Miglino Nicola, Toussaint Nora C, Ring Alexander, Bonilla Ximena, Tusup Marina, Gosztonyi Benedict, Mehra Tarun, Gut Gabriele, Jacob Francis, Chevrier Stephane, Lehmann Kjong-Van, Casanova Ruben, Jacobs Andrea, Sivapatham Sujana, Boos Laura, Rahimzadeh Parisa, Schuerch Manuel, Sobottka Bettina, Chicherova Natalia, Yu Shuqing, Wegmann Rebekka, Mena Julien, Milani Emanuela S, Goetze Sandra, Esposito Cinzia, Sarabia Del Castillo Jacobo, Frei Anja L, Nowak Marta, Irmisch Anja, Kuipers Jack, Baciu-Drăgan Monica-Andreea, Ferreira Pedro F, Singer Franziska, Bertolini Anne, Prummer Michael, Lischetti Ulrike, Aebersold Rudolf, Bacac Marina, Maass Gerd, Moch Holger, Weller Michael, Theocharides Alexandre P A, Manz Markus G, Beerenwinkel Niko, Beisel Christian, Pelkmans Lucas, Snijder Berend, Wollscheid Bernd, Heinzelmann Viola, Bodenmiller Bernd, Levesque Mitchell P, Koelzer Viktor H, Rätsch Gunnar, Dummer Reinhard, Wicki Andreas
Department of Medical Oncology and Hematology, University of Zurich and University Hospital, Zurich, Switzerland.
NEXUS Personalized Health Technologies, ETH Zurich, Zurich, Switzerland.
Nat Med. 2025 May 27. doi: 10.1038/s41591-025-03715-6.
There is limited evidence supporting the feasibility of using omics and functional technologies to inform treatment decisions. Here we present results from a cohort of 116 melanoma patients in the prospective, multicentric observational Tumor Profiler (TuPro) precision oncology project. Nine independent technologies, mostly at single-cell level, were used to analyze 126 patient samples, generating up to 500 Gb of data per sample (40,000 potential markers) within 4 weeks. Among established and experimental markers, the molecular tumor board selected 54 to inform its treatment recommendations. In 75% of cases, TuPro-based data were judged to be useful in informing recommendations. Patients received either standard of care (SOC) treatments or highly individualized, polybiomarker-driven treatments (beyond SOC). The objective response rate in difficult-to-treat palliative, beyond SOC patients (n = 37) was 38%, with a disease control rate of 54%. Progression-free survival of patients with TuPro-informed therapy decisions was 6.04 months, (95% confidence interval, 3.75-12.06) and 5.35 months (95% confidence interval, 2.89-12.06) in ≥third therapy lines. The proof-of-concept TuPro project demonstrated the feasibility and relevance of omics-based tumor profiling to support data-guided clinical decision-making. ClinicalTrials.gov identifier: NCT06463509 .
支持使用组学和功能技术为治疗决策提供信息的可行性的证据有限。在此,我们展示了前瞻性、多中心观察性肿瘤剖析(TuPro)精准肿瘤学项目中116名黑色素瘤患者队列的结果。使用了9种独立技术,大多在单细胞水平,对126份患者样本进行分析,在4周内每个样本产生高达500 Gb的数据(40,000个潜在标志物)。在既定和实验性标志物中,分子肿瘤委员会选择了54个以提供其治疗建议。在75%的病例中,基于TuPro的数据被判定对提供建议有用。患者接受标准治疗(SOC)或高度个体化、多生物标志物驱动的治疗(超出SOC)。在难治性姑息性、超出SOC的患者(n = 37)中,客观缓解率为38%,疾病控制率为54%。接受TuPro指导治疗决策的患者的无进展生存期在≥三线治疗中为6.04个月(95%置信区间,3.75 - 12.06)和5.35个月(95%置信区间,2.89 - 12.06)。概念验证性TuPro项目证明了基于组学的肿瘤剖析支持数据引导的临床决策的可行性和相关性。ClinicalTrials.gov标识符:NCT06463509 。