While precision medicine has focused on tumor biology, this research suggests a broader "precision care" approach is needed. This involves tailoring treatment, such as drug dosage, based on patient-specific factors like physiology, functional reserve, and personal goals, not just genomic markers.
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The endgame for CZI's work is hyper-personalized, "N of one" medicine. Instead of the current empirical approach (e.g., trying different antidepressants for months), AI models will simulate an individual's unique biology to predict which specific therapy will work, eliminating guesswork and patient suffering.
Genomics (DNA/RNA) only provides the 'sheet music' for cancer. Functional Precision Medicine acts as the orchestra, testing how live tumor cells respond to drugs in real time. AI serves as the conductor, optimizing the 'performance' for superior outcomes.
The traditional drug-centric trial model is failing. The next evolution is trials designed to validate the *decision-making process* itself, using platforms to assign the best therapy to heterogeneous patient groups, rather than testing one drug on a narrow population.
An individual tumor can have hundreds of unique mutations, making it impossible to predict treatment response from a single genetic marker. This molecular chaos necessitates functional tests that measure a drug's actual effect on the patient's cells to determine the best therapy.
The future of medicine isn't about finding a single 'best' modality like CAR-T or gene therapy. Instead, it's about strategic convergence, choosing the right tool—be it a bispecific, ADC, or another biologic—based on the patient's specific disease stage and urgency of treatment.
The next frontier in CSCC isn't just about new drugs, but about optimizing existing ones. A key research area is determining the minimum number of immunotherapy doses required for an optimal response—potentially just one or two—to limit toxicity, reduce treatment burden, and personalize care for high-risk patients.
Data on Enfortumab Vedotin suggests that for modern therapies, maintaining patients on treatment longer via a lower, more tolerable starting dose is more important than administering the maximum labeled dose upfront, a concept inherited from the cytotoxic chemotherapy era.
Despite billions invested over 20 years in targeted and genome-based therapies, the real-world benefit to cancer patients has been minimal, helping only a small fraction of the population. This highlights a profound gap and the urgent need for new paradigms like functional precision oncology.
The progress of AI in predicting cancer treatment is stalled not by algorithms, but by the data used to train them. Relying solely on static genetic data is insufficient. The critical missing piece is functional, contextual data showing how patient cells actually respond to drugs.
Modern breast cancer treatment has shifted from a 'one-size-fits-all' aggressive approach to a highly individualized one. By de-escalating care—doing smaller surgeries, minimizing radiation, and sometimes omitting chemotherapy or lymph node biopsies—clinicians can achieve better outcomes with fewer long-term complications for patients with favorable disease characteristics.
