ASCO 2025 daily highlights: Saturday 31st May
Future of Multi-Cancer Detection (MCD) Tests in Cancer Screening
Speakers emphasized the transformative potential of multi-cancer detection (MCD) tests in revolutionizing cancer screening. These tests identify cancer-related biological signatures in blood, such as DNA fragments, methylation, RNA, or proteins, using advanced algorithms powered by machine learning or artificial intelligence to predict the tissue of origin. The critical balance between high specificity to avoid false positives and high sensitivity to detect cancers was highlighted. MCD tests represent the initial step in a comprehensive screening program, with success hinging on factors like sensitivity, timely evaluation, early-stage disease curability, and screening accessibility. The session concluded that while MCD tests could redefine the screening paradigm, it is crucial to recognize that screening is a multistep process requiring robust evidence from randomized trials to ensure safety and efficacy.
Strategic Next Steps for the Pharma Industry:
- Invest in Advanced Algorithms: Focus on developing and refining machine learning and artificial intelligence algorithms to enhance the accuracy and reliability of MCD tests.
- Collaborate on Clinical Trials: Engage in partnerships to conduct robust randomized trials that provide high-quality evidence for the safety and efficacy of MCD tests.
- Enhance Screening Programs: Develop comprehensive screening programs that integrate MCD tests with other diagnostic tools to ensure timely evaluation and early-stage disease curability.
- Improve Accessibility: Work towards making MCD tests widely accessible to diverse populations, ensuring that advancements in cancer detection benefit all patients.
- Balance Specificity and Sensitivity: Prioritize the development of MCD tests that achieve a critical balance between high specificity and high sensitivity to minimize false positives and maximize cancer detection.
Pioneering the Future of Cancer Treatment: The Transition of Gene Therapy into Solid Tumors and Lessons from Gene Therapies Adopted in Inherited Diseases
Gene therapy for solid tumors is no longer a theoretical ambition—it is a clinical reality. With continued regulatory collaboration and stakeholder engagement, these therapies are poised to redefine standards of care and expand access for patients worldwide. Recent FDA approvals and clinical breakthroughs underscore a new era of precision medicine, where T cell therapies are delivering real-world impact.
Breakthrough Therapies, Real-World Impact:
- Afami-cel: The first FDA-approved TCR therapy targeting MAGE-A4, marks a pivotal moment in the treatment of synovial sarcoma.
- Lete-cel: Targeting NY-ESO-1, is demonstrating strong clinical potential in both synovial sarcoma and myxoid round-cell liposarcoma, reinforcing the viability of TCR therapies in solid tumors.
- Lifileucel: A TIL therapy, has gained FDA approval for metastatic melanoma, validating the clinical utility of tumor-infiltrating lymphocyte approaches.
The afternoon session on gene therapy drew lessons from gene therapy in hereditary diseases, highlighting the life-changing successes of exa-cel gene therapy in sickle cell anemia. However, it also emphasized the often-prohibitive limitations of gene therapy treatments, including very high costs, limited scalability and access (especially in patient populations who would benefit most), long manufacturing times, and severe toxicities. Promising alternatives are being developed by MD Anderson Cancer Center with the use of natural killer (NK) cell therapies, which can be developed as off-the-shelf treatments derived from donors or cell lines. This enables faster and more scalable production, with lower toxicity profiles, contributing to decreased overall treatment costs.
Strategic Steps for the Pharma Industry:
- Invest in Research: Focus on mechanistic insights and durability enhancements to improve therapeutic outcomes.
- Collaborate with Regulators: Work closely with regulatory bodies to address challenges and ensure patient access for all.
- Expand Applications: Explore the potential of T and NK cell constructs in other cancer types.
- Enhance Safety Profiles: Prioritize the understanding and monitoring of safety profiles for effective clinical use.
Evolution of Kidney Cancer Biomarkers
The evolution of biomarkers in kidney cancer over the last decade was highlighted, showcasing advancements in imaging, blood-based biomarkers for detecting minimal residual disease, and emerging gene expression signatures like single-cell sequencing and spatial biology. These tools have helped subtype clear cell renal cell carcinomas and predict treatment responses, with ongoing trials aiming to improve outcomes.
Various new biomarkers in kidney cancer were showcased, including serum KIM1, which has the potential to be a significant predictor of treatment outcomes with atezolizumab in high-risk resected renal cell carcinoma (RCC). The development of a machine learning-based model that integrates diverse biomarkers, including PD-L1 score, AI platform analysis of images, and examination of extracellular matrix (ECM) factors, was also discussed. However, several limitations were acknowledged, including the need for extensive validation and incorporation of additional features like transcriptional state and genetics. Researchers exploring the microtome of RCC showed the importance of circulating MAdCAM-1 levels as a prognostic biomarker in metastatic RCC and suggested that patients with low MAdCAM-1 levels may benefit from microbiota-centered interventions. Finally, the potential to induce significant pathologic responses in a subset of patients with high-risk RCC, leading to improved disease-free survival, was highlighted. The presence of CD8+CD39+ T-cells following neoadjuvant avelumab and axitinib may be a valuable predictive biomarker for treatment outcomes.
Strategic Steps for the Pharma Industry:
- Invest in Translational Research: Prioritize translational research to bridge the gap between laboratory discoveries and clinical applications.
- Validate and Collaborate: Validate machine-learning-based models to leverage the power of integrated biomarker analysis to create a comprehensive predictive tool and collaborate with regulatory bodies to ensure future access.
- Target the Microbiome for Enhanced Efficacy: Conduct cutting-edge trials to confirm the impact of the gut microbiome on the efficacy and toxicity of immune checkpoint inhibitors.
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