Evidence map›Paper›PMID 31668800›Full record

ArticleCell systems2020

Genotype-Fitness Maps of EGFR-Mutant Lung Adenocarcinoma Chart the Evolutionary Landscape of Resistance for Combination Therapy Optimization.

Patrick O Bolan, Asaf Zviran, Lisa Brenan, Joshua S Schiffman, Neville Dusaj, Amy Goodale, Federica Piccioni, Cory M Johannessen, Dan A Landau

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

Article in Cell systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
0.8field-weighted citation impact, top 32% 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

10 citing papers in PubMed, 17 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 4 institutions in 1 country.

Patrick O BolanWeill Cornell Medicine, New York, NY 10065, USA.
Asaf ZviranDivision of Hematology and Medical Oncology, Department of Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10065, USA; New York Genome Center, New York, NY 10013, USA; Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10065, USA.
Lisa BrenanBroad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Joshua S SchiffmanDivision of Hematology and Medical Oncology, Department of Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10065, USA; New York Genome Center, New York, NY 10013, USA; Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10065, USA.
Neville DusajWeill Cornell Medicine, New York, NY 10065, USA.
Amy GoodaleBroad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Federica PiccioniBroad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Cory M JohannessenBroad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Electronic address: johannes@broadinstitute.org.
Dan A LandauDivision of Hematology and Medical Oncology, Department of Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10065, USA; New York Genome Center, New York, NY 10013, USA; Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10065, USA. Electronic address: dlandau@nygenome.org.
Broad Institute · USWeill Cornell Medicine · USCornell University · USNew York Genome Center · US

Funding

Shapeshifters in Cancer: Defining the Fundamental Forces of Leukemia EvolutionDP2CA239065 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI LANDAU, DAN · 2018 to 2018
$2.5M
NCI NIH HHS DP2 CA239065
6 · The paper itself

Abstract

Cancer evolution poses a central obstacle to cure, as resistant clones expand under therapeutic selection pressures. Genome sequencing of relapsed disease can nominate genomic alterations conferring resistance but sample collection lags behind, limiting therapeutic innovation. Genome-wide screens offer a complementary approach to chart the compendium of escape genotypes, anticipating clinical resistance. We report genome-wide open reading frame (ORF) resistance screens for first- and third-generation epidermal growth factor receptor (EGFR) inhibitors and a MEK inhibitor. Using serial sampling, dose gradients, and mathematical modeling, we generate genotype-fitness maps across therapeutic contexts and identify alterations that escape therapy. Our data expose varying dose-fitness relationship across genotypes, ranging from complete dose invariance to paradoxical dose dependency where fitness increases in higher doses. We predict fitness with combination therapy and compare these estimates to genome-wide fitness maps of drug combinations, identifying genotypes where combination therapy results in unexpected inferior effectiveness. These data are applied to nominate combination optimization strategies to forestall resistant disease.

Indexed as

MutationAcrylamidesAdenocarcinoma of LungAniline CompoundsAntineoplastic Combined Chemotherapy ProtocolsBenzimidazolesDrug Resistance, NeoplasmErbB ReceptorsErlotinib HydrochlorideGenetic FitnessGenotypeHumansIndolesLung NeoplasmsMAP Kinase Signaling SystemPyrimidinesAcrylamidesAniline CompoundsBenzimidazolesbinimetinibEGFR protein, humanErbB ReceptorsErlotinib HydrochlorideIndolesosimertinibPyrimidinescancer resistancecombination therapyEGFR inhibitorevolutionary dynamicsgenotype-fitness maplung adenocarcinomaMEK inhibitortargeted-therapy resistancetumor evolutiontumor heterogeneity

Identifiers

PMID31668800
PMCPMC6981068
OpenAlexW2982272279

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.