» Articles » PMID: 29713020

Pathway-based Dissection of the Genomic Heterogeneity of Cancer Hallmarks' Acquisition with SLAPenrich

Overview
Journal Sci Rep
Specialty Science
Date 2018 May 2
PMID 29713020
Citations 16
Authors
Affiliations
Soon will be listed here.
Abstract

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.

Citing Articles

Charting the transcriptomic landscape of primary and metastatic cancers in relation to their origin and target normal tissues.

Sanghvi N, Calvo-Alcaniz C, Rajagopal P, Scalera S, Canu V, Sinha S Sci Adv. 2024; 10(49):eadn0220.

PMID: 39642223 PMC: 11623296. DOI: 10.1126/sciadv.adn0220.


Unraveling druggable cancer-driving proteins and targeted drugs using artificial intelligence and multi-omics analyses.

Lopez-Cortes A, Cabrera-Andrade A, Echeverria-Garces G, Echeverria-Espinoza P, Pineda-Alban M, Elsitdie N Sci Rep. 2024; 14(1):19359.

PMID: 39169044 PMC: 11339426. DOI: 10.1038/s41598-024-68565-7.


A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics.

Hoang D, Dinstag G, Shulman E, Hermida L, Ben-Zvi D, Elis E Nat Cancer. 2024; 5(9):1305-1317.

PMID: 38961276 DOI: 10.1038/s43018-024-00793-2.


Prediction of DNA methylation-based tumor types from histopathology in central nervous system tumors with deep learning.

Hoang D, Shulman E, Turakulov R, Abdullaev Z, Singh O, Campagnolo E Nat Med. 2024; 30(7):1952-1961.

PMID: 38760587 DOI: 10.1038/s41591-024-02995-8.


An efficient and effective method to identify significantly perturbed subnetworks in cancer.

Yang L, Chen R, Goodison S, Sun Y Nat Comput Sci. 2023; 1(1):79-88.

PMID: 37346964 PMC: 10284573. DOI: 10.1038/s43588-020-00009-4.


References
1.
Forbes S, Beare D, Gunasekaran P, Leung K, Bindal N, Boutselakis H . COSMIC: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Res. 2014; 43(Database issue):D805-11. PMC: 4383913. DOI: 10.1093/nar/gku1075. View

2.
Wain H, Bruford E, Lovering R, Lush M, Wright M, Povey S . Guidelines for human gene nomenclature. Genomics. 2002; 79(4):464-70. DOI: 10.1006/geno.2002.6748. View

3.
Subramanian A, Tamayo P, Mootha V, Mukherjee S, Ebert B, Gillette M . Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005; 102(43):15545-50. PMC: 1239896. DOI: 10.1073/pnas.0506580102. View

4.
Garraway L, Lander E . Lessons from the cancer genome. Cell. 2013; 153(1):17-37. DOI: 10.1016/j.cell.2013.03.002. View

5.
West N, McCuaig S, Franchini F, Powrie F . Emerging cytokine networks in colorectal cancer. Nat Rev Immunol. 2015; 15(10):615-29. DOI: 10.1038/nri3896. View