Evidence map›Paper›PMID 32989323›Full record

ArticleNature genetics2020

Discovering functional evolutionary dependencies in human cancers.

Marco Mina, Arvind Iyer, Daniele Tavernari, Franck Raynaud, Giovanni Ciriello

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in Nature genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
1.1field-weighted citation impact, top 25% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 34 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Marco MinaDepartment of Computational Biology, University of Lausanne, Lausanne, Switzerland.
Arvind IyerDepartment of Computational Biology, University of Lausanne, Lausanne, Switzerland.ORCID http://orcid.org/0000-0002-8247-700X
Daniele TavernariDepartment of Computational Biology, University of Lausanne, Lausanne, Switzerland.ORCID http://orcid.org/0000-0001-5981-7363
Franck RaynaudDepartment of Computational Biology, University of Lausanne, Lausanne, Switzerland.
Giovanni CirielloDepartment of Computational Biology, University of Lausanne, Lausanne, Switzerland. giovanni.ciriello@unil.ch.ORCID http://orcid.org/0000-0003-2021-8683
SIB Swiss Institute of Bioinformatics · CHUniversity of Geneva · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer cells retain genomic alterations that provide a selective advantage. The prediction and validation of advantageous alterations are major challenges in cancer genomics. Moreover, it is crucial to understand how the coexistence of specific alterations alters response to genetic and therapeutic perturbations. In the present study, we inferred functional alterations and preferentially selected combinations of events in >9,000 human tumors. Using a Bayesian inference framework, we validated computational predictions with high-throughput readouts from genetic and pharmacological screenings on 2,000 cancer cell lines. Mutually exclusive and co-occurring cancer alterations reflected, respectively, functional redundancies able to rescue the phenotype of individual target inhibition, or synergistic interactions, increasing oncogene addiction. Among the top scoring dependencies, co-alteration of the phosphoinositide 3-kinase (PI3K) subunit PIK3CA and the nuclear factor NFE2L2 was a synergistic evolutionary trajectory in squamous cell carcinomas. By integrating computational, experimental and clinical evidence, we provide a framework to study the combinatorial functional effects of cancer genomic alterations.

Indexed as

Computational BiologyEvolution, MolecularCell Line, TumorCell SurvivalClass I Phosphatidylinositol 3-KinasesCohort StudiesDatasets as TopicGenes, NeoplasmHumansNeoplasmsPhosphatidylinositol 3-KinasesSelection, GeneticClass I Phosphatidylinositol 3-KinasesPhosphatidylinositol 3-KinasesPIK3CA protein, human

Identifiers

PMID32989323
OpenAlexW3090808618

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.