Issue 14 Understanding Science

Patient tumour avatars improving treatment outcomes

🕒 4 min

With more than 19 million cases diagnosed per year and around 10 million deaths worldwide, cancer represents a big challenge in health care and an important cause of mortality and morbidity. Some of the most diagnosed cancer types like lung, female breast, and colorectal cancer account for a third of this incidence and mortality rate. So, how come we are still so ineffective in treating cancer? Part of the answer is tremendous tumour heterogeneity: between different types, between two people having the same type, or within one single tumour in one single person. And this biological phenomenon has been challenging scientists for a long time now.

Spatial and temporal tumour heterogeneity

Tumour heterogeneity manifests in two ways: spatial and temporal heterogeneity. Spatial heterogeneity means that tumours possess significant variations in distinct regions of the same tumour. This is a fundamental biological feature of the tumour microenvironment – a mesh of cellular and noncellular components surrounding tumour cells. The microenvironment represents cellular components like stromal fibroblasts that secrete extracellular matrix (ECM), multi-functional cells pericytes, endothelial and immune cells, as well as ECM components like collagen, glycoprotein fibronectin, and intermediate filaments lamins. The composition of the microenvironment is governed by cancer cells, mainly through the complex network of inflammatory mediators, growth factors, and matrix remodelling enzymes.

On top of that, tumours change over time and stages of tumour progression, which is termed temporal heterogeneity.
Heterogeneity provides the fuel for resistance, but also a great challenge in treating tumors as the drugs may not be effective in killing every single tumour cell. Some may, because of different mutations they possess, be resistant to a drug, whilst others may be very responsive. Cells that do not get killed are often responsible for generating even more aggressive tumours that can spread faster.

Bearing in mind the heterogeneous nature of cancer, it is important to note how effective treatments do require an individual approach, largely known as precision medicine. Multi-region sampling, research autopsies, and single-cell sequencing are all emerging informative platforms that help us uncover this intricate tumour heterogeneity. But how do we decide what drugs will be effective for an individual? One option is to use something called the patient-derived tumour explants (PDEs) platform.

Figure 1. Tumour heterogeneity. Source: Polyak et al., 2012 (Nature Reviews Cancer)

Patient-derived tumour explants (PDEs) platform

The discrepancy between preclinical models and patient outcomes represents a big problem in translating research into the clinic, especially when it comes to drug development: up to 90% of oncology drugs tested in Phase I clinical trials, the first phase that includes human subjects, do not reach the market. Moreover, it is quite common that patients with the same tumour type respond differently to the same drug. All due to tumour heterogeneity.

Therefore, it is crucial to develop platforms that will enable rapid, affordable but, most importantly, reliable prediction of a patient’s drug response. One such preclinical model system is the PDE platform derived from fresh surgically resected tumours. As such, we can imagine them as patient avatars because they do not require deconstruction of the original tumour – they conserve original tumour heterogeneity and architecture. After a PDE is generated, they can be treated with a range of antitumor drugs. Careful analysis of such PDEs can then give us an accurate prediction of how a patient will react when given a certain treatment.

So far, the PDE platform has shown to give promising results in the case of non-small lung and endometrial cancer, and there are plenty of other studies adopting PDEs for other cancer types.

Figure 2. Processing pipeline for PDEs. Source: Powley et al., 2020 (British Journal of Cancer)

PDEs are superior to other models like tumour cell lines, which fail to fully capture the biology of native tumours, resulting in poor prediction of clinical outcomes. However, several shortcomings limit their wider use. One of them is short-term nature of PDE-viability, where tissue integrity is typically preserved for up to 48–72h, so drug response studies must be performed within this timeframe.

Furthermore, PDEs are still not the standard clinical practice – many labs are not adapted for this technology and there is a lack of standardised pipelines for their generation and data analysis.

However, we can conclude that PDEs offer many advantages, including the ability to correlate drug responses with tumour pathology, tumour heterogeneity and changes in the tumour microenvironment. As such, PDEs are a powerful model of choice for cancer drug and biomarker discovery programmes and should be developed further.

Below, there are a couple of links for those who want to know more about the PDE platform:

  1. Patient-derived explants (PDEs) as a powerful preclinical platform for anti-cancer drug and biomarker discovery
  2. Patient-derived explants, xenografts and organoids: 3-dimensional patient-relevant pre-clinical models in endometrial cancer

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By Ivana Osredek

Ivana is a genetics student with a great interest in computational biology and big data analysis. She spends her free time doing trail running and preparing for a marathon.

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