PROJECT SUMMARY / ABSTRACT
Improved treatment strategies for patients with cancer require enhanced tools to better predict patient response,
model clonal heterogeneity, and identify novel treatment options for individual patients. Patient-derived cancer
organoids (PDCOs) are a major advance providing more representative models of the human disease, including
the maintenance of molecular alterations, cell-cell communication, and the 3D architecture found in cancers. Our
groups have significant experience using PDCOs to predict treatment response for patients across cancer types.
These models, however, have unique challenges when used for translational studies, including (1) heterogeneity
between organoids from the same patient, (2) assessment techniques that require sample fixation or reagents
that prevent time-course studies of clonal evolution, and (3) lack of single organoid assessment and low-
throughput culture techniques that limit screens for new drugs.
We have developed optical metabolic imaging (OMI) using two-photon (2P) microscopy to measure treatment
response without a need for reagents (e.g., dyes, labels) or fixation. Our prior studies demonstrated that 2P OMI
can predict treatment response for patients with cancer. However, 2P microscopy is high cost, low throughput,
and complex to operate. To expand the use of OMI across multi-center translational studies of PDCOs, more
readily accessible imaging and analysis methods are needed for high/moderate throughput drug screening and
assessments of organoid metabolic heterogeneity over time. To enhance the accessibility of this technology to
more laboratories and facilitate the expanded use of PDCOs, we have developed a one-photon wide-field (WF)
OMI technique with single-organoid tracking and leading-edge segmentation methods for significantly reduced
cost, reduced complexity, and increased throughput compared to 2P microscopy.
The goal of this proposal is to validate WF OMI techniques for PDCOs that can be widely used for patient
treatment planning, heterogeneity analyses, and new drug development. OMI non-invasively images
response in a 3D sample using the intrinsic fluorescence of the metabolic co-enzymes NAD(P)H and FAD. OMI
can dynamically quantify heterogeneous drug response over a treatment time-course. We have and will continue
to develop hundreds of PDCO lines from metastatic colorectal cancer (CRC) patients. These cultures will be
used to validate new methods for more widely accessible OMI tools to predict patient response, identify
metabolic/genetic heterogeneity that underlies resistance to targeted therapy, and perform screens of new drug
candidates. The completion of this work will create technologies to perform high-sensitivity patient-matched drug
screens in a clinical setting, predict the evolution of drug resistance for individual patients, and perform new drug
development in samples that reflect the diversity of human cancers.
