Exploring preferred amino acid mutations in cancer genes: Applications to identify potential drug targets

Paruchuri, Anoosha; Sakthivel, R.; Gromiha, M. Michael

doi:10.1016/j.bbadis.2015.11.006

Cited by 34 publications

(37 citation statements)

References 58 publications

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“…Thus, we predict this substitution to be the most destabilizing in both START domains. This is also in line with global analyses of the COSMIC dataset, which found that arginine-to-glutamine and arginine-to-cysteine substitutions occur with high frequency in driver mutations [42]. The serine-to-phenylalanine mutations (S1077F in DLC-1 and S1100F in DLC-2) also abolish multiple hydrogen bonds and replace a small polar side chain with a large hydrophobic side chain.…”

Section: Cosmic Missense Mutations Disrupt Tertiary-level Interactionsupporting

confidence: 84%

Identifying Cancer-Relevant Mutations in the DLC START Domain using Evolutionary and Structure-Function Analyses

Holub

Bouley

Petreaca

et al. 2020

Preprint

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Rho GTPase signaling promotes proliferation, invasion, and metastasis in a broad spectrum of cancers. Rho GTPase activity is regulated by the Deleted in Liver Cancer (DLC) family of bonafide tumor suppressors which directly inactivate Rho GTPases by stimulating GTP hydrolysis. In addition to a RhoGAP domain, DLC proteins contain a StAR-related lipid transfer (START) domain. START domains in other organisms bind hydrophobic small molecules and can regulate interacting partners or co-occurring domains through a variety of mechanisms. In the case of DLC proteins, their START domain appears to contribute to tumor suppressive activity. However, the nature of this START-directed mechanism, as well as the identities of relevant functional residues, remain virtually unknown. Using the Catalogue of Somatic Mutations in Cancer (COSMIC) dataset, and evolutionary and structure-function analyses, we identify several conserved residues likely to be required for START-directed regulation of DLC-1 and DLC-2 tumor suppressive capability. This pan-cancer analysis shows that conserved residues of both START domains are highly-overrepresented in cancer cells from a wide range tissues. Interestingly, in DLC-1 and DLC-2, three of these residues form multiple interactions at the tertiary structural level. Further, mutation of any of these residues is predicted to disrupt interactions and thus destabilize the START domain. As such, these mutations would not have emerged from traditional hotspot scans of COSMIC. We propose that evolutionary and structure-function analyses are an underutilized strategy which could be used to unmask cancer-relevant mutations within COSMIC. Our data also suggest DLC-1 and DLC-2 as high- priority candidates for development of novel therapeutics targeting their START domain.Simple SummaryDeleted in Liver Cancer (DLC) proteins are tumor suppressors that contain a StAR-related lipid transfer (START) domain. Little is known about the DLC START domain including the residues that mediate its activation. Using the Catalogue of Somatic Mutations in Cancer (COSMIC) dataset, evolutionary, and structure-function analyses, we identify key functional residues of DLC START domains. Mutations in these residues are significantly overrepresented in numerous cancers in multiple tissue types. In other contexts, START domains bind hydrophobic small molecules and stimulate regulatory outputs of interacting partners or co-occurring domains. The identification of functional residues in the DLC START domain may thus have implications for targeted manipulation of DLC tumor suppressive activity. Critically, these residues would not have been identified using traditional queries of COSMIC. Thus, we propose tandem evolutionary and structure-function approaches are an underutilized strategy to unmask cancer-relevant mutations within COSMIC.

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Section: Cosmic Missense Mutations Disrupt Tertiary-level Interactionsupporting

confidence: 84%

Identifying Cancer-Relevant Mutations in the DLC START Domain using Evolutionary and Structure-Function Analyses

Holub

Bouley

Petreaca

et al. 2020

Preprint

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“…Identifying driver mutations in the haystack of passenger mutations is a major outstanding problem in cancer research. Several approaches to discriminate between driver and passenger mutations have been developed, based on factors such as mutation frequency [5][6][7] , gene expression 8 , protein domain analysis 9,10 , markers of positive selection 11 , network enrichment analysis 12 and recurrently amino acid change analysis [13][14][15][16] .…”

Section: Mutational Likeliness and Entropy Help To Identify Driver Mumentioning

confidence: 99%

Mutational likeliness and entropy help to identify driver mutations and their functional role in cancer

Mattiuz

Giorgio²,

Tofani

et al. 2018

Preprint

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Alterations in cancer genomes originate from mutational processes taking place throughout oncogenesis and cancer progression. We show that likeliness and entropy are two properties of somatic mutations crucial in cancer evolution, as cancer-driver mutations stand out, with respect to both of these properties, as being distinct from the bulk of passenger mutations. Our analysis can identify novel cancer driver genes and differentiate between gain and loss of function mutations.. CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. Since driver mutations are under positive selection 11 , their mutational pattern might diverge from that observed in the much more numerous passenger mutations. In order to test this notion, we made use of a dataset of cancer mutations derived from the one generated by Chang et al. 16 using several cancergenome resources. The full dataset comprises ~2 million single-nucleotides variants present in over 11,000 cancer exomes from patients who had one of 41 tumour types. In order to calculate the probability of non-synonymous mutations, we applied on this dataset a Markov model trained on synonymous mutations, as they are mostly neutral. We preferred a zero-order rather than a higher order model, as we are dealing with coding sequences where, by virtue of the triplet genetic code, higher order patterns are confounded by constraints related to the protein sequence. Having worked out the parameters of the transition matrix of our model based on synonymous mutations, we refer to this output as the Mutational Background Model (see methods, Fig.1a), as these mutations reflect the outcomes of errors in the replicative/repair pathways and/or exposure to mutagens during cancer onset and progression. Next, we used the background model to calculate for each group of non-synonymous . CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/354324 doi: bioRxiv preprint first posted online Jun. 24, 2018; Mattiuz et al.3 mutations (GNSM: the set of all mutations hitting the same codon in a given transcript among all patients) two scores. (a) Mutational likeliness: this measures the probability for a given GNSM to result simply from the background model. A negative value of this parameter indicates a nucleotide change that does not conform to the overall mutational pattern of the tumour; in other words, a decreased likeliness of an individual mutation may reflect selective pressure on that mutation. (b) Mutational entropy: this score, calculated by applying the Shannon entropy to each mutated codon, measures the bias towards a specific amino acid that is encoded by a GNSM compared to the expectations from the background model (Fig.1a). Mutational entropy is m...

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“…Somatic mutation data were downloaded from COSMIC Cell Lines Project ( Missense mutations and frame-shift mutations were selected and mutations reported in disagreement between individual data sources were excluded. Next, missense mutations were classified into driver and passenger and driver as proposed by Anoosha et al (Anoosha et al 2016). Putative passenger mutations were excluded and the remaining mutations were kept for downstream analysis.…”

Section: Selection Of Somatic Mutationsmentioning

confidence: 99%

Towards an Integrated Map of Genetic Interactions in Cancer Cells

Rauscher

Heigwer

Henkel

et al. 2017

Preprint

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Exploring preferred amino acid mutations in cancer genes: Applications to identify potential drug targets

Cited by 34 publications

References 58 publications

Identifying Cancer-Relevant Mutations in the DLC START Domain using Evolutionary and Structure-Function Analyses

Identifying Cancer-Relevant Mutations in the DLC START Domain using Evolutionary and Structure-Function Analyses

Mutational likeliness and entropy help to identify driver mutations and their functional role in cancer

Towards an Integrated Map of Genetic Interactions in Cancer Cells

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