The same cancer-driving mutation behaves differently depending on the cell's internal "wiring." For example, a drug targeting a mutation works in melanoma but induces resistance in colorectal cancer due to a bypass pathway. This cellular context is why genetic data alone is insufficient.
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Experiments show that transferring a cancer cell's dysfunctional mitochondria—but not its nucleus—into a healthy cell is what induces cancer. This disruptive finding supports the view of cancer as a metabolic disease that can be targeted by starving its mitochondria of fuels like glucose.
Simple cell viability screens fail to identify powerful drug combinations where each component is ineffective on its own. AI can predict these synergies, but only if trained on mechanistic data that reveals how cells rewire their internal pathways in response to a drug.
BTK degraders work despite most kinase inhibitor resistance mutations. However, resistance to degraders themselves alters the BTK binding pocket so significantly that subsequent targeting with any BTK kinase inhibitor is unlikely to be effective, positioning them as a potential end-of-line therapy.
Step Pharma's synthetic lethality approach targets two redundant enzymes in the same pathway. Deleting one makes cancer cells entirely dependent on the other. This direct dependency is harder for biology to circumvent compared to approaches targeting different, interconnected pathways, creating a "cleaner" kill mechanism.
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.
When a patient has a BRCA2 mutation, clinicians on the panel view it as such a dominant predictive biomarker that they would prioritize a PARP inhibitor-based triplet regimen. This single genetic finding often outweighs other clinical factors or even the potential addition of docetaxel in treatment decisions.
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.
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.
Cellcuity's drug is effective in breast cancer patients without PIK3CA mutations (wild type). This challenges the dominant precision medicine model that requires a specific genetic marker, showing that a pathway's aberrant activity can be a sufficient therapeutic target on its own.
