Primary Lung Cribriform Adenocarcinoma With Squamoid Morules Harboring Somatic <i>CTNNB1</i> Mutation in a Never-Smoked Healthy Adolescent

Wu, Hao; Ye, Qiqi; Razzano, Dana; Tugal, Oya; Rosenblum, Jeremy; Weigel, Tracey L.; Zhong, Minghao

doi:10.1177/1093526620957219

Cited by 1 publication

(1 citation statement)

References 25 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…D.Barggio et al [100], demonstrated the apoptotic resistance of desmoid cells that harbor the p.SF45F β-catenin mutation with a downregulation of the RUNX3 protein expression. CTNNB1 mutations may result to rare cases, such as the case report of a never-smoked adolescent with a healthy family history diagnosed as the first reported pediatric case with lung aede-nocarcinomas, where the molecular testing and sequencing panels found that CTNNB1 mutation is the only alteration in the 17 years old patient [101].…”

Section: Uncommon Cancer Variants With Important Clinical Impact Are ...mentioning

confidence: 99%

Efficient Bioinspired Feature Selection and Machine Learning Based Framework Using Omics Data and Biological Knowledge Data Bases in Cancer Clinical Endpoint Prediction

et al. 2023

View full text Add to dashboard Cite

Cancer Research has advanced during the past few years. Using high throughput technology and advances in artificial intelligence, it is now possible to improve cancer diagnosis and targeted therapy, by integrating the investigation and analysis of clinical and omics profiles. The high dimensionality and class imbalance of the majority of available data sets represent a serious challenge to the development of computational methods and tools for cancer diagnosis and biomarker discovery. Taking into account multi-omics data further complicates the undertaking. In this paper, we describe a five-step integrative architecture for dealing with the three aforementioned problems by incorporating proteomics data, proteinprotein interaction networks, and signaling pathways in order to identify protein biomarkers with a direct association to cancerous patients' overall survival (OS) and progression free interval (PFI). The core parts of this architecture are a cluster based grey wolf optimization algorithm (CB-GWO) for feature selection and a deep stacked canonical correlation autoencoder (DSCC-AE) for clinical endpoint prediction. A thorough experimental study was carried out to evaluate the performance of the proposed optimization algorithm for feature selection, as well as the performance of the deep learning model in terms of Mathew coefficient correlation (MCC) and Area under the curve (AUC) on breast, lung, colon, and rectum cancers. The results were compared to other methods in the literature. The results are very promising and show the effectiveness of the proposed framework and its ability to outperform the other algorithms and models in terms of AUC (0.91) and MCC (0.64).

show abstract

Section: Uncommon Cancer Variants With Important Clinical Impact Are ...mentioning

confidence: 99%

Efficient Bioinspired Feature Selection and Machine Learning Based Framework Using Omics Data and Biological Knowledge Data Bases in Cancer Clinical Endpoint Prediction

et al. 2023

View full text Add to dashboard Cite

show abstract

Primary Lung Cribriform Adenocarcinoma With Squamoid Morules Harboring Somatic CTNNB1 Mutation in a Never-Smoked Healthy Adolescent

Cited by 1 publication

References 25 publications

Efficient Bioinspired Feature Selection and Machine Learning Based Framework Using Omics Data and Biological Knowledge Data Bases in Cancer Clinical Endpoint Prediction

Efficient Bioinspired Feature Selection and Machine Learning Based Framework Using Omics Data and Biological Knowledge Data Bases in Cancer Clinical Endpoint Prediction

Contact Info

Product

Resources

About