3D cell culture systems reveal drug efficacy previously undetectable in traditional 2D monolayer systems

A new paper exploring the application of patient-derived organoids (PDOs) in the study of novel inhibitors of stem cell activity has recently been published in the journal PLOS ONE (Badder et al., 2020).

The study utilized 3D image-based morphometric analysis to quantify over 600 different features from individual organoids following treatment with inhibitors of the tankyrase protein (TNKSi). While the morphometric analysis approach mirrored the trend seen in traditional biochemical assays, importantly this more sophisticated method was able to detect subtle alterations in growth and morphology in response to TNKSi with much greater accuracy. This leads to the conclusion that whilst traditional biochemical assays still have value in detecting compounds that merit further investigation in early stage drug discovery, combining these with 3D morphological analysis could be the key to unlocking the full potential of organoids in predictive drug testing at a much larger scale.

The study was led by Cellesce founding director Professor Trevor Dale’s Cardiff University-based academic research group working together with Cellesce and other partners. It describes the derivation of a novel set of colorectal cancer PDOs. The PDO models are then used as a platform to test the response of colorectal cancer to Wnt pathway modulation using small molecule TNKSi. The work utilizes a range of analysis techniques and highlights 3D quantitative image analysis in particular as having the potential to greatly enhance the high throughput prediction of compound efficacy in pre-clinical testing.

In recent years, there has been a shift within the drug discovery industry to focus on the development of compounds targeting ‘cancer stem cell’ populations within tumors. Historically, conventional chemotherapeutics have aimed to target the tumor bulk, to kill as many tumor cells as possible; the effects of which are usually to drive tumor regression in the short-term, albeit with greater side-effects - and a high chance of patient relapse. It is now widely understood that, in order to permanently prevent tumor growth, the initiating cancer stem cell population must be removed or inhibited. In the patient, this might have a relatively small impact initially on overall tumor size, but a longer term more effective treatment caused not by killing the cells, but by a more subtle change in the behavior of the cells within the tumor.

The study of such targeted compounds has led to demand for better predictive model systems. While historical drug discovery has relied heavily on the predictive power of 2D cancer cell lines, their lack of cellular heterogeneity and relevant phenotypic behavior leaves them largely unsuited for the study of cancer stem cell inhibitors, and far from ideally placed for anti-cancer drug development in general.

PDOs – which retain intra-tumoral complexity and, crucially, stem cell function - are now gaining increasing momentum as predictive in vitro models in the drug discovery field, with the potential to reduce compound attrition rates and development costs, ultimately increasing the number of successful compounds available for use in the clinic. A more complex model, the study argues, demands a more comprehensive method of analysis that is capable of capturing the complete range of changes that may occur in response to treatment.

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