ReviewCellular and molecular life sciences : CMLS2019
Computational algorithms for in silico profiling of activating mutations in cancer.
Review in Cellular and molecular life sciences : CMLS, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 18 citations in OpenAlex.
- Frequency and Clinical Significance of Clonal and Subclonal Driver Mutations in High-Risk Neuroblastoma at Diagnosis: A Children's Oncology Group Study.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2025Trial
- Gene duplication is associated with gene diversification and potential neofunctionalization in lung cancer evolution.Genome research · 2026Article
- Benzo[ACS omega · 2026Article
- Activating mutations drive human MEK1 kinase using a gear-shifting mechanism.The Biochemical journal · 2023Article
- Mechanism of activation and the rewired network: New drug design concepts.Medicinal research reviews · 2022Review
- The Yin and Yang of ERBB4: Tumor Suppressor and Oncoprotein.Pharmacological reviews · 2022Review
- Molecular-based precision oncology clinical decision making augmented by artificial intelligence.Emerging topics in life sciences · 2021Review
- Computational studies of anaplastic lymphoma kinase mutations reveal common mechanisms of oncogenic activation.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- A survey of multiscale modeling: Foundations, historical milestones, current status, and future prospects.AIChE journal. American Institute of Chemical Engineers · 2021Article
- Associations betweenTranslational pediatrics · 2021Article
- Machine Learning Predictions of Cancer Driver Mutations.Proceedings of the 2014 6th International Advanced Research Workshop on In Silico Oncology and Cancer Investigation : the CHIC Project Workshop (IARWISOCI) : Athens, Greece, 3-4 November 2014. International Advanced Research Workshop on... · 2014Article
- An interdomain helix in IRE1α mediates the conformational change required for the sensor's activation.The Journal of biological chemistryArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
Methods to catalog and computationally assess the mutational landscape of proteins in human cancers are desirable. One approach is to adapt evolutionary or data-driven methods developed for predicting whether a single-nucleotide polymorphism (SNP) is deleterious to protein structure and function. In cases where understanding the mechanism of protein activation and regulation is desired, an alternative approach is to employ structure-based computational approaches to predict the effects of point mutations. Through a case study of mutations in kinase domains of three proteins, namely, the anaplastic lymphoma kinase (ALK) in pediatric neuroblastoma patients, serine/threonine-protein kinase B-Raf (BRAF) in melanoma patients, and erythroblastic oncogene B 2 (ErbB2 or HER2) in breast cancer patients, we compare the two approaches above. We find that the structure-based method is most appropriate for developing a binary classification of several different mutations, especially infrequently occurring ones, concerning the activation status of the given target protein. This approach is especially useful if the effects of mutations on the interactions of inhibitors with the target proteins are being sought. However, many patients will present with mutations spread across different target proteins, making structure-based models computationally demanding to implement and execute. In this situation, data-driven methods-including those based on machine learning techniques and evolutionary methods-are most appropriate for recognizing and illuminate mutational patterns. We show, however, that, in the present status of the field, the two methods have very different accuracies and confidence values, and hence, the optimal choice of their deployment is context-dependent.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.