Personalized models of heterogeneous 3D epithelial tumor microenvironments: Ovarian cancer as a model

Abstract

Intractable human diseases such as cancers, are context dependent, unique to both the individual patient and to the specific tumor microenvironment. However, conventional cancer treatments are often nonspecific, targeting global similarities rather than unique drivers. This limits treatment efficacy across heterogeneous patient populations and even at different tumor locations within the same patient. Ultimately, this poor efficacy can lead to adverse clinical outcomes and the development of treatment-resistant relapse. To prevent this and improve outcomes, it is necessary to be selective when choosing a patient's optimal adjuvant treatment. In this review, we posit the use of personalized, tumor-specific models (TSM) as tools to achieve this remarkable feat. First, using ovarian cancer as a model disease, we outline the heterogeneity and complexity of both the cellular and extracellular components in the tumor microenvironment. Then we examine the advantages and disadvantages of contemporary cancer models and the rationale for personalized TSM. We discuss how to generate precision 3D models through careful and detailed analysis of patient biopsies. Finally, we provide clinically relevant applications of these versatile personalized cancer models to highlight their potential impact. These models are ideal for a myriad of fundamental cancer biology and translational studies. Importantly, these approaches can be extended to other carcinomas, facilitating the discovery of new therapeutics that more effectively target the unique aspects of each individual patient's TME. STATEMENT OF SIGNIFICANCE: In this article, we have presented the case for the application of biomaterials in developing personalized models of complex diseases such as cancers. TSM could bring about breakthroughs in the promise of precision medicine. The critical components of the diverse tumor microenvironments, that lead to treatment failures, include cellular- and extracellular matrix- heterogeneity, and biophysical signals to the cells. Therefore, we have described these dynamic components of the tumor microenvironments, and have highlighted how contemporary biomaterials can be utilized to create personalized in vitro models of cancers. We have also described the application of the TSM to predict the dynamic patterns of disease progression, and predict effective therapies that can produce durable responses, limit relapses, and treat any minimal residual disease.

Keywords: Biomaterial; Cancer stem-like cells; Cancers; Chemoresistance; Extracellular matrix; Immune cells; Mechanics; Mechanobiology; Ovarian cancers; Personalized; Predict relapse; Residual disease; Tumor microenvironment.

Figure 1:. Distinct and Heterogenous Microenvironments are Present in Cancers.

Human diseases, including cancers, are complex heterogeneous ecosystems. Although cancers can be categorized based on histological subtype, there are patient-specific individual variations in the tumor microenvironments (TME) constituents, which are demonstrated as varying cellular and ECM features in the schematic above. Biomaterials-based technologies enable the personalized models of patient-specific tumors, based on the analysis of the distinct TME components identified and characterized, as shown in the schematic. In this review, we describe new paradigms of personalized therapies to improve cure rates, that can be discovered and validated with the patient-specific biomaterials-based tumor models.

Figure 2:. Distinct and heterogenous cellular and ECM composition and organization are present in the primary and metastatic ovarian cancers.

(Left) Production of ECM in the primary ovarian tumor gradually dysregulates over time, depositing new proteins normally not found in the ovarian germinal epithelium and tunica albuginea. The basement membrane is degraded in the primary tumor aiding in dissemination. The primary tumor is infiltrated by tumor associated macrophages, T-cells, carcinoma associated fibroblasts and endothelial cells. (Middle) The malignant ascites contains suspended single cells and cellular aggregates comprising of cancer cells, cancer stem-like cells (CSC), fibroblasts, mesothelial cells, macrophages and T-cells. ECM such as fibronectin, hyaluronan, and collagen type I are found within cellular aggregates, and in and around the fluidic ascites. (Right) Secondary omental metastases consist of colonizing cellular spheroids from the ascites. In these sites, cancers cells begin to produce new basement membrane, collagens, and hyaluronan. In all three TME, the cell and ECM subtypes are indicated with the representative schematic (not drawn to scale).

Figure 3:. Selected examples of contemporary 3D platforms that can be applied towards personalized biomaterials-based patient-specific multiscale tumor models.

A) Heterospheroids in 3D suspension co-culture, that comprise of small number of ovarian cancer stem-like cells and M2-like alternatively activated macrophages, as described in [95]. B) 3D patient-derived in vitro ovarian cancer organoids grown in Matrigel, as characterized in [204]. C) In vivo patient derived xenograft (PDX) models in immune compromised animals described in [207]. D) 3D hydrogels based on a single ECM component, such as collagen type I, as described in [145]. E) 3D interpenetrating hydrogel created from multiple ECM components, for example, alginate-agarose-collagen type I as described in [223]. F) Self-assembling 3D scaffold, for example, peptide amphiphiles and keratin hydrogel, as described in [229]. (All schematics are original works based on previously published methods.)

Figure 4.

Proposed clinical workflow for patient-derived tumor-specific 3D models which can predict therapy response and identify the most effective yet non-toxic therapies or combinations, leading to sustained and durable responses.