Evidence map›Paper›PMID 26284497›Full record

ArticleeLife2015

Perturbation biology nominates upstream-downstream drug combinations in RAF inhibitor resistant melanoma cells.

Anil Korkut, Weiqing Wang, Emek Demir, Bülent Arman Aksoy, Xiaohong Jing, Evan J Molinelli, Özgün Babur, Debra L Bemis, Selcuk Onur Sumer, David B Solit and 2 more

Open access · goldAbstract read
In one paragraph

Article in eLife, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.

0numbers the graph read from it
0cells of the map it votes in
65citing papers in PubMed
5.4field-weighted citation impact, top 4% 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

65 citing papers in PubMed, 119 citations in OpenAlex.

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5 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 3 institutions in 1 country.

Anil KorkutComputational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.
Weiqing WangComputational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.
Emek DemirComputational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.
Bülent Arman AksoyComputational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.
Xiaohong JingComputational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.
Evan J MolinelliComputational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.
Özgün BaburComputational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.
Debra L BemisComputational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.
Selcuk Onur SumerComputational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.
David B SolitHuman Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, United States.
Christine A PratilasThe Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, United States.
Chris SanderComputational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.
Memorial Sloan Kettering Cancer Center · USJohns Hopkins University · USTri-Institutional PhD Program in Chemical Biology · US

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
TR&D 3 - Network Guided Machine LearningP41GM103504 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI IDEKER, TREY · 2012 to 2024
$17.3M
Variability of Cellular Responses to Growth Factors and Drugs During TumorgenesisU54CA148967 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI ALTAN-BONNET, GREGOIRE · 2010 to 2014
$13.8M
Pathway Commons: Research Resource for Biological PathwaysU41HG006623 · NHGRI · SLOAN-KETTERING INST CAN RESEARCH · PI SANDER, CHRIS · 2012 to 2017
$5.7M
NCI NIH HHS CA148967NCI NIH HHS U54 CA148967NHGRI NIH HHS U41 HG006623NIGMS NIH HHS GM 103504NIGMS NIH HHS P41 GM103504
6 · The paper itself

Abstract

Resistance to targeted cancer therapies is an important clinical problem. The discovery of anti-resistance drug combinations is challenging as resistance can arise by diverse escape mechanisms. To address this challenge, we improved and applied the experimental-computational perturbation biology method. Using statistical inference, we build network models from high-throughput measurements of molecular and phenotypic responses to combinatorial targeted perturbations. The models are computationally executed to predict the effects of thousands of untested perturbations. In RAF-inhibitor resistant melanoma cells, we measured 143 proteomic/phenotypic entities under 89 perturbation conditions and predicted c-Myc as an effective therapeutic co-target with BRAF or MEK. Experiments using the BET bromodomain inhibitor JQ1 affecting the level of c-Myc protein and protein kinase inhibitors targeting the ERK pathway confirmed the prediction. In conclusion, we propose an anti-cancer strategy of co-targeting a specific upstream alteration and a general downstream point of vulnerability to prevent or overcome resistance to targeted drugs.

Indexed as

Drug ResistanceAntineoplastic AgentsCell Line, TumorComputational BiologyCytological TechniquesDrug CombinationsGene Regulatory NetworksHumansMelanomaModels, BiologicalModels, Theoreticalraf KinasesAntineoplastic AgentsDrug Combinationsraf Kinasescancer drug resistancecell biologycellular signalingcomputational biologydrug synergyhumanmelanomanetwork modelingproteomicssystems biology

Identifiers

PMID26284497
PMCPMC4539601
OpenAlexW2116944463

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.