Detection of gene mutations in gastric cancer tissues using a commercial sequencing panel

Ito, Tomoaki; Matoba, Ryo; Maekawa, Hiroshi; Sakurada, Mutsumi; Kushida, Tomoyuki; Orita, Hajime; Wada, Ryo; Sato, Koichi

doi:10.3892/mco.2019.1926

Cited by 5 publications

(4 citation statements)

References 37 publications

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“…This study demonstrated the feasibility of DL-based prediction of mutations in CDH1, ERBB2, KRAS, PIK3CA, and TP53 genes, which are prevalent in both the TCGA and SSMH GC datasets, from tissue slide images of GC. Other studies have also shown that mutations in these genes are frequently observed in GC[ 3 , 5 ]. Furthermore, many studies have attempted to evaluate the prognostic value of these mutations[ 3 , 5 , 38 ].…”

Section: Discussionmentioning

confidence: 99%

“…Other studies have also shown that mutations in these genes are frequently observed in GC[ 3 , 5 ]. Furthermore, many studies have attempted to evaluate the prognostic value of these mutations[ 3 , 5 , 38 ]. However, the clinical relevance of these mutations for prognosis and treatment response has not been completely determined because the studies often presented discordant results.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, the clinical significance of many mutations is still not well understood. For example, mutation profiling of gastric cancer (GC) is still proceeding, and the meaning of each mutation is not clearly understood[ 3 ]. GC is the fifth most common cancer and the third leading cause of cancer-related deaths worldwide[ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach

Jang¹,

Lee²,

Kang³

et al. 2021

WJG

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BACKGROUND Studies correlating specific genetic mutations and treatment response are ongoing to establish an effective treatment strategy for gastric cancer (GC). To facilitate this research, a cost- and time-effective method to analyze the mutational status is necessary. Deep learning (DL) has been successfully applied to analyze hematoxylin and eosin (H and E)-stained tissue slide images. AIM To test the feasibility of DL-based classifiers for the frequently occurring mutations from the H and E-stained GC tissue whole slide images (WSIs). METHODS From the GC dataset of The Cancer Genome Atlas (TCGA-STAD), wild-type/mutation classifiers for CDH1, ERBB2, KRAS, PIK3CA, and TP53 genes were trained on 360 × 360-pixel patches of tissue images. RESULTS The area under the curve (AUC) for the receiver operating characteristic (ROC) curves ranged from 0.727 to 0.862 for the TCGA frozen WSIs and 0.661 to 0.858 for the TCGA formalin-fixed paraffin-embedded (FFPE) WSIs. The performance of the classifier can be improved by adding new FFPE WSI training dataset from our institute. The classifiers trained for mutation prediction in colorectal cancer completely failed to predict the mutational status in GC, indicating that DL-based mutation classifiers are incompatible between different cancers. CONCLUSION This study concluded that DL could predict genetic mutations in H and E-stained tissue slides when they are trained with appropriate tissue data.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach

Jang¹,

Lee²,

Kang³

et al. 2021

WJG

View full text Add to dashboard Cite

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“…In addition to these classifications, GCs can be characterised on the molecular level [ 11 ]. Research has reported mutations in KRAS , APC , ARID1A , PIK3CA , ERBB3 , HLA-B , SMAD4 , STK11 , PTEN , and BMPR1A , amongst others, with the most frequent reports of mutations in CDH1 and TP53 [ 11 , 12 , 13 , 14 , 15 ]. The TME also plays a role in tumour progression, as it has been shown that a high amount of tumour stroma is associated with worse five-year survival in GC [ 16 ].…”

Section: Difficult-to-treat Gastrointestinal Cancersmentioning

confidence: 99%

Multicellular Modelling of Difficult-to-Treat Gastrointestinal Cancers: Current Possibilities and Challenges

Hakuno

Michiels²,

Kuhlemaijer

et al. 2022

IJMS

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Cancers affecting the gastrointestinal system are highly prevalent and their incidence is still increasing. Among them, gastric and pancreatic cancers have a dismal prognosis (survival of 5–20%) and are defined as difficult-to-treat cancers. This reflects the urge for novel therapeutic targets and aims for personalised therapies. As a prerequisite for identifying targets and test therapeutic interventions, the development of well-established, translational and reliable preclinical research models is instrumental. This review discusses the development, advantages and limitations of both patient-derived organoids (PDO) and patient-derived xenografts (PDX) for gastric and pancreatic ductal adenocarcinoma (PDAC). First and next generation multicellular PDO/PDX models are believed to faithfully generate a patient-specific avatar in a preclinical setting, opening novel therapeutic directions for these difficult-to-treat cancers. Excitingly, future opportunities such as PDO co-cultures with immune or stromal cells, organoid-on-a-chip models and humanised PDXs are the basis of a completely new area, offering close-to-human models. These tools can be exploited to understand cancer heterogeneity, which is indispensable to pave the way towards more tumour-specific therapies and, with that, better survival for patients.

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Identification of Recurrent TERT Promoter Mutations in Intrathyroid Thymic Carcinomas

et al. 2020

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Detection of gene mutations in gastric cancer tissues using a commercial sequencing panel

Cited by 5 publications

References 37 publications

Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach

Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach

Multicellular Modelling of Difficult-to-Treat Gastrointestinal Cancers: Current Possibilities and Challenges

Identification of Recurrent TERT Promoter Mutations in Intrathyroid Thymic Carcinomas

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