Dataset Information

Large-scale pathogenicity prediction analysis of cancer-associated kinase mutations reveals variability in sensitivity and specificity of computational methods.

ABSTRACT:

Background

Mutations in kinases are the most frequent genetic alterations in cancer; however, experimental evidence establishing their cancerous nature is available only for a small fraction of these mutants.

Aims

Predicition analysis of kinome mutations is the primary aim of this study. Further objective is to compare the performance of various softwares in pathogenicity prediction of kinase mutations.

Materials and methods

We employed a set of computational tools to predict the pathogenicity of over forty-two thousand mutations and deposited the kinase-wise data in Mendeley database (Estimated Pathogenicity of Kinase Mutants [EPKiMu]).

Results

Mutations are more likely to be drivers when being present in the kinase domain (vs. non-kinase domain) and belonging to hotspot residues (vs. non-hotspot residues). We identified that, while predictive tools have low specificity in general, PolyPhen-2 had the best accuracy. Further efforts to combine all four tools by consensus, voting, or other simple methods did not significantly improve accuracy.

Discussion

The study provides a large dataset of kinase mutations along with their predicted pathogenicity that can be used as a training set for future studies. Furthermore, a comparative sensitivity and selectivity of commonly used computational tools is presented.

Conclusion

Primary-structure-based in silico tools identified more cancerous/deleterious mutations in the kinase domains and at the hot spot residues while having higher sensitivity than specificity in detecting deleterious mutations.

SUBMITTER: Akula S

PROVIDER: S-EPMC10501281 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Large-scale pathogenicity prediction analysis of cancer-associated kinase mutations reveals variability in sensitivity and specificity of computational methods.

Akula Sravani S Mullaguri Sai Charitha SC Melton Niklas Max NM Katta Archana A Naga Venkata Sai Giridhar Reddy VSGR Kandula Shyamson S Pedada Raj Kumar RK Subramanian Janakiraman J Kancha Rama Krishna RK

Cancer medicine 20230706 16

<h4>Background</h4>Mutations in kinases are the most frequent genetic alterations in cancer; however, experimental evidence establishing their cancerous nature is available only for a small fraction of these mutants.<h4>Aims</h4>Predicition analysis of kinome mutations is the primary aim of this study. Further objective is to compare the performance of various softwares in pathogenicity prediction of kinase mutations.<h4>Materials and methods</h4>We employed a set of computational tools to predi ...[more]

PMID: 37409618

Similar Datasets

Project description:Anti-cancer peptides (ACPs) are known as potential therapeutics for cancer. Due to their unique ability to target cancer cells without affecting healthy cells directly, they have been extensively studied. Many peptide-based drugs are currently evaluated in the preclinical and clinical trials. Accurate identification of ACPs has received considerable attention in recent years; as such, a number of machine learning-based methods for in silico identification of ACPs have been developed. These methods promote the research on the mechanism of ACPs therapeutics against cancer to some extent. There is a vast difference in these methods in terms of their training/testing datasets, machine learning algorithms, feature encoding schemes, feature selection methods and evaluation strategies used. Therefore, it is desirable to summarize the advantages and disadvantages of the existing methods, provide useful insights and suggestions for the development and improvement of novel computational tools to characterize and identify ACPs. With this in mind, we firstly comprehensively investigate 16 state-of-the-art predictors for ACPs in terms of their core algorithms, feature encoding schemes, performance evaluation metrics and webserver/software usability. Then, comprehensive performance assessment is conducted to evaluate the robustness and scalability of the existing predictors using a well-prepared benchmark dataset. We provide potential strategies for the model performance improvement. Moreover, we propose a novel ensemble learning framework, termed ACPredStackL, for the accurate identification of ACPs. ACPredStackL is developed based on the stacking ensemble strategy combined with SVM, Naïve Bayesian, lightGBM and KNN. Empirical benchmarking experiments against the state-of-the-art methods demonstrate that ACPredStackL achieves a comparative performance for predicting ACPs. The webserver and source code of ACPredStackL is freely available at http://bigdata.biocie.cn/ACPredStackL/ and https://github.com/liangxiaoq/ACPredStackL, respectively.

Dataset Information

Large-scale pathogenicity prediction analysis of cancer-associated kinase mutations reveals variability in sensitivity and specificity of computational methods.

Background

Aims

Materials and methods

Results

Discussion

Conclusion

Publications

Large-scale pathogenicity prediction analysis of cancer-associated kinase mutations reveals variability in sensitivity and specificity of computational methods.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets