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

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. 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. 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). 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. NCT05735392.

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Publication Details

Journal
Journal of Experimental & Clinical Cancer Research
Published
2026-01-20
DOI
https://doi.org/10.1186/s13046-025-03605-2
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Luisa Carbognin, Matteo Allegretti, Stefania Gori, Chiara Mandoj et al.
Journal of Experimental & Clinical Cancer Research
Cancer Genomics and Diagnostics
article

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

Luisa Carbognin, Matteo Allegretti, Stefania Gori, Chiara Mandoj, E. Bria, Ida Paris, Giovanni Scambia, Grazia Arpino, Elena Ricciardi, Diana Giannarelli, Patrizio Giacomini, Andrea Botticelli, Alessandra Fabi, Claudia Omarini, Alberto Zambelli, Francesco Cognetti, Elena Giordani
article en

Abstract

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. 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. 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). 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. NCT05735392.

Journal of Experimental & Clinical Cancer ResearchVol. 45(1)
Università Cattolica del Sacro Cuore (IT), University of Modena and Reggio Emilia (IT), Agostino Gemelli University Polyclinic (IT), Ospedale Sacro Cuore Don Calabria (IT), Ospedale Papa Giovanni XXIII (IT), Federico II University Hospital (IT), National Cancer Institute (MY), Policlinico San Matteo Fondazione (IT), Istituti di Ricovero e Cura a Carattere Scientifico (IT), University of Milano-Bicocca (IT), Sapienza University of Rome (IT)
Ministero della Salute, Associazione Italiana per la Ricerca sul Cancro, Università Cattolica del Sacro Cuore
Good health and well-being
Openalex Percentile: Top 83%
Cancer Genomics and Diagnostics
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