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Genomic Medicine: What's Next?

Jason Armstrong | April 25, 2025

As sequencing costs fall and interpretation tools advance, the potential of genomic medicine is becoming ever clearer. But how far can genomic medicine really go? And what does that mean for diagnosis, treatment, and care?

This article explores clinical and technological progress in genomic medicine, focusing on diagnostics, treatment, and implementation. Legal and ethical considerations are also a major part of the future of genomic medicine; however, we have chosen not to cover them in depth here, as they warrant a dedicated discussion.

The Era of Accessible Genomics

The Human Genome Project completed the first full sequence in 2003, at a cost of around $3 billion. Today, it is possible to sequence a human genome for a few hundred dollars¹, with NGS technology manufacturers chasing the “$100 genome”. This fall in price has a profound impact, helping move genomic analysis from a research luxury or diagnostic last resort to a part of routine practice.

The falling cost of sequencing helps enable genomics at scale. As more genomes are sequenced and more conditions are investigated at the genetic level, the amount and quality of available data increases. For rare disease patients, this means more timely answers and potentially better outcomes. As databases grow and algorithms improve, diagnostic yields climb. This has practical consequences. Earlier, more accurate, diagnoses mean fewer invasive tests; more targeted surveillance; and, in some cases, access to life-changing therapies.

Lower sequencing costs can also encourage innovation in other domains, such as monitoring cancer DNA in blood or sequencing pathogens for infection control, which were previously too expensive to consider broadly.

Another example is companion diagnostics (CDx) in cancer, where genomic testing guides treatment selection and improves therapeutic outcomes. CDx tests identify specific genetic variants that predict a patient’s response to targeted therapies, ensuring that the right patients receive the right drugs. As sequencing becomes more affordable, these tests will be more readily integrated into clinical pathways, helping to reduce trial-and-error prescribing and improve overall treatment efficacy.

Interpretation is Everything

More genomes mean more data, but this doesn’t automatically mean more insight. Sequencing alone doesn’t deliver answers. Expert interpretation does. That’s where classification frameworks, variant databases, scientific publications, and AI come in. This shifts the bottleneck from sequencing capacity to the time and expertise it takes to understand the data. Tools that support consistent, evidence-based classification are essential. This is particularly true in clinical settings where speed and accuracy are key.

For now, ambiguity remains a challenge; Variants of Uncertain Significance (VUS) still account for many findings. As datasets and supporting evidence grow, and classification frameworks become more robust, the appropriate interpretation of these enigmatic variants will improve, helping to decrease uncertainty for clinicians and patients.

Reanalysis of genomic data is becoming a recognized clinical strategy, particularly for rare disease cases that initially provide no definitive diagnosis. As variant databases expand and interpretation frameworks advance, cases once labelled ‘unsolved’ are being reclassified with more definitive findings. Studies suggest that reanalysing genomic data after 12–18 months can yield new diagnoses in up to 20% of previously inconclusive cases².

Beyond Diagnosis

Genomics is not just about what we can learn, but what it enables us to do. In oncology, genomics is already guiding treatment: matching therapies to tumor profiles, helping to avoid ineffective drugs, and identifying patients for immunotherapy. The result is higher efficacy and fewer unwanted side effects. For example, the list of approved CDx for oncology continues to grow³, allowing more patients to be matched with the most suitable drugs. Additionally, targeted therapies for rare⁴, metabolic⁵, and neuromuscular diseases⁶ are emerging. In oncology, CDx are increasingly used to determine whether a patient is eligible for a targeted therapy. The FDA has approved many CDx tests, spanning cancers of the lung, breast, colon, and more. These tests are a central part of precision oncology, ensuring patients get treatments tailored to the molecular features of their disease.

Gene therapy is also transitioning from experimental to actionable. For example, treatments have been approved for inherited retinal disorders⁷ and spinal muscular atrophy⁸. Meanwhile, CRISPR-based therapies have shown promising results in clinical trials and may soon offer durable cures for sickle cell disease and beta-thalassaemia⁹.

Looking ahead, the rise of antisense oligonucleotides and modular gene editing platforms is laying the groundwork for hyper-personalised, 'n-of-1' therapies - potentially enabling treatment for patients with ultra-rare or unique mutations¹⁰. Though still largely experimental, these cases show what’s possible when diagnosis and therapy are genetically aligned.

Pharmacogenomics (PGx), too, is moving into routine care. Incorporating PGx into care pathways can reduce adverse drug reactions and optimize efficacy, especially in psychiatry¹¹, cardiology¹², and oncology¹³. A patient’s response to antidepressants, statins, or chemotherapy can be partially predicted by their DNA. This relevance becomes especially clear in clinical areas where treatment outcomes vary widely and adverse effects can be serious. By integrating PGx data into electronic health records, clinicians are better equipped to tailor prescriptions, reduce trial-and-error prescribing, and avoid harmful reactions, helping to move PGx from insight to action.

Inclusive Genomics

Non-invasive prenatal testing (NIPT) is now widely used to detect chromosomal conditions. Its high uptake demonstrates how genomics can become routine when integrated into well-accepted care pathways. As costs drop, NIPT panels are expanding to include single-gene disorders and microdeletions, raising new questions about scope, consent, and clinical utility¹⁴.

Carrier screening, embryo testing, and even whole genome sequencing (WGS) of newborns are emerging. WGS is being piloted for use in newborn screening programmes in countries including the UK¹⁵ and the US¹⁶. These programmes aim to identify treatable genetic conditions early, sometimes before symptoms emerge. However, they also raise questions about long-term data storage, reanalysis, and the psychological burden of early information.

A lack of ancestral diversity in genomic databases limits the accuracy of variant interpretation for non-European populations. This isn’t just a research gap, but a clinical risk. Without diverse data, genomic medicine will widen, rather than close, health disparities. While projects like the UK’s 100,000 Genomes Project¹⁹ and the USA’s All of Us programme²⁰ have taken meaningful steps towards inclusion, systemic barriers and mistrust continue to limit participation among underrepresented communities. Achieving equity in genomic medicine will require continued efforts to improve access, understanding, and trust.

However, population-scale initiatives like GenomeAsia 100K¹⁷ and the Saudi Human Genome Program¹⁸ are specifically designed to address the underrepresentation of non-European populations in genomic datasets. By focusing on ancestral groups that have historically been neglected in research, these efforts are helping to improve the accuracy and equity of genomic medicine globally.

What Comes Next?

In the short term, genomic testing is likely to become even more embedded in frontline medicine, not only for rare diseases and cancer, but for cardiovascular conditions, PGx decision-making, and more. Several health systems are exploring the use of whole genome sequencing in newborn screening^16,21, aiming to detect treatable genetic conditions before symptoms appear. Similarly, genomic risk screening for adults is being piloted as a public health strategy^22,23.

AI is also poised to enhance clinical decision-making. Genomic alerts, triggered by electronic health record data and matched to variant interpretation frameworks, could soon support clinicians in prescribing and surveillance decisions. These tools may reduce uncertainty and cognitive burden, especially as AI models trained on large datasets help improve variant prioritisation and interpretation.

We may also begin to see early applications of digital twin models; virtual representations of an individual’s biology that simulate health outcomes based on genetic and environmental inputs²⁴. While still experimental, this concept hints at a future where genomics contributes to continuously updated models of individual risk and response.

Looking further ahead, the boundaries of genomic medicine will likely shift again. N-of-1 therapies remain applicable only in exceptional cases, but are becoming more feasible thanks to antisense oligonucleotide platforms and improved regulatory pathways.

Synthetic genomics may enable the design of DNA constructs that perform therapeutic functions, such as targeting cancer cells or modulating immune responses. Advances in synthetic biology could also allow for engineered microbes or cells to deliver treatments from within the body.

Perhaps the most controversial possibility is the potential for preventative genome editing, including germline modifications. While clinical use is not on the immediate horizon, proof-of-concept studies exist. Scientific tools are advancing faster than the ethical frameworks, and any move in this direction will require rigorous, international governance.

Beyond these emerging therapies, other innovations are beginning to surface. Wearable sequencing sensors²⁵. DNA-based data storage²⁶. Personalised therapies, developed in weeks²⁷. These ideas remain experimental for now, but reflect active areas of research that could reshape clinical practice.

Questions Worth Asking

How do we ensure interpretation keeps pace with sequencing?
Who has access to preventive genomics, and who doesn’t?
What counts as “actionable” in a world of real-time genomics?
How do we ensure that genomic alerts and reanalyses are clinically useful and safely implemented?
How do we best build diverse datasets that improve accuracy for everyone?
What ethical boundaries must be established as personalised, even programmable, therapies become more feasible?

Conclusions

Genomics is moving fast. What was once cutting-edge is now everyday practice. But it remains a field defined as much by its unanswered questions as by its discoveries.

For those working at the intersection of data and care, the question is no longer whether genomic medicine will change clinical practice. It already has. The questions now are how we apply it, who benefits, and how we respond as new capabilities emerge.

As tools become more powerful, so must our frameworks for using them responsibly. DNA Day is a chance to take stock, not only of what’s been achieved, but of what still needs to be built.