Precision oncology continues to generate innovative tools in this new era of cancer research, helping clinicians better understand cancer biology and personalize treatment decisions. But as several presentations at the 2026 ASCO Annual Meeting demonstrated, many emerging biomarkers face a significant challenge: proving they can predict which treatments individual patients should receive, rather than simply identifying who has a higher-risk disease.
During the presentation, “Precision Oncology: How to Apply New Biomarkers in Clinical Practice,” Joshua Michael Lang, MD, MS, Professor of Hematology, Medical Oncology, and Palliative Care at the University of Wisconsin, reviewed several studies involving transcriptomics, genomic classifiers, circulating biomarkers, and AI-driven pathology models that can help identify which patients may benefit from specific therapies.
While the technologies seem promising, Dr. Lang emphasized that many remain prognostic rather than predictive, which may raise concerns among clinicians and patients seeking definitive answers about the most effective treatments.
Precision oncology has historically focused on identifying specific mutations or molecular alterations to guide targeted treatment. However, newer approaches are exploring more complex biomarkers that assess tumor biology, gene expression patterns, and treatment response.
Several presentations focused on prostate cancer and whether advanced genomic tools can help identify patients who may benefit from treatment intensification.
One study explored clinic-transcriptomic risk stratification to guide the use of abiraterone, an androgen biosynthesis inhibitor and antiandrogen, in patients with high-risk prostate cancer. Another study evaluated whether the Decipher Prostate Genomic Classifier could identify patients who could achieve greater clinical benefit from adding docetaxel (a chemotherapy drug) to androgen deprivation therapy plus enzalutamide.
According to Dr. Lang, these studies “confirm that the Decipher test does have prognostic utility for poor overall survival,” reinforcing its potential value for risk stratification in prostate cancer.
At the same time, he also noted that many analyses were limited by tissue availability and retrospective study design, making it challenging to determine whether the biomarkers could reliably guide treatment decisions.
Another study that Dr. Lang discussed evaluated an AI model’s ability to analyze routine pathology slides and infer gene expression patterns without requiring additional molecular testing.
He described the technology as part of a rapidly evolving era in cancer research. “This is the most exciting time in cancer research with the technological capabilities I think we’ve really only been dreaming about,” he said during the presentation.
The model appeared capable of identifying patients with metastatic hormone-sensitive prostate cancer who were more likely to benefit from docetaxel. However, additional research is necessary before such tools can be implemented in clinical practice.
Dr. Lang also highlighted the first interim analysis of SWOG S1823, which examined the operating characteristics of circulating microRNA 371a-3p (miR371) in predicting active germ cell malignancy in patients with early-stage testicular cancer.
The assay demonstrated strong specificity, suggesting that circulating biomarkers could eventually play a larger role in surveillance and recurrence monitoring. However, researchers are still working to determine how the test should influence clinicians’ treatment decisions, with Dr. Lang asking, “Could the test lead us in the wrong direction?”
As biomarker-driven care continues to expand, clinicians still face challenges when deciding how to apply emerging tests in patient care.
Key considerations may include:
While many of these technologies show promise, additional research and validation are necessary before some biomarkers become widely adopted in routine oncologic care.
This ASCO presentation highlighted both the promise and challenges of precision oncology. Emerging biomarkers, AI-driven tools, genomic classifiers, and circulating biomarkers may improve risk stratification and help clinicians better understand cancer biology. Yet, additional validation is required before they can reliably guide clinicians’ treatment decisions.
At the presentation’s conclusion, Dr. Lang made one thing clear: while biomarkers are helping clinicians predict cancer prognosis, proving which therapies benefit individual patients remains the next hurdle for precision oncology.
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