Circulating tumor DNA and Response Evaluation Criteria In Solid Tumors: ctDNA-RECIST proof-of-concept in HER2-positive metastatic breast cancer

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Authors: Alessandra Fabi, Elena Giordani, Elena Ricciardi, Grazia Arpino, Matteo Allegretti, Gianluigi Ferretti, Claudia Omarini, Alberto Zambelli, Chiara Mandoj, A. Botticelli, Emilio Bria, Stefania Gori
+6 more Luisa Carbognin, Ida Paris, Giovanni Scambia, Francesco Cognetti, D. Giannarelli, Patrizio Giacomini
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Abstract

Abstract Background Response Evaluation Criteria In Solid Tumors (RECIST 1.1) and circulating tumor DNA (ctDNA) recapitulate and anticipate response to treatment, respectively. However, ctDNA-RECIST (cRECIST) and ctDNA-guided End of Treatment (cEoT) are not applied routinely. Methods To provide proof-of-concept for RECIST1.1/cRECIST integration, HER2-positive metastatic breast cancer patients (n = 50) were enrolled in the multi-center prospective GIM21 study to receive Trastuzumab-emtansine (T-DM1). CT scans (113 tumor lesions) were longitudinally assessed for classical Objective Responses (ORs: progressive disease/stable disease/partial response/complete response; PD/SD/PR/CR) applying default RECIST 1.1 cut-offs (SD/PD ≥ 20%; SD/PR ≤ 30%). Likewise, bespoke NGS/dPCR (78 genomic alterations; 466 time points) were converted into ctDNA-Objective Responses (cORs: cPD/cSD/cPR/cCR) exploring wide cPD/cSD/cCR cut-off ranges, both default (RECIST 1.1-like) and alternative. Results Whichever the cut-off, cORs were much deeper than ORs, leading to RECIST 1.1/cRECIST divergence in 27 cPD-positive patients. Moreover, due to complex ctDNA trajectories (multiple successive ctDNA increases/decreases, termed ctDNA waving), cPD (the earliest ctDNA increase) correlated with outcome in broad patient subsets but not individual patients. To deconvolute ctDNA waving, cPD was combined with three-point ctDNA Trends (Tr), resulting in a personalized cEoT clinical algorithm that, once retrofitted to the 27 cPD-positive patient dataset, aligned with PFS much better than cPD (cEoT/PFS vs cPD/PFS linear regression: R2 = 0.85 vs 0.35). Conclusions Even in difficult ctDNA scenarios, the cEoT algorithm may help to: (a) predict treatment efficacy during drug development, (b) adaptively randomize for patient-specific, timely treatment switch in clinical trials, and (c) prevent premature treatment withdrawal in long-responders. Future randomized studies are warranted for cRECIST/RECIST 1.1 integration/personalization in different tumors/settings. Trial registration NCT05735392.

Fields of Study & Subjects

Cancer Genomics and Diagnostics HER2/EGFR in Cancer Research Cancer Immunotherapy and Biomarkers

Publication details

Journal / source
Figshare
Publication date
20 January 2026
Language
EN