Identification of driver pathways in cancer based on combinatorial patterns of somatic gene mutations

Ht, Li; Zhang, J; Xia, Junfeng; Zheng, Chuansheng

doi:10.4149/neo_2016_007

Cited by 6 publications

(7 citation statements)

References 30 publications

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“…On the basis of these cost functions, optimal sub-networks are identified and interpreted as novel cancer driver pathways 22 – 24 . However, at the moment there is no consensual method to rigorously define a mathematical metric for mutual exclusivity and compute its statistical significance, and a number of interpretations exist 22 , 23 , 25 – 27 .…”

Section: Resultsmentioning

confidence: 99%

Pathway-based dissection of the genomic heterogeneity of cancer hallmarks’ acquisition with SLAPenrich

Iorio

García-Alonso

Brammeld

et al. 2018

Sci Rep

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Cancer hallmarks are evolutionary traits required by a tumour to develop. While extensively characterised, the way these traits are achieved through the accumulation of somatic mutations in key biological pathways is not fully understood. To shed light on this subject, we characterised the landscape of pathway alterations associated with somatic mutations observed in 4,415 patients across ten cancer types, using 374 orthogonal pathway gene-sets mapped onto canonical cancer hallmarks. Towards this end, we developed SLAPenrich: a computational method based on population-level statistics, freely available as an open source R package. Assembling the identified pathway alterations into sets of hallmark signatures allowed us to connect somatic mutations to clinically interpretable cancer mechanisms. Further, we explored the heterogeneity of these signatures, in terms of ratio of altered pathways associated with each individual hallmark, assuming that this is reflective of the extent of selective advantage provided to the cancer type under consideration. Our analysis revealed the predominance of certain hallmarks in specific cancer types, thus suggesting different evolutionary trajectories across cancer lineages. Finally, although many pathway alteration enrichments are guided by somatic mutations in frequently altered high-confidence cancer genes, excluding these driver mutations preserves the hallmark heterogeneity signatures, thus the detected hallmarks’ predominance across cancer types. As a consequence, we propose the hallmark signatures as a ground truth to characterise tails of infrequent genomic alterations and identify potential novel cancer driver genes and networks.

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Section: Resultsmentioning

confidence: 99%

Pathway-based dissection of the genomic heterogeneity of cancer hallmarks’ acquisition with SLAPenrich

Iorio

García-Alonso

Brammeld

et al. 2018

Sci Rep

View full text Add to dashboard Cite

show abstract

“…On the basis of these cost functions, optimal sub-networks are identified and interpreted as novel cancer driver pathways [22][23][24] . However, at the moment there is no consensual method to rigorously define a mathematical metric for mutual exclusivity and compute its statistical significance, and a number of interpretations exist 22,23,[25][26][27] .…”

Section: Problem Definition and Methods Overviewmentioning

confidence: 99%

Pathway-based dissection of the genomic heterogeneity of cancer hallmarks’ acquisition with SLAPenrich

Iorio

García-Alonso

Brammeld

et al. 2016

Preprint

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Cancer hallmarks are evolutionary traits required by a tumour to develop. While extensively characterised, the way these traits are achieved through the accumulation of somatic mutations in key biological pathways is not fully understood. To shed light on this subject, we characterised the landscape of pathway alterations associated with somatic mutations observed in 4,415 patients across ten cancer types, using 374 orthogonal pathway gene-sets mapped onto canonical cancer hallmarks. Towards this end, we developed SLAPenrich: a computational method based on population-level statistics, freely available as an open source R package. Assembling the identified pathway alterations into sets of hallmark signatures allowed us to connect somatic mutations to clinically interpretable cancer mechanisms. Further, we explored the heterogeneity of these signatures, in terms of ratio of altered pathways associated with each individual hallmark, assuming that this is reflective of the extent of selective advantage provided to the cancer type under consideration. Our analysis revealed the predominance of certain hallmarks in specific cancer types, thus suggesting different evolutionary trajectories across cancer lineages. Finally, although many pathway alteration enrichments are guided by somatic mutations in frequently altered high-confidence cancer genes, excluding these driver mutations preserves the hallmark heterogeneity signatures, thus the detected hallmarks' predominance across cancer types. As a consequence, we propose the hallmark signatures as a ground truth to characterise tails of infrequent genomic alterations and identify potential novel cancer driver genes and networks.

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“…2). Three papers [33,46,51] used Genomic Variation data as a single source of feature, whereas 23 (i.e., 56.09%) used it in combination with other categories. A long-standing hypothesis in the discovery of cancer drivers is that driver genes are mutated more frequently than expected as compared to a background mutation rate (BMR) estimated from cancer samples for a given cancer type [11].…”

Section: Genomic Variationmentioning

confidence: 99%

“…Mutation hotspots were also detected using scores computed by OncoDriveCLUST [26,59,64] and applying density estimates to aggregate closelyspaced missense mutations into peaks and compute mutation fraction inside the highest peak [53]. The normalized number of SNPs in the exon where the mutation is located [24], mutations' distance to closest Transcribed Sequence Start (TSS) and closest Transcribed Sequence End (TSE) [43], and a genelevel binary matrix summarizing mutation occurrence across all samples [33,51] were also adopted.…”

Section: Genomic Variationmentioning

confidence: 99%

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Machine learning methods for prediction of cancer driver genes: a survey paper

Andrades,

Recamonde-Mendoza

2021

Preprint

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Identifying the genes and mutations that drive the emergence of tumors is a major step to improve understanding of cancer and identify new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in identifying genomic patterns associated with cancer drivers and developing models to predict driver events. Machine learning (ML) has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.

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Identification of driver pathways in cancer based on combinatorial patterns of somatic gene mutations

Cited by 6 publications

References 30 publications

Pathway-based dissection of the genomic heterogeneity of cancer hallmarks’ acquisition with SLAPenrich

Pathway-based dissection of the genomic heterogeneity of cancer hallmarks’ acquisition with SLAPenrich

Pathway-based dissection of the genomic heterogeneity of cancer hallmarks’ acquisition with SLAPenrich

Machine learning methods for prediction of cancer driver genes: a survey paper

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