Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2017 Sep;58(9):1367-1372.
doi: 10.2967/jnumed.117.192534. Epub 2017 Jun 6.

Functional Optical Imaging of Primary Human Tumor Organoids: Development of a Personalized Drug Screen

Affiliations
Review

Functional Optical Imaging of Primary Human Tumor Organoids: Development of a Personalized Drug Screen

Alex J Walsh et al. J Nucl Med. 2017 Sep.

Abstract

Primary tumor organoids are a robust model of individual human cancers and present a unique platform for patient-specific drug testing. Optical imaging is uniquely suited to assess organoid function and behavior because of its subcellular resolution, penetration depth through the entire organoid, and functional endpoints. Specifically, optical metabolic imaging (OMI) is highly sensitive to drug response in organoids, and OMI in tumor organoids correlates with primary tumor drug response. Therefore, an OMI organoid drug screen could enable accurate testing of drug response for individualized cancer treatment. The objective of this perspective is to introduce OMI and tumor organoids to a general audience in order to foster the adoption of these techniques in diverse clinical and laboratory settings.

Keywords: 3D culture; cancer drug screens; fluorescence imaging; multiphoton microscopy; optical; primary human spheroids.

PubMed Disclaimer

Figures

FIGURE 1.
FIGURE 1.
Clinical workflow of organoid-based anticancer drug screen.
FIGURE 2.
FIGURE 2.
(A) Example fluorescence lifetime data (blue dots), system response (green line), and exponential decay fit (red line). Fluorescence lifetime decays, I(t), are often fit to 2-component exponential decay model, with mean lifetime (τ m ) computed as weighted average of short and long lifetimes (τ 1 and τ 2) and proportion of short and long lifetimes (α 1 and α 2). χ2 provides goodness-of-fit measure. At right are representative redox ratio image (B), NADH τ m image (C), and FAD τ m image (D) of organoid derived from human breast cancer biopsy. (Reprinted from (20).)
FIGURE 3.
FIGURE 3.
OMI of primary (HER2-positive) human tumor organoids that develop resistance to paclitaxel chemotherapy over 72 h of treatment (upper row) and that respond to experimental drug cocktail of trastuzumab (anti-HER2) plus paclitaxel plus XL147 (lower row). A longer NADH mean lifetime, as evidenced by more red and orange tones, indicates resistance, whereas a shorter NADH mean lifetime, as evidenced by blue, indicates response. Scale bar is 100 μm.
FIGURE 4.
FIGURE 4.
(A) NADH fluorescence lifetime image of organoid derived from HER2-positive human breast cancer biopsy sample demonstrates intraorganoid heterogeneity (reprinted from (20)). (B) Population modeling of representative organoid data, where blue bars are data and red lines are gaussian fits. (C) Population-density models of human-derived breast cancer organoids treated with control, trastuzumab plus paclitaxel plus XL147 (H+P+X), and trastuzumab plus paclitaxel plus tamoxifen plus XL147 (H+P+T+X). H+P+X-treated organoids demonstrate responding and nonresponding populations, whereas H+P+T+X-treated organoids demonstrate greater proportion of responding cells.

Similar articles

Cited by

References

    1. Holohan C, Van Schaeybroeck S, Longley DB, Johnston PG. Cancer drug resistance: an evolving paradigm. Nat Rev Cancer. 2013;13:714–726. - PubMed
    1. Almendro V, Cheng YK, Randles A, et al. . Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity. Cell Reports. 2014;6:514–527. - PMC - PubMed
    1. Polyak K. Tumor heterogeneity confounds and illuminates: a case for Darwinian tumor evolution. Nat Med. 2014;20:344–346. - PubMed
    1. Fisher R, Pusztai L, Swanton C. Cancer heterogeneity: implications for targeted therapeutics. Br J Cancer. 2013;108:479–485. - PMC - PubMed
    1. Boj SF, Hwang CI, Baker LA, et al. . Organoid models of human and mouse ductal pancreatic cancer. Cell. 2015;160:324–338. - PMC - PubMed

MeSH terms

LinkOut - more resources