Evidence map›Paper›PMID 28287179›Full record

ArticleScientific reports2017

Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution.

Vanessa D Jonsson, Collin M Blakely, Luping Lin, Saurabh Asthana, Nikolai Matni, Victor Olivas, Evangelos Pazarentzos, Matthew A Gubens, Boris C Bastian, Barry S Taylor and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
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

18 citing papers in PubMed.

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  14. Second-line treatment ofTherapeutic advances in medical oncology · 2019
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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

12 authors.

Vanessa D JonssonDepartment of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA.
Collin M BlakelyDepartment of Medicine, University of California, San Francisco, CA, USA.
Luping LinDepartment of Medicine, University of California, San Francisco, CA, USA.
Saurabh AsthanaDepartment of Medicine, University of California, San Francisco, CA, USA.
Nikolai MatniDepartment of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA.
Victor OlivasDepartment of Medicine, University of California, San Francisco, CA, USA.
Evangelos PazarentzosDepartment of Medicine, University of California, San Francisco, CA, USA.
Matthew A GubensDepartment of Medicine, University of California, San Francisco, CA, USA.
Boris C BastianHelen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA.
Barry S TaylorHuman Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, USA.
John C DoyleDepartment of Control and Dynamical Systems, California Institute of Technology, Pasadena, CA, USA.
Trever G BivonaDepartment of Medicine, University of California, San Francisco, CA, USA.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

The success of targeted cancer therapy is limited by drug resistance that can result from tumor genetic heterogeneity. The current approach to address resistance typically involves initiating a new treatment after clinical/radiographic disease progression, ultimately resulting in futility in most patients. Towards a potential alternative solution, we developed a novel computational framework that uses human cancer profiling data to systematically identify dynamic, pre-emptive, and sometimes non-intuitive treatment strategies that can better control tumors in real-time. By studying lung adenocarcinoma clinical specimens and preclinical models, our computational analyses revealed that the best anti-cancer strategies addressed existing resistant subpopulations as they emerged dynamically during treatment. In some cases, the best computed treatment strategy used unconventional therapy switching while the bulk tumor was responding, a prediction we confirmed in vitro. The new framework presented here could guide the principled implementation of dynamic molecular monitoring and treatment strategies to improve cancer control.

Indexed as

Computer SimulationModels, BiologicalAdenocarcinomaAdenocarcinoma of LungCell Line, TumorCombined Modality TherapyHumansLung Neoplasms

Identifiers

PMID28287179
PMCPMC5347024

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.