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Applications of next-generation sequencing in oncology

This article outlines how next-generation sequencing (NGS) technologies are helping scientists to uncover the underlying causes of cancer; highlighting what needs to be done to unearth the relevant information from the reams of data it generates.

NGS technologies as powerful cancer diagnostic and prognostic tools

Cancer is a complex disease resulting from the accumulation of many DNA mutations. This leads to the disruption of cell function, particularly cell cycle control. Understanding these underlying mutations is necessary to cure the resultant cancer or manage the deleterious downstream consequences. NGS tools provide a powerful means of understanding cancer genetics and biology. However, due to the amount of data these platforms generate, the ability to analyse and screen efficiently necessitates powerful software. This article gives a short account of sample case studies on NGS application in cancer research and clinical diagnosis, as well as some examples of NGS software tools used to study cancer genetic profiles and how the outcomes compare to traditional techniques such as fluorescence in situ hybridisation (FISH).

Case studies

NGS assays target the genome at different scales depending on the purpose of application. These assays can be used to study the complex mutations, gene copy number changes and rearrangements often associated with different types of cancers. For these purposes, NGS assays can be aimed at whole genome, whole exomes and targeted panel studies. One drawback is the information overload (thousands and thousands of variable regions) can make it difficult to identify the variant responsible for the disease being studied, requiring a massive dataset (many genomes) to identify the collection of mutations consistently found in affected patients. However, it is the best option for detecting rare mutations associated with hereditary cancers1 and software for NGS analysis is becoming increasingly user friendly.

The possibilities of what could be achieved with the rise of next-generation technologies are endless and very exciting”

Using NGS for translocation detection is superior to conventional detection methods, since it more accurately defines breakpoints, enables identification of other partner genes and detection of some cryptic rearrangements2 in a single reaction and dataset. NGS technologies make it possible, for instance, to track translocation partners for promiscuous genes such as KMT2A.

FISH, however, requires highly trained individuals to score rearrangements by fluorescent microscopy and is an inherently low-resolution method that may be confounded by complex, multiway rearrangements. It may also require numerous probes to fully elucidate translocation partners for promiscuous genes, such as the mixed-lineage leukaemia gene, KMT2A. In this regard, NGS tools can be seen as the superior option for disease study.

Role of precision medicine in clinical trials

Cancer precision medicine focuses on adjusting treatment choice and dosage based on the specific genetic profile of the cancer and of the individual.3 Pharmacogenomics studies investigate how genetic variants in the regions coding for drug metabolism and transportation proteins impact drug target receptors, enzymes and signalling proteins and, in turn, affect the drug efficacy and patient’s response to it.4

For example, the effect of genomic alterations in cancer drug targets such as EGFR and CDK4/65,6 can be studied in drug trials in order to ascertain necessary treatment adjustments to optimise the efficacy of the treatment. The role of NGS platforms in personalised medicine has thus become increasingly important, particularly in the development of laboratory-developed protocols (LDPs) optimised to accommodate as wide a range as possible of different cancer profiles.7

Comparing NGS platforms in oncology

Using NGS for translocation detection is superior to conventional detection methods”

The FoundationOne (F1; Foundation Medicine) test is an NGS test designed to detect the exons of 315 genes associated with cancer and introns from 28 genes known to be involved in rearrangements.8 The Guardant360 (G360; Guardant Health) NGS test can sequence 70 genes found in DNA circulating in the blood.8 In a recent study8, researchers tested the congruency of the results of both tests when used in the same patients (n=9). The results from both tests were also used to compare the treatment routes recommended to patients. The tests identified 45 changes in the genes tested – only 10 of which were shared between both tests – and of the 36 drugs appearing in the cases, only nine were shared between the two groups. These differences are important and have major implications in the clinical setting since these tests are performed on thousands of patients.

For NGS technologies to become embedded in clinical practice, they must be rigorously tested for regulatory approval, accepted as routine diagnostic tools and implemented into clinical trials to ultimately improve patient outcomes.

Challenges and conclusion

The possibilities of what could be achieved with the rise of next-generation technologies are endless and very exciting. However, filtering, analysing and storing the high-throughput generated data currently requires some powerful NGS software and thus high-performance computers. Storing the large datasets is also very challenging in the long term. In addition, there is an urgent need to streamline and ensure congruency in outputs of different NGS software tools. The more data generated, the harder it becomes to sift through it all and make sense of what is and is not relevant for the question at hand. This creates the needle in a haystack (or perhaps, needle stack) dilemma.

Even when data has been shifted to only include variants found in afflicted patients that were not present in the controls, it is still difficult to identify the subset of variants associated with the disease of interest. In addition, tumour-promoting mutations can be found with passenger mutations and distinguishing these may be difficult and further complicated by the fact that the roles of some of these variants can change depending on the development stage of the tumour. Given the complexity of most cancers and the varieties that exist, the size of the sample size required will also bring with it a plethora of noise variants that may hinder the filtering process.

In addition, the amount of data sharing that may be required to make this a fruitful endeavour has huge ethical implications. The level of disclosure that this undertaking will require will also increase the risk of the information being intentionally or unintentionally used, thus introducing biases related to genetic information.

Nonetheless, when considering the age of meta-omics purely from the research and clinical standpoint, we are living in an exciting age where personalised medicine is progressing towards being an option to treat a wide range of cancers. In instances where treatment is yet to be developed, NGS technologies are providing key insights to genetic and epigenetic causes of some of the most debilitating ailments of this era.

About the author

Ximena Rodriguez is a freelance writer with an interest in the fields of healthcare, science and lifestyle. She works to spread awareness about health issues and is always researching and keeping up to date with the latest research and developments in the healthcare sector.

References

  1. Guan et al, 2012
  2. Abel et al, 2014
  3. Low et al, 2018
  4. Low et al, 2018
  5. Esfahani et al, 2014
  6. Xu et al, 2017
  7. Cheng et al. 2015
  8. Heath 2017

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