Instead of just measuring the presence or quantity of proteins, new technology analyzes their physical proximity and co-localization on a cell's surface. This protein "geography" creates a unique spatial fingerprint that can more accurately distinguish healthy regenerating cells from residual cancer cells post-treatment.

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.

AI identified circulating tumor DNA (ctDNA) testing as a highly sensitive method for detecting cancer recurrence earlier than scans or symptoms. Despite skepticism from oncologists who deemed it unproven, the speaker plans to use it for proactive monitoring—a strategy he would not have known about otherwise.

The InVigor11 study was the first to show that detecting recurrence via a ctDNA test before it's visible on scans is not just a prognostic sign, but an actionable clinical state. Intervening with therapy at this early stage was proven to improve patient outcomes, establishing a new paradigm for cancer surveillance.

After immunotherapy, many colorectal cancer patients have residual nodules on scans that appear to be partial responses. However, ctDNA testing can confirm these are often just scar tissue, not active disease. This provides the confidence to stop therapy at the two-year mark and avoid unnecessary surgeries for what are effectively complete responses.

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.

The NCI-supported MyeloMatch trial is pioneering a new standard for AML diagnostics, providing comprehensive genomic, FISH, and karyotype analysis within 72 hours. This rapid turnaround allows for immediate risk stratification and assignment to appropriate clinical trials.

Biomarkers provide value beyond predicting patient response. Their core function is to answer 'why' a treatment succeeded or failed. This explanatory power informs sequential therapy decisions and provides crucial scientific insights that advance the entire medical field, not just the individual patient's case.