Application Note

Disease modeling methods with cancer patient-derived organoids

  • Demonstrate using patient -derived cells for disease modelling
  • Automate compound treatment and processing of 3D micro-tissues
  • Show multi-parametric approach in analysis compound effects on cancer micro-tumors

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Matthew Hammer, Oksana Sirenko | Molecular Devices
Evan F Cromwell | Protein Fluidics, Inc
Margarite Matossian, Matthew E Burow | Tulane University School of Medicine


The use of patient-derived tumor tissue has transformed the field of drug and target discovery research, providing a translational tool and physiologically relevant system to evaluate tumor biology. There has been a transition from 2D assays to 3D cell model systems because they more accurately recapitulate the human in vivo environment and are a more predictive tool for drug discovery. An example of this is the use of patient-derived organoids (PDO) for oncology research. PDOs are generated from tumor biopsies or surgical procedures and can serve as models to understand patient-specific drug responses and investigate cancer cell growth. Digested tumor pieces and cells harvested from patient-derived tumors exhibit cancer stem cell (CSC)-like qualities and can be expanded over multiple passages to produce large numbers of organoids that maintain molecular characteristics of the original tumor.1 We present results for breast cancer disease modeling using PDOs formed from primary cells isolated from a patient-derived tumor, TU-BcX-4IC.2 TU-BcX-4IC represents a rare breast cancer subtype, metaplastic breast cancer (MBC), and is classified as a triple negative breast cancer (TNBC) histological subtype. The tumor exhibited rapid pre-operative growth despite combination of neoadjuvant therapy with adriamycin, cyclophosphamide, and paclitaxel. PDOs were incubated in the presence of targeted anti-cancer compounds for 24–48 hours and then stained for either viability markers or E-cadherin/CD44 markers. Resistance of PDOs to chemotherapeutic agents observed in our experiments were consistent with tumor response in the patient. To facilitate this work, we used a Pu·MA System and flowchips for semi-automated organoid assays coupled with high-content imaging. This novel assay method using microfluidics enables automation of 3D cell-based cultures that mimic in vivo conditions and allows a wide range of assay detection modalities. The work described here demonstrates the utility of the Pu·MA System for in vitro drug discovery research in testing structurally delicate 3D systems and can be applied to all solid tumor types.

Pu·MA System assays

Pu·MA System flowchips are designed with chambers and reservoirs arranged in a convenient multi-well plate format (384-well spacings) and provide up to 32 organoid samples per plate. Once tumoroids and reagents are loaded into the flowchips, the plate is placed into the Pu·MA System and reagent exchanges are done automatically through microfluidic channels connected to the protected sample chamber3.

Multiple reagent exchanges are performed with assay protocols using a built-in program. Assay protocols are pre-loaded into the system and run using an intuitive touchscreen interface. The whole Pu·MA System can be placed in an incubator to run assays at 37°C and CO2 (Figure 1). The system architecture and use of pneumatics to move fluids provides gas exchange to the sample chambers to maintain cell health.

Schematic of the semi-automated tumoroid assay workflow

Figure 1. Schematic of the semi-automated tumoroid assay workflow coupled with high-content 3D imaging.

Schematic of the semi-automated tumoroid assay workflow


Pu·MA System and 3D flowchip features:

The assay protocols can be edited via the Pu·MA System software.

ImageXpress® Micro Confocal HighContent Imaging System includes:

The system is controlled by MetaXpress® High-Content Image Acquisition & Analysis Software

Tumoroid formation & staining

Workflow for tumoroid formation

Figure 2. Top: Workflow for tumoroid formation. Bottom: Schematic of the protocol for magnetic labeling and positioning of tumoroids. Transmitted light images of tumoroids are shown on the Right. Tumoroids are centered in the wells with no optical artifacts from NanoShuttle particles.

mages of tumoroids treated with romidepsin

Figure 3. Images of tumoroids treated with romidepsin (Top) and trametinib (Bottom). Images are maximum projections of confocal stacks acquired with a 10X objective.

Microfluidic flowchip technology

Each Pu·MA System flowchip contains eight lanes of reagent wells connected by microfluidic channels. Four flowchips are placed in holder that locates all wells in a 384 multiwell plate format providing for 32 samples per assay. The compound wells in each lane hold media, compounds, or additional assay reagents. Tumoroids are placed into the sample well and located in a protected chamber at the bottom of the well. This allows reagents to be directed in and out of the sample well without disturbing or drying out the microtissue.

Protocol steps for Tumoroid assays

Figure 4. Protocol steps for Tumoroid assays.3 Tumoroids were treated overnight with compounds and then stained with viability dyes (1 μM calcein AM, 3 μM EthD-1, 33 μM Hoechst 33342) or FL-labeled primary antibodies to E-Cadherin and CD44 (BD Biosciences). Conditioned media can be removed for additional analyses.

Cancer disease modeling

Tumoroids incubated with compounds for E-Cadherin and CD44

Figure 5. Tumoroids incubated with compounds for 24 hr and labeled for E-Cadherin (Green) and CD44 (Red). Images are maximum projection of confocal stacks taken with 20X water immersion (WI) objective. Treatments results in loss of E-Cadherin and disruption of organoids. EC50 values measured by percentage CD44 positive cells.

Concentration response curves from viability assays for tumoroids

Figure 6. Concentration response curves from viability assays for tumoroids treated with romidepsin, trametinib, and paclitaxel. EC50 values are shown in table from both viability and CD44 staining. Values for romidepsin and trametinib were found to be within normal ranges while paclitaxel was significantly higher. This is consistent with the response of the patient’s tumor to treatment2.



  1. Drug resistance profiling if a new triple negative breast cancer patient-derived xenograft model. Matossian, M.D. et al (2019) 19(1): 205, BMC Cancer
  2. In-depth characterization of a new patient-derived xenograft model for metaplastic breast carcinoma to identify viable biologic targets and patterns of matrix evolution within rare tumor types. Matossian, M.D. et al (2021) Clin. & Trans. Oncology (in revision)
  3. Disease modeling with 3D cell-based assays using a novel flowchip system and high-content imaging. Cromwell, E.F. et al (2021) 26(3): 237, SLAS Technol.
  4. Three-dimensional tissue culture based on magnetic cell levitation. Souza, G.R. et al (2010) 5(4): 291, Nat. Nanotechnol.
  5. High-Content Assays for Characterizing the Viability and Morphology of 3D Cancer Spheroid Cultures. Sirenko, O. et al (2015) 13, 402, Assay and Drug Dev. Tech.

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