Evidence map›Paper›PMID 37331782›Full record

ReviewSeminars in radiation oncology2023

Radiation Sensitivity: The Rise of Predictive Patient-Derived Cancer Models.

Liliana L Berube, Kwang-Ok P Nickel, Mari Iida, Sravani Ramisetty, Prakash Kulkarni, Ravi Salgia, Deric L Wheeler, Randall J Kimple

Abstract readReview
In one paragraph

Review in Seminars in radiation oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Liliana L BerubeDepartment of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI; Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI.
Kwang-Ok P NickelDepartment of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI.
Mari IidaDepartment of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI.
Sravani RamisettyDepartment of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center, Duarte, CA.
Prakash KulkarniDepartment of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center, Duarte, CA.
Ravi SalgiaDepartment of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center, Duarte, CA.
Deric L WheelerDepartment of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI; University of Wisconsin Carbone Cancer Center, Madison, WI.
Randall J KimpleDepartment of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI; Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI; University of Wisconsin Carbone Cancer Center, Madison, WI. Electronic address: rkimple@humonc.wisc.edu.

Funding

UW COMPREHENSIVE CANCER CENTER SUPPORTP30CA014520 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI Justine Yang Bruce · 1985 to 2026
$142.6M
Project 3: Modulation of the head and neck tumor immune microenvironment by targeting the TAM family of receptorsP50CA278595 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI David J Beebe · 2022 to 2026
$12.5M
Improving the translational value of head and neck cancer patient-in-mouse modelsR37CA255330 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI Randall J. Kimple · 2021 to 2026
$3.5M
Cooperation of the TAM and Abl family kinases in therapeutic resistance in HNCR01CA262292 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI Ravi Salgia, DERIC L WHEELER · 2022 to 2026
$2.9M
NCI NIH HHS P30 CA014520NCI NIH HHS P50 CA278595NCI NIH HHS R01 CA262292NCI NIH HHS R37 CA255330
6 · The paper itself

Abstract

Patient-derived cancer models have been used for decades to improve our understanding of cancer and test anticancer treatments. Advances in radiation delivery have made these models more attractive for studying radiation sensitizers and understanding an individual patient's radiation sensitivity. Advances in the use of patient-derived cancer models lead to a more clinically relevant outcome, although many questions remain regarding the optimal use of patient-derived xenografts and patient-derived spheroid cultures. The use of patient-derived cancer models as personalized predictive avatars through mouse and zebrafish models is discussed, and the advantages and disadvantages of patient-derived spheroids are reviewed. In addition, the use of large repositories of patient-derived models to develop predictive algorithms to guide treatment selection is discussed. Finally, we review methods for establishing patient-derived models and identify key factors that influence their use as both avatars and models of cancer biology.

Indexed as

NeoplasmsZebrafishAnimalsDisease Models, AnimalHumansMiceRadiation Tolerance

Identifiers

PMID37331782
PMCPMC10287034

What OpenQuestion holds

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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.