The RSClin tool integrates a patient's Oncotype DX score with their unique clinical-pathologic features, such as tumor size and grade. This provides a more accurate and personalized risk assessment, as the same genomic score can represent significantly different prognoses for patients who have low versus high clinical risk factors.
Related Insights
Real-world data demonstrates that a subset of node-negative (N0) breast cancer patients with high-risk features has a recurrence and mortality rate nearly identical to that of node-positive (N1) patients. This finding justifies intensifying adjuvant therapy with agents like CDK4/6 inhibitors for this seemingly lower-risk group, as was done in the NATALEE trial.
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.
In a subset analysis of the high-risk MONARCH-E trial, an inferred Oncotype score did not identify which patients benefited from the CDK4/6 inhibitor abemaciclib. This indicates that while such scores assess prognostic risk and guide chemotherapy decisions, they are not predictive biomarkers for selecting patients for this targeted therapy.
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 Rampart study's use of the Leibovic score for risk stratification is a key strength. Unlike traditional TNM staging, this score more heavily weights tumor grade, which clinicians find to be a more granular and clinically relevant predictor of recurrence risk than just tumor size.
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.
Oncotype DX risk scores are more influenced by estrogen-related genes, while other assays like MammaPrint are driven more by genes related to cell proliferation. This fundamental difference in their underlying biology can inform an oncologist's choice of which genomic test is most appropriate for a given patient's tumor.
TP53-mutated AML carries an extremely poor prognosis, significantly worse than other adverse-risk subtypes. When TP53 patients are excluded from analyses, the survival gap between the remaining adverse-risk and intermediate-risk patients narrows considerably, clarifying risk stratification.
The successful KEYNOTE-564 trial intentionally used a pragmatic patient selection model based on universally available pathology data like TNM stage and grade. This approach avoids complex, inconsistently applied nomograms, ensuring broader real-world applicability and potentially smoother trial execution compared to studies relying on more niche scoring systems.