, p < 0.000001) and M3OS (HR 0.0942, p < 0.000001) were independently associated with OS. Conclusion In our retrospective cohorts, machine learning models demonstrated the ability to prognosticate long-term survival outcomes in patients with lung adenocarcinoma. Furthermore, tumor lesions could be characterized according to their histologic grade and predominant growth pattern risk with high accuracy.

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“…At present, the main treatment methods for PEAC are surgery and systemic chemotherapy. Although many case reports and studies have described the detection of different gene mutations in patients with PEAC, few have been administered targeted therapy ( 2 ).…”

Section: Introductionmentioning

confidence: 99%

Hyperprogressive Disease After Immunotherapy: A Case Report of Pulmonary Enteric Adenocarcinoma

Shi

Dong

et al. 2022

Front. Oncol.

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Primary pulmonary enteric adenocarcinoma (PEAC) is a rare invasive adenocarcinoma clinically similar to metastatic colorectal adenocarcinoma (MCRC). Although many studies have addressed the differential diagnosis of PEAC, few have described the treatment of PEAC, especially using immunotherapy. This report describes a 61-year-old man who presented initially with pain in the ribs. Pathological analysis of biopsy samples shows malignant tumors of the right pleura, and next-generation sequencing of 26 genes showed a KRAS gene mutation. Positron emission tomography-computed tomography (PET-CT) found no evidence of gastrointestinal malignancy. Due to multiple metastases, the patient could not undergo radical surgery. The patient was treated with a combination chemotherapy regimen of paclitaxel plus carboplatin, along with sindilizumab immunotherapy, but, after one cycle of treatment, the tumor showed a hyperprogressive state. The patient is still being monitored regularly. These findings indicate that chemotherapy combined with immunotherapy may be ineffective in the treatment of primary PEAC with positive driver genes.

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Pulmonary enteric adenocarcinoma: a literature review

Cited by 23 publications

References 32 publications

DNA sequencing and gene-expression profiling assists in making a tissue of origin diagnosis in cancer of unknown primary

DNA sequencing and gene-expression profiling assists in making a tissue of origin diagnosis in cancer of unknown primary

PET/CT radiomics and machine learning enable non-invasive survival stratification and histologic tumor risk profiling in patients with lung adenocarcinoma

Hyperprogressive Disease After Immunotherapy: A Case Report of Pulmonary Enteric Adenocarcinoma

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