{rfName}
In

Indexed in

License and use

Icono OpenAccess

Citations

236

Altmetrics

Grant support

N.L.-B. acknowledges funding from the European Research Council (consolidator grant 682398) and ERDF/Spanish Ministry of Science, Innovation and Universities - Spanish State Research Agency/DamReMap Project (RTI2018-094095-B-I00) and Asociacion Espanola Contra el Cancer (AECC) (GC16173697BIGA). IRB Barcelona is a recipient of a Severo Ochoa Centre of Excellence Award from the Spanish Ministry of Economy and Competitiveness (MINECO; Government of Spain) and is supported by CERCA (Generalitat de Catalunya). O.P. is the recipient of a BIST PhD fellowship supported by the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia, and the Barcelona Institute of Science and Technology (BIST). This publication and the underlying research were partly facilitated by the Cancer Genome Atlas project, the TARGET project and the Hartwig Medical Foundation and the Center for Personalized Cancer Treatment (CPCT), which have generated, analysed and made available data for this purpose. The schematic DNA double helices in Extended Data Fig. 9a, e were created using Biorender (N.L.-B. premium membership).

Analysis of institutional authors

Martinez-Jimenez, FAuthorPich, OAuthorLopez-Bigas, NCorresponding Author

Share

August 30, 2021
Publications
>
Article
Sí

In silico saturation mutagenesis of cancer genes

Publicated to:Nature. 596 (7872): 428-+ - 2021-08-19 596(7872), DOI: 10.1038/s41586-021-03771-1

Authors: Muiños, F; Martínez-Jiménez, F; Pich, O; Gonzalez-Perez, A; Lopez-Bigas, N

Affiliations

Barcelona Inst Sci & Technol, Inst Res Biomed IRB Barcelona, Barcelona, Spain - Author
Inst Catalana Recerca Estudis Avanca ICREA, Barcelona, Spain - Author
Univ Pompeu Fabra, Res Program Biomed Informat, Barcelona, Spain - Author

Abstract

Despite the existence of good catalogues of cancer genes(1,2), identifying the specific mutations of those genes that drive tumorigenesis across tumour types is still a largely unsolved problem. As a result, most mutations identified in cancer genes across tumours are of unknown significance to tumorigenesis(3). We propose that the mutations observed in thousands of tumours-natural experiments testing their oncogenic potential replicated across individuals and tissues-can be exploited to solve this problem. From these mutations, features that describe the mechanism of tumorigenesis of each cancer gene and tissue may be computed and used to build machine learning models that encapsulate these mechanisms. Here we demonstrate the feasibility of this solution by building and validating 185 gene-tissue-specific machine learning models that outperform experimental saturation mutagenesis in the identification of driver and passenger mutations. The models and their assessment of each mutation are designed to be interpretable, thus avoiding a black-box prediction device. Using these models, we outline the blueprints of potential driver mutations in cancer genes, and demonstrate the role of mutation probability in shaping the landscape of observed driver mutations. These blueprints will support the interpretation of newly sequenced tumours in patients and the study of the mechanisms of tumorigenesis of cancer genes across tissues.

Keywords

Cell transformation, neoplasticComputer simulationConsequencesHumansMachine learningModels, geneticMutagenesisMutationMutational landscapeNeoplasmsOncogenesOrgan specificityPrecision medicineProbabilityProteinReproducibility of resultsSelectionSiftSignaturesVariants

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Nature due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2021, it was in position 1/74, thus managing to position itself as a Q1 (Primer Cuartil), in the category Multidisciplinary Sciences. Notably, the journal is positioned above the 90th percentile.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 3.41. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Field Citation Ratio (FCR) from Dimensions: 23.03 (source consulted: Dimensions Jul 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-20, the following number of citations:

  • WoS: 66
  • Scopus: 5
  • Europe PMC: 55
  • Google Scholar: 40

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-20:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 312.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 317 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 282.7.
  • The number of mentions on the social network X (formerly Twitter): 330 (Altmetric).
  • The number of mentions in news outlets: 11 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: Last Author (Lopez Bigas, Nuria).

the author responsible for correspondence tasks has been Lopez Bigas, Nuria.