The impact of automated genome sequencing in cancer diagnosis & treatment
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– Genomic technologies
– Sequencing challenges
– Automation in sequencing
New technologies are revolutionising scientists’ approach to cancer genome sequencing from diagnosis to treatment. Discover the role that lab automation plays.
What is cancer genome sequencing?
Cancer genome sequencing is the DNA (or RNA) sequencing of the whole, or regions of the genome of cancer cells. The first cancer whole-genome sequence was published in 2008 by Ley et al., of an acute myeloid leukaemia(1). Genome sequencing has opened up a world of opportunities for clinicians to improve the diagnosis of cancers and develop precision treatments targeted to individual patients.
In comparing the genome sequence of cancer cells to normal, healthy tissues to identify mutations, researchers can better understand the molecular biology behind the cause of cancers and their growth and assess individual patient prognosis and how likely they are to respond to certain treatments.(2)
Cancer genomics technologies
Cancer genome sequencing can detect both germline (inherited) and somatic (acquired) mutations. Next-generation sequencing (NGS) enables high throughput screening to rapidly generate genome sequences. This information allows for the development of novel therapeutics.
These precision therapies cause fewer side effects than chemotherapy, due to their specific targeting of proteins characteristic to genetic alterations. In addition, integrating proteomic profiles alongside genomic sequencing, known as proteogenomics, can be used to aid the understanding of cancers at a molecular level.
Genomic sequencing has improved research into the evolution of cancers, enabling the tracking of tumour development through genome analysis and tumour sampling. For example, TRACERx is a study of non-small cell lung cancer patients conducted in 2014, in which they tracked genetic mutations during cancer’s development and monitored patients’ health outcomes(3 – 5).
Challenges of cancer genome sequencing
Sample quality
One of the primary challenges for effective cancer genome sequencing is the efficient use of high-quality samples. The mutations involved in tumour growth are often very specific. Testing is required to search for many different genetic alterations, and biological samples can be rapidly exhausted. Therefore, procuring enough high-quality tumour samples can be problematic. Alternative extraction methods are currently under development, such as liquid biopsy, as non-invasive diagnostic tests are typically conducted using blood.
Data rationalisation
Sequencing a cancer genome produces vast amounts of data. Analysing this data requires significant computational resources and sophisticated bioinformatics tools. Moreover, cancer genomes often contain complex structural variations, copy number variations, and a high mutation burden, making data interpretation challenging.
Integrating genomic data with other omics data (e.g. transcriptomic, proteomics) to get a comprehensive view of the cancer’s biology is complex and combining genomic data with clinical data to make informed decisions is still an evolving area.
While behavioural, biophysical, and biomedical data has significantly enhanced our understanding of public health issues and expanded the range of possible responses, much of that data was not originally collected for scientific purposes, and so those developing treatments face the challenge of rationalising complex data sets.
Looking ahead: the role of lab automation in cancer genomics
Genomics provides a wealth of opportunities to improve the diagnosis and treatment of cancers. However, the next generation of cancer genomics requires a ‘work smarter, not harder’ approach with lab automation at the forefront.
- Improving the quality and accessibility of data
Newer automation like LINQ can collect more data points than ever before while transferring that information—from an entire experimental tech stack—into any data lake for immediate contextualisation of experiment results, meaning they can be repeated and reproduced.
- Removing bottlenecks
There are many bottlenecks in the process of NGS sequencing that can limit workflow. Sample preparation is a laborious and time-consuming step of NGS, susceptible to human-induced error, for example, while labs are struggling to hire enough lab staff to maintain an efficient workflow, with researcher’s time consumed in low-value processes.
Automating sample preparation of libraries can free up valuable team members for higher-value tasks.6,7
- Facilitating scale
Lab automation can unlock large-scale experimentation capabilities without the need for additional staff in a way that ensures more control over variables, easier access to conditional data, and the ability to increase throughput by extending working hours, increasing the speed of tasks, and parallelising touchpoints for maximum lab efficiency.
Genomics labs need to start adopting automation at the workflow, lab, and ultimately network levels to reduce the cost of genome sequencing, achieve the required cancer diagnostic throughput, and to get therapeutics to market faster.
LINQ by Automata
LINQ is a next-generation lab automation platform that can eliminate manual interactions and fully automate your genomic workflows, end to end. Explore how automation could revolutionise your lab’s capabilities: book an exploration call today.
We’re partnering with the NHS on genomic testing
For the first time in the UK, robotic technology is being used to support genomic testing for cancer patients following a partnership between The Royal Marsden NHS Foundation Trust and Automata.
The innovative installation will double the Trust’s genomics testing capacity and expand the range of tests.
References
1. Fletcher, M. Sequencing the secrets of the cancer genome. Nature Research (2020) doi:10.1038/d42859-020-00075-8.
2. Nogrady, B. How cancer genomics is transforming diagnosis and treatment. Nature 579, S10–S11 (2020).
3. Kelly, A. Key genomic technologies of 2020: treatments old and new. Genomics Education Programme https://www.genomicseducation.hee.nhs.uk/blog/key-genomic-technologies-of-2020-treatments-old-and-new/ (2021).
4. Genomics, F. L. & Mobley, I. Cancer Genomics: From Diagnosis to Treatment. Front Line Genomics https://frontlinegenomics.com/cancer-genomics-from-diagnosis-to-treatment/ (2021).
5. Cancer genome research and precision medicine – NCI. https://www.cancer.gov/about-nci/organization/ccg/cancer-genomics-overview (2015).
6. Muscarella, L. A. et al. Automated Workflow for Somatic and Germline Next Generation Sequencing Analysis in Routine Clinical Cancer Diagnostics. Cancers (Basel) 11, 1691 (2019).7. Keefer, L. A. et al. Automated next-generation profiling of genomic alterations in human cancers. Nat Commun 13, 2830 (2022).
