Cancer remains one of the most devastating diagnoses for any family. Despite decades of progress, cancer is still the number one cause of death by disease among children and the leading killer of men, and the second leading cause of death among women under fifty. Pediatric cancer survival rates have improved, yet too many children relapse or suffer lasting complications from therapies designed decades ago. Even today, treatment decisions often rely on trial-and-error approaches that overlook each child’s biological uniqueness. For example, my son Trey was among those children who relapsed after standard treatment protocols, some of which had not changed in nearly fifty years, and ultimately lost his battle with cancer.
There is an urgent need for a new paradigm that unites biology, genomics, and artificial intelligence (AI) to deliver personalized and predictive care. This convergence represents the inflection point that can redefine how cancers in children and adults are treated.
Traditional cancer care is guided by population-level data: what worked best for most patients becomes the standard of care. While evidence-based medicine has saved countless lives, it cannot account for the deep biological differences between individuals, especially in children. Pediatric cancers are rare, genetically diverse, and underrepresented in large clinical trials, leaving oncologists to make treatment decisions with limited data. Too often, we treat children as if they are “little adults”, but they are not. Their bodies are biologically distinct, still growing, developing, and transforming as part of the natural progression toward adulthood. Two patients with the same diagnosis can respond completely differently to the same therapy, underscoring the need for approaches that tailor treatment to the individual, rather than relying on averages.
Functional Precision Medicine: Moving From Prediction to Proven, Personalized Treatment
Traditional precision medicine represented a breakthrough by using genomic data to identify mutations or biomarkers linked to potential drug responses. But in practice, this approach often stops at prediction, targeting the identified mutation and hoping the drug works. Functional Precision Medicine (FPM) represents a significant advancement beyond genomics. Instead of relying solely on genetic profiling, FPM tests a patient’s living cancer cells directly against hundreds of FDA-approved drugs and combinations, validating what truly works for that individual. By integrating these direct test results with genomic information and applying advanced AI analysis, FPM creates a comprehensive, evidence-based map that reveals each tumor’s unique vulnerabilities. This approach moves cancer care from a generalized ‘trial and error’ process toward an evidence-based, individualized testing framework.
The Role of Artificial Intelligence in Functional Precision Medicine
AI plays a critical role in this evolution, but not all AI is the same.
Large language models (LLMs), such as those used across various industries, are trained on massive, generalized datasets, much like the standard of care was established on aggregated clinical outcomes. These systems are powerful but impersonal. The AI applied to functional precision medicine, however, operates on an entirely different principle. Instead of learning from millions of patients, it learns from one, the individual. Each patient becomes their own control. Their biological and genomic data train the AI in real-time, allowing for an unprecedented view into how that specific cancer behaves and responds to drugs.
By combining AI-driven analytics with drug sensitivity testing on living tumor cells and genomic sequencing, physicians can utilize this data to identify effective therapies within days, rather than weeks. This is not theoretical: the Functional Precision Medicine approach is already being applied in research and clinical settings to help guide treatment for refractory cancer patients, including children with limited therapeutic options. These systems are designed to continuously refine their insights as more data becomes available, potentially improving the precision of future treatment recommendations.
The convergence of biology, genomics, and AI is redefining what is possible in cancer treatment. For the first time, clinicians can move beyond static genetic snapshots and observe, in real time, how a patient’s cancer responds to therapy. When analyzed by AI, this data ecosystem becomes self-learning, continually refining treatment guidance.
Every day, families hear the words “no more options”, but today, there is a scientific foundation capable of changing that reality. The Functional Precision Medicine approach is currently being implemented in research and clinical settings, reflecting a broader movement toward data-driven personalization in cancer care.
Jim Foote
Jim Foote is the Co-Founder and CEO of First Ascent Biomedical, a company pioneering Functional Precision Medicine to personalize cancer care. A technologist turned healthcare innovator, Jim’s work bridges AI, biology, and robotics to help physicians identify the right treatment for each patient the first time. His mission was born from personal experience. After his son’s cancer diagnosis, he made it his life’s work to ensure no family ever has to rely on “try and hope” medicine again.
