By Houda Hachad, Vice President Clinical Operations, Aranscia
LinkedIn: Houda Hachad
LinkedIn: Aranscia
Cancer care is undergoing a structural transformation. Advances in early detection, targeted therapies, and immunotherapies have improved outcomes. According to the American Cancer Society, the five-year survival rate for all cancers combined has now reached 70%, reflecting meaningful progress in treatment and care delivery. But as treatment options expand and become more personalized, the complexity of managing those therapies is increasing as well.
This shift is raising the stakes on how medications are selected, dosed, and monitored over time. Drug-gene testing (also known as pharmacogenomics, or PGx) can provide valuable insights into medication response and toxicity risk, but in oncology, it remains largely confined to narrow, reactive use cases. This creates a disconnect between advancements in cancer care and how treatment decisions are supported.
This gap is becoming more pronounced as cancer care continues to evolve. Oncology continues to dominate FDA drug approvals, with a growing share of therapies designed for targeted and biomarker-defined populations, increasing the need for more comprehensive approaches to patient-specific treatment.
At the same time, more patients are living longer, often navigating years of therapy, survivorship, and chronic disease management. Cancer is becoming a long-term, medication-managed condition rather than an acute episode.
Within this landscape, oncologists must be aware of the different types of genetic testing that play distinct roles in guiding care. PGx focuses on inherited differences in enzymes and pathways that influence how patients process medications over their lifetime, with many clinically relevant drug-gene interactions documented in resources such as the FDA table of Pharmacogenetic Associations and ClinPgx Annotated Gene-Drug Pairs. Somatic tumor profiling identifies mutations within tumors to guide targeted therapies.
Together, these approaches are complementary but serve distinct functions in oncology.
Moving Beyond Narrow Use Cases
PGx testing has traditionally been applied through focused interventions. Recent regulatory developments further underscore the growing importance of PGx in oncology. The FDA has updated labeling for fluorouracil and capecitabine to emphasize DPYD genetic testing prior to treatment initiation, citing the risk of severe or even fatal toxicity in patients with certain variants. This shift reflects a broader recognition that differences in drug response are often biologically predictable rather than random, and that genetic insights should be incorporated earlier in care.
These applications are clinically important, but they do not reflect the full scope of medication complexity in oncology.
Patients often receive a wide range of therapies over time, including anticancer agents, supportive care medications, and treatments for coexisting conditions. Many of these medications are influenced by genetic variability across multiple pathways. A single-gene testing approach cannot adequately support this complexity. A broader multi-gene-based strategy enables PGx to function as a continuous source of medication intelligence, informing decisions across therapies, care settings, and phases of disease.
Preemptive Testing in a Time-Sensitive Environment
Oncology care is defined by urgency. Treatment decisions often need to be made quickly, with limited tolerance for delays.
Preemptive PGx testing helps ensure that genetic insights are available before prescribing decisions occur. Instead of reacting to a specific medication, clinicians can access a comprehensive profile from a multi-gene PGx panel at the point of care.
This becomes especially valuable as patients transition across care environments. Survivors frequently move between oncology, primary care, and specialty services, often accumulating medications over time.
Evidence suggests that cancer survivors remain engaged in decisions about their ongoing treatments and are interested in improving how medications work for them. This underscores that medication decisions are continuous rather than episodic.
In this context, PGx can be viewed as a form of “medication genome”, a stable, inherited layer of information that does not change over time but can be applied repeatedly across treatment decisions. Unlike tumor profiles that evolve, this germline foundation provides a consistent reference point for medication management throughout the patient journey.
Building a Scalable Model for Implementation
The challenge is no longer whether PGx testing has value, but how to implement it in a scalable and sustainable way. Many organizations remain in pilot phases, where testing is episodic and disconnected from clinical workflows. Scaling requires a more integrated approach.
PGx testing must be embedded into routine care pathways, such as at diagnosis or treatment planning. Results should be stored to allow reuse across encounters and over time. Clinical decision support is essential. Clinicians need actionable recommendations that translate genetic insights into clear prescribing guidance at the point of care.
Increasingly, leading organizations are moving beyond static test-and-report models toward systems that continuously translate genomic signals into clinical actions and learn from outcomes over time. This shift enables PGx to function not just as a diagnostic input, but as part of an adaptive, data-driven care model.
Integrating Genetics with Clinical Context
Genetic data alone does not determine medication response.
The next phase of genetic-guided prescribing in oncology lies in combining genetic insights with other clinical variables, including drug interactions, organ function, prior therapies, and comorbid conditions. Increasingly, oncology is recognizing that treatment-related toxicity is not simply an unavoidable side effect but is, in many cases, a predictable consequence of underlying biology. Regulatory guidance has begun to emphasize individualized dosing strategies that incorporate patient-specific factors, reinforcing the need to integrate genetic insights with broader clinical data.
Advances in data infrastructure and analytics are making it possible to operationalize this multi-factorial model at scale, enabling more consistent and personalized care.
Comprehensive PGx strategies also create opportunities in clinical research. As trials become more targeted, identifying eligible patients based on molecular characteristics is increasingly important. Health systems with searchable structured datasets are better positioned to match patients to trials efficiently.
A New Expectation for Precision Medicine
The oncology landscape continues to evolve. Longer survival, increasing treatment complexity, and growing data availability are redefining expectations for care delivery. PGx testing must evolve with it. Moving beyond single-gene testing toward a comprehensive preemptive and longitudinal approach allows health systems to enhance medication decisions across the full arc of a patient’s journey.
When supported by scalable technology and integrated data strategies, PGx becomes an enabling layer of precision medicine and a foundational component of how treatment decisions are made over time.
As more patients live longer after diagnosis, the goal is no longer simply to avoid toxicity at a single point in time. It is to support the right medication decisions across the full trajectory of care.