An official website of the United States government
Patient-derived organoids (PDOs) can model personalized therapy responses; however, current screening technologies cannot reveal drug response mechanisms or how tumor microenvironment cells alter therapeutic performance. To address this, we developed a highly multiplexed mass cytometry platform to measure post- translational modification (PTM) signaling, DNA damage, cell-cycle activity, and apoptosis in >2,500 colorectal cancer (CRC) PDOs and cancer-associated fibroblasts (CAFs) in response to clinical therapies at single-cell resolution. To compare patient- and microenvironment-specific drug responses in thousands of single-cell da- tasets, we developed ‘‘Trellis’’—a highly scalable, tree-based treatment effect analysis method. Trellis single- cell screening revealed that on-target cell-cycle blockage and DNA-damage drug effects are common, even in chemorefractory PDOs. However, drug-induced apoptosis is rarer, patient-specific, and aligns with cancer cell PTM signaling. We find that CAFs can regulate PDO plasticity—shifting proliferative colonic stem cells (proCSCs) to slow-cycling revival colonic stem cells (revCSCs) to protect cancer cells from chemotherapy.
more »
« less
A pipeline for malignancy and therapy agnostic assessment of cancer drug response using cell mass measurements
Abstract Functional precision medicine offers a promising complement to genomics-based cancer therapy guidance by testing drug efficacy directly on a patient’s tumor cells. Here, we describe a workflow that utilizes single-cell mass measurements with inline brightfield imaging and machine-learning based image classification to broaden the clinical utility of such functional testing for cancer. Using these image-curated mass measurements, we characterize mass response signals for 60 different drugs with various mechanisms of action across twelve different cell types, demonstrating an improved ability to detect response for several slow acting drugs as compared with standard cell viability assays. Furthermore, we use this workflow to assess drug responses for various primary tumor specimen formats including blood, bone marrow, fine needle aspirates (FNA), and malignant fluids, all with reports generated within two days and with results consistent with patient clinical responses. The combination of high-resolution measurement, broad drug and malignancy applicability, and rapid return of results offered by this workflow suggests that it is well-suited to performing clinically relevant functional assessment of cancer drug response.
more »
« less
- Award ID(s):
- 2026060
- PAR ID:
- 10386975
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Date Published:
- Journal Name:
- Communications Biology
- Volume:
- 5
- Issue:
- 1
- ISSN:
- 2399-3642
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Colorectal cancer, a significant cause of cancer-related mortality, often exhibits drug resistance, highlighting the need for improved tumor models to advance personalized drug testing and precision therapy. We generated organoids from primary colorectal cancer cells cultured through the conditional reprogramming technique, establishing a framework to perform short-term drug testing studies on patient-derived cells. To model interactions with stromal cells in the tumor microenvironment, we combined cancer cell organoids with carcinoma-associated fibroblasts, a cell type implicated in disease progression and drug resistance. Our organotypic models revealed that carcinoma-associated fibroblasts promote cancer cell proliferation and stemness primarily through hepatocyte growth factor–MET paracrine signaling and activation of cyclin-dependent kinases. Disrupting these tumor–stromal interactions reduced organoid size while limiting oncogenic signals and cancer stemness. Leveraging this tumor model, we identified effective drug combinations targeting colorectal cancer cells and their tumorigenic activities. Our study highlights a path to incorporate patient-derived cells and tumor–stromal interactions into a drug testing workflow that could identify effective therapies for individual patients.more » « less
-
Traditional drug screening models are often unable to faithfully recapitulate human physiology in health and disease, motivating the development of microfluidic organs-on-a-chip (OOC) platforms that can mimic many aspects of human physiology and in the process alleviate many of the discrepancies between preclinical studies and clinical trials outcomes. Linsitinib, a novel anti-cancer drug, showed promising results in pre-clinical models of Ewing Sarcoma (ES), where it suppressed tumor growth. However, a Phase II clinical trial in several European centers with patients showed relapsed and/or refractory ES. We report an integrated, open setting, imaging and sampling accessible, polysulfone-based platform, featuring minimal hydrophobic compound binding. Two bioengineered human tissues – bone ES tumor and heart muscle – were cultured either in isolation or in the integrated platform and subjected to a clinically used linsitinib dosage. The measured anti-tumor efficacy and cardiotoxicity were compared with the results observed in the clinical trial. Only the engineered tumor tissues, and not monolayers, recapitulated the bone microenvironment pathways targeted by linsitinib, and the clinically-relevant differences in drug responses between non-metastatic and metastatic ES tumors. The responses of non-metastatic ES tumor tissues and heart muscle to linsitinib were much closer to those observed in the clinical trial for tissues cultured in an integrated setting than for tissues cultured in isolation. Drug treatment of isolated tissues resulted in significant decreases in tumor viability and cardiac function. Meanwhile, drug treatment in an integrated setting showed poor tumor response and less cardiotoxicity, which matched the results of the clinical trial. Overall, the integration of engineered human tumor and cardiac tissues in the integrated platform improved the predictive accuracy for both the direct and off-target effects of linsitinib. The proposed approach could be readily extended to other drugs and tissue systems.more » « less
-
Abstract Drug screening data from massive bulk gene expression databases can be analyzed to determine the optimal clinical application of cancer drugs. The growing amount of single-cell RNA sequencing (scRNA-seq) data also provides insights into improving therapeutic effectiveness by helping to study the heterogeneity of drug responses for cancer cell subpopulations. Developing computational approaches to predict and interpret cancer drug response in single-cell data collected from clinical samples can be very useful. We propose scDEAL, a deep transfer learning framework for cancer drug response prediction at the single-cell level by integrating large-scale bulk cell-line data. The highlight in scDEAL involves harmonizing drug-related bulk RNA-seq data with scRNA-seq data and transferring the model trained on bulk RNA-seq data to predict drug responses in scRNA-seq. Another feature of scDEAL is the integrated gradient feature interpretation to infer the signature genes of drug resistance mechanisms. We benchmark scDEAL on six scRNA-seq datasets and demonstrate its model interpretability via three case studies focusing on drug response label prediction, gene signature identification, and pseudotime analysis. We believe that scDEAL could help study cell reprogramming, drug selection, and repurposing for improving therapeutic efficacy.more » « less
-
Abstract In search of effective therapeutics for breast cancers, establishing physiologically relevant in vitro models is of great benefit to facilitate the clinical translation. Despite extensive progresses, it remains to develop the tumor models maximally recapturing the key pathophysiological attributes of their native counterparts. Therefore, the current study aimed to develop a microsphere‐enabled modular approach toward the formation of in vitro breast tumor models with the capability of incorporating various selected cells while retaining spatial organization. Poly (lactic‐co‐glycolic acid) microspheres (150‐200 mm) with tailorable pore size and surface topography are fabricated and used as carriers to respectively lade with breast tumor‐associated cells. Culture of cell‐laden microspheres assembled within a customized microfluidic chamber allowed to form 3D tumor models with spatially controlled cell distribution. The introduction of endothelial cell‐laden microspheres into cancer‐cell laden microspheres at different ratios would induce angiogenesis within the culture to yield vascularized tumor. Evaluation of anticancer drugs such as doxorubicin and Cediranib on the tumor models do demonstrate corresponding physiological responses. Clearly, with the ability to modulate microsphere morphology, cell composition and spatial distribution, microsphere‐enabled 3D tumor tissue formation offers a high flexibility to satisfy the needs for pathophysiological study, anticancer drug screening or design of personalized treatment.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s42003-022-04270-3
