Evidence map›Paper›PMID 27489532›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2016

Preclinical models for interrogating drug action in human cancers using Stable Isotope Resolved Metabolomics (SIRM).

Andrew N Lane, Richard M Higashi, Teresa W-M Fan

Open access · greenAbstract read
In one paragraph

Article in Metabolomics : Official journal of the Metabolomic Society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
1.7field-weighted citation impact, top 18% 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

27 citing papers in PubMed, 30 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

3 authors at 1 institution in 1 country.

Andrew N LaneCenter for Environmental and Systems Biochemistry, University of Kentucky.
Richard M HigashiCenter for Environmental and Systems Biochemistry, University of Kentucky.
Teresa W-M FanCenter for Environmental and Systems Biochemistry, University of Kentucky.
University of Kentucky · US

Funding

University of Kentucky Markey Cancer Center Support Grant ECIA SupplementP30CA177558 · NCI · UNIVERSITY OF KENTUCKY · PI KATHLEEN L. O'CONNOR · 2013 to 2026
$38.3M
SAMPLE COREU24DK097215 · NIDDK · UNIVERSITY OF KENTUCKY · PI FAN, TERESA WHEI-MEI, HIGASHI, RICHARD M · 2013 to 2017
$8.7M
Systems Biochemistry in Lung Cancer: Toward a Mechanistic Understanding of NSCLCP01CA163223 · NCI · UNIVERSITY OF LOUISVILLE · PI FAN, TERESA WHEI-MEI, FAN, TERESA WHEI-MEI · 2013 to 2017
$6.5M
Integrated Chemoselective and Informatic Platform for Large-Scale MetabolomicsR01ES022191 · NIEHS · UNIVERSITY OF LOUISVILLE · PI FAN, TERESA WHEI-MEI, HIGASHI, RICHARD M · 2012 to 2016
$3.4M
Mitochondrial Metabolic Reprogramming and DNA Damage in Arsenic CarcinogenesisR21ES025669 · NIEHS · UNIVERSITY OF KENTUCKY · PI FAN, TERESA WHEI-MEI, IZUMI, TADAHIDE · 2015 to 2016
$413k
NCI NIH HHS P01 CA163223NCI NIH HHS P30 CA177558NIDDK NIH HHS U24 DK097215NIEHS NIH HHS R01 ES022191NIEHS NIH HHS R21 ES025669
6 · The paper itself

Abstract

aimsIn this review we compare the advantages and disadvantages of different model biological systems for determining the metabolic functions of cells in complex environments, how they may change in different disease states, and respond to therapeutic interventions.

backgroundAll preclinical drug-testing models have advantages and drawbacks. We compare and contrast established cell, organoid and animal models with ex vivo organ or tissue culture and in vivo human experiments in the context of metabolic readout of drug efficacy. As metabolism reports directly on the biochemical state of cells and tissues, it can be very sensitive to drugs and/or other environmental changes. This is especially so when metabolic activities are probed by stable isotope tracing methods, which can also provide detailed mechanistic information on drug action. We have developed and been applying Stable Isotope-Resolved Metabolomics (SIRM) to examine metabolic reprogramming of human lung cancer cells in monoculture, in mouse xenograft/explant models, and in lung cancer patients in situ (Lane et al. 2011; T. W. Fan et al. 2011; T. W-M. Fan et al. 2012; T. W. Fan et al. 2012; Xie et al. 2014b; Ren et al. 2014a; Sellers et al. 2015b). We are able to determine the influence of the tumor microenvironment using these models. We have now extended the range of models to fresh human tissue slices, similar to those originally described by O. Warburg (Warburg 1923), which retain the native tissue architecture and heterogeneity with a paired benign versus cancer design under defined cell culture conditions. This platform offers an unprecedented human tissue model for preclinical studies on metabolic reprogramming of human cancer cells in their tissue context, and response to drug treatment (Xie et al. 2014a). As the microenvironment of the target human tissue is retained and individual patient's response to drugs is obtained, this platform promises to transcend current limitations of drug selection for clinical trials or treatments. CONCLUSIONS AND FUTURE WORK: Development of ex vivo human tissue and animal models with humanized organs including bone marrow and liver show considerable promise for analyzing drug responses that are more relevant to humans. Similarly using stable isotope tracer methods with these improved models in advanced stages of the drug development pipeline, in conjunction with tissue biopsy is expected significantly to reduce the high failure rate of experimental drugs in Phase II and III clinical trials.

Indexed as

cell culturemetabolismPDX modelsSIRMtissue slices

Identifiers

PMID27489532
PMCPMC4968890
OpenAlexW2465746198

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.