Finding the right cancer drug with high-content screening

Listen to this article on the DDW Podcast:

Lassi Paavolainen, Principal Investigator at the Institute for Molecular Medicine Finland (FIMM), Vilja Pietiäinen, Adjunct Professor in Cell and Molecular Biology, and Amanda Jones, Life Science Research Strategy Leader at PerkinElmer explain how functional precision medicine using ex vivo drug testing and HCS on patient-derived 3D cancer cell cultures shows promise for improving individual’s treatment journey. 

Each cancer is personal: Managing drug response and resistance 

Over the past decade we have seen dramatic progress in our understanding of the underlying causes of many cancers, giving rise to more targeted treatments which exploit specific molecules and pathways involved in cancer growth, progression, and spread. 

Despite impressive gains with these more attuned drugs, the fact remains that no two cancers act the completely same and no two patients’ bodies respond to treatment in the same way. Many patients, especially those in advanced and metastasized disease stages, experience drug resistance caused by a combination of factors including tumour microenvironment and intratumour heterogeneity. Additionally, while the genetic background of the patient, such as certain somatic cancer mutations, would be known, many of these mutations are not directly druggable. 

To help fight this 1×1 resistance challenge, researchers need to look beyond pure genomics processes to multi-dimensional assessments — including both genomic and phenotypic analysis. This can, in turn, help inform personalised and more effective treatment approaches and lead to better outcomes for patients. 

Inner workings: Leveraging HCS and AI to pinpoint drug sensitivities at the cellular and organoid level 

A promising approach, currently being used at the Institute for Molecular Medicine Finland (FIMM), University of Helsinki, leverages automated fluorescence microscopy and image analysis (HCS) with AI data analytics to investigate the response of patient-derived 3D cancer cell cultures against panels of anti-cancer drugs more quickly, easily, deeply, and accurately. FIMM’s researchers are currently investigating both haematological cancers as well as a wide range of solid tumours and are also part of the iCAN Digital Precision Cancer Medicine Flagship.   

By characterising the phenotypic alterations caused by the drug perturbations at a single-cell level, it is possible to build an understanding of the range of cellular responses providing additional layers of information in comparison to genomic sequencing alone. This approach also allows for hundreds of compounds to be tested at once meaning many more drugs or combinations can be analysed than would otherwise be possible on an individual cancer.  

Here’s how it works: 

Functional, ex vivo drug testing with HCS involves testing a panel of drugs on cancer cells directly derived from individual patients, and then imaging and analysing the phenotypic alterations caused by the compounds.  

Patient tissue samples are collected and then instantly processed to create short term cell cultures that retain the relevant physiology. Once enough cells have grown, various biomarkers are tested to confirm the phenotypic representativity when compared to the original tumour tissue. Additionally, the cells can be treated with more than 500 drugs or customised libraries prior to the staining. Automated robotics provide consistent liquid handling through this process before fixing, staining and imaging.  

Software is used to extract quantitative descriptions of the cells and the data is analysed. AI and machine learning models, trained with a set of reference images, are then used to tease apart the often-subtle differences in phenotypic signatures to analyse drug treatments. This overall process allows drug sensitivity to be characterised at a single cell level, which is a significant gain in knowledge given the heterogeneous nature of cancer and that what happens to one cell type may not happen in another.  

Compared to working with other precision medicine assays, this imaging, plus AI approach is more data driven where larger volumes of data are created and can be mined to highlight patterns in fresh and unbiased ways. Untypical responses, that might be missed by more traditional assay methods or require manual checking in the past can also be spotted.  

Depending on the cell culture, the whole process can take a team between one and six weeks to get from samples to actionable insights– compared to sequential in-patient treatments with long term follow up. Providing a physician with a drug sensitivity profile, along with tumour genomics and eg. transcriptomics can help generate more predictive clinical responses and more effective treatments.  

Collaboration and standardisation are important for future success 

As with any burgeoning field of study, collaboration and the creation of standards to help support research quality, reproducibility and wider adoption will help propel new findings and approaches forward and increase clinical relevance. Collaborating more fluidly and increasingly across academic, clinical and geographic lines can play a big role.  

The FIMM team, for example, is working as part of the ERA PerMed-funded project on clinical implementation of multidimensional phenotypical drug sensitivities in paediatric precision oncology (COMPASS). This is bringing together clinicians and researchers across multiple countries to conduct drug sensitivity testing on paediatric solid cancers using the same standardised drug plates and protocols. One of the main goals for COMPASS is building an international, standardised, and validated platform for drug testing based on image analysis and accompanying molecular analysis that characterises and classifies different types of tumours for their response to different drugs.  

Other areas that could benefit from standardisation include building unbiased pre-trained models that can extract information directly from cell images. Datasets like those being worked on by the Joint Undertaking in Morphological Profiling-Cell Painting (JUMP-CP) consortium can help support this. The consortium, spearheaded by the Broad Institute of MIT and Harvard, which brings together leading pharmaceutical companies, non-profit researchers and organisations like PerkinElmer, are focused on creating and sharing the world’s largest, public cell imaging dataset to help scientists determine the mechanism of action of new therapeutics before they are introduced into patients in clinical trials. When completed, the dataset will feature information from over one billion cells responding to more than 140,000 small molecule and genetic perturbations.  

Further, development of complex co-culture models, like organoids with immune cells and other stromal cells, would also be beneficial. This will come with challenges, including creating accurate image segmentation, balancing demands and needs around microscope power and live cells as well as throughput.  


Given the high level of heterogeneity of cancer and that many patients do not respond or become resistant to standard therapies, a more personalised approach to cancer treatment is necessary. 

State-of-the-art approaches, such as drug sensitivity testing using image-based analysis of patient-derived cancer cells can give clinicians a more comprehensive picture of an individual’s cancer and potentially improve outcomes for patients that have exploited other treatment options.  

Finally, in addition to operating in the realm of cancer research, image-based HCS analysis is also playing an increasing role in rare and inherited diseases, neuroscience, viral research and most recently on SARS-CoV-2, where morphological or functional changes due to a perturbation can be explored. 

Volume 23, Issue 1 – Winter 2021/22

About the authors 

Lassi Paavolainen, PhD, Academy of Finland Fellow, is Principal Investigator at the Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki. His research focuses on uncovering complex information from bioimages using machine learning. In 2017, he co-created the FIMM High-Content Imaging and Analysis core unit (FIMM-HCA) and worked as the first Head of the Unit. In addition to research activities, Dr. Paavolainen is President of the CytoData Society, and Information Officer on the board of the Nordic Microscopy Society. 

Vilja Pietiäinen, PhD in virology, Adj. Prof. in Cell and Molecular Biology, is a team leader and Senior Researcher at FIMM, with expertise in functional precision medicine, patient-derived 2D-3D cell models, and high-content microscopy. Her key interest is to explore the cancer pathogenesis by imaging and improve cancer therapies for functional precision cancer medicine. She is also a co-director of FIMM-HCA. 

Amanda Jones is a Life Science Research Strategy Leader at PerkinElmer. Her key interests include developing innovative discovery solutions (including instrumentation, automation, reagents and software) to help the world’s scientists and biotech organisations enhance cancer research. 

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