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York - A Vital Tool for Prostate Cancer Primary Cell Analysis

 

In 2020 prostate cancer accounted for 7.3% of new cancer cases globally and lead to over 375,000 deaths [1]. Treatments for the disease are limited, often involving androgen deprivation therapy (ADT) which eventually leads to resistance [2]. Recent drug developments are promising new avenues for treatment. However, high levels of drug candidate attrition exhibited in oncology highlights the necessity for better models of prostate cancer in drug development research [3]. Currently the drug discovery pipeline is overdependent upon established cell lines which do not adequately reflect the complex, heterogeneous nature of prostate cancers. This leads to wasting time and money on progressing drugs which go on to fail in downstream trials.

 

Primary cells taken from patients provide an opportunity to improve treatment discovery outcomes by seeing effects on more physiologically accurate representations of the target cancer. It also gives scope for personalising treatments to individuals, in line with shifts in modern medicine.

 

Dr Fiona Frame

The need for more complex cellular models

 

Current prostate cancer cell lines are incapable of accurately modelling the behaviour of prostate tumours due to loss of the complex cell signalling pathways present in heterogeneous cultures [4]. Phenotypes between patient cancer cells can also vary wildly and in a multitude of ways, starting at the genetic and epigenetic level and working up to multi-focal cancers and inter-patient variation. This variation is not reflected in established cell lines – emphasising the need for more complex cellular models for developing and screening cancer treatments.

 

To address the cell line problem, Professor Norman Maitland’s research group at the University of York looked at using primary prostate cells to study new cancer treatments [5]. This allowed them to study cellular interactions within a tumour and represent different, more clinically relevant, cancer types.

 

Dr Amanda Noble

Limited options for analysing heterogeneous cell cultures

 

In analysing primary prostate cancer cells researchers at the University of York, Dr Fiona Frame and Dr Amanda Noble, found that established options for characterising cells in a heterogeneous culture are suboptimal for readily gaining accurate data. Initially they looked at exposing a population of cells to the hypothesised treatment and then separating subtypes into individual cultures, which was laborious and led to changes in cellular activity [6]. The alternative option was via fluorescent labelling, which leads to phototoxicity caused by release of reactive oxygen species [7].

 

They sought to use Livecyte, a label free high content kinetic cytometer, to identify their different subpopulations within the heterogeneous culture using its quantitative phase imaging modality.

 

Livecyte: Automated cell tracking and analysis

 

Livecyte produces high contrast label-free images of cell culture using ptychography, a Quantitative Phase Imaging technique. Automated analysis algorithms produce accurate segmentation of each cell, allowing investigators to automatically track individual cells and extract valuable information about their behaviour such as motility, proliferation, and dry mass.

 

Using Livecyte, Dr Frame et al [5] were able to illustrate the behavioural disparity between commonly used cell lines and primary cells. This confirmed that tumour heterogeneity cannot adequately be represented by single cell lines during treatment development. They demonstrated that primary cells were larger, and faster, however increased in dry mass and cell count at a lower rate than the cell lines (Figure 1). These differences in growth and proliferation would impact the duration of time treatments would take to be effective.

 

Figure 1: Livecyte imaging shows that primary prostate cultures divide less frequently than cell lines but undertake significantly more movement in 2D culture. A panel of prostate cell lines was grown alongside a primary prostate epithelial culture and time-lapse imaging was carried out. (A) Brightfield images of each cell type; (B) Livecyte images of each cell type with cell segmentation outlines (coloured lines) and cell tracking ID (coloured numbers) shown; (C) 2D representation of tracking of each cell type (X-axis, x position; Y-axis, y position); (D) 3D representation of tracking of each cell type (as for 2D but including a Z-axis, time); (E) mean cell area is plotted for each cell type. Each dot represents a single cell track; (F) mean speed is plotted for each cell type. Each dot represents a single cell track; (G) the total dry mass of each frame of the time-lapse video is plotted, which is indicative of cell growth and proliferation. [5]

Find out more about Motility measurements on Livecyte

 

 

Livecyte: An even greater depth of analysis

 

The team were further able to distinguish subpopulations of Transit Amplifying (TA) cancer progenitor cells and Committed Basal (CB) differentiated cells taken from primary culture [5]. TA cells are an active component of tumours, where abnormal differentiation causes cell accumulation resulting in cancer growth [8]. The characteristics of isolated TA and CB cell populations were analysed on Livecyte, finding that CB cells were less spherical, and larger in size and dry mass than the TA cells. Unique cellular fingerprints were developed for both cell types from these findings, leading to categorisation of CB and TA cells within a heterogeneous culture (Figure 2). The team could then observe heterogeneous primary cell dynamic responses to a docetaxel treatment, label-free in real time. This proves the possibility of rapidly screening and personalising prostate cancer treatment from patients’ biopsied tumours.

 

Additional published work by Dr Frame et al further investigated the changes in primary cell morphology upon dosing with docetaxel [6]. Increased dosage led to a decrease in velocity and meandering index, as well as an increase in sphericity as cellular mitosis was disrupted. The team discovered bi-modal and tri-modal responses in cell sphericity indicating drug resistance in small subsets of cells; some cells flattened and died, whereas others maintained their sphericity and survived (Figure 3).

 

Upon observing the time-lapse images, outlier cells could be identified. These cells moved more erratically, became spherical upon attempting mitosis, and kept moving after failing division. Further work is needed to confirm the resistance of these outlier cells, however, if this is the case, cells which could ultimately lead to post-treatment cancer recurrence could be identified. In treating every cell as a data-point, Livecyte was able to reveal details otherwise masked with more traditional assays which look at the average of a population.

Figure 2: Signatures of two populations of cells within primary prostate cultures can be characterized from Livecyte data and used to identify different cell populations within heterogeneous cultures (A) Livecyte images of TA and CB cells showing cell segmentation outlines (coloured lines). Data from Livecyte analysis of each cell type was measured including (B) mean cell area, (C) mean cell dry mass and (D) cell sphericity; (E) analysis of a mixed culture of cells with gates applied to separate out the two cell populations based on cell area. Data from the whole population (WP) and each cell type was measured and plotted as mean cell area and mean cell dry mass. [5]

 

 

Figure 3: Dynamic responses of primary prostate cells to Docetaxel can be extracted from the Livecyte data. Each cell is measured, and patterns of response recorded. Bimodal sphericity responses are observed with some parameters that can be related to the cell behaviour (turquoise circles). [6]

 

 

Conclusion

Due to the inherent variability of prostate cancer, single cell models lack the complex cell signalling pathways and phenotypes present in real heterogeneous prostate cell cultures. More complex, clinically relevant cellular models are vital to improve the efficacy of developing and screening cancer treatments. However, such complexity presents far greater challenges for characterising cell behaviour.

 

Professor Norman Maitland’s research group at the University of York used primary prostate cells to study new cancer treatments. In analysing primary prostate cancer cells researchers Dr Fiona Frame and Dr Amanda Noble determined that established options for characterising heterogeneous cultures, such as fluorescent labelling or separating subtypes, led to changes in cellular behaviour. Using Livecyte, however, Dr Frame and Dr Noble were able to perform long-term timelapse imaging whilst using the inbuilt processing algorithms to automatically segment and track cells in their heterogeneous prostate cell cultures. They could distinguish subpopulations of TA and CB differentiated cells and quantify their behaviour, independently and whilst cocultured, in response to docetaxel treatment. Additionally, they used Livecyte to identify bi-modal and tri-modal responses in primary cell morphology to docetaxel pointing to drug resistance in a small subset of cells.

 

Livecyte opens the realms of possibilities within cancer research, enabling investigators to identify therapy resistance in individual cells. Furthermore, it takes us considerably closer to the goal of rapid screening of treatments and personalisation of therapies to cancer patients, a prospect which would improve many millions of cancer prognoses.

Find out more about Livecyte here

 

 

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4. PMID: 33538338.
  2. Nakazawa M, Paller C, Kyprianou N. Mechanisms of Therapeutic Resistance in Prostate Cancer. Curr Oncol Rep. 2017 Feb;19(2):13. doi: 10.1007/s11912-017-0568-7. PMID: 28229393; PMCID: PMC5812366.
  3. D. L. Jardim, E. S. Groves, P. P. Breitfeld, R. Kurzrock, Factors associated with failure of oncology drugs in late-stage clinical development: A systematic review. Cancer Treat Rev 52, 12-21 (2017)
  4. Butler DE, Marlein C, Walker HF, Frame FM, Mann VM, Simms MS, Davies BR, Collins AT, Maitland NJ. Inhibition of the PI3K/AKT/mTOR pathway activates autophagy and compensatory Ras/Raf/MEK/ERK signalling in prostate cancer. Oncotarget 2017;8:56698-713.
  5. Frame FM, Noble AR, Klein S, Walker HF, Suman R, Kasprowicz R, Mann VM, Simms MS, Maitland NJ. Tumor heterogeneity and therapy resistance - implications for future treatments of prostate cancer. J Cancer Metastasis Treat 2017;3:302-14.
  6. Frame, Fiona M, Noble, Amanda R, O'Toole, Peter et al (2019) Assessing the Advantages, Limitations and Potential of Human Primary Prostate Epithelial Cells as a Pre-clinical Model for Prostate Cancer Research. Advances in experimental medicine and biology. pp. 109-118. ISSN 0065-2598
  7. Dixit R, Cyr R. 2003. Cell damage and reactive oxygen species production induced by fluorescence microscopy: effect on mitosis and guidelines for non-invasive fluorescence microscopy. Plant J 36:280–90 [8] Sell S. (2010). On the stem cell origin of cancer. The American journal of pathology, 176(6), 2584–2494. https://doi.org/10.2353/ajpath.2010.091064