Ultra-Sensitive TP53 Sequencing for Cancer Detection Reveals Progressive Clonal Selection in Normal Tissue over a Century of Human Lifespan

Salk, Jesse J.; Loubet-Senear, Kaitlyn; Maritschnegg, Elisabeth; Valentine, Charles C.; Williams, Lindsey N.; Higgins, Jacob E.; Horvat, Reinhard; Vanderstichele, Adriaan; Nachmanson, Daniela; Baker, Kathryn T.; Emond, Mary J.; Loter, Emily; Tretiakova, Maria; Soussi, Thierry; Loeb, Lawrence A.; Zeillinger, Robert; Speiser, Paul; Risques, Rosa Ana

doi:10.1016/j.celrep.2019.05.109

Cited by 74 publications

(76 citation statements)

References 50 publications

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“…Automatic pulmonary disease detection using computer-aided diagnosis (CAD) is based on the correct segmentation of anatomical structures, such as the lungs, heart, and clavicle bones [2]. With the success of deep learning, artificially intelligent algorithms can help medical experts and ophthalmologists to detect and diagnose the disease and increase diagnostic throughput [14][15][16][17][18][19][20]. Semantic segmentation is a special branch of deep learning that involves pixel-wise classification of the image, which is important to accurately locate the infected areas for disease analysis [21,22].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Arsalan

Owais

Mahmood

et al. 2020

JCM

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Automatic chest anatomy segmentation plays a key role in computer-aided disease diagnosis, such as for cardiomegaly, pleural effusion, emphysema, and pneumothorax. Among these diseases, cardiomegaly is considered a perilous disease, involving a high risk of sudden cardiac death. It can be diagnosed early by an expert medical practitioner using a chest X-Ray (CXR) analysis. The cardiothoracic ratio (CTR) and transverse cardiac diameter (TCD) are the clinical criteria used to estimate the heart size for diagnosing cardiomegaly. Manual estimation of CTR and other diseases is a time-consuming process and requires significant work by the medical expert. Cardiomegaly and related diseases can be automatically estimated by accurate anatomical semantic segmentation of CXRs using artificial intelligence. Automatic segmentation of the lungs and heart from the CXRs is considered an intensive task owing to inferior quality images and intensity variations using nonideal imaging conditions. Although there are a few deep learning-based techniques for chest anatomy segmentation, most of them only consider single class lung segmentation with deep complex architectures that require a lot of trainable parameters. To address these issues, this study presents two multiclass residual mesh-based CXR segmentation networks, X-RayNet-1 and X-RayNet-2, which are specifically designed to provide fine segmentation performance with a few trainable parameters compared to conventional deep learning schemes. The proposed methods utilize semantic segmentation to support the diagnostic procedure of related diseases. To evaluate X-RayNet-1 and X-RayNet-2, experiments were performed with a publicly available Japanese Society of Radiological Technology (JSRT) dataset for multiclass segmentation of the lungs, heart, and clavicle bones; two other publicly available datasets, Montgomery County (MC) and Shenzhen X-Ray sets (SC), were evaluated for lung segmentation. The experimental results showed that X-RayNet-1 achieved fine performance for all datasets and X-RayNet-2 achieved competitive performance with a 75% parameter reduction.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Arsalan

Owais

Mahmood

et al. 2020

JCM

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“…To accommodate the error rate of NGS, large volumes of blood are needed, and higher read depth is required, which increases the overall costs and errors associated with oversampling (Haque & Elemento, 2017; Phallen et al, 2017). In addition, the frequency of somatic mutations in healthy individuals may be similar to those of patients with early stages of cancer (Salk et al, 2019; Xia et al, 2017). Further, the high correlation between mutations found in white blood cells and cfDNA suggests that some mutations may not indicate the presence of a tumor (Chin et al, 2019; Xia et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Single‐molecule detection of cancer mutations using a novel PCR‐LDR‐qPCR assay

et al. 2020

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Detection of low-abundance mutations in cell-free DNA is being used to identify early cancer and early cancer recurrence. Here, we report a new PCR-LDR-qPCR assay capable of detecting point mutations at a single-molecule resolution in the presence of an excess of wild-type DNA. Major features of the assay include selective amplification and detection of mutant DNA employing multiple nested primer-binding regions as well as wild-type sequence blocking oligonucleotides, prevention of carryover contamination, spatial sample dilution, and detection of multiple mutations in the same position. Our method was tested to interrogate the following common cancer somatic mutations: BRAF:c.1799T>A

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“…Recently, one of the major mutational mechanisms driving genetic mosaicism in humans has been described as oxidative stress and spontaneous deamination of methylated cytosines [8]. How these mutant lineages expand or disappear in healthy tissues during the development has been a highly active research area in the last years [6,[9][10][11][12][13][14][15] and fits within the neutral theory of mutagenesis and genetic drift. However, in this review, we will focus on a unique type of mutagenesis: point mutations in the RTK and its pathway components (e.g., RAS) that change the function of the protein and lead to the clonal growth of the cell.…”

Section: Pzm and Selfish Mutationsmentioning

confidence: 99%

Pathogenic postzygotic mosaicism in the tyrosine receptor kinase pathway: potential unidentified human disease hidden away in a few cells

et al. 2020

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Mutations occurring during embryonic development affect only a subset of cells resulting in two or more distinct cell populations that are present at different levels, also known as postzygotic mosaicism (PZM). Although PZM is a common biological phenomenon, it is often overlooked as a source of disease due to the challenges associated with its detection and characterization, especially for very low-frequency variants. Moreover, PZM can cause a different phenotype compared to constitutional mutations. Especially, lethal mutations in receptor tyrosine kinase (RTK) pathway genes, which exist only in a mosaic state, can have completely new clinical manifestations and can look very different from the associated monogenic disorder. However, some key questions are still not addressed, such as the level of mosaicism resulting in a pathogenic phenotype and how the clinical outcome changes with the development and age. Addressing these questions is not trivial as we require methods with the sensitivity to capture some of these variants hidden away in very few cells. Recent ultra-accurate deep-sequencing approaches can now identify these low-level mosaics and will be central to understand systemic and local effects of mosaicism in the RTK pathway. The main focus of this review is to highlight the importance of low-level mosaics and the need to include their detection in studies of genomic variation associated with disease.

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Ultra-Sensitive TP53 Sequencing for Cancer Detection Reveals Progressive Clonal Selection in Normal Tissue over a Century of Human Lifespan

Cited by 74 publications

References 50 publications

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Single‐molecule detection of cancer mutations using a novel PCR‐LDR‐qPCR assay

Pathogenic postzygotic mosaicism in the tyrosine receptor kinase pathway: potential unidentified human disease hidden away in a few cells

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