Evidence map›Paper›PMID 31340155›Full record

ArticleCell reports2019

Beyond Synthetic Lethality: Charting the Landscape of Pairwise Gene Expression States Associated with Survival in Cancer.

Assaf Magen, Avinash Das Sahu, Joo Sang Lee, Mahfuza Sharmin, Alexander Lugo, J Silvio Gutkind, Alejandro A Schäffer, Eytan Ruppin, Sridhar Hannenhalli

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
2.6field-weighted citation impact, top 9% 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

25 citing papers in PubMed, 40 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 1 country.

Assaf MagenCenter for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA; Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Avinash Das SahuCenter for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA; Department of Biostatistics and Computational Biology, Harvard School of Public Health, Boston, MA, USA; Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA.
Joo Sang LeeCenter for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA; Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Mahfuza SharminCenter for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA; Department of Genetics, Stanford University, Stanford, CA, USA.
Alexander LugoCenter for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA.
J Silvio GutkindMoores Cancer Center, University of California San Diego, La Jolla, CA, USA.
Alejandro A SchäfferCancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. Electronic address: alejandro.schaffer@nih.gov.
Eytan RuppinCenter for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA; Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. Electronic address: eytan.ruppin@nih.gov.
Sridhar HannenhalliCenter for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA; Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. Electronic address: sridhar@umiacs.umd.edu.
National Institutes of Health · USUniversity of Maryland, College Park · USHarvard University · USUniversity of California San Diego · USUniversity of Maryland, Baltimore · US

Funding

Discovery of genetic interactions in cancerZIABC011802 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI RUPPIN, EYTAN · 2018 to 2025
$19.6M
Area C: Genome-wide identification and targeting of resistance to cancer therapyR33CA225291 · NCI · UNIV OF MARYLAND, COLLEGE PARK · PI GUTKIND, JORGE SILVIO, RUPPIN, EYTAN · 2017 to 2017
$1.5M
Intramural NIH HHS ZIA BC011802NCI NIH HHS R33 CA225291
6 · The paper itself

Abstract

The phenotypic effect of perturbing a gene's activity depends on the activity level of other genes, reflecting the notion that phenotypes are emergent properties of a network of functionally interacting genes. In the context of cancer, contemporary investigations have primarily focused on just one type of functional relationship between two genes-synthetic lethality (SL). Here, we define the more general concept of "survival-associated pairwise gene expression states" (SPAGEs) as gene pairs whose joint expression levels are associated with survival. We describe a data-driven approach called SPAGE-finder that when applied to The Cancer Genome Atlas (TCGA) data identified 71,946 SPAGEs spanning 12 distinct types, only a minority of which are SLs. The detected SPAGEs explain cancer driver genes' tissue specificity and differences in patients' response to drugs and stratify breast cancer tumors into refined subtypes. These results expand the scope of cancer SPAGEs and lay a conceptual basis for future studies of SPAGEs and their translational applications.

Indexed as

Gene Expression Regulation, NeoplasticAntineoplastic AgentsCarcinogenesisGenes, NeoplasmHumansNeoplasmsOrgan SpecificitySurvival AnalysisSynthetic Lethal MutationsAntineoplastic Agentscancer stratificationdata miningdrug responsegene interactionspatient survival analysissynthetic lethality

Identifiers

PMID31340155
PMCPMC8261641
OpenAlexW2963037532

What OpenQuestion holds

Textmetadata
LicenceTDM
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.