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PEER-TO-PEER CLINICAL CONVERSATIONS
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Genomics, Risk Stratification, and Treatment Personalization
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Genomic Classifiers and AI for Personalizing Prostate Cancer Radiation Therapy
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Jason Efstathiou, MD, DPhil, FASTRO, FACRO
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| Jason Efstathiou discusses how genomic classifiers and AI-based tools may help personalize radiation therapy for prostate cancer, including how molecular tumor biology and digital pathology-based risk models can complement traditional clinical factors.
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Correlation Between Genomic Classifier Scores and Upfront Treatment Selection in Localized Prostate Cancer: A Real-World U.S. Population-Based Cohort Study
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Michael Leapman, MD, MHS
Michael Leapman presents a population-level analysis linking SEER registry with Decipher® genomic classifier data in low and intermediate-risk prostate cancer patients. Higher Decipher® scores were independently associated with greater odds of treatment versus active surveillance across grade group one and two patients.
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Prior Therapy, Genomics, and Patient Factors in mCRPC Treatment Selection
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Kristine Lacuna, MD
Kristine Lacuna discusses treatment selection in mCRPC with Neeraj Agarwal, emphasizing how prior ARPI exposure, genomic testing, HRR/BRCA alterations, MSI-high/TMB-high status, PSMA PET findings, and patient-specific factors can inform sequencing decisions across PARP inhibitors, pembrolizumab, lutetium-177 PSMA therapy, chemotherapy, and bone-targeted strategies.
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| Real-World Genetic Testing Patterns in Prostate Cancer Assessed via AI: Implications for NCCN Guideline Implementation
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| Mitchell Singstock, MD
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| In a large community oncology practice, NCCN-indicated germline and somatic testing for prostate cancer was frequently underused, although testing rates improved from 2023 to 2025. An AI-based approach to applying NCCN criteria showed high agreement with manual review and could help scale identification of patients who should receive genetic testing.
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| Germline and Somatic Genetic Testing in Advanced Prostate Cancer: Practical Considerations and Challenges
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| Channing Paller, MD
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| Channing Paller emphasized that all men with advanced metastatic prostate cancer should undergo both germline and somatic testing when possible, because the results can guide PARP inhibitor, platinum, and immunotherapy choices and inform family cascade testing. She also noted that archival tumor tissue is often the best starting point, while liquid biopsy can help when tissue is unavailable but has limitations such as false positives from clonal hematopoiesis.
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| Clinical and Transcriptomic Characterization of Metastatic Hormone-Sensitive Prostate Cancer Patients with Low PTEN Expression - Beyond the Abstract
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| Marta Garcia de Herreros, Natalia Jiménez, and Begoña Mellado
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| Low PTEN expression in mHSPC was linked to worse outcomes, including shorter castration-resistant prostate cancer–free survival and overall survival, and it marked a tumor state with heightened cell-cycle/DNA-repair programs plus an immunosuppressive, immune-excluded microenvironment. The authors derived a 39-gene PTEN-low signature that captured the biology of this subgroup and may help guide future personalized strategies, including AKT-based and immune-combination approaches.
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| Artificial Intelligence in Diagnostic, Prognostic, and Predictive Genomic Biomarkers for Prostate Cancer: Ready for Prime Time? - Beyond the Abstract
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| Andrey Bazarkin, Mark Taratkin, Stanislav Vovdenko et al.
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| A systematic review of 16 studies suggests AI can meaningfully improve prostate cancer genomics by discovering new diagnostic markers, refining prognosis, and helping predict treatment response. The big caveat is that most models still need prospective clinical validation before they are ready for routine use.
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| Genomic and Transcriptomic Correlates of Deep PSA Response in Patients with mHSPC
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| Emmanuel Antonarakis, MD
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| In 525 men with mHSPC, a deep PSA response at 6 months (<0.1<0.1<0.1 ng/mL) was strongly associated with better survival, including 83% 36-month overall survival versus 66% in men with higher PSA and an adjusted HR of 0.51. SPOP alterations were more common in deep responders, while ZFHX3 alterations were enriched in poorer responders, but no major transcriptomic differences were seen for targets like PSMA, TROP2, B7-H3, or STEAP1.
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