Cancer driver mutations: predictions and reality

Ostroverkhova, Daria; Przytycka, Teresa M.; Panchenko, Anna R.

doi:10.1016/j.molmed.2023.03.007

Cited by 53 publications

(28 citation statements)

References 110 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also evaluated the frequencies of AQ, CP, SB, and HBAQ interactions among proteins with defined oncogenic mutations. Although several databases list cancer genes and their associated somatic mutations, the identification of driver mutations remains a challenge (Ostroverkhova et al, 2023). In an attempt to overcome this challenge, we used recent somatic mutation models provided by the boostDM machine learning algorithm (Muinos et al, 2021).…”

Section: Resultsmentioning

confidence: 99%

AQcalc: A web server that identifies weak molecular interactions in protein structures

Afshinpour,

Smith,

Chakravarty

2023

Protein Science

View full text Add to dashboard Cite

Weak molecular interactions play an important role in protein structure and function. Computational tools that identify weak molecular interactions are, therefore, valuable for the study of proteins. Here, we present AQcalc, a web server (https://aqcalcbiocomputing.com/) that can be used to identify anion–quadrupole (AQ) interactions, which are weak interactions involving aromatic residue (Trp, Tyr, and Phe) ring edges and anions (Asp, Glu, and phosphate ion) both within proteins and at their interfaces (protein‐protein, protein‐nucleic acids, and protein‐lipid bilayer). AQcalc identifies AQ interactions as well as clusters involving AQ, cation–π, and salt bridges, among others. Utilizing AQcalc we analyzed weak interactions in protein models, even in the absence of experimental structures, to understand the contributions of weak interactions to deleterious structural changes, including those associated with oncogenic and germline disease variants. We identified several deleterious variants with disrupted AQ interactions (comparable in frequency to cation–π disruptions). Amyloid fibrils utilize AQ to bury anions at frequencies that far exceed those observed for globular proteins. AQ interactions were detected three and five times more frequently than the hydrogen‐bonded AQ (HBAQ) in fibril structures and protein‐lipid bilayer interfaces, respectively. By contrast, AQ and HBAQ interactions were detected with similar frequencies in globular proteins. Collectively, these findings suggest AQcalc will be effective in facilitating fine structural analysis. As other web utilities designed to identify protein residue interaction networks do not report AQ interactions, wide use of AQcalc will enrich our understanding of residue interaction networks and facilitate hypothesis testing by identifying and experimentally characterizing these comparably weak but important interactions.This article is protected by copyright. All rights reserved.

show abstract

Section: Resultsmentioning

confidence: 99%

AQcalc: A web server that identifies weak molecular interactions in protein structures

Afshinpour,

Smith,

Chakravarty

2023

Protein Science

View full text Add to dashboard Cite

show abstract

“…Pairs such as CDH1_N:TP53_M (DIALECT 𝑝-value = 0.002) and AKT1_M:PIK3CA_M (DIALECT 𝑝-value = 0.015) have been found to reflect distinct functional modules within breast cancer, e.g. TP53, CDH1, AKT1, and PIK3CA are all known breast cancer driver genes [57,37,62].…”

Section: Analysis Of Mutations In Tcgamentioning

confidence: 99%

A latent variable model for evaluating mutual exclusivity and co-occurrence between driver mutations in cancer

Shuaibi,

Chitra,

Raphael

2024

Preprint

View full text Add to dashboard Cite

A key challenge in cancer genomics is understanding the functional relationships and dependencies between combinations of somatic mutations that drive cancer development. Such driver mutations frequently exhibit patterns of mutual exclusivity or co-occurrence across tumors, and many methods have been developed to identify such dependency patterns from bulk DNA sequencing data of a cohort of patients. However, while mutual exclusivity and co-occurrence are described as properties of driver mutations, existing methods do not explicitly disentangle functional, driver mutations from neutral, passenger mutations. In particular, nearly all existing methods evaluate mutual exclusivity or co-occurrence at the gene level, marking a gene as mutated if any mutation - driver or passenger - is present. Since some genes have a large number of passenger mutations, existing methods either restrict their analyses to a small subset of suspected driver genes - limiting their ability to identify novel dependencies - or make spurious inferences of mutual exclusivity and co-occurrence involving genes with many passenger mutations. We introduce DIALECT, an algorithm to identify dependencies between pairs of driver mutations from somatic mutation counts. We derive a latent variable mixture model for drivers and passengers that combines existing probabilistic models of passenger mutation rates with a latent variable describing the unknown status of a mutation as a driver or passenger. We use an expectation maximization (EM) algorithm to estimate the parameters of our model, including the rates of mutually exclusivity and co-occurrence between drivers. We demonstrate that DIALECT more accurately infers mutual exclusivity and co-occurrence between driver mutations compared to existing methods on both simulated mutation data and somatic mutation data from 5 cancer types in The Cancer Genome Atlas (TCGA).

show abstract

“…P533Q is reported as associated with Niemann-Pick disease type A and a ClinVar review status of 1. We also identify this variant as a somatic one in cancer samples, where most mutations are expected to be passenger and neutral 32 . Fluorescence-based enzymatic activity assay reported that another variant at P533 (P533L, reported in ClinVar with conflicting interpretation) causes a very mild reduction of ASM activity (i.e., > 70% residual activity, Table S3 33 ) suggesting that this variant is unlikely to have a damaging effect on the protein function.…”

Section: Analysis Of Variants Reported As Pathogenic or Benign In Cli...mentioning

confidence: 99%

ASM Variants in the Spotlight: A Structure-Based Atlas for Unraveling Pathogenic Mechanisms in Lysosomal Acid Sphingomyelinase

Scrima,

Lambrughi,

Tiberti

et al. 2023

Preprint

View full text Add to dashboard Cite

Lysosomal acid sphingomyelinase (ASM), a critical enzyme in lipid metabolism encoded by the SMPD1 gene, plays a crucial role in sphingomyelin hydrolysis in lysosomes. ASM deficiency leads to acid sphingomyelinase deficiency, a rare genetic disorder with diverse clinical manifestations, and the protein can be found mutated in other diseases. We employed a structure-based framework to comprehensively understand the functional implications of ASM variants, integrating pathogenicity predictions with molecular insights derived from molecular dynamics simulations in a lysosomal membrane environment. Our analysis, encompassing over 400 variants, establishes a structural atlas of missense variants of lysosomal ASM, associating mechanistic indicators with pathogenic potential. Our study highlights variants that influence structural stability or exert local and long-range effects at functional sites. To validate our predictions, we compared them to available experimental data on residual catalytic activity in 135 ASM variants. Notably, our findings also suggest applications of the resulting data for identifying cases suited for enzyme replacement therapy. This comprehensive approach enhances the understanding of ASM variants and provides valuable insights for potential therapeutic interventions.

show abstract

Cancer driver mutations: predictions and reality

Cited by 53 publications

References 110 publications

AQcalc: A web server that identifies weak molecular interactions in protein structures

AQcalc: A web server that identifies weak molecular interactions in protein structures

A latent variable model for evaluating mutual exclusivity and co-occurrence between driver mutations in cancer

ASM Variants in the Spotlight: A Structure-Based Atlas for Unraveling Pathogenic Mechanisms in Lysosomal Acid Sphingomyelinase

Contact Info

Product

Resources

About