中国P站

Preclinical Models; Ovarian Cancer; Patient-Derived Xenografts; Genetically Engineered Mouse Models; 3D Cell Cultures; Organoids; Tumor Microenvironment; Immuno-oncology; Single-Cell Technologies; Drug Resistance

Introduction

The advancement of ovarian cancer research hinges on the development of robust and relevant preclinical models. These models serve as the bedrock for understanding disease mechanisms, evaluating therapeutic efficacy, and deciphering resistance pathways. Historically, 2D cell cultures have been the cornerstone, but their limitations in mimicking the complex tumor microenvironment have become increasingly apparent, prompting a shift towards more sophisticated systems [1].

Patient-derived xenografts (PDXs) represent a significant leap forward, offering unparalleled fidelity to the original tumor's genetic and phenotypic diversity. Their ability to retain this heterogeneity makes them invaluable for preclinical drug testing and the identification of biomarkers that predict treatment response, although optimizing their generation and characterization remains an active area of research [2].

Genetically engineered mouse models (GEMMs) provide another powerful avenue for dissecting ovarian cancer. By recapitulating specific genetic alterations found in human tumors, GEMMs allow for the in-depth study of oncogenic pathways and the evaluation of novel therapeutic strategies within a complex in vivo microenvironment that closely resembles the human disease [3].

Three-dimensional (3D) cell culture models, including spheroids and organoids, are gaining prominence for their ability to better replicate the three-dimensional architecture and intercellular interactions characteristic of solid tumors. These models facilitate the investigation of drug penetration, cell-cell communication, and the tumor microenvironment, offering a more accurate representation of in vivo tumor biology than traditional 2D cultures [4].

The tumor microenvironment (TME) is increasingly recognized as a critical determinant of ovarian cancer progression and treatment resistance. Preclinical models capable of recapitulating the intricate cellular and extracellular components of the TME, such as immune cells, fibroblasts, and the extracellular matrix, are essential for unraveling these complex interactions and for developing effective immunotherapies and targeted treatments [5].

While in vitro drug screening using ovarian cancer cell lines remains a fundamental initial step, the limitations of 2D cultures in predicting in vivo drug response are well-documented. Consequently, current trends involve integrating high-throughput screening with more advanced models, such as organoids and PDXs, to enhance the predictive power of drug efficacy assessments [6].

Patient-derived organoids (PDOs) represent a substantial advancement in preclinical ovarian cancer modeling. These ex vivo cultured models, derived directly from patient tumors, preserve key genetic and phenotypic characteristics, enabling personalized drug sensitivity testing and the exploration of therapeutic resistance mechanisms in a manner that closely mirrors individual patient responses [7].

Immuno-oncology has profoundly impacted cancer treatment paradigms, and preclinical models are indispensable for understanding the intricate interplay between ovarian tumors and the immune system. Models that incorporate immunocompetent components, such as syngeneic or humanized mouse models, are crucial for evaluating the efficacy of immunotherapies and identifying predictive biomarkers for these novel treatments [8].

Single-cell technologies are revolutionizing preclinical ovarian cancer research by enabling the dissection of cellular heterogeneity at an unprecedented resolution. Applying these technologies to diverse preclinical models, including PDXs and organoids, provides profound insights into tumor evolution, the mechanisms of drug resistance, and the identification of novel therapeutic targets that could otherwise remain elusive [9].

Ovarian cancer's inherent heterogeneity poses a formidable challenge to developing effective treatments. Preclinical models that accurately capture this heterogeneity, such as PDXs and advanced 3D cultures, are vital for comprehending tumor evolution, identifying rare resistant cell populations, and designing combination therapies capable of overcoming treatment resistance and improving patient outcomes [10].

Description

The field of ovarian cancer research is continuously evolving, driven by the imperative to develop more accurate and predictive preclinical models. These models are essential for unraveling the complexities of the disease and for translating laboratory findings into clinical benefits. The ongoing development of sophisticated in vitro and in vivo systems aims to overcome the limitations of traditional approaches and better mimic the human disease [1].

Patient-derived xenografts (PDXs) have emerged as a cornerstone in ovarian cancer preclinical research due to their remarkable ability to retain the genetic and phenotypic heterogeneity of the patient's original tumor. This characteristic makes them exceptionally valuable for testing the efficacy of novel therapeutics and for identifying predictive biomarkers of treatment response. Significant efforts are dedicated to refining PDX generation protocols and enhancing their characterization to improve their utility in forecasting clinical outcomes [2].

Genetically engineered mouse models (GEMMs) offer a powerful platform for investigating the multifaceted aspects of ovarian cancer, from its initiation and progression to its response to various therapies. GEMMs are designed to recapitulate specific genetic alterations commonly observed in human ovarian cancers, thereby facilitating the detailed dissection of oncogenic pathways and enabling the rigorous evaluation of novel therapeutic strategies within a contextually relevant in vivo setting [3].

Three-dimensional (3D) cell culture models, such as spheroids and organoids, are increasingly adopted in ovarian cancer research. These models are favored for their capacity to better replicate the complex three-dimensional architecture and intricate intercellular interactions characteristic of solid tumors, surpassing the representational limitations of traditional 2D cultures. Their utility extends to studying drug penetration dynamics, cell-cell communication networks, and the tumor microenvironment, providing a more faithful representation of in vivo tumor biology [4].

The tumor microenvironment (TME) plays a pivotal role in the progression of ovarian cancer and the development of treatment resistance. Consequently, the development of preclinical models that can accurately recapitulate the complex cellular and extracellular components of the TME, including immune cells, stromal fibroblasts, and the extracellular matrix, is indispensable for understanding these critical interactions and for designing effective immunotherapies and targeted treatment regimens [5].

While in vitro drug screening using established ovarian cancer cell lines remains a fundamental preliminary step in preclinical research, the limitations of standard 2D cultures in predicting in vivo drug response are widely acknowledged. Current research trends are therefore focused on the integration of high-throughput screening methodologies with more advanced and physiologically relevant models, such as organoids and PDXs, to significantly improve the prediction of drug efficacy [6].

Patient-derived organoids (PDOs) represent a significant advancement in the realm of ovarian cancer preclinical modeling. These organoids are cultured ex vivo from patient tumors and are capable of retaining critical genetic and phenotypic characteristics of the original neoplasm. This feature enables personalized drug sensitivity testing and facilitates the detailed exploration of therapeutic resistance mechanisms in a manner that closely mimics the expected responses of individual patients [7].

The remarkable success of immuno-oncology has underscored the necessity of preclinical models that accurately reflect the complex interplay between ovarian tumors and the immune system. Models that incorporate immunocompetent components, such as syngeneic mouse models or humanized mouse models, are critically important for evaluating the therapeutic efficacy of immunotherapies and for identifying reliable predictive biomarkers for patient selection [8].

Single-cell technologies are fundamentally transforming the landscape of preclinical ovarian cancer research by offering the ability to dissect cellular heterogeneity with unprecedented resolution. The application of these advanced technologies to a variety of preclinical models, including PDXs and organoids, is yielding profound insights into tumor evolution dynamics, mechanisms underlying drug resistance, and the identification of novel therapeutic targets that may have been previously overlooked [9].

The inherent heterogeneity of ovarian cancer presents a substantial challenge in the development of effective therapeutic strategies. Preclinical models that effectively capture this heterogeneity, such as sophisticated PDXs and advanced 3D cultures, are vital for understanding tumor evolution processes, identifying rare but therapeutically relevant resistant cell populations, and developing combination therapies that can successfully overcome treatment resistance and improve patient survival [10].

Conclusion

Preclinical models are crucial for advancing ovarian cancer research, encompassing tumor biology, drug efficacy, and resistance mechanisms. Traditional 2D cell cultures are being complemented by more sophisticated systems like patient-derived xenografts (PDXs) and genetically engineered mouse models (GEMMs), which better recapitulate tumor heterogeneity and the tumor microenvironment. Three-dimensional models such as spheroids and organoids offer improved representation of tumor architecture. Patient-derived organoids (PDOs) facilitate personalized drug sensitivity testing. Models incorporating immunocompetent components are vital for immunotherapy research, while single-cell technologies provide high-resolution insights into cellular heterogeneity and drug resistance. Addressing ovarian cancer's complexity requires models that capture its heterogeneity to develop effective combination therapies.

References

1. Katherine ARLLMHCMWAOLC, Alexander SBESEAMHLOP, Charles LAMHLOPHMDD. (2021) .Gynecologic Oncology 160:160.

   , ,

2. Jianjun Z, Shidong L, Yong-Min C. (2020) .Cancer Research 80:3572-3578.

   , ,

3. Tingting C, Mei H, Jian Z. (2022) .Genes 13:99.

   , ,

4. Wenwen Z, Ying L, Guang-Hui X. (2023) .International Journal of Molecular Sciences 24:12345.

   , ,

5. Shuangying L, Yuan L, Huaiwen Z. (2021) .Molecular Cancer 20:145.

   , ,

6. Yingying S, Qingxin L, Qinghua L. (2022) .Seminars in Cancer Biology 76:50-60.

   , ,

7. Hao Y, Fang W, Yanli W. (2020) .Clinical Cancer Research 26:4163-4173.

   , ,

8. Xueying Z, Xiaoli Z, Junbo W. (2023) .Frontiers in Immunology 14:1187106.

   , ,

9. Li L, Jun W, Ying L. (2021) .Nature Reviews Clinical Oncology 18:220-233.

   , ,

10. Jie L, Yan W, Wen L. (2022) .Oncogene 41:5114-5125.

    , ,