Evidence map›Paper›PMID 35332332›Full record

ArticleNature chemical biology2022

Systematic identification of biomarker-driven drug combinations to overcome resistance.

Matthew G Rees, Lisa Brenan, Mariana do Carmo, Patrick Duggan, Besnik Bajrami, Michael Arciprete, Andrew Boghossian, Emma Vaimberg, Steven J Ferrara, Timothy A Lewis and 8 more

Abstract read
PubMed Publisher
In one paragraph

Article in Nature chemical biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.1field-weighted citation impact, top 13% 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

13 citing papers in PubMed, 22 citations in OpenAlex.

  1. Article
  2. Frontiers in cell and developmental biology · 2026
    Article
  3. Article
  4. Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Evolution-Informed Strategies for Combating Drug Resistance in Cancer.International journal of molecular sciences · 2023
    Review
  13. Review
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

18 authors at 1 institution in 2 countries.

Matthew G ReesBroad Institute of MIT and Harvard, Cambridge, MA, USA. reesm@broadinstitute.org.ORCID http://orcid.org/0000-0002-2987-7581
Lisa BrenanBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Mariana do CarmoBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Patrick DugganBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Besnik BajramiBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Michael ArcipreteBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Andrew BoghossianBroad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-7008-8138
Emma VaimbergBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Steven J FerraraBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Timothy A LewisBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Danny RosenbergBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Tenzin SangpoBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Jennifer A RothBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Virendar K KaushikBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Federica PiccioniBroad Institute of MIT and Harvard, Cambridge, MA, USA.
John G DoenchBroad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-3707-9889
David E RootBroad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0001-5122-861X
Cory M JohannessenBroad Institute of MIT and Harvard, Cambridge, MA, USA. johannes@broadinstitute.org.ORCID http://orcid.org/0000-0002-2618-1036
Broad Institute · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ability to understand and predict variable responses to therapeutic agents may improve outcomes in patients with cancer. We hypothesized that the basal gene-transcription state of cancer cell lines, coupled with cell viability profiles of small molecules, might be leveraged to nominate specific mechanisms of intrinsic resistance and to predict drug combinations that overcome resistance. We analyzed 564,424 sensitivity profiles to identify candidate gene-compound pairs, and validated nine such relationships. We determined the mechanism of a novel relationship, in which expression of the serine hydrolase enzymes monoacylglycerol lipase (MGLL) or carboxylesterase 1 (CES1) confers resistance to the histone lysine demethylase inhibitor GSK-J4 by direct enzymatic modification. Insensitive cell lines could be sensitized to GSK-J4 by inhibition or gene knockout. These analytical and mechanistic studies highlight the potential of integrating gene-expression features with small-molecule response to identify patient populations that are likely to benefit from treatment, to nominate rational candidates for combinations and to provide insights into mechanisms of action.

Indexed as

Histone DemethylasesMonoacylglycerol LipasesBiomarkersCell SurvivalDrug CombinationsHumansBiomarkersDrug CombinationsHistone DemethylasesMonoacylglycerol Lipases

Identifiers

PMID35332332
OpenAlexW4221065598

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.