Transforming diagnostics and personalized medicine amidst challenges

Genomic testing, an advanced diagnostic tool, delves deep into the analysis of DNA, RNA, and proteomics to uncover genetic mutations, assess disease risks, and provide critical insights for medical decisions. The field has seen transformative progress with the advent of technologies like next-generation sequencing, enabling the early detection of diseases, particularly cancers.

This cutting-edge approach has significantly enhanced diagnostic precision, making it a cornerstone in personalized medicine. However, challenges persist. Restricted insurance coverage, high costs, and the complexity of interpreting vast genomic data continue to hinder its widespread adoption. These obstacles underscore the need for robust frameworks to ensure accessibility and affordability.
To shed light on this evolving field and its implications for healthcare, we turn to Dr. Kirti Chadha, Chief Scientific and Innovation Officer at Metropolis Healthcare Limited.

Q: What is genomic testing, and how does it differ from traditional diagnostic tests?
A: In 2003, the Human Genome Project that mapped an understanding of all human genes was completed and changed the entire testing scope impacting every clinical segment. Although genetic testing started way back in 1950 when Down (trisomy 21) and other syndromes were diagnosed by cytogenetic methods & evolved further in 1990s when the novel technique of PCR accelerated molecular genetic testing by enabling targeted testing for known pathogenic variants, it accelerated only when Next Generation Sequencing & other newer technologies added to the understanding of the human genome and its impact on disease. Testing has now evolved from comprehensive gene panels to exome and genome sequencing as routine tests, thereby revolutionizing the diagnosis of rare genetic diseases among others. As compared to traditional lab tests, genomic tests that study DNA, RNA , proteomics can identify mutations in our genes indicating if we have or not a genetic condition while also identifying our risk for developing a certain condition or passing on a genetic disorder to the next generation. One can have a deeper understanding and open multiple opportunities of intervention to detect, predict, treat & monitor.

Q: How does genomic testing help in the early detection of diseases, particularly cancer?
A: There is fair adoption of robust genomic technologies into early detection to prevent or treat conditions in early stage. Some success stories include newborn screening, non-invasive prenatal screening & early cancer screening. Additionally, cell-free DNA is emerging for the early detection of rejection of transplanted organ rejection. Next generation sequencing allows laboratories to start analyzing not just one gene at a time, but panels of genes for common genetic conditions, including hereditary breast/ovarian cancer and cardiomyopathies. Genomic tumor profiling has a crucial role in the management of patients with solid cancers, as it helps selecting apt intervention based on diagnostic, prognostic and predictive biomarkers, as well as identifying markers of hereditary cancers. There are well etched out international guidelines for breast, cervical, colorectal, and lung cancer and the use of these single-cancer screening tests has reduced cancer-related mortality for these malignancies while more are developing for unaddressed organ systems. In the absence of screening, an undetected early-stage cancer can progress to a more advanced stage before the presentation of clinical symptoms that would lead to a diagnosis, by which point the prognosis may have become less favorable.

Q: What are some of the challenges faced by healthcare systems in implementing genomic testing on a larger scale?
A: A current major hurdle is that one can detect more variation than the current resources or knowledge can fully interpret, whether in relation to variant pathogenicity, disease penetrance, or genetic-environment interactions. New AI algorithms identify and combine disparate knowledge to determine variant-disease relationships and the influence of environmental factors. Over time, AI based population health support will emerge to facilitate identification of patients who will benefit most from genomic testing, coupled with sophisticated real-time clinical decision support that can move beyond guidance for gene-drug pairs to multigene pathways, drug-drug interactions & other variables needed for effective clinical practice. Financial pressure on laboratories and policy makers with limited coverage by insurance companies are major hurdles, equally concerning is the possible decrease in test offerings, particularly to underserved communities or for rare disorders. Hope at laboratory end is that machine learning will likely reduce the time that pathologists spend making microscopic diagnoses, and more time placing these diagnoses in the context of increasing amounts of nucleic acid sequence and expression data (both RNA and proteomic), and an expanding array of therapeutics. Informatics tools while helping pathologists will also communicate important findings to healthcare professionals and patients. Over time, the clinical impact of these technological advances will help remove financial barriers to their implementation.

Q: How do insurance companies view genomic testing, and are there any challenges regarding coverage?
A: Insurers face challenges in form of difficulty to decide when to reimburse for genetic tests that health care providers have offered their patients as insurers may not be able to easily evaluate what type of genetic test was performed, whether the test was appropriate to perform and whether the test is scientifically valid. The number of insurance instances that exist are significantly fewer than the number of genetic tests available today. These companies are having trouble keeping up with the volume of new genetic and next-generation sequencing tests that are coming onto the market. There is also lack of data evaluating the economics of genomic testing. There are attempts by regulatory bodies to promote research into health benefit & cost-effectiveness of genetic testing with constantly evolving scenario. Scientific criteria that aid are – analytical validity, how well the test predicts the presence or absence of a particular gene or genetic change ; clinical validity – how well the genetic variant(s) being analyzed is related to the presence, absence, or risk of a specific disease; clinical utility – whether the test can provide information about diagnosis, treatment, management, or prevention of a disease that will be helpful to patients and their providers. Will the use of the test lead to improved health outcomes?
There are policies to enhance analytical validity regulation and expand oversight of the clinical validity of genetic tests. Clinical utility reference is from medical treatment data while frameworks to evaluate the clinical utility of genetic tests are being developed.