Rapid Fire 3

R. Salgado¹, R. Gray², G. Broeckx³, C. Desmedt⁴, A. Li⁵, G. Van den Eynden⁶, Z. Kos⁷, B. Acs⁸, T. Tramm⁹, E. Stovgaard¹⁰, C. Focke¹¹, L. Comerma Blesa¹², A. Hida¹³, M. Lacroix-Triki¹⁴, E. Provenzano¹⁵, F. Pareja¹⁶, S. Maley¹⁷, N. Villena¹⁸, H. Montgomery¹⁹, E. Li-Ning-Tapia²⁰, A. Lazar²¹, S. Badve²², S. Loi¹, J. Sparano²³, The International Immuno-Oncology Biomarker Working Group, TAILORx Investigators; ¹Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, AUSTRALIA, ²Biostatistics and Computational Biology, Dana-Farber Cancer Institute (DFCI), Boston, MA, ³Department of Pathology, ZAS Hospitals, Antwerp, BELGIUM, ⁴Laboratory for Translational Breast Cancer Research; Department of Oncology, KU Leuven, Leuven, BELGIUM, ⁵Department of Pathology, Massachusetts General Hospital, Boston, MA, ⁶Department of Pathology and Cytology, Heilig Hartziekenhuis Lier, Lier, BELGIUM, ⁷Department of Pathology and Laboratory Medicine, University of British Columbia & BC Cancer, Vancouver, BC, CANADA, ⁸Department of Oncology-Pathology, Karolinska Institutet, Stockholm, SWEDEN, ⁹Department of Pathology / Clinical Medicine, Aarhus University Hospital & Aarhus University, Aarhus N, DENMARK, ¹⁰Department of Clinical Medicine, University of Copenhagen, Copenhagen, DENMARK, ¹¹Department of Surgical Pathology, Dietrich Bonhoeffer Medical Centre, Neubrandenburg, GERMANY, ¹²Pathology Department, Hospital del Mar, Parc de Salut Mar, Barcelona, SPAIN, ¹³Department of Pathology, Matsuyama City Hospital, Matsuyama, JAPAN, ¹⁴Department of Pathology, Gustave Roussy, Villejuif Cedex, FRANCE, ¹⁵Department of Histopathology, Addenbrookes Hospital, Cambridge, UNITED KINGDOM, ¹⁶Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, ¹⁷NRG Oncology, NSABP Foundation, Pittsburgh, PA, ¹⁸Pathology Department, Aalborg University Hospital, Aalborg, DENMARK, ¹⁹NA, PaigeAI, New York, NY, ²⁰Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, ²¹Pathology, Genomic Medicine, Dermatology, Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, ²²Department of Pathology and Laboratory Medicine, Emory University School of Medicine / Winship Cancer Institute, Atlanta, GA, ²³NA, Montefiore Medical Center-Weiler Hospital, Bronx, NY.

Background: Approximately15% of invasive breast cancers are Invasive Lobular Cancer (ILC) with differingclinical outcomes than non-lobular breast cancer (NLC). Digital pathologyenables high-throughput biomarkers and morphology analysis. The TME,particularly TILs is a robust prognostic factor in localized breast cancer.Here, we detail the prognostic importance of ductal vs lobular histology,manually and with AI, combined with manual and novel AI-derived TME metrics inearly (5y) recurrence with 15-years of follow-up inTAILORx.Methods: H&E whole slideimages (WSI) from 8,422 patients were reviewed by 16 breast pathologists who completedcentral review for grade, TILs (www.tilsinbreastcancer.org) and histology (WHOclassification). The same H&E dataset was evaluated with an AI-based CDH1-classifier(Paige.AI). In addition, a zero-shot AI model (Case45) generated a panel of TMEbiomarkers (TIL abundance, spatial TIL levels with proximity to cancer cellsand degree of cancer cell-fibroblast contact) combined into a single TME-riskscore. Clinical outcomes were evaluated using multivariable Cox models adjustedfor age, tumor size, Oncotype DX 21-gene recurrence score (RS; Exact Sciences),adjuvant therapy (endocrine vs chemo-endocrine), and centrally determined grade,histology (ILC vs NLC), and manual TILs. Similar analysis were performed usingthe CDH1-classifier rather than central histology, and TME risk score. Results: Centralpathology review revealed ILC in 11.9% of cases, and NLC in 88.1% (77.7% ductal,10.4% non-ductal). Concordances ratesbetween the pathologists were 0.837 and 0.494 (Fleiss’ Kappa) for histology andgrade, respectively, with ICC of 0.968 for TILs. Both centralized pathologyreview (11.9%) and the CDH1-AI classifier (11.2%) demonstrated that ILC has consistentlyhigher risk of recurrence than NLC between years 5-15, but not before (Figure1); there was a 4.9% overall survival (OS)-difference between ILC vs NLC at 15years. For manual TILs, estimated hazard ratio (HR) for a 10-point differencein TILs was 1.06 (95% CI 1.00, 1.13 p=0.04) for distance recurrence freeinterval (DRFI). AI-derived TME-risk stratified DRFI from 95.7% to 90.9% at 10years, and from 92.1% to 86.9% at 15 years (HR per standard deviation (SD)1.27, 95% CI 1.15-1.40, p<0.0001). This association remained significantafter adjustment for clinicopathologic factors including 21-gene RS (HR per SD1.14, 95% CI 1.04-1.25, p=0.005). Conclusion: ILC(identified by manual review or CDH1-AI classifier) is associated with higherlate recurrence risk and death than NLC at 15 years after diagnosis in TAILORx, the majority of whom received a 5-yearcourse of adjuvant endocrine therapy. Furthermore, both manual TILs andAI-derived TME analysis provide independent risk stratification in addition to 21-geneRS in HR+/HER2− node-negative breast cancer. These findings haveimplications for considering up to a 10-year course of adjuvant ET in womenwith ER+, HER2−, node-negative ILC, even when there is a low 21-gene RS.

C. Denkert¹, S. Rachakonda², T. Karn³, A. Schneeweiss⁴, C. Solbach⁵, P. Rastogi⁶, F. Moreno⁷, T. Freeman⁶, T. Link⁸, J. Mouta⁹, M. Reinisch¹⁰, R. Meyer¹¹, Á. Rodríguez Lescure¹², V. Bjelic-Radisic¹³, P. A. Fasching¹⁴, M. Balic⁶, M. Untch¹⁵, K. Rhiem¹⁶, J. Teply-Szymanski¹, K. Lüdtke-Heckenkamp¹⁷, J. Huober¹⁸, S. Morales¹⁹, I. Blancas²⁰, J. Holtschmidt², V. Nekljudova², N. Wolmark⁶, C. E. Geyer⁶, S. Loibl²¹; ¹Institute of Pathology, Philipps-University Marburg and University Hospital Marburg (UKGM), Marburg, GERMANY, ²Medicine and Research Departement, GBG c/o GBG Forschungs GmbH, Neu-Isenburg, GERMANY, ³Department of Gynecology and Obstetrics, University of Frankfurt, UCT Frankfurt-Marburg, Frankfurt am Main, GERMANY, ⁴Division of Gynaecological Oncology, National Center for Tumor Diseases (NCT), University Hospital and German Cancer Research Center, Heidelberg, GERMANY, ⁵Breast Unit, University Medical Center, Goethe-University Frankfurt, Frankfurt am Main, GERMANY, ⁶NSABP Foundation, Inc., University of Pittsburgh School of Medicine, and UPMC Hillman Cancer Center, Pittsburgh, PA, ⁷Medical Oncology Department, Hospital Universitario Clínico San Carlos, GEICAM Spanish Breast Cancer Group, Madrid, SPAIN, ⁸Department of Gynecology and Obstetrics, Medical Faculty and University Hospital Carl Gustav Carus, National Center for Tumor Diseases, Technische Universität Dresden, Dresden, GERMANY, ⁹Product Development Medical Affairs Oncology, Roche Farmacêutica Química Lda, Amadora, PORTUGAL, ¹⁰Interdisciplinary Breast Unit, University Medical Center Mannheim, University of Heidelberg, Mannheim, GERMANY, ¹¹Department of Internal Medicine, Hematology and Oncology, Hämato-Onkologie Praxis im Medicum, Bremen, GERMANY, ¹²Medical Oncology Department, Hospital General Universitario de Elche, GEICAM Spanish Breast Cancer Group, Elche, SPAIN, ¹³Department of Obstetrics and Gynecology, Helios University Clinic Wuppertal, University Witten/Herdecke, Wuppertal, GERMANY, ¹⁴Department of Obstetrics and Gynecology, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Erlangen, GERMANY, ¹⁵Department of Obstetrics and Gynecology, Interdisciplinary Breast Cancer Center, Medical School Berlin, Helios Kliniken Berlin-Buch, Berlin, GERMANY, ¹⁶Center for Hereditary Breast and Ovarian Cancer, University Hospital Cologne, Cologne, GERMANY, ¹⁷Department of Internal Medicine, Hematology and Oncology, Niels-Stensen-clinics GmbH, Georgsmarienhütte, GERMANY, ¹⁸Breast center, HOCH Health Ostschweiz, Cantonal Hospital, St. Gallen, SWITZERLAND, ¹⁹Oncology Department, Hospital Arnau de Vilanova, GEICAM Spanish Breast Cancer Group, Lleida, SPAIN, ²⁰Medicine Department, Granada University, Hospital Universitario Clínico San Cecilio, Instituto de Investigación Biosanitaria de Granada (ibs.Granada), GEICAM Spanish Breast Cancer Group, Granada, GERMANY, ²¹Medicine and Research Departement, GBG c/o GBG Forschungs GmbH, Neu Isenburg, Goethe University Frankfurt, Frankfurt am Main, GERMANY.

Introduction: The NSABP-B59/GBG-96-GeparDouze trial study was designed to evaluate the value of adding atezolizumab to standard neoadjuvant chemotherapy in TNBC. As reported at the SABCS 2024 meeting, the addition of atezolizumab to neoadjuvant chemotherapy followed by adjuvant atezolizumab did not result in statistically significant improvement in event-free survival (EFS) over the control arm (Geyer et al. SABCS 2024, GS 3-05). Here, we report the first biomarker analyses for subtyping of triple-negative breast cancer and evaluation of predictive signatures, based on previous analyses that were performed in the GeparNuevo trial. Methods: We evaluated a total of 494 pre-therapeutic (pre-Tx) core biopsies for gene expression of 2549 genes using the HTG EdgeSeq system. For subtyping of tumors, we used the AIMS approach as well as the TNBC subtyping. Endpoints were pathological complete response (pCR) as well as EFS. Predefined gene expression signatures for immune genes, proliferation genes and stromal genes were used based on previous evaluations in the GeparNuevo trial (Denkert et al. Cell Rep. Med. 2024, PMID 39566464). Results: Based on classical AIMS subtyping of this TNBC cohort, the main subtypes were Basal-like (70.5%) and HER2-enriched (23.8%). With TNBC subtyping, the main subtypes were immunomodulatory (IM, 25.5%), Mesenchymal (23.9%), Basal-like-1 (BL1, 17.6%), Mesenchymal stem-like (MSL, 15.2%), Basal-like-2 (BL2, 10.1%) and Luminal androgen receptor (LAR, 7.7%). pCR rates for all subtypes were: IM (74.6%), M (38.1%), BL1 (58.6%), MSL (46.7%), BL2 (54.0%) and LAR (21.1%). The IM subtype had a similar pCR rate in both therapy arms (atezolizumab: 76.2% vs. placebo 73.02%). Numerically increased pCR rates in the atezolizumab arm were observed for BL2 (atezolizumab: 60.8% vs. placebo 48.2%) and LAR (atezolizumab: 31.3% vs. placebo 13.6%) Kaplan-Meier analysis showed significant differences for the six TNBC subtypes overall. Detailed EFS data for the separate subgroups will be presented. When considering a predefined gene set of 126 genes that had been defined in the GeparNuevo trial, with a focus on immune genes, proliferation genes and stromal genes, the expression of most immune and proliferation genes was significantly associated with pCR in both therapy arms. For EFS, a subset of immune genes including HLA-A, HLA-B and IL6R was significant only in the atezolizumab arm (with a positive test for interaction for IL6R; interaction p-value 0.017). In a combined analysis of the two endpoints pCR and EFS, a positive association between the expression of immune genes and improvement in both endpoints was confirmed, while expression of stromal genes was associated with reduced response and reduced survival. Conclusion: In this study we have observed a considerable heterogeneity of TNBC subtypes for pCR and EFS in the context of neoadjuvant immune-chemotherapy. We have been able to confirm previous results regarding the contribution of stromal genes, immune genes and proliferation genes to response to neoadjuvant immune-chemotherapy in TNBC. The similarities regarding predictive genes for pCR and prognostic genes for EFS suggest that the interaction between immune cells and tumor cells is relevant in the placebo-arm, as well. Expression of selected immune genes is significant only in the atezolizumab arm, these genes are candidates for further validations.

C. E. Geyer¹, M. Filipits², N. Harbeck³, N. Harbeck³, J. Zhang6⁴, P. Rastogi¹, A. Piehler⁴, T. Freeman⁵, M. Balic¹, W. Zwerink⁴, H. Kreipe⁶, D. Hlauschek⁷, S. Anderson⁸, J. R. Griffin⁴, D. Kates-Harbeck⁹, M. Gnant², N. Wolmark¹⁰; ¹NSABP Foundation and UPMC Hillman Cancer Center, Pittsburgh, PA, Pittsburgh, PA, ²Austrian Breast and Colorectal Cancer Study Group (ABCSG), Vienna, AUSTRIA, Vienna, Austria, ³Breast Center, Dept. OB/GYN and CCC Munich, LMU University Hospital, Munich, GERMANY, Munich, Germany, ⁴ArteraAI, Mountain View, CA, ⁵NSABP and Department of Pathology, University of Pittsburgh, Pittsburgh, PA, Pittsburgh, PA, ⁶Department of Pathology, Medical School Hannover, Hannover, GERMANY, Hannover, Germany, ⁷Center for Cancer Research, Medical University of Vienna, Vienna, AUSTRIA, Vienna, Austria, ⁸NSABP and Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, Pittsburgh, PA, ⁹West German Study Group, Moenchengladbach, GERMANY, Moenchengladbach, Germany, ¹⁰NSABP and Department of Surgery, University of Pittsburgh, Pittsburgh, PA., Pittsburgh, PA

Background:Accurate risk stratification for distantmetastasis (DM) in hormone receptor-positive (HR+) early-stage breast cancer(EBC) is important in guiding decisions on selection of adjuvant therapies,particularly use of chemotherapy (CT). Genomic assays have improveddecision-making in these patients; however, global accessibility can be limiteddue to cost and logistical constraints. The Multimodal Artificial Intelligence(MMAI) breast cancer model validated for prognosis in ABCSG-08 integrateshistopathological images and clinical data to stratify risk of DM as apotential lower-cost alternative. Here, we evaluated the MMAI algorithm in thelandmark NSABP B-20 Trial – a randomized phase III trial of tamoxifen (TAM)alone, TAM plus methotrexate and fluorouracil (MFT) or TAM pluscyclophosphamide, methotrexate, and fluorouracil (CMFT) in women with nodenegative (N0) HR+ EBC. Methods:This study included patients from B-20 withavailable digitized H&E-stained surgical slides, complete clinical data(age and tumor size), and long-term follow-up. The evaluation was conductedusing locked MMAI scores and the pre-specified risk group classifications (Low,Intermediate, High). The primary endpoint was DM. Prognostic performance wasassessed in the TAM arm using Fine-Gray (FG) regression models, with sub-distributionhazard ratios (sHR) and associated 95% confidence interval (CI) reported. Forevaluation of CT benefit, interactions between treatment arm and MMAI (eithercontinuous score or dichotomized, i.e., low vs. intermediate/high-risk) wereassessed by FG regression both in the entire cohort and by age groups¹ defined as in the original B-20 follow-up. Results:A total of 1763patients (75% of the B-20 population), with median follow-up time of 14.6 years,had MMAI scores generated for analyses. Low-risk scores were assigned in 1,189pts (67%), intermediate risk in 180 pts (10%) and high risk in 394 pts (22%).In the TAM arm, both the MMAI score as a continuous variable and MMAI riskgroups were significantly associated with risk of DM: MMAI score (sHR [95% CI]= 1.94 [1.57-2.41], p<.001) and MMAI risk group (high vs low sHR [95% CI] =3.97 [2.57-6.16], p<.001; intermediate vs low 2.78 [1.47-5.23], p=.002). Inthe entire cohort, no statistically significant interaction effect betweentreatment arms and either continuous or dichotomized MMAI was found. However, the interaction was significant (p=.01) in patients aged ≥ 50: In MMAI high/intermediate-risk patients (32%), addition of CT was associated with a 52% relative 10-year DM riskreduction (10% in CMFT vs 21% in TAM), compared to (10-year DM rate: 7% in CMFTvs. 5% in TAM) in low-risk patients. The predictive interaction of dichotomizedMMAI was not significant in patients aged <50; chemotherapy added benefit inboth low and high/intermediate MMAI groups. Conclusion:The MMAI demonstrated strong independentprognostic performance in all patients with HR+ N0 EBC from the NSABP B-20 trialbut was not predictive of chemotherapy benefit in the entire population.However, in an exploratory analysis by age, in patients aged ≥ 50 the MMAI risk groups (high/intermediate vs. low) werepredictive for benefit of CMF. These findings supportthe potential use of MMAI as a lower cost, non-tissue consumptive alternativeto genomic testing for guiding CT decisions in older HR+ N0 EBC patients. Keywords:Artificial intelligence, breast cancer,HR-positive, early-stage, distant metastasis, multi-modal model, prognosticbiomarker, chemotherapy predictive Support:U10 CA180868, -180822; U24 CA196067

H. A. Parsons¹, K. Ballman², E. Heitzer³, M. Watson⁴, M. Balic⁵, D. Hlauschek⁶, D. Renner⁷, E. Kalashnikova⁷, G. Steger⁸, J. M. Balko⁹, Y. Novik¹⁰, M. Martin¹¹, A. A. Rodriguez⁷, Z. Dayao¹², A. Chan¹³, E. Nili Gal-Yam¹⁴, M. C. Liu⁷, C. Isaacs¹⁵, M. Los¹⁶, M. Gil Gil¹⁷, B. Felder¹⁸, C. Denkert¹⁹, P. Fasching²⁰, F. Liu²¹, T. O’Donnell²², E. L. Mayer²³, M. Gnant²⁴, W. F. Symmans²⁵, A. DeMichele²⁶; ¹Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, ²Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, MN, ³Liquid Biopsies Research Unit, Medical University Graz, Graz, AUSTRIA, ⁴Biospeciman Bank, Washington University, St. Louis, MO, ⁵Division of Oncology, Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, ⁶Statistics Department, ABCSG, Austrian Breast and Colorectal Cancer Study Group, Vienna, AUSTRIA, ⁷Oncology, Natera, Austin, TX, ⁸Hemato-Oncology, BIG/ABCSG, Medical University Vienna, Vienna, AUSTRIA, ⁹Molecular Oncology, Vanderbilt University Medical Center, Nashville, TN, ¹⁰Breast Oncology, New York University Langone Health, New York, NY, ¹¹Medical Oncology Service, Hospital General Universitario Gregorio Marañón, Madrid, SPAIN, ¹²Division of Hematology/Oncology, University of New Mexico, Albuquerque, NM, ¹³Medical Oncology, Breast Cancer Research Centre – WA, Nedlands, AUSTRALIA, ¹⁴Department of Breast Malignances, Chaim Sheba Medical Centre, Tel Aviv-Yafo, ISRAEL, ¹⁵Breast Cancer Program, Georgetown University, Washington, DC, ¹⁶Department of Oncology, St. Antonius Hospital Nieuwegein, Nieuwegein, NETHERLANDS, ¹⁷Breast Cancer Unit, Department of Medical Oncology, Catalan Institute of Oncology, Barcelona, SPAIN, ¹⁸Translational Research, GBG Forschungs GmbH, Neu-Isenburg, GERMANY, ¹⁹Institute of Pathology, Philipps-Universität Marburg, Marburg, GERMANY, ²⁰Gynecologic Oncology Center, University Hospital Erlangen and Comprehensive Cancer Center, Erlangen, GERMANY, ²¹Clinical Pharmacology and Translational Sciences, Oncology, Pfizer Inc., San Diego, CA, ²²Clinical Operations, Alliance Foundation Trials, LLC, Boston, MA, ²³Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, ²⁴Comprehensive Cancer Center, Medical University of Vienna, Vienna, AUSTRIA, ²⁵Department of Anatomical Pathology, MD Anderson Cancer Center, Houston, TX, ²⁶Medical Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.

Background. PALLAS (PALbociclib CoLlaborative Adjuvant Study, NCT02513394) is a randomized phase III trial comparing two years of the CDK4/6 inhibitor palbociclib combined with the physician’s choice of adjuvant endocrine therapy, versus endocrine therapy alone for patients with Stage II-III hormone receptor-positive, HER2-negative (HR+/HER2-) breast cancer. Molecular residual disease (MRD) detection via circulating tumor DNA (ctDNA) testing was a predefined biomarker analysis to identify patients at highest risk of recurrence. Methods. PALLAS participants with available tumor block, at least a baseline (pre-study treatment) plasma sample, and either whole blood or buffy coat were randomly selected from the overall study population. Plasma samples on cycle 1 day 1 (C1D1), cycle 6 day 1 (C6D1), and at the end of treatment (EOT) were collected, processed, and stored. Nucleic acids were extracted from primary tumor tissue or, when primary tissue was insufficient, from post-neoadjuvant tumor tissue, provided the sample had sufficient material and tumor cellularity (>25 mm² tissue area with ≥20% tumor nuclei). Data from next generation sequencing of tumor tissue and matched normal DNA were used to create a personalized ctDNA Signatera^TM assay (Natera, Inc.) for MRD assessment in the peripheral blood. ctDNA was reported as mean tumor molecules/mL (MTM/mL) of plasma. The primary endpoint was distant recurrence-free interval (DRFI) based on ctDNA status. Cox Proportional Hazards models were used to evaluate prognosis and predictive interactions. To minimize bias, the biorepository and laboratory teams were blinded to participant identity. Results. In total, 1280 participants were randomly selected for inclusion in this preplanned ctDNA sub-study. WES and WGS are being conducted on tissue, and Signatera results will be generated. Results for association of MRD status (positive/negative) and MTM/mL with DRFI in the context of 7 year median follow up of this study cohort will be reported. Conclusions. The planned analysis is ongoing and the presented work will provide data from the first randomized phase 3 adjuvant study in HR+/HER2- breast cancer to report MRD status by treatment arm.

N. Cunningham¹, R. J. Cutts², C. Swift¹, K. Dunne¹, M. Dewan³, L. Kilburn³, K. Goddard³, P. Hall⁴, C. Harper-Wynne⁵, T. Hickish⁶, I. Macpherson⁷, A. Okines⁸, A. Wardley⁹, S. Waters¹⁰, C. Palmieri¹¹, M. Winter¹², J. Bliss³, D. Dustin¹³, M. Ellis¹³, I. Garcia- Murillas², N. C. Turner²; ¹Ralph Lauren Centre for Breast Cancer Research, Royal Marsden Hospital, London, UNITED KINGDOM, ²Breast Cancer Now Research Centre, Institute of Cancer Research, London, UNITED KINGDOM, ³Clinical Trials and Statistics Unit, Institute of Cancer Research, London, UNITED KINGDOM, ⁴University of Edinburgh, University of Edinburgh, Edinburgh, UNITED KINGDOM, ⁵Maidstone Hospital, Maidstone and Tunbridge Wells NHS Trust, London, UNITED KINGDOM, ⁶University Hospitals Dorset NHS Foundation Trust, University Hospitals Dorset NHS Foundation Trust, Bournemouth, UNITED KINGDOM, ⁷The Beatson West of Scotland Cancer Centre, The Beatson West of Scotland Cancer Centre, Glasgow, UNITED KINGDOM, ⁸The Breast Unit, Royal Marsden Hospital, London, UNITED KINGDOM, ⁹Outreach Research & Innovation Group Ltd, Outreach Research & Innovation Group Ltd, Manchester, UNITED KINGDOM, ¹⁰Velindre Cancer Centre, Velindre University NHS Trust, Cardiff, UNITED KINGDOM, ¹¹Clatterbridge Cancer Centre NHS Foundation Trust, Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, UNITED KINGDOM, ¹²Weston Park Cancer Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UNITED KINGDOM, ¹³Guardant Health, Guardant Health, Palo Alto, CA.

Background: Detection of circulating tumour DNA (ctDNA) in plasma predicts future recurrence in early breast cancer (EBC). Previous studies have relied on tumour-informed approaches with personalised assays. In this study, we performed orthogonal testing on plasma samples collected prospectively in the c-TRAK-TN study to compare a tissue-free assay with digital droplet PCR (ddPCR) for ctDNA analysis. Methods: c-TRAK-TN recruited 161 patients with early triple negative breast cancer (TNBC) deemed clinically high risk of relapse. Patients underwent ctDNA analysis by ddPCR starting within 4 weeks of completion of treatment and continuing every 3 months for up to 2 years. ddPCR tracked 1 or 2 patient specific variants as previously described. We further analysed 1062 timepoints from a total of 159 patients (median 7 per patient, range 1-11) using Guardant Reveal assay, a tissue-free approach that exploits differential methylation patterns in breast cancer compared with normal tissue. Results: Samples were successfully analysed from 95.3% (1012/1062) timepoints from 159 patients with the tissue-free Reveal assay. CtDNA was detected in 34.0% (54/159) of patients in at least 1 timepoint, with a median methylation tumour fraction of 0.266% (range 0.002 to 51.0%). Detection of ctDNA with the tumour agnostic assay strongly associated with future risk of relapse (HR 20.62, p<0.0001; logrank). Of patients who relapsed by 24 months follow up, 89.5% (34/38) of relapses were detected by prior ctDNA analysis. There were 159 patients available for comparison between ddPCR and the tissue-free assay (Reveal), in 1005 timepoints (median 7 per patient, range 1-11). Concordant test results were observed in 950/1005 timepoints with an overall agreement rate of 94.5%. In this cohort, 63.5% (101/159) of patients did not have ctDNA detected by either assay; 26.4% (42/159) had ctDNA detected by both assays and 10.1% (16/159) had discordant results. Of those detected, 44.8% (26/58) were detected by the tissue-free assay first and 6.9% (4/58) by ddPCR (p<0.0001, Fisher exact test). The median time from first detection of ctDNA to relapse for the tissue-free assay was 7.9 months (95% confidence interval (CI): 5.7-10.5 months) compared to ddPCR assay with 5.7 months (95% CI: 2.9-9.7 months) (HR = 0.6, p=0.0574; mixed effects cox model). Conclusion: Detection of ctDNA with a tissue-free assay anticipated relapse with high accuracy, in patients with early TNBC. The tissue-free assay frequently detected ctDNA at an earlier timepoint than ddPCR, trending towards a longer lead time from detection of ctDNA to relapse. Tissue-free approaches might offer simpler workflows for ctDNA analysis than tumour-informed approaches, and further assessment in clinical trials is warranted.

S. S. Badve¹, F. Zhao², S. Reid³, Y. Gokmen-Polar¹, I. Mayer³, C. L. Arteaga⁴, W. F. Symmans⁵, B. H. Park⁶, B. L. Burnette⁷, D. F. Makower⁸, M. Block⁹, M. A. Kimberly¹⁰, C. R. Jani¹¹, C. Mescher¹², S. J. Dewani¹³, B. Tawfik¹⁴, B. Tawfik¹⁴, L. E. Flaum¹⁵, E. L. Mayer¹⁶, W. M. Sikov¹⁷, E. T. Rodler¹⁸, A. M. DeMichele¹⁹, J. A. Sparano²⁰, A. C. Wolff²¹, K. D. Miller²², ECOG-ACRIN; ¹Pathology, Emory School of Medicine, Atlanta, GA, ²Biostatistics, DFCI, Boston, MA, ³Hem Onc, Vanderbilt University Medical Center, Nashville, TN, ⁴Hem Onc, UT Southwestern/Simmons Cancer Center-Dallas, Dallas, TX, ⁵Pathology, M D Anderson Cancer Center, Houston, TX, ⁶Hem Onc, Vanderbilt University/Ingram Cancer Center, Nashville, TN, ⁷Hem Onc, Saint Vincent Hospital Cancer Center Green Bay, Green Bay,, WI, ⁸Hem Onc, Montefiore Medical Center, Bronx, NY, ⁹Hem Onc, Alegent Health Bergan Mercy Medical Center, Omaha, NE, ¹⁰Hem Onc, Saint Joseph Mercy Hospital, Ypsilanti, MI, ¹¹Hem Onc, Phoebe Putney Memorial Hospital, Albany, GA, ¹²Hem Onc, Metro-Minnesota Community Oncology Research Consortium, St Louis Park, MN, ¹³Hem Onc, Columbus Oncology and Hematology Associates Inc, Columbus, OH, ¹⁴Hem Onc, University of New Mexico Cancer Center, Albuquerque, NM, ¹⁵Hem Onc, Northwestern University, Chicago, IL, ¹⁶Hem Onc, Beth Israel Deaconess Medical Center, Boston, MA, ¹⁷Hem Onc, Women and Infants Hospital, Providence, RI, ¹⁸Hem Onc, Fremont – Rideout Cancer Center, Marysville, CA, ¹⁹Hem Onc, University of Pennsylvania/Abramson Cancer Center, Philadelphia, PA, ²⁰Hem Onc, Mt Sinai Medical Center, New York, NY, ²¹Hem Onc, Johns Hopkins Univ/Sidney Kimmel Cancer Center, Baltimore, MD, ²²Hem Onc, Indiana Univ/Melvin and Bren Simon Cancer Center, Indianapolis, IN.

Background: Patients with residual triple negative breast cancer (TNBC) after neoadjuvant chemotherapy (NAC) have a higher risk of recurrence and worse survival outcomes. Prior studies have shown that higher stromal tumor-infiltrating lymphocytes (sTILs) post-NAC in patients with residual TNBC is associated with improved survival outcomes. In this secondary analysis of EA1131, we evaluated the association between sTILs, intrinsic subtype, and invasive disease-free survival (iDFS), stratified by race in patients with residual TNBC after completion of NAC. Patients and methods: EA1131 enrolled patients with clinical stage II or III TNBC with ≥1.0 cm residual disease (RD) in the breast post-NAC were randomly assigned to receive platinum (carboplatin or cisplatin) once every 3 weeks for four cycles or capecitabine on days 1-14 of a 21-day cycle for six cycles. Stromal TIL density was assessed on full-face hematoxylin and eosin (H&E)-stained tumor sections. Patients were categorized into four groups based on sTILs level (<30% vs. ≥30%) and tumor intrinsic subtype (basal vs. non-basal): (1) low sTILs/basal, (2) low sTILs/non-basal, (3) high sTILs/basal, and (4) high sTILs/non-basal. Cox proportional hazard models were used to examine the association between sTILs and intrinsic subtype group and iDFS, adjusting for available baseline factors. The association between sTILs category and race and intrinsic subtype were examined using Fisher exact test. Results: Among 410 participants with TNBC and RD, H&E slides were available for all 342 who had intrinsic subtype (264 basal, 78 non-basal). Most patients (93%, n=319) had low sTILs (70%: n=3). There was no difference in sTILs by intrinsic subtype (p=0.31) or race (p=0.5). Compared to all groups, patients with low TILs and basal subtype (n=244) had the worst iDFS (p=0.005) and had numerically worse OS (p=0.084). Other significant risk factors for iDFS in this group included high histologic grade (vs intermediate and low), positive lymph node (vs negative), higher pathologic T stage (as a continuous variable). Differences according to race could not be analyzed due to small sample size. Conclusion: In the EA1131 clinical trial, patients with residual basal-like TNBC and low sTILs had the worse iDFS, delineating a particularly high-risk subgroup. These findings underscore the prognostic relevance of post-treatment immune microenvironment characteristics and suggest that patients with basal-like, TIL-low residual TNBC may derive limited benefit from current standard adjuvant therapies. Further investigation is warranted to identify and develop tailored therapeutic approaches for this biologically aggressive subgroup to improve long-term outcomes.

E. Mamounas¹, V. Wang², M. Chen³, J. A. Sparano⁴, R. J. Gray², M. Rahman³, Y. Cheng³, P. Rastogi⁵, E. Amidi³, C. E. Geyer, Jr.⁵, T. Boucher³, T. J. Freeman⁵, M. Ramzanpour³, M. Varma³, H. Ghani³, C. Cheng³, C. Bales³, J. R. Ribeiro³, N. Stransky³, M. R. Miglarese³, M. Oberley³, D. Spetzler³, M. Radovich³, G. W. Sledge³, N. Wolmark⁵; ¹National Surgical Adjuvant Breast and Bowel Project and AdventHealth Cancer Institute, Orlando, FL, ²ECOG-ACRIN Biostatistical Center, Dana-Farber Cancer Institute, Boston, MA, ³Caris Life Sciences, Irving, TX, ⁴Icahn School of Medicine at Mount Sinai, Tisch Cancer Institute, New York, NY, ⁵National Surgical Adjuvant Breast and Bowel Project and University of Pittsburgh School of Medicine, UPMC Hillman Cancer Center, Pittsburgh, PA

Background: Clarity BCR is a multimodal-multitask (MMMT) deep learning algorithm developed to estimate late distant recurrence (DR) risk in hormone receptor–positive (HR+) breast cancer and to identify patients likely to benefit from extended endocrine therapy (EET). The algorithm was initially developed and validated in the NSABP B-42 trial, a pivotal randomized phase III study evaluating EET in postmenopausal women with HR+ early breast cancer. In B-42, Clarity BCR stratified patients into high- and low-risk groups with strong prognostic performance (HR = 5.71; 95% CI: 3.50-9.32, p < 0.001). It identified a clinically meaningful differential EET benefit for the 49% of patients in the high-risk group (10-year absolute benefit 4.09%). In contrast, among the 51% classified as low risk, the 10-year DR risk was 1.93% without EET and 1.44% with EET, yielding a minimal absolute benefit of 0.49%, supporting its clinical utility in sparing EET. Building on these findings, we externally validated Clarity BCR in the TAILORx translational cohort. TAILORx was a large prospective trial designed to optimize adjuvant therapy for patients with ER-positive, HER2-negative early breast cancer. Methods: We included 6,516 TAILORx patients with digitized hematoxylin and eosin (H&E) slides and relevant clinical data. Slides were scanned using the Pramana SpectralHT scanner. Clarity BCR integrates image features from whole-slide images (WSIs) and clinical variables (age, surgery type, pathological node status) to produce a continuous risk score and a dichotomized risk group (high vs. low), using a prespecified threshold. A subset of 4,469 patients who completed at least 4.5 years of endocrine therapy (ET) and remained disease-free at 5 years post-randomization was included in the late DR analysis. The primary endpoint was distant recurrence–free interval (DRFI). The primary objective was to validate Clarity BCR for late DR prognostication. The secondary objective was overall DR risk estimation across the full follow-up period. Univariable and multivariable Cox models were used. Results: In the overall cohort, 1,134 (17.4%) patients were classified as Clarity BCR high risk and 5,382 (82.6%) as low risk. In the late DR analysis cohort (n = 4,469), 770 were high risk and 3,699 were low risk. High-risk patients exhibited significantly worse late DR outcomes (HR = 1.88; 95% CI: 1.43-2.48; p < 0.001). The estimated 15-year DRFI was 86.4% (95% CI: 83.1-89.9%) in the high-risk group vs. 93.0% (95% CI: 92.0-94.0%) in the low-risk group. The prognostic discrimination C-index was 0.59 for Clarity BCR and 0.54 for Oncotype DX.In a multivariable analysis adjusting for age, tumor size, grade, Oncotype DX recurrence score, surgery type, therapy type, and menopausal status (n = 4,368), Clarity BCR remained independently prognostic (HR = 1.54; 95% CI: 1.11-2.13; p = 0.010).Across the entire follow-up period (DRFI from randomization), patients in the high-risk experienced significant worse DR outcomes (HR = 1.73; 95% CI: 1.40-2.15; p < 0.001), with 15-year DRFI of 83.7% in the high-risk group vs. 90.8% in the low-risk group. Multivariable analysis (n = 6,354) confirmed Clarity BCR’s independent prognostic value (HR = 1.32; 95% CI: 1.02-1.72; p = 0.037). The C-index for DRFI was 0.59 for Clarity BCR and 0.60 for Oncotype DX. Conclusions: Clarity BCR demonstrated robust, independent prognostic performance for late and overall DR up to 15 years in TAILORx. Together with prior NSABP B-42 findings, these results support its potential clinical utility to guide long-term treatment decisions in HR+ breast cancer. Research Support: Supported by NIH/NCI U10CA180820, U10CA180794. NSABP B-42 trial funded by NIH (U10CA180868, U10CA180822, UG1CA189867, U24CA196067) and Novartis.