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Cited by 281

The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.

Functional precision oncology: Testing tumors with drugs to identify vulnerabilities and novel combinations

Open access
Letai, Anthony, Bhola, Patrick, Welm, Alana L.·2022·Cancer Cell

Abstract

Functional precision medicine is a strategy whereby live tumor cells from affected individuals are directly perturbed with drugs to provide immediately translatable, personalized information to guide therapy. The heterogeneity of human cancer has led to the realization that personalized approaches are needed to improve treatment outcomes. Precision oncology has traditionally used static features of the tumor to dictate which therapies should be used. Static features can include expression of key targets or genomic analysis of mutations to identify therapeutically targetable "drivers." Although a surprisingly small proportion of individuals derive clinical benefit from the static approach, functional precision medicine can provide additional information regarding tumor vulnerabilities. We discuss emerging technologies for functional precision medicine as well as limitations and challenges in using these assays in the clinical trials that will be necessary to determine whether functional precision medicine can improve outcomes and eventually become a standard tool in clinical oncology.

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Metadata

Authors
Letai, A., Bhola, P., & Welm, A. L.
Venue
Cancer Cell
Publisher
Elsevier BV
Published
2022-01
Volume
40
Issue
1
Pages
26-35
ISSN
1535-6108 (ISSN-L)
OpenAlex
W4200579603
Cited by
281
References
68
FWCI
13.69
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Functional precision oncology: Testing tumors with drugs to identify vulnerabilities and novel combinations. https://app.cochat.ai/papers/10.1016/j.ccell.2021.12.004
The paper
Letai, A., Bhola, P., & Welm, A. L. (2022). Functional precision oncology: Testing tumors with drugs to identify vulnerabilities and novel combinations. Cancer Cell. https://doi.org/10.1016/j.ccell.2021.12.004

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.