Integrated genomics is an emerging field that has the potential to transform the management of cancer and rare diseases with no known cures. The size of the genomics market in the US is expected to surpass the $30 billion mark this year. Genomics investigates the core of cancers and rare diseases from the genotype level and therefore provides a rare opportunity for precise personalized treatment.
Initially, cancer treatment was based on the location of the tumor and the type of cells involved. Analyzing tumors at the genetic level provides an understanding of the genetic mutations that may trigger the growth or proliferation of the tumor and hence device an effective and personalized treatment. For example, 10 years ago, lung cancer was categorized as either small cell or non-small cell cancer. With the evolution of genomics, it can now be categorized based on 30 different genetic mutations.
Cancers mostly arise from mutations that occur at the genetic level. Some genetic mutations affect tumor-suppressor genes that usually protect against cancer. For example, mutations of BRCA1 and BRCA2 are associated with an increased risk for breast, ovarian, and prostate cancer. The presence of a mutation on the BCR-ABL gene is a risk factor for Chronic Myeloid Leukemia (CML) which is also associated with the presence of the Philadelphia chromosome.
The National Institute of Health defines a rare disease as a disease that affects less than 200,000 people in a country. This includes diseases such as muscular dystrophy, Spina bifida, Klinefelter syndrome, Abetalipoproteinemia, and Bloom syndrome among others. Rare diseases are usually quite expensive to treat and since most are chronic, they impose a huge burden on patients and their families. Most rare diseases are genetic and therefore would greatly benefit from genomic research.
Integrated genomics employs different genomic technologies such as Next-Generation Sequencing (NGS) to advance cancer and rare disease research. Scientists are seeking to understand the Genomics drivers of cancer (the Genomics profile of specific tumors) and use this information to create personalized treatments. This is called cancer genome profiling and is carried out on patients opting for this option. This is necessary to increase the “effective treatment options” for those with the highest mortality risk since it may not be possible to do this for every patient. This may include off-label treatments and enrollment in experimental drugs.
Genomics not only provides an understanding of the triggers of cancer but also provides insights into how the cancer is likely to progress and how it’s likely to respond to treatment.
Cancer Treatment Based on Genomics
A genome refers to the wholesome genetic instructions while the expression of specific mRNA molecules is the transcriptome. The translation of RNA molecules into protein constitutes the proteome while the metabolites from the protein activity make up the metabolome. These factors combined make up the omics of precision-based cancer treatment.
Several treatments have been created to target specific cancer-causing mutations. For example, patients with HER2- positive breast cancer are usually treated with trastuzumab while those with AML are treated with imatinib.
Some mutations will not offer an alternative effective treatment but will indicate a poor response to treatment. This can help the family and clinical team plan how to best care for the patient under such a circumstance. Genetic sequencing is therefore not just important for identifying treatable mutations but also for identifying potential targets and treatment options.
Genomics is a field that is still under research and facing a number of teething challenges, including the following:
Lack of Diversity
Earlier genome projects only included white Europeans, but this is changing. The National Human Genome Research Institute is currently running over 25 initiatives to increase the diversity of human genomics.
Challenging Data Management
Genomics involves a profound amount of data and metadata that can be overwhelming for a laboratory. The storage and interpretation of the data can also be challenging given the diverse languages and protocols in a global context. Informed consent must also be collected in a way that is consistent with global ethical standards and this presents yet another challenge.
Genomics is no Replacement for Standard-of-Care
For now, genomics is only used when the standard-of-care treatment has been tried and failed. It will be a while before genomics is incorporated into the physicians’ diagnostic toolkit.
Integration with Other Omics
Genomics doesn’t exist on its own, it integrates with other omics such as transcriptomics, epigenomics, metabolomics, and proteomics. This integration is likely to expand our understanding of cancers and rare diseases but will require extensive research and resources.
The Need for Machine Learning
Data generated from the different omics can be overwhelming. Machine Learning can help streamline and manage the data in a way that it can be used to predict disease occurrence and treatment outcomes, though this is still under investigation.
How a Diagnostics LIMS can Streamline Genomics
A Laboratory Information Management System (LIMS) is a modern laboratory software that can be used to automate workflows and data management in a laboratory environment. Given the large volume of data that can be generated during genome sequencing, a Diagnostics LIMS is vital in ensuring efficient and error-free data management. A diagnostics LIMS can also be integrated with data analysis tools for downstream data analysis and decision-making.
Genomics is the Future of Cancer and Rare Disease Management
As the global burden of disease shifts from communicable to chronic diseases, there’s an urgent need to better understand cancers and rare diseases at the genome level. A better understanding of genomics is the foundation for precision medicine and personalized treatment for cancers and rare diseases. A diagnostics LIMS comes in handy to streamline the management of the volumes of data generated through genomics and other related omics.