General Session 1

M. Lambertini¹, F. Samy², E. Agostinetto³, L. Arecco⁴, P. Freire⁵, A. Sonnenblick⁶, G. Arpino⁷, L. Del Mastro⁸, A. Choudhury⁹, N. Harbeck¹⁰, I. Vaz-Luis¹¹, V. Kaklamani¹², A. Wolff¹³, A. Partridge¹⁴, S. Loi¹⁵, S. Fielding¹⁶, M. Piccart¹⁷, S. Di Cosimo¹⁸, E. de Azambuja¹⁷; ¹Medical Oncology, University of Genova – IRCCS Ospedale Policlinico San Martino, Genoa, ITALY, ²Statistics, Frontier Science Scotland, Kingussie, UNITED KINGDOM, ³Oncology, Institut Jules Bordet, Hôpital Universitaire de Bruxelles (H.U.B) and Université Libre de Bruxelles (U.L.B.), Brussels, BELGIUM, ⁴Oncology, Department of Oncology, Institut Jules Bordet, Hôpital Universitaire de Bruxelles (H.U.B) and Université Libre de Bruxelles (U.L.B.), Brussels, BELGIUM, ⁵Medical Oncology, Oncologia D’Or Hospital de Câncer de Pernambuco, Recife, BRAZIL, ⁶Medical Oncology, The Oncology Division, Tel Aviv Sourasky Medical Center, Grey Faculty of Medicine, Tel Aviv University, Tel Aviv, ISRAEL, ⁷Medical Oncology, Università di Napoli Federico II, Naples, ITALY, ⁸Medical Oncology, University of Genova – IRCCS Ospedale Policlinico San Martino, Genova, ITALY, ⁹-, Novartis, Hyderabad, INDIA, ¹⁰Oncology, Dept. OB&GYN and CCC Munich, LMU University Hospital, Munich, GERMANY, ¹¹Medical Oncology, Institut Gustave Roussy, Villejuif, FRANCE, ¹²Medical Oncology, UTH San Antonio, San Antonio, TX, ¹³Medical Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, ¹⁴Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, ¹⁵Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, AUSTRALIA, ¹⁶Stat, Frontier Science Scotland, Kingussie, UNITED KINGDOM, ¹⁷Medical Oncology, Department of Oncology, Institut Jules Bordet, Hôpital Universitaire de Bruxelles (H.U.B) and Université Libre de Bruxelles (U.L.B.), Brussels, BELGIUM, ¹⁸Medical Oncology, Istituto Nazionale dei Tumori (INT), Milano, ITALY.

Background: The optimal adjuvant endocrine therapy (ET) for patients (pts) with hormone receptor-positive (HR+)/HER2-positive (HER2+) early breast cancer (EBC) remains controversial. The present analysis investigated the efficacy of different types of ET in pts with centrally tested HR+/HER2+ EBC treated with modern chemotherapy (CT) and trastuzumab (T)-based regimens at 10-year follow-up. Patients and methods: ALTTO (BIG 2-06) is an international phase 3 trial in pts with HER2+ EBC randomized to 4 adjuvant anti-HER2 treatments with CT: T alone, lapatinib (L) alone, their sequence (T->L) or their combination (T+L). Pts in the L alone arm, pts with HR-/HER2+ disease and pts with HR+ tumours who did not start adjuvant ET were excluded from the analysis. HER2 and HR status were centrally tested for all pts. Disease-free survival (DFS), time to distant recurrence (TTDR) and overall survival (OS) were compared between pts who received a selective estrogen receptor modulator (SERM) vs. those who received an aromatase inhibitor (AI). To avoid the risk of immortal time bias, pts with HR+/HER2+ disease who switched from one ET to another during the follow-up were excluded. A pre-planned subgroup analysis according to menopausal status at baseline was performed. Among premenopausal women, a comparison was made between SERM alone, SERM + ovarian function suppression (OFS) and AI±OFS (depending on post-CT menopausal status). Multivariable Cox proportional hazards regression models were used to model survival outcomes with adjuvant ET as the predictor. Other covariates in the model were CT timing (concurrent/sequential), tumour grade (G1/2, G3, GX), age (continuous) and tumour size (T, continuous) and a strata variable of axillary lymph node (N0, N1-3, N>=4 nodes, N/A). SERM were used as the reference category for adjusted hazard ratios (aHR). Results: This analysis included 2,651 pts with HR+/HER2+ of whom 1,518 (57.3%) received SERM (99.5% tamoxifen) and 1,133 (42.7%) AI. Among 1,259 premenopausal pts, 903 (71.7%) received SERM alone, 238 (18.9%) SERM+OFS and 118 (9.4%) AI±OFS. Median follow-up was 9.9 years (IQR 7.0-10.0 years). Overall, 10-year DFS was 80.1% and 76.5% in the AI and SERM groups, respectively (aHR 0.65; 95% CI 0.52-0.82). Compared to pts treated with SERM, those who received AI had fewer local (0.9% vs. 2.4%) and distant (9.3% vs. 12.1%) recurrences. In subgroup analyses, AI was superior to SERM irrespective of pts (age, menopausal status, body mass index), tumor (T, N, G, HR+ levels, HER2 FISH ratio) and treatment (anti-HER2 arm, CT type and timing) characteristics. In the AI and SERM groups, 10-year TTDR was 85.7% and 83.1% (aHR 0.65; 95% CI 0.50-0.85), and 10-year OS was 88.9% and 89.1% (aHR 0.73; 95% CI 0.53-1.00), respectively. Among premenopausal pts only, 10-year DFS was 90.0%, 77.3% and 77.6% with AI±OFS, SERM+OFS and SERM, respectively (AI±OFS vs. SERM: aHR 0.44; 95% CI 0.23-0.85; SERM+OFS vs. SERM: aHR 0.87; 95% CI 0.61-1.23). In the AI±OFS, SERM+OFS and SERM groups, 10-year TTDR was 92.8%, 82.9% and 85.1% (AI±OFS vs. SERM: aHR 0.57; 95% CI 0.27-1.18; SERM+OFS vs. SERM: aHR 0.94; 95% CI 0.62-1.41), and 10-year OS was 95.6%, 88.7% and 91.3% (AI±OFS vs. SERM: aHR 0.68; 95% CI 0.27-1.73; SERM+OFS vs. SERM: aHR 0.98; 95% CI 0.58-1.66), respectively. Conclusions: In this large 10-year follow-up analysis of pts with centrally tested HR+/HER2+ EBC treated with modern CT+anti-HER2-based therapy in the ALTTO trial, the use of AI was associated with significantly improved DFS and TTDR without differences in OS. The DFS benefit of AI was observed in both premenopausal and postmenopausal pts. These data may help optimizing adjuvant ET choices in pts with HR+/HER2+ EBC and shed light on the need of designing ad hoc clinical trials in this setting.

S. S. Badve¹, F. Zhao², N. Tung³, Y. Gokmen-Polar¹, C. C. O’Sullivan⁴, A. Prat⁵, E. P. Winer⁶, J. L. Wright⁷, A. Recht³, A. C. Weiss⁸, J. A. Tjoe⁹, S. M. Feldman¹⁰, G. B. Rocque¹¹, M. Smith¹², N. Unni¹³, S. Sardesai¹⁴, S. Tang¹⁵, S. Modi¹⁶, W. J. Irvin¹⁷, P. Villagrassa⁵, C. Battelli¹⁸, A. K. Krie¹⁹, N. Bagegni²⁰, M. A. George²¹, M. L. Telli²², V. F. Borges²³, N. D’Abreo²⁴, P. Shah²⁵, K. D. Miller²⁶, A. H. Partridge²⁷, L. A. Carey²⁸, A. M. DeMichele²⁵, A. C. Wolff²⁹, ECOG-ACRIN; ¹Pathology, Emory School of Medicine, Atlanta, GA, ²Biostatistics, DFCI, Boston, MA, ³Hem Onc, Beth Israel Deaconess Medical Center, Boston, MA, ⁴Hem Onc, Mayo Clinic, Rochester, MN, ⁵Reveal Genomics, Reveal Genomics, Barcelona, SPAIN, ⁶Hem Onc, Yale University, New Haven, CT, ⁷Hem Onc, Lineberger Comprehensive Cancer Center, Chapel Hill, NC, ⁸Oncology, University of Rochester Medical Center, Rochester, NY, ⁹Hem Onc, Green Bay Oncology, Neenah, WI, ¹⁰Hem Onc, Montefiore Einstein Center for Cancer Care, Bronx, NY, ¹¹Hem Onc, O’Neal Comprehensive Cancer Center at UAB, Birmingham, AL, ¹²Patient Advocacy, Research Advocacy Network, Naperville, IL, ¹³Hem Onc, UT Southwestern Medical Center, Dallas, TX, ¹⁴Hem Onc, The James Cancer Hospital at Ohio State University, Columbus, OH, ¹⁵Hem Onc, Louisiana State University Health Sciences Center, New Orleans, LA, ¹⁶Hem Onc, Memorial Sloan Kettering Cancer Center, New York, NY, ¹⁷Hem Onc, Bon Secours Health System, Marriottsville, MD, ¹⁸Hem Onc, New England Cancer Specialists, Scarborough,, GA, ¹⁹Hem Onc, Allina Health, Minneapolis,, MN, ²⁰Hem Onc, Washington University School of Medicine, St. Louis, MO, ²¹Hem Onc, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, ²²Hem Onc, Stanford University School of Medicine, Stanford, CA, ²³Hem Onc, University of Colorado Anschutz Medical Campus, Aurora, CO, ²⁴Hem Onc, NYU Winthrop Hospital, Mineola, NY, ²⁵Hem Onc, University of Pennsylvania/Abramson Cancer Center, Philadelphia, PA, ²⁶Hem Onc, Indiana Univ/Melvin and Bren Simon Cancer Center, Indianapolis, IN, ²⁷Hem Onc, Dana-Farber Cancer Institute, Boston, MA, ²⁸Hem Onc, Lineberger Comprehensive Cancer Center, , Chapel Hill, NC, ²⁹Hem Onc, Johns Hopkins Univ/Sidney Kimmel Cancer Center, Baltimore, MD.

Background: The association of TILs with pathologic complete response (pCR) and survival in HER2+ breast cancer is not well established with several prior studies showing inconsistent results. In this secondary analysis of EA1181, we evaluated the association between stromal TILs (sTILs) and pCR rates. Methods: EA1181 (NCT04266249) enrolled patients (pts) with anatomic clinical stage II/III HER2+ breast cancer who preoperatively received 4 cycles of trastuzumab and pertuzumab with 12 weeks of paclitaxel or docetaxel q3w x 4 (THP), followed by surgery. sTILs density was assessed on full-face hematoxylin and eosin (H&E)-stained tumor biopsy sections. sTILs scores were analyzed per protocol as a continuous variable (every 10% increment) and as a categorical variable (60% as per Denkert et al, 2018), and also with an exploratory 30% cutoff commonly used in TNBC. Cox proportional hazards models were used to examine the association between sTILs and pCR, adjusting for available baseline factors. The associations between sTILs (by category) and clinicopathologic characteristics were examined using Fishers’ exact test. Results: Among 2141 pts with HER2+ BC treated on EA1181, H&E slides were available for 1328 (62%). The study population evaluated for sTILs was comparable to the overall population. pCR rates were 44.5% overall, 64% for HER2+/ER- and 33% for HER2+/ER+ disease. sTILs distribution was: 623 (47%) 60%, which led us to merge the latter two groups (705 [53%] had ≥10% sTILs). In univariable and multivariable analyses, increasing sTILs (as a continuous variable) were associated with increasing pCR rates in both HER2+/ER+ and HER2+/ER- disease (p<0.001; Table). sTILs analyzed as a categorical variable (<10% vs ≥10%) were significantly associated with pCR for all patients (combined) and for those with HER2+/ER+ disease. In exploratory analysis using a cutoff of <30% vs ≥30% sTILs, an association with pCR was also seen in univariable analysis for both HER2+/ER+ disease (p30% remained a significant predictor for pCR only in HER2+/ER+ disease. Additional exploratory analysis with other molecular biomarkers, intrinsic subtypes, and immune signatures will be presented. Conclusion: sTILs were associated with pCR after THP, further supporting the important role of immune mechanisms in HER2+ breast cancer, and highlighting a potentially robust predictive tool to assess pathologic response. Baseline sTILs could potentially inform the preoperative design of future trials of therapy optimalization. Association of baseline sTILs with recurrence free survival in EA1181 will be reported in the future.

R. Salgado¹, L. E. Lara Gonzalez¹, F. Giudici², F. Rojo³, L. Comerma⁴, S. Wienert⁵, J. Palacios⁶, Z. Kos⁷, S. L. De Haas⁸, A. Rodriguez Lescure⁹, G. Viale¹⁰, Y. Zheng¹¹, D. Gao⁷, A. Kiermaier¹², F. Andre¹³, S. Loibl¹⁴, M. J. PIccart¹⁵, R. Gelber¹⁶, D. Cameron¹⁷, I. E. Krop¹⁸, P. Savas¹, T. O. Nielsen⁷, C. Denkert¹⁹, S. Michiels²⁰, S. Loi¹, APHINITY Steering Committee and Investigators, The International Immuno-Oncology Biomarker Working Group.; ¹Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, AUSTRALIA, ²Unit of Cancer Epidemiology, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, ITALY, ³Department of Pathology, Fundacion Jimenez Diaz University Hospital Health Research Institute (IIS-FJD, UAM)—CIBERONC, Madrid, SPAIN, ⁴Pathology Department, Hospital del Mar, Barcelona, SPAIN, ⁵Institute of Pathology, Universitätsmedizin Berlin (a corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin), Berlin, GERMANY, ⁶Pathology Department, University Hospital Ramón y Cajal, IRYCIS, CIBERONC, Universidad de Alcala, Madrid, SPAIN, ⁷Department of Pathology and Lab Medicine, University of British Columbia and BC Cancer, Vancouver, BC, CANADA, ⁸NA, F. Hoffmann-La Roche Ltd, Basel, SWITZERLAND, ⁹Medical Oncology Department, Elche General University Hospital, Alicante, SPAIN, ¹⁰Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, ITALY, ¹¹Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, ¹²Product Development Data Sciences, F. Hoffmann-La Roche Ltd, Basel, SWITZERLAND, ¹³Department of Medical Oncology, Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, Paris, FRANCE, ¹⁴NA, GBG Forschungs GmbH, Neu-Isenburg, GERMANY, ¹⁵NA, Institut Jules Bordet, Université Libre de Bruxelles (U.L.B.), Bruxelles, BELGIUM, ¹⁶Harvard T.H. Chan School of Public Health, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, ¹⁷Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UNITED KINGDOM, ¹⁸Medical Oncology, Yale Cancer Center, New Haven, CT, ¹⁹Institute of Pathology, Philipps University Marburg, Marburg, GERMANY, ²⁰Service de Biostatistique et d’Epidémiologie, Gustave Roussy, Villejuif Cedex, FRANCE.

Background: Stromal tumor-infiltrating lymphocytes (sTILs) are prognostic and predictive biomarkers for HER2-targeted therapy in early-stage HER2-positive breast cancer (BC). Manual sTIL scoring demonstrates high reproducibility but may underrepresent immune infiltration. Digital pathology and artificial intelligence (AI) offer automated sTIL quantification and spatial assessment but require validation against clinical endpoints. Aim: To compare manual, digital (non-AI), and AI-based sTIL quantification methods, including AI-derived spatial metrics in the phase III APHINITY trial. Objectives included interobserver reproducibility, method concordance, prognostic performance for invasive disease-free survival (iDFS) and overall survival, enhancement of prognostic models by AI, and identification of patients benefiting from adjuvant pertuzumab. Methods: Of 4,804 APHINITY trial participants, 4,306 (90%) had evaluable archival H&E whole-slide images for all methods. Manual scoring was performed by an experienced pathologist (RS), with reproducibility assessed on 262 slides independently reviewed by five pathologists using international TIL guidelines. Digital quantification used a standard image-analysis algorithm, and AI-percentage lymphocyte scoring employed Case45’s zero-shot deep-learning biomarker pipeline. Two AI-derived spatial metrics were computed: density of lymphocytes adjacent to cancer nests and quantification of dense lymphoid aggregates (immune hotspots). High-TIL status was defined as ≥75th percentile for percentage scores and ≥50th percentile for spatial metrics. Concordance was measured using intraclass correlation coefficient (ICC) and pairwise agreement. Prognostic and predictive performance were assessed via multivariable Cox regression adjusted for treatment arm, age, chemotherapy regimen, hormone-receptor status, nodal status, tumor size, and grade, with likelihood-ratio tests for incremental prognostic gains. Predictive benefit was evaluated by treatment-by-TIL interaction hazard ratios (HR) for iDFS in high-TIL groups. Results: Manual scoring reproducibility was excellent (ICC 0.87). Continuous sTIL quantification showed modest to moderate concordance across methods (ICC 0.37-0.62), with digital and AI consistently scoring lower than manual. High/low TIL classification achieved 80% overall agreement. Every 10% increment in sTIL was associated with reduced recurrence risk (HR: manual 0.93, digital 0.92, AI 0.87; all p < 0.001). AI spatial immune hotspots outperformed percentage metrics (HR = 0.41; p < 0.001) and, when combined with manual scores, provided the greatest additional discrimination (p < 0.001). In high-TIL patients, pertuzumab reduced iDFS events by 64% (manual HR = 0.36; p int = 0.003), 52% (digital HR = 0.48; p int = 0.025), and 54% (AI HR = 0.46; p int = 0.01). Manual scoring alone identified 562/2,573 (22%) node-positive patients as high-TIL and likely pertuzumab-responsive, whereas AI-percentage lymphocyte identified 625 (24%) thus contributing to further detect 253 node-positive patients who would benefit from addition of pertuzumab (a 10% larger group). AI spatial metrics were not predictive. Conclusions: Despite modest concordance, manual, digital and AI-derived sTIL assessments independently demonstrated prognostic and predictive value for identifying HER2-positive BC patients benefiting from adjuvant pertuzumab. AI-driven sTIL quantification matches and slightly improves prognostic accuracy, importantly both AI and manual sTIL can identify a larger group of pertuzumab-responsive patients. Integrating AI-derived quantitative and spatial metrics into multiparameter models can further individualize HER2-targeting therapy.

A. Llombart-Cussac¹, J. Pérez-García², M. Ruiz-Borrego³, A. Stradella⁴, B. Bermejo⁵, S. Escrivá-de-Romaní⁶, C. Reboredo⁷, N. Ribelles⁸, A. Cortés-Salgado⁹, C. Albacar¹⁰, M. Colleoni¹¹, G. Antonarelli¹², G. Notini¹³, M. Gion¹⁴, J. García-Mosquera¹⁵, L. Sanz¹⁶, E. Martínez-García¹⁷, P. González-Alonso¹⁷, A. Amaya-Garrido¹⁸, J. Guerrero¹⁹, J. Rodríguez-Morató¹⁸, L. Mina¹⁹, G. Martrat²⁰, M. Quintana²¹, F. Riva²², D. Dustin²², H. Zhang²², M. Mancino¹⁷, J. Cortés²³; ¹Oncology, Hospital Arnau de Vilanova; Translational Oncology Group, Facultad de Ciencias de la Salud, Universidad Cardenal Herrera-CEU; Medica Scientia Innovation Research (MedSIR), Valencia, SPAIN, ²Oncology, International Breast Cancer Center (IBCC), Pangaea Oncology,  Quiron  Group;Medica Scientia Innovation Research (MedSIR), Barcelona, SPAIN, ³Oncology, Hospital Universitario Virgen del Rocío, Sevilla, SPAIN, ⁴Oncology, Institut  Català  d’Oncologia, Barcelona, SPAIN, ⁵Oncology, Hospital Clínico Universitario de Valencia, Biomedical Research Institute INCLIVA; Oncology Biomedical Research National Network (CIBERONC-ISCIII), Madrid, Valencia, SPAIN, ⁶Oncology, Vall  d’Hebron  University Hospital, Vall  d’Hebron  Institute of Oncology (VHIO), Barcelona, SPAIN, ⁷Oncology, Complejo Hospitalario Universitario A Coruña (CHUAC), A Coruña, SPAIN, ⁸Oncology, Hospital Universitario Virgen de la Victoria; Instituto de Investigaciones Biomédicas de Málaga (IBIMA), Málaga, SPAIN, ⁹Oncology, Medical Oncology Department, Hospital Universitario Ramon y Cajal (YRYCIS), Madrid, SPAIN, ¹⁰Oncology, Institut d’Oncologia de la Catalunya Sud (IOCS), Hospital Universitari Sant Joan de Reus, IISPV, Reus, SPAIN, ¹¹Oncology, Instituto Europeo di Oncologia (IEO), IRCCS, Milano, ITALY, ¹²Oncology and Hemato-Oncology (DIPO), University of Milan; European Institute of Oncology, IRCCS, Milan, ITALY, ¹³Oncology, Medica Scientia Innovation Research (MedSIR), Barcelona, SPAIN, ¹⁴Oncology, Hospital Ramón y Cajal; IOB Madrid, Hospital Beata María Ana, Madrid, SPAIN, ¹⁵Oncology, Hospital Universitari Dexeus; Medica Scientia Innovation Research (MedSIR), Barcelona, SPAIN, ¹⁶Oncology, IOB Madrid, Hospital Beata María Ana, Madrid, SPAIN, ¹⁷Translational, Medica Scientia Innovation Research (MEDSIR), Barcelona, SPAIN, ¹⁸Scientific Impact, Medica Scientia Innovation Research (MEDSIR), Barcelona, SPAIN, ¹⁹Data Analytics, Medica Scientia Innovation Research (MEDSIR), Barcelona, SPAIN, ²⁰Scientific, Medica Scientia Innovation Research (MEDSIR), Barcelona, SPAIN, ²¹Strategic Services, Medica Scientia Innovation Research (MEDSIR), Barcelona, SPAIN, ²²Oncology, Guardant Health, Palo Alto, CA, ²³Oncology, International Breast Cancer Center (IBCC), Pangaea Oncology,  Quiron  Group; IOB Madrid; Hospital Beata María Ana; Universidad Europea de Madrid; Hospital Universitario Torrejón, Ribera Group; Medica Scientia Innovation Research (MedSIR), Madrid, SPAIN.

IntroductionHER2-directed therapies have dramatically improved the outcome of patients (pts) with HER2[+]EBC, leading to the investigation of different de-escalation approaches. ctDNA is an emerging tool for risk stratification and real-time monitoring in EBC, offering the potential to personalize treatment decisions. The PHERGain study showed the feasibility of a FDG positron emission tomography (PET)-guided, pathological complete response (pCR)-adapted strategy to safely omit chemotherapy (CT) in pts with stage I-IIIA HER2[+] EBC undergoing neoadjuvant dual HER2 blockade with trastuzumab and pertuzumab (HP). The PET- and pCR-guided approach allowed omission of CT in 37.9% of pts, achieving a 3-year invasive-disease free survival (iDFS) rate (94.8%) in the overall adaptive group. In this sub-study, we assess a tumor-uninformed epigenomic-based ctDNA assay for minimal residual disease detection to improve the prediction of pCR and 3-year iDFS, thereby enabling more tailored (neo)adjuvant treatment strategies for HER2[+] EBC pts within the framework of the PHERGain study. MethodsDetails of the trial design and study population have been previously reported. Out of 356 randomized pts, 63 in group A (standard treatment) and 267 in group B (adaptive treatment) proceeded to surgery. The primary objective of the PHERGuide sub-study was to assess the correlation between ctDNA clearance after two treatment cycles and pCR (ypT0/is ypN0) in all included pts. Secondary objectives evaluated the association between ctDNA levels and patient outcomes. Blood samples were collected at baseline, after cycle 2 of neoadjuvant treatment, and pre-surgery. A total of 932 samples from 351 pts (336 baseline, 311 cycle 2, and 285 pre-surgery) were analyzed using Guardant Reveal^TM, a tumor-uninformed epigenomic assay that provides a binary ctDNA detection result along with an estimated tumor fraction. ctDNA clearance was defined as detectable ctDNA at baseline but undetectable at a subsequent time point. Categorical variables were analyzed using logistic regression models, and survival outcomes were assessed with Cox proportional hazards regression. ResultsOf the 932 samples, 801 were eligible for this sub-study (161 in group A and 640 in group B). ctDNA was detected in 204 of 288 (71%) baseline samples suitable for analysis. Detection rates were significantly correlated with disease stage: 33% (9/27) in stage I, 73% (154/217) in stage II, and 93% (41/44) in stage III tumors (p < 0.001). A total of 126/167 (75.4%) and 124/149 (83.2%) pts with detectable baseline ctDNA showed ctDNA clearance after two treatment cycles and prior to surgery, respectively. ctDNA clearance after two treatment cycles (p = 0.003) and at the pre-surgical time point (p < 0.001) was significantly associated with achieving a pCR. No significant correlation was observed between baseline ctDNA status and pCR (p = 0.583). No patient with detectable ctDNA prior to surgery (n = 25) achieved a pCR. ctDNA positivity at baseline was associated with worse 3-year iDFS (92.5% vs. 100%, HR 0.20; 95%CI 0.02-0.98; p = 0.046). ConclusionsThese findings demonstrate a significant correlation between ctDNA clearance and pCR in HER2[+] EBC pts undergoing neoadjuvant HER2-targeted therapy. Moreover, detectable ctDNA at baseline is associated with inferior 3-year outcomes. Further prospective studies are needed to confirm these results.