Identifying Driver Interfaces Enriched for Somatic Missense Mutations in Tumors

Öztürk, Kıvılcım; Carter, Hannah

doi:10.1007/978-1-4939-8967-6_4

Cited by 4 publications

(8 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This indicates that not only protein structural localisation of variants indicates driver status (as previously reported, e.g. [29]), but also a reflection of the clonality of mutations. We further observe, using Gene Set Enrichment Analysis (GSEA) [30], that clonal mutations at interacting interfaces perturb important signaling pathways and cell adhesion, while those at the core perturb proteins such as transporters and molecules responsible for binding ions and nucleotides (Supplementary Figure S3).…”

Section: Resultssupporting

confidence: 79%

“…loss-of-function mutations that target the protein cores of tumour suppressor genes [18], which are likely to be positively selected during tumour evolution. The distribution of driver mutations in protein surface, core and interface has been widely discussed [29]; our data additionally suggest that driver mutations located in protein core and interface are more clonal (Figure 3B), supporting the link between mutational frequency and such cost-benefit balance. Experimentally, to override the under-sampling problem of damaging variants, one potential solution is to use dedicated methods such as ultra-deep sequencing [44] or emerging single-nucleus sequencing approaches [45] to generate mutational profiles from biological samples.…”

Section: Discussionsupporting

confidence: 80%

“…This indicates that not only protein structural localisation of variants indicates driver status (as previously reported, e.g. [29]), but also a reflection of the clonality of mutations.…”

Section: Protein Structural Localisation Is Indicative Of Driver Status Clonality and Selection Pressuresupporting

confidence: 79%

See 2 more Smart Citations

The “dark matter” of protein variants carries a distinct DNA signature and predicts damaging variant effects

Fraternali

2021

Preprint

View full text Add to dashboard Cite

Signatures of DNA motifs associated with distinct mutagenic exposures have been defined for somatic variants, but little is known about the consequences different mutational processes pose to the cancer cell, particularly the distribution of the resulting variants in the implied proteins and their structural regions (surface, core, interacting interface). Here we first compare the protein-level consequences of six mutational signatures (Aging, APOBEC, POLE, UV, 5-FU and Platinum) characterised by clear DNA motif preferences. By mapping individual substitution events observed in tumours to three-dimensional protein structures, we show that these common somatic mutational signatures are biased against the protein core, consistent with the lower tolerability of substitutions at such structurally important regions. On the other hand, deep mutational scanning (DMS) data allow us to probe the "dark matter" of somatic mutational landscape, exploring variants which are otherwise removed in purifying selection. A computational DMS analysis identifies mutational contexts (5'-G/C[T>G]A/G-3') which are associated with damaging mutations, by altering physicochemical characteristics of amino acids at the protein core. We argue that comprehensive DMS analysis can contribute to classification of variants according to their true impact to the stability/activity of the affected protein, decoupling this from pathogenicity prediction offered by conventional variant impact classifiers.

show abstract

Section: Resultssupporting

confidence: 79%

Section: Discussionsupporting

confidence: 80%

See 1 more Smart Citation

The “dark matter” of protein variants carries a distinct DNA signature and predicts damaging variant effects

Fraternali

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Drivers at protein-protein interaction, or ligand-binding (active) sites are orthosteric. These sites are often known; if unknown, there are tools to predict them [70][71][72][73]. Allosteric drivers map to allosteric sites that can be predicted [74][75][76][77][78] or detected by functional impacts experimentally or predicted computationally [21] or revealed by structural techniques.…”

Section: Box 1 Strategies To Identify Rare Oncoprotein Driver Mutationsmentioning

confidence: 99%

Why Are Some Driver Mutations Rare?

Nussinov

Tsai

Jang

2019

Trends in Pharmacological Sciences

View full text Add to dashboard Cite

Understanding why driver mutations that promote cancer are sometimes rare is important for precision medicine since it would help in their identification. Driver mutations are largely discovered through their frequencies. Thus, rare mutations often escape detection. Unlike high-frequency drivers, low-frequency drivers can be tissue specific; rare drivers have extremely low frequencies. Here, we discuss rare drivers and strategies to discover them. We suggest that allosteric driver mutations shift the protein ensemble from the inactive to the active state. Rare allosteric drivers are statistically rare since, to switch the protein functional state, they cooperate with additional mutations, and these are not considered in the patient cancer-specific protein sequence analysis. A complete landscape of mutations that drive cancer will reveal tumorspecific therapeutic vulnerabilities. Mutations That Drive Cancer Can Be Frequent or RareHere we ask: Why are some driver mutations rare? Both frequent and rare drivers bestow a selective advantage to a clone in its microenvironment by promoting cell survival and proliferation; thus why are driver mutations sometimes rare? This question is important. If understood, it would help not only in uncovering the mechanisms of driver mutations, but also their identification and drug discovery. Yet, to date it has not been asked.Precision medicine efforts to uncover actionable mutations that drive cancer in patients have galvanized big-data initiatives. They have spurred development of experimental approaches to accumulate data [e.g., The Cancer Genome Atlas (TCGA) [1], Cancer Genome Project (CGP) [2], and Catalogue of Somatic Mutations in Cancer (COSMIC) [3]], and prompted the formation of the International Cancer Genome Consortium [4,5], and advances in computational algorithms to search and analyze it [6][7][8][9][10][11][12][13]. The outcome witnessed an increase in experimental and efficient computational methods, analyses tools and applications, servers, and reviews [5][6][7][8][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. At the same time, they have also shown that the state-of-the-art computational strategies that integrate and prioritize the data [30,31] have been unsuccessful in predicting all clinically diagnosed cancer drivers. This implies some deficiencies, not necessarily of the software itself, but its underlying conceptual biological basis. Among the emerging flaws have been the unidentified, yet clinically diagnosed, rare driver mutations. This raised the challenging question of how to identify rare driver mutations [8,[32][33][34]. Driver mutations contribute to cancer development. They can be statistically frequent or rare, at the tail of the distribution, as are passenger mutations that do not promote cancer [35]. Rare drivers are likely to be in oncogenes or tumor suppressors and are present in <1% of cancers. If direct functional or signaling data are unavailable, which is often the case, how then can we distinguish between rare driver and pa...

show abstract

“…[87][88][89][90][91] However, frequent mutations are not necessarily drivers. 19 If it is not at the binding site, it acts by shifting the landscape of the protein. 19 If it is not at the binding site, it acts by shifting the landscape of the protein.…”

Section: The Free Energy Landscape and Allosteric Mutationsmentioning

confidence: 99%

Autoinhibition can identify rare driver mutations and advise pharmacology

2019

View full text Add to dashboard Cite

Identification of protein mutations that drive cancer is a major challenge. A primary reason is that driver mutations are principally identified by their high frequency even though they can also be rare. Driver mutations can locate at functional (binding or active) sites. We dub these orthosteric drivers. However, often they are allosteric drivers. Identification is particularly formidable for rare allosteric drivers. Autoinhibition, where a segment of the protein covers its functional site, is a common allosteric regulation mechanism. A modest shift in the equilibrium can switch the system from the autoinhibited to the active state. This can suggest why (i) mutations are likely to evolve to target it; (ii) inhibitors can straightforwardly relieve the autoinhibition but not vice versa; and why (iii) mutations that relieve the autoinhibition are likely to be drivers—even if they are rare. We explain in simple terms the linkage between allosteric driver mutations, release of autoinhibition, free energy landscapes, and targeted pharmacology in precision medicine. We review the literature and propose new concepts in identification of rare drivers in this framework.

show abstract

Identifying Driver Interfaces Enriched for Somatic Missense Mutations in Tumors

Cited by 4 publications

References 42 publications

The “dark matter” of protein variants carries a distinct DNA signature and predicts damaging variant effects

The “dark matter” of protein variants carries a distinct DNA signature and predicts damaging variant effects

Why Are Some Driver Mutations Rare?

Autoinhibition can identify rare driver mutations and advise pharmacology

Contact Info

Product

Resources

About