It is a problem that many translational scientists have faced. You’ve got some great results from testing a compound in vitro, providing a valuable piece of evidence that the compound is effective against cancer. But what next? Before you rush out to test the drug in a clinical trial, most therapies will require further evidence that the compound works in vivo. In the era of targeted therapy, this poses a challenge: what is the best preclinical cancer model to use for in vivo testing and how do you find it? The key issue is finding an animal model that has the molecular characteristics of the patient subgroup that you hope to treat with your drug. For example, those molecular characteristics might be a specific DNA mutation, a gene expression signature or structural DNA variants.
This blog post is part of a series looking at some of the challenges with finding the perfect preclinical cancer model and how the Cancer Models Scout Team at Repositive might be able to help!
Searching for your perfect preclinical cancer model
The first step is understanding your requirements. For example, are you looking for a PDX (patient-derived xenograft) model or a syngeneic mouse model? Do you need a specific primary site or is the intended patient subgroup agnostic of cancer type? Are there multiple mutational profiles that you wish to evaluate or just a single driver mutation?
We’ve put together a downloadable checklist which helps you gather all this information in one place.
You can then start a search for a cancer model to use. This might be via Google, PubMed, or simply emailing some vendors you know (see how Repositive can help you below!). The query can sometimes be quite difficult to put into words, especially since cancer subtypes can have multiple names for the same disease, or genes that go by different names. Ultimately you want to find a model that has had its tumour DNA sequenced to confirm the presence of the mutation you are interested in.
If you are interested in a rare cancer type, or a rare genotype, then a candidate model that you find is less likely to have DNA sequencing data available. Before you can get started on planning your in vivo experiments, you would need to organise some exome sequencing, which can take several weeks. Once you’ve found your preclinical cancer model and obtained its data, it’s time to dive into the sequencing analysis.
Filtering out signal from noise
While next-gen DNA sequencing is now comparatively cheap and accessible, running the analysis to interpret the results still requires significant bioinformatics know-how. For the scientist trying to validate an anti-cancer compound, understanding the difference between FASTQ, BAM & SAM files, which aligner to use, and which analysis package is best for variant calling, isn’t really what you set out to do when you started looking for a model. In particular, there are several issues commonly encountered with preclinical cancer model sequencing data:
1. De-mousing: For patient-derived xenograft (PDX) models that have been passaged in mice prior to sequencing, there is a need to remove any contaminating sequencing reads that have come from the mouse host tissue.
2. Lack of a normal reference sample: Many preclinical cancer models are isolated from patient samples, but don’t have a matched normal tissue sample from the same patient. This makes identifying somatic mutations vs. germline SNPs difficult.
3. Variant filtering: The unfiltered variant calls can include many false positives that arise due to reasons such as incorrect alignments, poor quality base calls, tumour heterogeneity or read bias. Ensuring that these are correctly filtered out is challenging, but necessary to produce a list of high confidence variants.
Once these challenges are overcome, you should be able to confirm that the model you’re evaluating does have the genomic features you are looking to test your therapy against.
Congratulations! If you’ve made it this far and the analysis confirms the presence of your desired genomic feature, you’re almost ready to progress to preclinical validation and a step closer to entering clinical trials. However, the technical hurdle of bioinformatics analysis, plus the associated time and money required, means that this individual, step-wise approach isn’t a viable option for many scientists. Especially given there is no guarantee that the preclinical model will have the mutation you’re after. Or worse, the risk of a poor analysis returning an incorrect result that could lead to months of wasted effort either in preclinical or even human trials. Yet the growing rise of precision medicine approaches across cancer research increasingly means that this step is not one biotech companies can afford to skip when selecting validation models.
How Repositive can help
Repositive offers a confidential Cancer Models Scout service that can help you to identify the perfect preclinical cancer model for your experiment. We have curated the world’s largest directory of cancer models from across the globe, many of which have molecular characterisation data available already. Where possible, we have brought all the gene sequencing results together and filtered by confidence and functional relevance. This means we can quickly tell you whether there are models that match your genomic criteria and connect you to the relevant providers, saving you from countless hours searching Google, PubMed and sending numerous emails!
Image credit: ©Convit - stock.adobe.com