Cancer is a heterogeneous, constantly evolving disease that still represents a global health burden. The complexity of cancer is, however, based on a small number of underlying principles that facilitate the transformation to malignancy. Hanahan and Weinburg first conceptualized these complexities into ‘The Hallmarks of Cancer’ in a paradigm-changing publication at the turn of the Century. Two decades later, these concepts have been reworked and redefined into 7 core cancer traits: selective proliferative advantage, altered stress response, self-vascularization, invasion and metastasis, metabolic changes, immune modulation and finally, an abetting microenvironment.
In this special feature dedicated to cancer research, Frankie MacDonald, from SelectScience’s editorial team, brings you the latest techniques and technologies to better understand the mechanisms behind these cancer capabilities, and target key signaling pathways with rational drug design.
Revisit the hallmarks of cancer with live cell analysis
Recent developments in our understanding of cancer cell behaviour have emerged from advanced in vitro translational models and live cell analysis platforms. Using the IncuCyte S3 system, for example, scientists have been able to monitor and quantify NAD, nicotinamide adenine dinucleotide, a ubiquitous coenzyme that is emerging as a novel target in cancer therapy. Download this guide to find out more about how live cell analysis is evolving our understanding of the 7 core cancer hallmarks.
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Identify mutations and drug resistance using ddPCR
One of the main roadblocks to finding a cancer cure is innate or acquired drug resistance. Hence, many researchers are focused on understanding the mechanisms behind this and identifying the subpopulation of cancers, pre-determined by genetics, to be recalitrant to therapy. Using digital droplet PCR to monitor patients on therapy, clinical pharmacologist Dr. Marzia Del Re can identify increasing mutations in liquid biopsies that indicate resistance, and use this information to prevent malignant dissemination.
Cancer cell counting: there's an app for that
Migration, proliferation, viability and cytotoxicity assays provide us with vital insights into cancer cell behavior, that are easily performed on in vitro cell lines. Yet the speed of the assay often outweighs the time-consuming data collection and analysis steps that follow. Bertin Instruments has recently lauched a set of apps to compliment their cell imaging platform, removing the hassle and human error from cell counting. A minimum of 4 images captured in phase contrast are analyzed, and a mean value calculated, in a fully-automated fashion.
CO2 incubators: the ideal design for cancer cell culture
With so many cell incubators available, it's probably more important than you realize to consider the key features compatible to your culturing needs. For example, how easy are the incubators to dismantle for de-contamination purposes? How effective is the humidity mangement system, and is the CO2 sensor on the inside? Helpfully, Binder has put together a comprehensive chamber buying guide to help you make more informed purchasing decisions to perfectly compliment your culturing.
Advanced mutational analysis with qPCR
Gene expression analysis and mutational detection techniques are becoming more routine in the diagnosis and treatment of cancer. Quantitative PCR is an established DNA and RNA measuring principle that can be performed to accurately determine the molecular changes in tumors that influence clinical regimens. Yet, this technology is limited by the efforts and skills required by the user for sample preparation. Recent developments in qPCR platforms have sought to address these challenges. The qTOWER384, from Analytik Jena, is a high-performance qPCR platform that analyzes samples in 384-well format in just 6 seconds with automated set-up capacity. Find out how the qTOWER384 is advancing genetic analyses in this brochure.
Monitor cell health in real time
Cell health monitoring is key to cytotoxicity and apoptosis experiments in basic and pre-clinical labs. DRAQ7 has been developed to facilitate this with fluorescent labeling of treated cells in culture. A non-membrane permeant dye, DRAQ7 specifically labels apoptotic cells and can be used in conjunction with existing membrane-permeant dyes, such as Hoechst 33342, due it's spectral separation. Find out how DRAQ7 has benefitted translational research in cancer.
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Isolating extracellular cancer vesicles with flow cytometry
Flow cytometry is a powerful tool in cancer cell analysis, both in a clinical and research setting. Coupled with cell separation platforms, flow cytometry enables the detection, quantification and selection of single cells in an extremely high-throughput manner. The sensitivity of the technique makes it particularly useful for monitoring patients in remission and predicting cancer cell recurrence. New developments in flow cytometry technology enable up to seven assays to be run in parallel, further accelerating its application in cancer research. Learn how the sensitivity of newer flow cytometry platforms such as the CellStream™ is enabling researchers to isolate and examine extracellular vesicles in cancer.
Deplete non-malignant cells in your primary culture
Malignant tumor cells possess unique capabilities that permit resistance to conventional therapies. Hypothetically, selective inhibition of this cancer-driving population, alongside more conventional chemo or radiation therapies, will prevent tumor repropagation. However, much of the difficulty in this field remains in finding supportive cell culture systems and selective cell markers to study the molecular characteristics of patient-derived malignant cells. The PromoCell Primary Cancer Culture System has been developed to overcome many of these existing caveats in cancer cell research. In this white paper, discover how to reliably deplete non-malignant cells from primary culture and enrich the malignant population to accelerate discovery biology in vivo.
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Addressing the challenges of pre-clinical research
In vitro 2D and 3D models often fail to recapitulate complex cellular interactions within the tumor microenvironment. One way to increase the statistical reliability of your research is to use multiple cancer cell lines. In this study, researchers used a large panel of 120 cancer lines, available from ProQinase, to determine the combinatorial effect of the MEK1 inhibitor, Selumetinib, and the pan-Raf inhibitor AZ-628. Using this comprehensive panel of cell lines enabled researchers to identify populations that exhibit mutual synergy of therapeutic effect, up to 500-fold. Download the poster to read more about ProQinase's CL100-ProliFiler service.
Create tumor 3D maps with mass spec imaging
It has recently come to light that using the principles of mass spectrometry, we can 'map' the spatial distribution of molecules in 3D tissue, by generating mass spectra at multiple points and planes within a sample. The ability to generate spatial resolution with this technique is enabling advanced tumor analysis that is opening up new avenues in cancer research. In this video, Dr. Bindesh Shrestha explains how he uses mass spectrometry imaging, within a fully-automated workflow, to generate a high volume of molecular images from patient samples, for 3D mapping of spheroids and tumor classification.
Radical advances in redox biology
Changes in cellular redox are known to be present in multiple diseases, including cancer. But our understanding of the role for redox species in cellular homeostasis is lacking. Research has recently shown, for example, that certain species can act as second messengers, and may play a part in regulating metabolic time-keeping in cells. The Morgan group from the University of Kaiserslautern, Germany, is using genetically-encoded fluorescent redox sensors to investigate this. Find out more about the technology they use and read about their research in this article.
From outer space to cancer research
Most tumors exhibit constant, global cellular dynamics that means, when analyzing the effect of novel therapeutics, scientists should decipher average ‘background changes’ that might have occurred without intervention. Researchers from the University of Manchester, however, have applied their machine learning technique, initially developed for mapping planetary features on Mars, to pre-clinical therapy studies in order to address these complexities when measuring the effects of treatments on tumors.