The impact of automated genome sequencing in cancer diagnosis & treatment


From diagnosis to treatment, new technologies are revolutionising scientists’ approach to cancer genome sequencing. 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 that are 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.

Scaling to local lab level

Another significant challenge to genomic sequencing is the scaling of validated assays to the local laboratory level. Despite the availability of whole genome sequencing as a technology, it is yet to achieve widespread adoption in smaller-scale labs. This is because genome profiling requires the analysis of complicated and sophisticated bioinformatics data. Expertise is needed to interpret this data, especially as it requires the management of very large datasets. It is these challenges that lab automation has the ability to alleviate.

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.

There are many bottlenecks in the process of NGS sequencing that can limit workflow. For example, sample preparation is a laborious and time-consuming step of NGS, susceptible to human-induced error. Many labs are struggling to hire enough lab staff to maintain an efficient workflow, with researchers’ time consumed in low-value processes. Automating sample preparation of libraries can free up valuable lab members for higher-value tasks (6, 7).

Automation reduces costs and limits data variability. Equipment, and thus assays, can be standardized through automation, using identical operating conditions to ensure lab-to-lab reproducibility. Validated assays in a tandem, an end-to-end workflow can assist the scalability of cancer genomic technologies to smaller-scale laboratories.

At Automata, we believe that in order to usher in the coming genomics revolution, labs need to think about automation differently. They need to embrace open, integrated automation.

LINQ: A new open, integrated laboratory automation platform

Our new open, integrated automation solution – LINQ – features a unique laboratory bench, with integrated automation capabilities and accompanying powerful, proprietary lab orchestration software.

As a result, labs can easily reduce human touchpoints and increase efficiency and accuracy without needing to dedicate additional lab space to bulky equipment.  


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 (2021).

4. Genomics, F. L. & Mobley, I. Cancer Genomics: From Diagnosis to Treatment. Front Line Genomics (2021).

5. Cancer genome research and precision medicine – NCI. (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).