Advanced technological tools to study multidrug resistance in cancer
Introduction
Cancers comprise various cell populations with distinct phenotypic and genotypic profiles as well as with an inherent potential for metastasis (Marusyk and Polyak, 2010). A recent definition describes cancers as epigenetic disorders where cells emerge and compete under a robust evolutionary pressure (Vitale et al., 2019). The extracellular environment can produce a positive selection of subsets of pre-malignant cells with a fitness advantage towards metastasis and colonization of healthy tissues (Martincorena et al., 2017; Ostrow et al., 2014). Heterogeneity leads to the expansion of diverse niches, including hypoxic or perivascular regions that might support the development of cancer stem cell phenotypes and drug-resistant cell populations (Fu et al., 2015; Junttila and de Sauvage, 2013; Mao et al., 2013; McGranahan and Swanton, 2017; Tellez-Gabriel et al., 2016).
Cancer multidrug resistance (MDR) has been a subject of intensive research in the past four decades in an attempt to decipher the molecular mechanisms underlying MDR and develop novel modalities to surmount this major therapeutic impediment (Cui et al., 2018; Li et al., 2016b; Livney and Assaraf, 2013; Robey et al., 2018; Zhitomirsky and Assaraf, 2016). Still, the implementation of the significant amount of the generated findings in this field into clinical practice is rather insufficient. New emerging tools are necessary to address many burning questions regarding cancer MDR. Characterization of different MDR phenotypes (Assaraf and Borgnia, 1994; Goler-Baron et al., 2012; Goler-Baron and Assaraf, 2011; Ifergan et al., 2005; Zhitomirsky and Assaraf, 2017, 2015), correlation of their presence with cancer aggressive behavior and identification of new druggable targets to overcome MDR, are some of many important tasks in studying cancer MDR.
Cancer is a complex disease that systematically affects the whole organism. Characteristics of cancer cells and their tumor microenvironment (TME) favor the progression of the disease and invasion of remote organs, distinct from the primary location of the cancer. These characteristics or hallmarks of cancer have been described and revised by Hanahan and Weinberg (Hanahan and Weinberg, 2011, 2000). In its latest revision, 10 characteristics of cancer were identified that are hard to study with classical two-dimensional (2D) cell culture methodology.
Cell-culture based screening of anticancer drug effects has significantly evolved in the past decades. For a long time, conventional 2D cell cultures have been employed as the sole in vitro model to test the anticancer activity of new drugs. In this respect, the NCI-60 human tumor cell lines screen using a panel of 60 human tumor cell lines of distinct cell lineage was introduced by the National Cancer Institute in Bethesda, MD, USA. These cytotoxicity studies on cells grown under monolayer conditions are quite affordable and easy to perform but have great disadvantages. Generally, these simple in vitro 2D models are unable to mimic the complexity of the tumor tissue and therefore their cytotoxicity results are rather different from those obtained in vivo (Pampaloni et al., 2007).
Animal models are important to study complex interactions with surrounding cells and tissues in cancer research. However, animal models including patient-derived xenografts, are far away from genuine tumors in cancer patients. Therefore, complementary methods capable of creating a complex in vitro microenvironment using microfluidic technology, have emerged. Three-dimensional (3D) cell cultures were introduced in anticancer drug screening to gain more valid in vitro data that could more faithfully represent the in vivo results of drug sensitivity. 3D culturing of human cells hence mimicking the conditions of the genuine tumors can surpass both practical and ethical obstacles in using animal models. Spheroids are the simplest 3D cell culture models that mimic cell-to-cell interactions and hypoxic conditions and therefore provide a more realistic drug response than conventional 2D cultures (Costa et al., 2016). However, spheroids still have some limitations primarily including the lack of extracellular matrix (ECM), with its complex physical and chemical characteristics (Valente et al., 2017). For that reason, various scaffold-based 3D cell cultures were developed in recent years. They represent biocompatible 3D networks designed to provide structural support to the cells, with physicochemical characteristics that resemble ECM present in genuine tumor tissue (Hoarau-Véchot et al., 2018). Although these are more advanced 3D cultures, they are characterized by static conditions and lack fluid interstitial flow (i.e. blood) that is normally present in any tissue which significantly affects various phenotypes including response to cytotoxic drugs.
Other tools like next-generation sequencing (NGS) which has become irreplaceable in discovering mutations, gene expression and cancer biomarkers, offers a broad spectrum of possibilities for studying MDR (Chandana et al., 2019; Cho et al., 2019; Kyrochristos et al., 2019; Li et al., 2015a, 2015b). In the last years, high throughput NGS technology revealed important findings regarding the genomic, epigenetic and transcriptomic diversity of cancers that otherwise would not be possible to acquire by standard histopathological analysis (Jiang et al., 2014; Teixeira et al., 2019; Turajlic et al., 2019). This is of paramount importance and clinical relevance especially for cancers displaying high levels of drug resistance (Røe et al., 2012).
Some less common techniques such as atomic force microscopy (AFM) and single live-cell imaging can facilitate the identification of MDR phenotypes in cancer patients’ specimens. AFM is a very versatile tool for biological research. Its multispectral capacity to monitor the topographical, mechanical adhesive and oscillatory patterns of living cells makes the instrument highly promising for cancer research and more specifically for drug development (Prusty et al., 2018). On the other hand, the analysis performed by tracking single cells in a heterogeneous cancer cell population can determine the fate of each cancer cell while studying different characteristics including proliferative capacity, motility, shape, size, signaling patterns, and intercellular communication.
In this review we discuss the potential of i) NGS application in identification of cancer MDR as well as research covering DNA-based sequencing, RNA-based sequencing and clinical applications of NGS; ii) AFM application in cancer research and anticancer drug screening; iii) Single live-cell imaging applicability for the identification of drug-resistant and aggressive clones of malignant cells; iv) The use of microfluidics-based 3D cell cultures to evaluate cancer characteristics and complex interactions with its surrounding including blood flow as well as, v) Anticancer drug efficacy and mechanisms of resistance emerging under selective conditions that mimic the genuine tumor.
Section snippets
Next-generation sequencing (NGS) in cancer MDR research
The rapid development of sequencing technologies for the human genome and transcriptome analysis has led to an enormous increase in our knowledge regarding the roles of genetic variability in various disorders including cancer. NGS technologies are useful in a broad spectrum of applications in biomedical research. These include a variant discovery by whole-genome sequencing (WGS), whole-exome sequencing (WES) (Hitomi and Tokunaga, 2017) or targeted sequencing of genomic regions or gene panels
Atomic Force Microscopy in biomedical research
A novel tool appeared recently among the instruments used in cancer research: the atomic force microscope (AFM). This device was invented in 1986 by the Nobel laureate Gerd Binnig to image, at high resolution, electrically insulating surfaces (Binnig et al., 1986). Since the instrument can operate in liquids, it quickly became popular among biologists. Soon after, it appeared that not only does it permit to acquire high-resolution images of biological samples in nearly physiological conditions,
Single live-cell tracking for identification of drug-resistant and aggressive cancer cells
Single live-cell imaging has become widely used to study cancer particularly when it can address intra-tumor and inter-tumor heterogeneity and complexity. Various cell tracking methodologies and software tools progress towards quantitative analysis of the spatio-temporal dynamics of heterogeneous cell populations present in tumors. However, direct monitoring (tracking) of many initial single cells and their descendants during several days in realistic 3D tumor environments has so far not been
Microfluidic technology to study the MDR phenotype in cancer
The microfluidic technology for cell culture applications, also known as organ-on-chip, designs and fabricates microfluidic devices made of silicones, glass or thermoplastic materials (polystyrene, cyclic olefin copolymer or similar) that are structured with cameras and channels to fit the cell scale. This scale allows the recreation of complex multicellular architectures (3D cultures surrounded by epithelial barriers and/or vascular networks, etc.) and the mechanical and chemical physiological
Microfluidic-based 3D cell cultures for drug screening
The development of microfluidic technology and its application in designing microfluidic-based 3D cell cultures have made breakthroughs in overcoming drawbacks in evaluating anti-cancer drug sensitivity/resistance in vitro.
Future directions in cancer MDR research
Future techniques that could explain the complex interaction between resistance mechanisms and TME can help to develop efficient personalized treatment strategies. The use of phenotype-driven 3D cultures for the investigation of patient-derived biopsies could be optimal for the establishment of the most appropriate in vivo mimicking model. Answering the key question of which cancer patients could benefit from the specific therapeutic regimen, should be prioritized in primary tumors’ treatment
Declaration of Competing Interest
Ignacio Ochoa Garrido is promoter and consultant for BeOnChip S.L. and EBERS Medical Technology S.L. (Zaragoza, Spain). Both cited companies have had no role in the decision to publish nor were involved in the writing of this manuscript. Mónica Suárez Korsnes declares that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest. She is the owner of the Upstart firm Korsnes Biocomputing (KoBio; //korsnesbiocomputing.no/
Acknowledgements
This article is based upon work from COST Action CA17104 “New diagnostic and therapeutic tools against multidrug resistant tumors” supported by COST (European Cooperation in Science and Technology) and upon collaboration of Tijana Stanković, Milica Pešić and Ignacio Ochoa Garrido within James S. Mc. Donnell Foundation 21 st Century Science Initiative in Mathematical and Complex Systems Approaches for Brain Cancer (Collaborative award 220020560). Sandor Kasas is funded by the Swiss National
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