Capturing the Complexity of Real Cancer Cases with Patient-Derived Xenograft Models
Patient-derived xenograft models enable researchers to explore novel therapies and diagnostic tools within the context of specific mutational signatures, treatment histories, and more
Michael J. Wick, PhD, is the CSO of START and co-founder of its preclinical division, XenoSTART.
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One of the most significant areas of advancement in oncology is the growing capacity to match patients with therapies tailored to their unique cancer. As more relevant biomarkers and druggable targets are identified, drug developers still face an evergreen challenge: narrowing down the most promising therapeutic candidate from hundreds of compounds is akin to looking for a needle in a haystack. Relevant preclinical models are vital to ensuring that time and money are invested in advancing the most effective drug candidates.
While traditional cell line-derived xenograft (CDX) models generated with established cancer cell lines are a valuable tool for initially assessing the efficacy of experimental drugs, these models fall short in capturing the intricacies of patients’ actual clinical situations. Patient-derived xenograft (PDX) models are a uniquely relevant preclinical tool, enabling researchers to explore novel drugs and diagnostic tools within the context of specific mutational signatures, treatment histories, and more.
Capturing the intricacies of real patient histories with PDX models
PDX models are in vivo cancer models created by implanting fresh tumor cells from patients into immunocompromised mice, generating models that recapitulate the features of that patient’s specific cancer. These models are more deeply relevant to today’s cancer patients and offer many distinct advantages over other preclinical models for the development of therapies and diagnostics. While CDX models leverage cell lines that are often decades old, providing a snapshot of the past, PDX models can be generated from patients within the last few years. They can thus reflect the genetic and spatial heterogeneity of each cancer, as well as the clinical state of patients treated with newer therapies, helping researchers explore how a novel drug might work as a second- or third-line treatment.
This level of information, as well as the knowledge of a patient’s specific mutation profile, can also be invaluable for identifying a subpopulation of patients within a specific indication or subtype who are best to target with one or more therapies. With this knowledge, clinicians can make treatment decisions that maximize the odds of success while avoiding treatment of patients unlikely to respond to a therapy. PDX models also enable researchers to maximize the utility of a single tumor sample by creating a renewable source of tissue that can be leveraged to create 3D organoids, ex vivo cell lines, and more, rather than exhausting a limited tissue sample in only a few applications. This flexibility expands researchers’ capacity to pinpoint relevant physical and molecular characteristics for a candidate drug or diagnostic out of a wide variety of models.
Maximizing the value of PDX models
My experience in developing PDX models has underscored the value of close collaboration between researchers and clinicians for reaching the full potential of these specialized models. Because patient cancer samples must be fresh and should be implanted into mice as quickly as possible, the process requires careful coordination. However, it’s not just about logistics—close relationships between researchers and clinicians can, in turn, foster trust with patients. By helping them understand the value of these models and how researchers are working to develop better treatments, patients may be more willing to consent to sampling.
PDX models are most useful when we know as much as possible about a patient’s case details, sequencing data, and treatment history. For example, models created from patients treated with multiple agents can help identify second- or third-line treatments for other patients for whom other drugs have failed. For our organization, the close relationship with our community cancer centers also enables us to work with samples longitudinally collected from the same patient. This means we can create PDX models that capture how a real patient’s cancer evolves over time and responds (or doesn’t) to different therapies and can observe how other therapies perform within that milieu of factors. XenoSTART is also uniquely positioned based on the diversity of indications and treatment profiles covered by our 2,500+ PDX models, including rare cancers that may not receive a great deal of investment from pharmaceutical companies. These models can serve as a tool for other entities in academia and the nonprofit sector to explore options for these uncommon cancer types, from repurposing existing drugs to exploring new therapeutic options.
Connecting patients to the most promising therapies for their cancer
PDX models are a distinctly valuable tool in accelerating the development of precision medicines. Community cancer care has an important role to play in both clinical and preclinical research, enabling researchers to generate collections of models that capture a more diverse range of cancer types, patient populations, and treatment histories. By fostering strong connections from bench to bedside, we can continue to build a deeper understanding of each patient’s unique cancer, drive more efficient drug development, and maximize every patient’s chance of treatment success.