#ASCO26 GU Oncology Spotlight 🚨
🔬 Precision Oncology: How to Apply New Biomarkers in Clinical Practice
Excellent discussion by Joshua M. Lang, MD, MS
@JoshLangMD
@OncoAlert
@ASCO
This session captured the real challenge of precision oncology in GU cancers:
A biomarker is only useful if it answers a clinical decision.
Not just:
➡️ Is the patient high risk?
➡️ Is the biomarker prognostic?
But:
➡️ What treatment decision changes?
➡️ Who should be intensified?
➡️ Who can safely avoid toxicity?
➡️ Can we reduce imaging, treatment burden, or overtreatment?
🟦 Prostate cancer: intensification is getting crowded
In metastatic hormone-sensitive prostate cancer and high-risk localized disease, we now have multiple active options:
• ADT
• ARPIs
• docetaxel
• abiraterone-based intensification
• PARP inhibitor strategies in selected patients
• radioligand approaches under study
But the clinical problem remains:
➡️ We often have only one chance to choose the best early strategy.
And biomarkers for therapy selection remain limited.
🟩 Abstract 5000: clinical genomic risk for abiraterone intensification
The clinico-transcriptomic model in high-risk localized prostate cancer reinforces an important concept:
Clinical risk alone is imperfect.
Adding genomic classifier information may better define who resembles the STAMPEDE M0 very-high-risk population — the group in whom abiraterone intensification has a strong rationale.
But caution is essential:
⚠️ pooled legacy trials
⚠️ no contemporary PSMA PET staging
⚠️ no abiraterone-treated patients in the NRG/RTOG cohorts
⚠️ model-based inference, not a definitive treatment-selection rule
So this is promising risk refinement — not yet a final decision tool.
🟨 Abstract 5001: ENZAMET Decipher
ENZAMET offered a unique opportunity to ask whether Decipher can help identify patients who benefit from adding docetaxel to ADT enzalutamide.
Key signal:
• DGC >0.85 was associated with poorer OS
• patients with higher Decipher scores had more adverse clinical features
• the analysis suggested potential enrichment for docetaxel benefit in selected biologically high-risk patients
But again, the key question is clinical utility:
➡️ Does the biomarker predict treatment benefit enough to change practice?
➡️ Or is it mainly identifying worse prognosis?
That distinction matters.
🟧 Abstract 5002: spatial transcriptomics computational pathology
ST-DoxPCa takes the field in another direction:
Can routine H&E images be linked to spatial transcriptomic biology to identify who benefits from docetaxel?
This is exciting because it points toward biomarkers that may be embedded in ordinary pathology workflows.
But implementation questions remain:
• retrospective analysis
• limited sample availability
• need for validation
• ADT docetaxel is no longer the full modern standard backbone
• generalizability to today’s ARPI-containing regimens remains uncertain
🟥 LBA5003 / SWOG S1823: miR371 in early-stage testicular cancer
In testicular germ cell tumors, miR371 showed:
• high specificity
• strong negative predictive value
• modest sensitivity overall
• better sensitivity with more advanced relapse
This may help refine surveillance, but the key question remains:
➡️ Does miR371 change imaging frequency?
➡️ Does it trigger treatment?
➡️ Can it safely reduce CT exposure?
That is the clinical utility test.
💬 My take
This was an excellent discussion because it emphasized the right standard for biomarkers.
A biomarker should not only be statistically impressive.
It should be:
✓ clinically actionable
✓ prospectively validated
✓ interpreted in the right population
✓ aligned with current treatment standards
✓ able to change a decision that matters
For GU oncology, the next era of precision medicine will not be defined by “more biomarkers.”
It will be defined by better biomarker-driven decisions.
#ASCO26 #GUOnc #ProstateCancer #TesticularCancer #PrecisionOncology #Biomarkers #Genomics #Decipher #miR371