Bridging the gap between tumor and disease: Innovating cancer and glioma models - Review

Sep 21, 2026

Recent advances in cancer biology and therapeutics highlight the importance of preclinical models, yet many existing systems fail to reflect the complexity and patient-specific nature of human tumors, especially gliomas. This review evaluates key strengths and limitations of current cancer and glioma models and argues for a new generation. By emphasizing tumor microenvironment and heterogeneity, it proposes integrating glioma biological insights with bioengineering and AI to generate more clinically relevant, patient-specific platforms that improve therapeutic development and outcomes.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages17

Reading Time43 min

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Introduction

Glioma models: Past and present

REVIEW Cancer Focus Bridging the gap between tumor and disease: Innovating cancer and glioma models Stefano M. Cirigliano1􀀁 and Howard A. Fine1,2􀀁 Recent advances in cancer biology and therapeutics have underscored the importance of preclinical models in understanding and treating cancer. Nevertheless, current models often fail to capture the complexity and patient-specific nature of human tumors, particularly gliomas. This review examines the strengths and weaknesses of such models, highlighting the need for anew generation of models. Emphasizing the critical role of the tumor microenvironment, tumor, and patient heterogeneity, we propose integrating our advanced understanding of glioma biology with innovative bioengineering and AI technologies to create more clinically relevant, patient-specific models. These innovations are essential for improving therapeutic development and patient outcomes. Introduction In the last decade, there have been unprecedented advances in our understanding and ability to treat cancer. Preclinical models of cancer have played a pivotal role in these advances as a cornerstone tool of cancer research. Nevertheless, despite the explosion of new cancer biology through advances in multiomics technologies and the advent of highly novel therapeutic strategies such as immunotherapy and precision medicine, with some notable exceptions, preclinical cancer modeling has not substantially changed in the last couple of decades (Garraway and Lander, 2013) . This raises the question of whether our legacy models are optimally constructed to represent the new biology and to screen for the next generation of innovative therapeutics. There have been numerous excellent and comprehensive reviews of cancer modeling previously published, and this article will not attempt to review the field as a whole (Ben-David et al., 2019; Akter et al., 2021; Antonica et al., 2022; Tuveson and Clevers, 2019; Ventura and Dow, 2018; Veninga and Voest, 2021; Tuveson and Clevers, 2019; Simian and Bissell, 2017; Smith and Tabar, 2019; Sajjad et al., 2021; Lai et al., 2017; Sampetrean and Saya, 2018; Kersten et al., 2017; Lenting et al., 2017; Ben-David et al., 2019; Morgan et al., 2016; Breitenbach and Hoffmann, 2018; Ogilvie et al., 2017; Lampreht Tratar et al., 2018; Klinghammer et al., 2017; Antonica et al., 2022; Zuckermann et al., 2015; Erices et al., 2023; Mulder et al., 2023; Winkler et al., 2023; Suv and Tirosh, 2020 ; Gimple et al., 2022; Ren et al. , 2023 ; Frederico et al. , 2023 ; Hicks et al. , 2021a, 2021b ; Xu et al., 2021; Da Hora et al., 2019) . Instead, we will discuss where the current state of cancer modeling resides within the context of the changing face of cancer research and therapeutic development. We will use glioblastoma (GBM), oneof the most therapeutically intractable and lethal human cancers, as a representative case for highlighting the strengths and weaknesses of these models. In doing so, we propose the central hypothesis that current glioma models excel at elucidating specific reductionist views of cancer biology but largely fail in therapeutic screening due to their inability to capture the multifaceted and patient-specific nature of human cancers—for, infact, cancer is not a cell-autonomous disease. We argue that the next generation of preclinical tumor models will need to be highly contextualized based on the specific tumor type and biological and clinical phenotypes unique to host variables. Glioma models: Past and present The earliest attempts to cultivate human glioma tissue in vitro date back to the early 20th century. A notable effort came in 1924 when Cushing and Bailey successfully maintained small sections of glioma tissue in plasma clots for several days. During this time, they observed cell division and growth. This approach, commonly referred to as the “hanging drop technique,” enabled researchers to conduct short-term studies of viable glioma cells in an external environm

FAQs

Why do current preclinical cancer models often fall short for gliomas?

They frequently cannot capture the multifaceted complexity and patient-specific heterogeneity of human tumors, particularly gliomas.

What does the review propose to improve next-generation glioma modeling?

It proposes integrating advanced glioma biology with innovative bioengineering and AI technologies to build more clinically relevant, patient-specific models.

What role do tumor microenvironment and heterogeneity play in the proposed model design?

The review emphasizes that accounting for the tumor microenvironment and both tumor and patient heterogeneity is critical for improving translational relevance and therapeutic screening.

Sep 21, 2026

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