Oncology models are controlled systems that advance cancer research and drug discovery. These models allow scientists to study cancer’s complexities, from its origins and progression to its response to various treatments. By mimicking aspects of human cancer, these systems provide a reproducible environment for researchers to test hypotheses and discover new therapeutic strategies. Their ongoing development accelerates the delivery of effective cancer treatments to patients.
What Are Oncology Models?
Oncology models are biological, computational, or combined systems designed to study cancer outside the human body. They replicate specific features of human cancer, enabling research, testing of potential therapies, and discovery of underlying mechanisms. These models allow scientists to investigate how cancer cells grow, spread, and react to different interventions.
These systems fall into broad categories: in vitro models, conducted in a lab environment; in vivo models, performed within living organisms; and computational models, which rely on mathematical and computer-based simulations. Each offers unique advantages for understanding cancer biology and evaluating potential treatments. The selection of an appropriate model depends on the specific research question, aiming to provide the most relevant data for drug development.
Types of Oncology Models and Their Use
Oncology models encompass diverse approaches, each providing unique insights into cancer biology and treatment responses.
In Vitro Models
These models include traditional 2D cell lines like HeLa and MCF-7, widely used for initial drug screening and fundamental biological studies. More advanced 3D models, such as patient-derived spheroids and organoids, offer improved physiological relevance by mimicking the three-dimensional structure and cell-to-cell interactions found in human tumors.
In Vivo Models
These typically involve animal subjects, with mice being the most common choice. Xenograft models, where human tumor cells or tissues are grown in immunocompromised mice, are frequently used to study tumor progression and treatment responses. Syngeneic models involve transplanting mouse tumor cells into genetically identical mice, allowing for the study of the immune system’s interaction with the tumor. Genetically engineered mouse models (GEMMs) are another type, where specific genes are altered in mice to induce cancer, providing a way to study cancer development from its earliest stages.
Computational Models
These models leverage mathematical equations and computer simulations to predict drug interactions, disease progression, or patient responses. These in silico models analyze complex biological data, helping researchers streamline drug development by simulating how drugs might interact with cancer cells before laboratory or clinical trials. They can also optimize drug properties and predict drug sensitivity based on molecular and clinical profiles.
Oncology Models in Drug Development
Oncology models are integrated throughout the drug discovery and development pipeline, influencing the speed and success of new cancer therapies. They are employed early to identify specific molecular targets or pathways that drive cancer growth and progression. By studying cellular responses in controlled environments, researchers can pinpoint vulnerabilities new drugs could exploit.
Following target identification, models are used for high-throughput compound screening. Thousands of potential drug molecules are rapidly tested for their ability to inhibit cancer cell growth or induce cell death. This allows for the efficient selection of promising candidates while also assessing their potential toxicity. In vitro cell-based assays and 3D spheroids are examples of systems used in this initial screening phase.
Preclinical testing, a step before human trials, relies on oncology models to evaluate the effectiveness, safety, and optimal dosage of drug candidates. Animal models, such as cell line-derived xenografts (CDX) and patient-derived xenografts (PDX), provide insights into how a drug behaves in a living system and its impact on tumor growth. This stage also helps in understanding pharmacokinetic and pharmacodynamic relationships, which relate to how a drug is absorbed, distributed, metabolized, excreted, and its effect on the body.
Models also contribute to biomarker discovery, identifying indicators that can predict a patient’s response to a specific drug or track disease progression. Patient-derived models are increasingly used in personalized medicine, allowing researchers to test various treatments on models derived from an individual patient’s tumor. This approach aims to tailor therapies to individual patients based on their unique cancer profiles, potentially improving treatment outcomes.
Improving Model Accuracy: Lab to Clinic
Despite their many benefits, oncology models face challenges in fully replicating the intricate complexity of human tumors, their microenvironment, and the vast diversity among patients. Traditional 2D cell cultures, while useful for initial studies, often lack the physiological relevance of actual tumors, which can lead to discrepancies between laboratory findings and clinical outcomes. Similarly, animal models, while providing a whole-organism context, have biological differences from humans that can limit their predictive power.
To address these limitations, newer and more sophisticated models are being developed.
Patient-Derived Organoids (PDOs)
These are lab-grown mini-organs derived directly from patient tumor biopsies. PDOs better retain the original tumor’s architecture, genetic makeup, and cellular composition. They can be expanded and used for high-throughput drug screening, offering a more accurate representation of patient-specific drug responses.
Patient-Derived Xenografts (PDX)
These are advanced models where patient tumor tissue is directly implanted into immunocompromised mice. PDX models are considered more representative of patient tumors than older cell line xenografts, as they maintain the genetic heterogeneity and histological features of the original tumor. They provide an in vivo environment that can recapitulate aspects of the tumor microenvironment, which is often difficult to achieve in in vitro cultures.
Organ-on-a-Chip Systems
Innovative technologies like microfluidic “organ-on-a-chip” systems are also advancing model accuracy. These platforms integrate 3D cell culture and microfluidics to create miniature tissue and organ systems that simulate human physiology more precisely. Organ-on-a-chip models can recreate complex structures and functions, including blood vessels and mechanical forces experienced by cells, enabling more detailed studies of cancer progression and drug efficacy and safety. These advancements collectively aim to improve the success rate of drugs in clinical trials and accelerate the development of personalized and effective treatments for cancer patients.