Preclinical Modeling of Tumor Metastasis and Resistance: Current Challenges and New Approaches
It’s no secret that cancer is a highly complex disease with ~90% of patient deaths resulting from metastasis and resistance commonly developing to first-line therapy. To improve cancer survival rates, pharma and biotech researchers must develop therapies that are safe and effective at treating not only primary tumors, but also metastatic growths and tumors that have acquired resistance.
To ensure unpromising drug candidates are failed earlier in the drug development process and successful therapies are accelerated to the clinic, pharma and biotech researchers must have access to more physiologically representative models of metastasis and drug resistance. While there are innovative preclinical models currently available that show promising results, they have limitations, including being unable to recapitulate tumor heterogeneity and model each stage of metastasis. In this article, I summarize my key takeaways from an insightful roundtable discussion with leading contract research organizations on this topic at AACR 2019.
The challenges of drug resistance and metastatic modeling in the laboratory
Traditionally, drug resistance has been assessed by dosing a sensitive cancer cell line with the candidate drug until there were signs of cell survival. The drug dose would then be increased in a step-wise fashion to see if a resistant population of cells would develop. These therapy-resistant cells could then be grafted into an immunocompromised mouse and used as a pre-treated background for assessing the efficacy of potential second or third-line therapies.
Traditional models of metastasis rely on genetically engineered mice prone to developing metastases, or orthotopic implantation of patient-derived xenograft (PDX) cells into mice via delicate surgery. In the unique case of bone metastasis, there are three main methods used – tumor cells can either be systemically delivered into the circulatory system of immune-compromised mice or implanted directly into the bone to assess their potential to form metastases. While these approaches to modeling tumor drug resistance and metastasis have shown promising results for assessing drug candidates in preclinical development, they have some key limitations.
One of the main issues to resolve is capturing the large heterogeneity of tumors in vivo. While most subclones of a tumor might appear sensitive to a drug or not harbor metastatic potential, it just takes one or two cells for the therapy to be ineffective or metastasis to occur. However, if cell lines are used to create pre-treated models of drug resistance for assessing next-generation therapies, the cells will be clones of each other, preventing researchers from accurately modeling the complex heterogeneity that we know exists within most tumors.
While PDX models have offered insights into how metastasis occurs, these xenograft models only allow part of the metastasis process to be examined, limited to cells in the circulatory system or bone. As a result, most mouse models of metastasis are focused on modeling the homing of tumor cells in the blood to secondary sites, rather than the entire metastasis process. Since few rodent species have shown spontaneous metastasis, evaluating the entire process in vivo remains difficult.
Asides from the challenges with modeling metastasis and resistance, pharma and biotech researchers also need to tackle how best to characterize mechanisms of acquired drug resistance and metastasis on the level of individual cells or subclones. Ideally, drug developers need to model how a patient’s tumor evolves from drug sensitivity to resistance over treatment time, based on the genetic and molecular changes that occur within each cell or subclone.
Another key challenge with assessing the potential for drug resistance to develop is the short timescale in which these studies are typically conducted. To meet tight project timelines, pharma and biotech researchers are often under pressure to assess if drug resistance develops in a primary tumor within 14–28 days. However, most cancer patients are treated in the clinic over several months. As a result, drug candidates could demonstrate promising results in preclinical studies only for resistance to develop when administered to patients in clinical trials. More clinically relevant timeframes for assessing tumor resistance should be incorporated into the preclinical stages of drug development to ensure only truly effective candidates are taken forward.
Bringing metastasis and resistance modeling to the forefront
With metastases responsible for the vast majority of cancer patient deaths, a key issued raised during the roundtable is that pharma and biotech companies do not routinely test drug candidates in metastatic models before moving into clinical trials. In addition, the number of cancer treatments being developed that are targeted to bone metastases (one of the most common locations for metastases to form) or for tumors that have developed resistance to second or third-line therapy are relatively low. The question is, why?
When targeting bone metastases, the challenge is overcoming the highly complex and poorly understood interplay between the bone cells and surrounding tumor microenvironment. Targeting such a dynamic interaction comes with its associated risks and there is a high chance that drug candidates could fail to show efficacy in preclinical studies – particularly if the bone metastasis models used are not able to accurately recapitulate the in vivo environment. However, the scientific challenge itself should not be a reason for pharma and biotech to not develop treatments for metastatic tumors. Furthermore, to take on the challenge, pharma and biotech companies must be able to access a large variety of metastasis and drug resistant models in order to account for the vast number of genomic and molecular differences amongst patients and uncover which subset of patients will respond best to a particular therapy. Currently only a few models of tumor metastasis are available, but new approaches and techniques for developing models of metastasis are looking promising.
Enhancing metastasis and resistance modeling with new approaches
It is important to recognize that, despite their limitations, traditional approaches used for modeling resistance and metastasis have played a pivotal role in the development of many cancer therapies. However, thanks to the latest research and technology developments, there are now a number of new models and approaches being used that are addressing some of the key challenges faced.
Humanized xenograft mouse models, for example, enable the tumor microenvironment to be modeled, including immune cells, stromal tissue and peripheral blood. With a big focus on developing immunotherapies, scientists recently devised an approach for humanizing mice with peripheral blood mononuclear cells. In addition, innovative scaffolds are being used in generate mouse models with humanized bone, with both approaches providing a more physiologically relevant environment to evaluate the effectiveness of immunotherapies.
To better model tumor heterogeneity in the laboratory, a clinical study called MATCH-R is currently underway where biopsy samples from 300 cancer patients who have developed resistance to targeted molecular therapies will be used to create PDX models of individual patients. By using patient biopsy samples, the models will contain a snapshot of the different subclones that exist within the native tumor, helping researchers to uncover the mechanisms of acquired drug resistance and providing a bank of models to aid in the development of novel therapeutics and treatment strategies for specific subgroups of patients who have acquired resistance to first-line therapy.
There are also a variety of innovative ex vivo approaches that are starting to gain popularity as the drive to replace less predictive animal models continues. For example, slices of bladder and prostrate tumors have been cultured ex vivo as a potential method of assessing the efficacy of therapies to an individual’s tumor. In addition, a novel ex vivo method of modeling bone metastasis has recently been shown to preserve many important aspects observed in vivo, including gene expression profiles and metastatic growth kinetics.
Making metastasis and tumor drug resistance a thing of the past
Creating preclinical cancer models that accurately model scenarios as complex as metastasis and drug resistance is a fundamental challenge for cancer therapy development. Over recent years, great progress has been made by contract research organizations and model providers to supply pharma and biotech scientists with innovative models that can enhance the predictivity of their drug development programs. However, with metastasis being responsible for the majority of cancer patient deaths, pharma and biotech must rise to the challenge of developing precision cancer therapies that are both safe and effective at targeting metastases. To support this and reduce the risk of metastasis therapies failing during clinical trials, contract research organizations and model providers need to create more physiologically representative models of metastasis.
Given the unexpected challenges cancer continues to throw at us, including the recently discovered phenomena of tumor hyper progression following immunotherapy, it’s important for the cancer research community to work closely together to develop better preclinical cancer models, increase access to the latest models and technologies available worldwide, and ultimately accelerate cancer therapies to patients.
Fiona Nielsen is a bioinformatics scientist-turned entrepreneur. Having worked at Illumina developing tools for interpretation of next-generation sequencing data and analyzing cancer genomes, Fiona realized the main bottleneck for genome interpretation and precision medicine is accessing the right data. She decided to disrupt outdated practices with the aim of accelerating drug discovery.
In 2013, Fiona founded DNAdigest as a charity to promote best practices for efficient and ethical data sharing, and in 2014, she co-founded Repositive to develop an online community platform and global exchange for genomic data.
Fiona’s familiarity with the industry ensured that Repositive’s platforms would provide data access solutions while also respecting the industry’s need for privacy, data governance and IP protection. This awareness was fundamental in securing venture capital investment and gaining support from worldwide partners including, among others, AstraZeneca, Boehringer Ingelheim, XenTech and Shanghai LIDE Biotech.
Fiona features on the 2018 WISE100 list, an index of the UK’s 100 most inspiring and influential women in social enterprise, and she regularly speaks on invited panels on the subjects of genomics, the future of medicine, and ethical data sharing.
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