Current perspectives on CHEK2 mutations in breast cancer

Apostolou, Panagiotis; Papasotiriou, Ioannis

doi:10.2147/bctt.s111394

Cited by 92 publications

(94 citation statements)

References 42 publications

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“…Similarly, we found that in BRCA samples ( Fig. 2e), for the prediction of expression levels for genes involved in cell-cycle regulation (Cell Cycle: G2/M DNA Damage Checkpoint Regulation, Cell cycle control of chromosomal Replication, Mitotic Roles of Polo-Like Kinase), but also for the prediction of expression levels for CHEK2 (a gene known to be mutated in breast cancer 40 and involved in its progression 41 ) and Cyclin E (known to be overexpressed in breast cancer 42 ). These results demonstrate that HE2RNA, despite training on a diverse range of cancer types, was able not only to predict expression levels for genes involved in immune regulation, but also to detect pathways deregulated in specific types of cancer.…”

supporting

confidence: 58%

Transcriptomic learning for digital pathology

Schmauch¹,

Romagnoni²,

Pronier³

et al. 2019

Preprint

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^,⋕ : These authors contributed equally.Deep learning methods for digital pathology analysis have proved an effective way to address multiple clinical questions, from diagnosis to prognosis and the prediction of treatment outcomes. They have also recently been used to predict gene mutations from pathology images, but no comprehensive evaluation of their potential for extracting molecular features from histology slides, has yet been performed. We propose a novel approach based on the integration of multiple data modes, and show that our deep learning model HE2RNA can be trained to predict systematically RNA-Seq profiles from whole-slide images alone, without the need for expert annotation. The model facilitates the virtual spatialization of gene expression, as validated by double-staining in an independent dataset. The results can therefore be interpreted in detail and this model opens up new opportunities for virtual staining. Finally, the transcriptomic representation learned by the model could be could be used to improve performances for other clinical tasks, particularly for small datasets. For example we studied the problem of predicting microsatellite instability from Hematoxylin & Eosin (H&E)-stained images. Greater prediction ability was achieved in such a realistic framework.

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supporting

confidence: 58%

Transcriptomic learning for digital pathology

Schmauch¹,

Romagnoni²,

Pronier³

et al. 2019

Preprint

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“…The interpretation of this challenge is problematic since the exact nature of the association between CHEK2 and breast cancer is debatable (Apostolou & Papasotiriou, ). Moreover, the frequency of the variants is too low to make pathogenicity annotations: Twenty‐four variants were seen once (either in a case or in a control), six variants two times, two variants three times, one variant four times, and one variant 17 times.…”

Section: Resultsmentioning

confidence: 99%

“…The interpretation of this challenge is problematic since the exact nature of the association between CHEK2 and breast cancer is debatable (Apostolou & Papasotiriou, 2017). Moreover, the frequency of the variants is too low to make pathogenicity annotations:…”

Section: Other Considerationsmentioning

confidence: 99%

CAGI5: Objective performance assessments of predictions based on the Evolutionary Action equation

Katsonis

Lichtarge

2019

Human Mutation

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Many computational approaches estimate the effect of coding variants, but their predictions often disagree with each other. These contradictions confound users and raise questions regarding reliability. Performance assessments can indicate the expected accuracy for each method and highlight advantages and limitations. The Critical Assessment of Genome Interpretation (CAGI) community aims to organize objective and systematic assessments: They challenge predictors on unpublished experimental and clinical data and assign independent assessors to evaluate the submissions. We participated in CAGI experiments as predictors, using the Evolutionary Action (EA) method to estimate the fitness effect of coding mutations. EA is untrained, uses homology information, and relies on a formal equation: The fitness effect equals the functional sensitivity to residue changes multiplied by the magnitude of the substitution. In previous CAGI experiments (between 2011 and 2016), our submissions aimed to predict the protein activity of single mutants. In 2018 (CAGI5), we also submitted predictions regarding clinical associations, folding stability, and matching genomic data with phenotype. For all these diverse challenges, we used EA to predict the fitness effect of variants, adjusted to specifically address each question. Our submissions had consistently good performance, suggesting that EA predicts reliably the effects of genetic variants.

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“…Checkpoint kinase 2 ( CHEK2 ) has been demonstrated to be an effector kinase in the DNA damage checkpoint pathway, thus variations in its sequence can lead to detrimental outcomes in downstream DNA replication processes (Chaturvedi et al, ). CHEK2 alterations by single nucleotide variants (SNVs) are linked to DNA damage that is implicated in cases of breast cancer (BC; Apostolou & Papasotiriou, ). BC is the most commonly diagnosed cancer among female Hispanics in the United States and the leading cause of cancer related deaths (Lynce et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…CHEK2 alterations by single nucleotide variants (SNVs) are linked to DNA damage that is implicated in cases of breast cancer (BC; Apostolou & Papasotiriou, 2017). BC is the most commonly diagnosed cancer among female Hispanics in the United States and the leading cause of cancer related deaths (Lynce et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Assessing the performance of in silico methods for predicting the pathogenicity of variants in the gene CHEK2, among Hispanic females with breast cancer

et al. 2019

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The availability of disease‐specific genomic data is critical for developing new computational methods that predict the pathogenicity of human variants and advance the field of precision medicine. However, the lack of gold standards to properly train and benchmark such methods is one of the greatest challenges in the field. In response to this challenge, the scientific community is invited to participate in the Critical Assessment for Genome Interpretation (CAGI), where unpublished disease variants are available for classification by in silico methods. As part of the CAGI‐5 challenge, we evaluated the performance of 18 submissions and three additional methods in predicting the pathogenicity of single nucleotide variants (SNVs) in checkpoint kinase 2 (CHEK2) for cases of breast cancer in Hispanic females. As part of the assessment, the efficacy of the analysis method and the setup of the challenge were also considered. The results indicated that though the challenge could benefit from additional participant data, the combined generalized linear model analysis and odds of pathogenicity analysis provided a framework to evaluate the methods submitted for SNV pathogenicity identification and for comparison to other available methods. The outcome of this challenge and the approaches used can help guide further advancements in identifying SNV‐disease relationships.

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Current perspectives on CHEK2 mutations in breast cancer

Cited by 92 publications

References 42 publications

Transcriptomic learning for digital pathology

Transcriptomic learning for digital pathology

CAGI5: Objective performance assessments of predictions based on the Evolutionary Action equation

Assessing the performance of in silico methods for predicting the pathogenicity of variants in the gene CHEK2, among Hispanic females with breast cancer

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