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.
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The CREST trial showed benefit driven by patients with carcinoma in situ (CIS), while the Potomac trial showed a lack of benefit in the same subgroup. This stark inconsistency demonstrates that subgroup analyses, even for stratified factors, can be unreliable and are a weak basis for regulatory decisions or label restrictions.
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.
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 panel suggests AKT inhibitor trials in prostate cancer have been disappointing due to suboptimal biomarker selection (e.g., PTEN IHC). A similar drug in breast cancer showed significant survival benefit when using a more precise NGS-based strategy, indicating a potential path forward if the right patient population is identified genetically.
Three 2025 trials (AMPLITUDE, PSMA-addition, CAPItello) introduced personalized therapy for metastatic hormone-sensitive prostate cancer. However, significant benefits were confined to narrow subgroups, like BRCA-mutated patients. This suggests future success depends on even more stringent patient selection, not broader application of targeted agents.
A study where celecoxib initially failed to show benefit was re-analyzed using ctDNA. The drug provided a substantial survival improvement (HR 0.55-0.58) specifically in ctDNA-positive patients. This demonstrates ctDNA's power not just for prognosis, but as a predictive biomarker to identify which patients will benefit from a targeted therapy.
Observational data from the BESPOKE study showed that the survival benefit from adjuvant chemotherapy was only seen in patients who tested positive for ctDNA post-surgery. In contrast, ctDNA-negative patients had overlapping survival curves whether they received chemotherapy or not, questioning its utility for that group.
The main barrier to widespread ctDNA use is not its proven ability to predict who will recur (prognostic value). The challenge is the emerging, but not yet definitive, data on its ability to predict a patient's response to a specific therapy (predictive value).
Data from the MONARCH-E and NATALY trials show that the benefit of adjuvant CDK4/6 inhibitors like abemaciclib and ribociclib persists and even increases after patients complete their 2-3 year treatment course. This sustained "carryover effect" suggests a lasting impact on disease biology rather than just temporary suppression.
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.