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ASCO 2026 | The future of the PALMARES-2 study in HR+/HER2- advanced breast cancer

Claudio Vernieri, MD, PhD, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy, comments the evolving landscape of the PALMARES-2 study (NCT06805812), which has grown to include over 6,000 patients across 37 Italian institutions. The next steps for the study include completing analysis on the first-line setting and training artificial intelligence models to optimize treatment sequences for individual patients. This interview took place during the 2026 American Society of Clinical Oncology (ASCO) Meeting in Chicago, IL.

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Transcript

PALMARES-2 is evolving very rapidly. So the analysis that I presented yesterday derived from the second study data lock in January 2025 with 4,236 patients. Now we have recently obtained the results from the third study data lock with more than 6,000 patients including across 37 Italian institutions. I want to take the opportunity to thank all our colleagues, collaborators, and friends who are contributing to this huge study...

PALMARES-2 is evolving very rapidly. So the analysis that I presented yesterday derived from the second study data lock in January 2025 with 4,236 patients. Now we have recently obtained the results from the third study data lock with more than 6,000 patients including across 37 Italian institutions. I want to take the opportunity to thank all our colleagues, collaborators, and friends who are contributing to this huge study. And at the ESMO meeting, 2026, I will present the final overall survival results regarding the CDK4-6 inhibitor comparison in terms of real-world overall survival. So the next steps are to, let’s say, to complete the analysis on the first-line setting, comparing the three CDK4-6 inhibitors, and also to confirm the results of different sub-analyses that we previously presented. And finally, I would like to highlight probably the most relevant perspective of this study, which is the training of artificial intelligence models based on clinical and biological features, and in the future also radiomic and digital pathology features. And these algorithms will be really useful to let us understand what is the best sequence of treatments from the first line to the second, the third and also subsequent lines of therapy for individual patients with the final goal of optimizing overall survival at an individual level it is really a huge effort but this is the final goal of this study because in this specific setting there is an increasing number of effective therapies but we do not know how to choose the best first, second, third and subsequent line of therapy for individual patients.

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