scholarly journals Association between Predicted Effects of TP53 Missense Variants on Protein Conformation and Their Phenotypic Presentation as Li-Fraumeni Syndrome or Hereditary Breast Cancer

2021 ◽  
Vol 22 (12) ◽  
pp. 6345
Author(s):  
Yaxuan Liu ◽  
Olga Axell ◽  
Tom van Leeuwen ◽  
Robert Konrat ◽  
Pedram Kharaziha ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Regression Model ◽  
Protein Conformation ◽  
Hereditary Breast Cancer ◽  
Logistic Regression Model ◽  
Tertiary Structure ◽  
Li Fraumeni Syndrome ◽  
Missense Variants ◽  
Li Fraumeni

Rare germline pathogenic TP53 missense variants often predispose to a wide spectrum of tumors characterized by Li-Fraumeni syndrome (LFS) but a subset of variants is also seen in families with exclusively hereditary breast cancer (HBC) outcomes. We have developed a logistic regression model with the aim of predicting LFS and HBC outcomes, based on the predicted effects of individual TP53 variants on aspects of protein conformation. A total of 48 missense variants either unique for LFS (n = 24) or exclusively reported in HBC (n = 24) were included. LFS-variants were over-represented in residues tending to be buried in the core of the tertiary structure of TP53 (p = 0.0014). The favored logistic regression model describes disease outcome in terms of explanatory variables related to the surface or buried status of residues as well as their propensity to contribute to protein compactness or protein-protein interactions. Reduced, internally validated models discriminated well between LFS and HBC (C-statistic = 0.78−0.84; equivalent to the area under the ROC (receiver operating characteristic) curve), had a low risk for over-fitting and were well calibrated in relation to the known outcome risk. In conclusion, this study presents a phenotypic prediction model of LFS and HBC risk for germline TP53 missense variants, in an attempt to provide a complementary tool for future decision making and clinical handling.

scholarly journals A Logistic Regression Model Based on the National Mammography Database Format to Aid Breast Cancer Diagnosis

2009 ◽  
Vol 192 (4) ◽  
pp. 1117-1127 ◽  
Author(s):  
Jagpreet Chhatwal ◽  
Oguzhan Alagoz ◽  
Mary J. Lindstrom ◽  
Charles E. Kahn ◽  
Katherine A. Shaffer ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Regression Model ◽  
Cancer Diagnosis ◽  
Logistic Regression Model ◽  
Breast Cancer Diagnosis ◽  
Model Based

An improved procedure for estimation of malignant breast cancer prevalence using partially rank ordered set samples with multiple concomitants

2015 ◽  
Vol 26 (6) ◽  
pp. 2552-2566 ◽  
Author(s):  
Armin Hatefi ◽  
Mohammad Jafari Jozani
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Ordered Set ◽  
Model Fitting ◽  
Simple Random Sampling ◽  
Breast Cancer Study ◽  
Training Samples ◽  
Malignant Breast

Rank-based sampling designs are widely used in situations where measuring the variable of interest is costly but a small number of sampling units (set) can be easily ranked prior to taking the final measurements on them and this can be done at little cost. When the variable of interest is binary, a common approach for ranking the sampling units is to estimate the probabilities of success through a logistic regression model. However, this requires training samples for model fitting. Also, in this approach once a sampling unit has been measured, the extra rank information obtained in the ranking process is not used further in the estimation process. To address these issues, in this paper, we propose to use the partially rank-ordered set sampling design with multiple concomitants. In this approach, instead of fitting a logistic regression model, a soft ranking technique is employed to obtain a vector of weights for each measured unit that represents the probability or the degree of belief associated with its rank among a small set of sampling units. We construct an estimator which combines the rank information and the observed partially rank-ordered set measurements themselves. The proposed methodology is applied to a breast cancer study to estimate the proportion of patients with malignant (cancerous) breast tumours in a given population. Through extensive numerical studies, the performance of the estimator is evaluated under various concomitants with different ranking potentials (i.e. good, intermediate and bad) and tie structures among the ranks. We show that the precision of the partially rank-ordered set estimator is better than its counterparts under simple random sampling and ranked set sampling designs and, hence, the sample size required to achieve a desired precision is reduced.

A logistic regression model predicting high axillary tumour burden in early breast cancer patients

2017 ◽  
Vol 19 (11) ◽  
pp. 1393-1399 ◽  
Author(s):  
I. Barco ◽  
M. García Font ◽  
A. García-Fernández ◽  
N. Giménez ◽  
M. Fraile ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Regression Model ◽  
Cancer Patients ◽  
Early Breast Cancer ◽  
Logistic Regression Model ◽  
Breast Cancer Patients ◽  
Tumour Burden

scholarly journals Deep learning generates custom-made logistic regression models for explaining how breast cancer subtypes are classified

2021 ◽  
Author(s):  
Takuma Shibahara ◽  
Chisa Wada ◽  
Yasuho Yamashita ◽  
Kazuhiro Fujita ◽  
Masamichi Sato ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Deep Learning ◽  
Regression Model ◽  
Linear Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Learning Approach ◽  
Logistic Regression Models ◽  
Custom Made

Breast cancer is the most frequently found cancer in women and the one most often subjected to genetic analysis. Nonetheless, it has been causing the largest number of women's cancer-related deaths. PAM50, the intrinsic subtype assay for breast cancer, is beneficial for diagnosis and stratified treatment but does not explain each subtype's mechanism. Nowadays, deep learning can predict the subtypes from genetic information more accurately than conventional statistical methods. However, the previous studies did not directly use deep learning to examine which genes associate with the subtypes. Ours is the first study on a deep-learning approach to reveal the mechanisms embedded in the PAM50-classified subtypes. We developed an explainable deep learning model called a point-wise linear model, which uses a meta-learning approach to generate a custom-made logistic regression model for each sample. Logistic regression is familiar to physicians and medical informatics researchers, and we can use it to analyze which genes are important for subtype prediction. The custom-made logistic regression models generated by the point-wise linear model for each subtype used the specific genes selected in other subtypes compared to the conventional logistic regression model: the overlap ratio is less than twenty percent. And analyzing the point-wise linear model's inner state, we found that the point-wise linear model used genes relevant to the cell cycle-related pathways. The results of this study suggest the potential of our explainable deep learning to play a vital role in cancer treatment.

scholarly journals New mutation of the TP53 gene associated with the hereditary breast cancer in a young Tuvinian woman

2022 ◽  
Vol 20 (6) ◽  
pp. 164-170
Author(s):  
P. A. Gervas ◽  
A. Yu. Molokov ◽  
A. A. Zarubin ◽  
A. A. Ponomareva ◽  
N. N. Babyshkina ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Hereditary Breast Cancer ◽  
Protein Modification ◽  
Tp53 Gene ◽  
Exon 2 ◽  
Li Fraumeni Syndrome ◽  
New Variant ◽  
Unknown Significance ◽  
Li Fraumeni ◽  
Ngs Data

Background. The identification of the ethnospecific mutations associated with hereditary breast cancer remains challenging. Next generation sequencing (Ngs) technology fully enables the compilation of germline variants associated with the risk for inherited diseases. Despite the success of the Ngs, up to 20 % of molecular tests report genetic variant of unknown significance (Vus) or novel variants that have never been previously described and their clinical significances are unknown. To obtain extended information about the variants of the unknown significance, it is necessary to use an alternative approach for the analysis of the Ngs data. To obtain extended characteristic about the unknown significance variants, it is necessary to search for additional tools for the analysis of the Ngs data. Material and methods. We reclassified the mutation of the unknown significance using the activedrivedb database that assessed the effect of mutations on sites of post-translational modifications, and the proteinpaint tool that complemented the existing cancer genome portals and provided a comprehensive and intuitive view of cancer genomic data. Results. In this study, we report a 44-year-old tuvinian woman with a family history of breast cancer. Based on the Ngs data, mutational analysis revealed the presence of the lrg_321t1: c.80c>t heterozygous variant in exon 2, which led to the proline to leucine change at codon 27 of the protein. In the dbpubmed database, this mutation was determined as unknown significance due to data limitation. According to the data of the activedriverdb tool, this mutation is located distally at the site of post-translational protein modification, which is responsible for binding to kinases that regulate genes of the cell cycle, etc. (atm, chek2, cdk, mapk). In accordance with proteinpaint tool, the lrg_321t1: c.80c>t mutation is located in functionally specialized transactivation domains and codon of the tp53 gene, where the pathogenic mutation associated with li-Fraumeni syndrome has been earlier described. Conclusion. This report is the first to describe a new variant in the tp53 gene (rs1555526933), which is likely to be associated with hereditary cancer-predisposing syndrome, including li-Fraumeni syndrome, in a tuvinian Bc patient with young-onset and familial Bc.

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