Correlation of SUV on Early Interim PET with Recurrence-Free Survival and Overall Survival in Primary Operable HER2-Positive Breast Cancer (the TBCRC026 Trial)
Predictive biomarkers of response to human epidermal growth factor receptor 2 (HER2)-directed therapy are essential to inform treatment decisions. The TBCRC026 trial reported that early declines in tumor SUVs corrected for lean body mass (SUL<sub>max</sub>) on <sup>18</sup>F-FDG PET/CT predicted a pathologic complete response (pCR) to HER2 therapy with neoadjuvant trastuzumab and pertuzumab (HP) without chemotherapy in estrogen receptor (ER)-negative, HER2-positive breast cancer. We hypothesized that <sup>18</sup>F-FDG PET/CT SUL<sub>max</sub> parameters would predict recurrence-free survival (RFS) and overall survival (OS). <strong>Methods:</strong> Patients with stage II/III ER-negative, HER2-positive breast cancer received neoadjuvant HP (<em>n</em> = 88). pCR after HP alone was 22% (18/83), additional nonstudy neoadjuvant therapy was administered in 28% (25/88), and the majority received adjuvant therapy per physician discretion. <sup>18</sup>F-FDG PET/CT was performed at baseline and at cycle 1, day 15 (C1D15). RFS and OS were summarized using the Kaplan-Meier method and compared between subgroups using logrank tests. Associations between <sup>18</sup>F-FDG PET/CT (≥40% decline in SUL<sub>max</sub> between baseline and C1D15, or C1D15 SUL<sub>max</sub> ≤ 3) and pCR were evaluated using Cox regressions, where likelihood ratio CIs were reported because of the small numbers of events. <strong>Results:</strong> Median follow-up was 53.7 mo (83/88 evaluable), with 6 deaths and 14 RFS events. Estimated RFS and OS at 3 y was 84% (95% CI, 76%-92%) and 92% (95% CI, 87%-98%), respectively. A C1D15 SUL<sub>max</sub> of 3 or less was associated with improved RFS (hazard ratio [HR], 0.36; 95% CI, 0.11-1.05; <em>P</em> = 0.06) and OS (HR, 0.14; 95% CI, 0.01-0.85; <em>P</em> = 0.03), the latter statistically significant. The association of an SUL<sub>max</sub> decline of at least 40% (achieved in 59%) with RFS and OS did not reach statistical significance. pCR was associated with improved RFS (HR, 0.25; 95% CI, 0.01-1.24; <em>P</em> = 0.10) but did not reach statistical significance. <strong>Conclusion:</strong> For the first time, we report a potential association between a C1D15 SUL<sub>max</sub> of 3 or less on <sup>18</sup>F-FDG PET/CT and RFS and OS outcomes in patients with ER-negative, HER2-positive breast cancer receiving neoadjuvant HP alone. If confirmed in future studies, this imaging-based biomarker may facilitate early individualization of therapy.
Authors
- Roisín M. Connolly (ORCID: https://orcid.org/0000-0003-0056-1070)
- Lilja B. Sólnes (ORCID: https://orcid.org/0000-0002-2192-7425)
- Richard L. Wahl (ORCID: https://orcid.org/0000-0002-7306-2590)
- Jennifer M. Specht (ORCID: https://orcid.org/0000-0003-1484-2113)
- Antonio C. Wolff (ORCID: https://orcid.org/0000-0003-3734-1063)
- Minetta C. Liu (ORCID: https://orcid.org/0000-0002-8206-5232)
- Ian E. Krop (ORCID: https://orcid.org/0000-0002-6380-5944)
- Rita Denbow (ORCID: https://orcid.org/0000-0002-6603-3863)
- Maeve Hennessy (ORCID: https://orcid.org/0000-0002-2369-3327)
- Ashley Cimino‐Mathews (ORCID: https://orcid.org/0000-0002-0638-7969)
- Lisa A. Carey (ORCID: https://orcid.org/0000-0003-2388-4649)
- Anna Maria Storniolo (ORCID: https://orcid.org/0000-0002-8701-7889)
- Vered Stearns (ORCID: https://orcid.org/0000-0003-4018-4708)
- Christos Vaklavas (ORCID: https://orcid.org/0000-0002-9919-2748)
- Eric P. Winer (ORCID: https://orcid.org/0000-0002-8819-1723)
- Mothaffar F. Rimawi (ORCID: https://orcid.org/0000-0002-4284-5656)
- Chiung‐Yu Huang (ORCID: https://orcid.org/0000-0003-2313-3562)
- Vandana G. Abramson (ORCID: https://orcid.org/0000-0002-6855-1322)
- Vicente Valero (ORCID: https://orcid.org/0000-0001-5913-7480)
- Jeffrey P. Leal (ORCID: https://orcid.org/0000-0002-8774-4541)
Institutions
- University of North Carolina at Chapel Hill (US)
Publication Details
- Journal
- UNC Libraries
- Published
- 2025-01-23
- DOI
- https://doi.org/10.17615/37vd-2t42
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European Society for Medical Oncology