DeMichele, Angie

Abstract Introduction: Pathological complete response (pCR) is a strong prognostic marker, but survival outcomes comparing treatment to control do not reliably align with treatment-control differences in pCR rates in breast cancer. A novel Bayesian hierarchical framework models treatments within trials, allowing us to predict treatment effects on distant recurrence-free survival (DRFS) from pCR with greater accuracy. Methods: We analyzed 12 neoadjuvant breast cancer trials (6,000 patients, all HR/HER2 subtypes; including I-SPY2). The framework of Burzykowski, Molenberghs & Buyse (2005) is extended to a novel arm-based hierarchical structure providing a distribution of pCR and DRFS treatment effects controlling for subtype (HR/HER2), N and T stage, grade, and calendar year. Three held-out trials (877 patients; 26 regimens; med follow-up >4 years) validate predictions of DRFS treatment benefit from pCR. All data was used to estimate treatment-effect correlation and the surrogate threshold effect (STE). Analyses were repeated for Residual Cancer Burden Index (binary RCB01, continuous RCB). Results: Predicted probability of DRFS benefit closely matched actual DRFS follow-up (mean absolute error 0.06; Pearson r = 0.9). Across all trials, pCR showed moderate surrogacy (ρ = 0.82, R2 = 0.67). A 60% increase in pCR odds achieves ≥95% probability of DRFS benefit (STE = OR of 1.60). RCB outperforms pCR across all metrics, with 92% sensitivity and 93% specificity for detecting DRFS benefit in 26 validation regimens (Table 1). Conclusion: This novel Bayesian meta-analytic framework reveals that pCR, RCB01 and continuous RCB reliably predict survival benefit across heterogeneous trials, with RCB performing the best in an external validation. This provides a statistical foundation for accelerated approval decisions using robust early biomarkers in modern neoadjuvant trial designs by accurately predicting survival at the treatment arm level. Citation Format: Keli S. Santos-Parker, Jessica R. Santos-Parker, W. Fraser Symmans, Laura J. Esserman, Christina Yau, Angie DeMichele, Laura van't Veer, Doug Yee, Fabien Reyal, Helena Earl, Jean Abraham, David Cameron, Peter Hall, Judy Boughey, Matthew Goetz, Gabe Sonke, Miguel Martín, Sara López-Tarruella, Priyanka Sharma, Rachel Freiberg, Jane Perlmutter, Aditya Bardia, Martin Eklund, Rachel Freiberg, Lajos Pusztai. A novel statistical framework for surrogate endpoint prediction of survival in neoadjuvant breast cancer trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1401.

Abstract Background: In MBC, CTC enumeration is highly prognostic for both progression free survival (PFS) and overall survival (OS). Multiparametric technologies enable for expanded phenotypic profiling of CTCs, including ER and HER2 assessment. Limited information is available on the concordance of CTC ER and HER2 levels compared to synchronously obtained tissue. We assessed the prognostic performance of CTC enumeration and phenotypic profiling of ER and HER2 on CTCs. Methods: The prospective, multi-institutional Aurora study enrolled pts with MBC of any subtype either at diagnosis of metastatic disease or at time of progression on any line of systemic therapy. Pts underwent a metastatic site biopsy, consented to archival primary tissue collection and donated blood samples serially every 3-4 months until progression. CTCs, from blood collected at study entry, were identified as nucleated, EpCAM+/ cytokeratin+/ CD45- cells using an automated imaging system (RareCyte, Seattle). ER and HER2 expression levels were measured by mean fluorescence intensity (MFI), with thresholds above 80 (ER) and 160 (HER2) considered positive. Comparisons were made to total RNAseq from paired metastatic tissue biopsies. PFS and OS were calculated from the time of study entry to last follow up or death. Results: Baseline CTC enumeration was performed in 154 pts. The most common clinical subtype was HR+/HER2- (59.7%), followed by HER2-positive (16.8%) and TNBC (15.5%). The median lines of prior therapy were 1 (range 0-10). At least 1 CTC was identified in 68/154 pts (44%), 30 (19%) had ≥ 5 CTCs, and 15 (9.7%) had CTC clusters. The median CTC value was 0 (0-877). The presence of CTCs (≥1 or ≥5) and CTC clusters was adversely associated with PFS and OS (Table 1 Table 1. CTC survival analyses Number Median PFS PFS HR (95% CI) Median OS OS HR (95% CI) Presence of CTC 0 CTC 86 7.37 (0.03 - 43.1) reference 20.72 (0.03 - 50.6) reference ≥ 1 CTC 68 4.7 (0.43 - 19.5) 1.65 (1.16-2.36) 12.04. (0.43-44.1) 2.13 (1.44-3.16) Presence of ≥ 5 CTC <5 CTC 124 7.7 (0.03 - 43.1) reference 19.31 (0.03 - 50.6) reference ≥ 5 CTC 30 2.88 (0.43 - 18.7) 1.77 (1.15 - 2.71) 5.74 (0.43 - 29.3) 3.59 (2.26-5.70) Presence of clusters 0 clusters 139 7.37 (0.03 - 43.1) reference 18.06 (0.03 - 50.6) reference ≥ 1 cluster 15 1.91 (0.43 - 18.7) 2.06 (1.16 - 3.68) 3.42 (0.43 - 18.7) 5.44 (2.94-10.09) ). Across all subtypes, the median CTC ER-MFI across was 36.2 (range 4.2-10,016.6) and the median HER2-MFI was 139.6 (range 4.2-2480.5). We observed high intra-patient heterogeneity of ER-MFI (CV: 0.65 [0.34-1.25]) and HER2-MFI (CV: 0.79 [0.42-1.54]). Clinical metastatic subtype from metastasis at study entry was available in 54/68 pts with CTCs. Pts with clinical ER+ MBC had a higher median ER-MFI compared to clinical TNBC (111.9 vs 68.6, p = 0.02). However, 11 pts (26.8%) with clinical ER+ MBC had all CTCs with an ER-MFI <80. Lower ER expression was observed in CTCs collected after prior exposure to a SERD (median ER-MFI: post-SERD [n=8] 27 vs post-aromatase inhibitor [n=4] 252, p<2.22e-16). No correlations were observed between the median CTC ER-MFI or HER2-MFI and paired tissue ESR1 or ERBB2 RNA expression (n=66). Conclusions: CTCs are strongly predictive of PFS and OS. CTC ER- and HER2-MFI were highly heterogeneous without clear correlation with bulk tissue RNA expression. The clinical impact of discordant clinical receptor status and CTC receptor status warrants further exploration. Citation Format: K. V. Giridhar, W. C. Nenad, H. Ye, G. L. Wheeler, T. Hinoue, A. R. Michmerhuizen, B. M. Felsheim, U. R. Chandran, B. J. Kelly, E. R. Mardis, P. W. Laird, J. M. Balko, A. C. Garrido-Castro, S. Coppens, J. Bowen, R. Nanda, N. U. Lin, C. K. Anders, C. Isaacs, J. J. Tao, J. D. Anampa, N. Jahan, L. Huppert, A. DeMichele, B. H. Park, S. Vinayak, A. L. Delson, M. M. Magbanua, M. Balic, H. A. Parsons, A. V. Lee, L. A. Carey, L. Norton, A. C. Wolff, N. E. Davidson, I. Krop, T. A. King, C. M. Perou, K. A. Hoadley, Aurora US Metastases Network. Characterizing circulating tumor cell (CTC) enumeration and phenotypic profiling in blood with synchronously obtained tissue in metastatic breast cancer (MBC) in TBCRC /AURORA US [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS2-07-17.

Abstract Background: Pembrolizumab (Pembro), an anti-PD-1 immune checkpoint inhibitor, has been approved for the treatment of a variety of cancers. Pembro was recently evaluated in HER2- breast cancer patients in the neoadjuvant I-SPY 2 TRIAL and graduated in the triple negative (TN), HR+HER2-, and HER2- signatures. TN breast cancers tend to have high numbers of immune infiltrates, including T cells and tumor associated macrophages (TAMs). We utilized multiplex multispectral imaging to evaluate the presence and spatial proximity of various immune cell populations as specific predictors of response to Pembro. Methods: Pre-treatment biopsies were available from 54 patients. FFPE sections were immunostained using Opal fluorescence reagent kits on a fully automated Ventana Discovery platform and imaged with a Vectra automated imaging system. Image analyses were performed with a variety of software packages. Immune biomarkers included CD3, CD8, FoxP3, CD68, PD-1, PD-L1, and cytokeratins. Cell densities were determined per area of tissue. Spatial analyses were performed to quantitate proximity of cell types. We used logistic regression with the likelihood ratio test to evaluate associations between pathologic complete response (pCR) with immune cell counts, ratios, and spatial proximity measurements. This analysis was also performed adjusting for HR status, and within receptor subsets, sample size permitting. Our statistics are descriptive rather than inferential and do not adjust for multiple hypothesis testing or multiplicities of other biomarkers outside this study. Results: Densities of CD3+ T cells ranged from 193-5155 cells/mm2, with CD8+ cytotoxic T cells (Tc) ranging from 103 to 3774 cells/mm2 and FoxP3+ Tregs from 0-416 cells/mm2. Densities of PD-1+ T cells and PD-1+ Tc cells ranged from 0-1341 cells/mm2 and 0-698 cells/mm2, respectively. Macrophage cell densities ranged from 84-2644 cells/mm2. PD-L1+ tumor cell densities ranged from 0-2981 cells/mm2. High PD-L1 expression (>=10% of tumor cells positive for PD-L1) was observed in 12% of the cases. Overall T cell density was positively associated with pathological complete response (pCR), as were Treg cell density and PD-1+ T cell density. The ratio of macrophages to Tc cells was negatively associated with response. Finally, the spatial distribution of CD3+ T cells in proximity to cancer cells correlated positively with pCR. Conclusion: Cell density, immune cell ratios and spatial resolution provide insight to response and resistance. We confirmed the observation that T cell density predicts pCR, but more specifically that Treg and PD1+ T cell densities increase the chance of pCR. An increase in macrophages relative to cytotoxic T cells predicts resistance. The closer the T cells are to the tumor, the better the response. These results demonstrate the utility of characterizing the immune microenvironment, in the context of therapy with immuno-oncology agents, not only to predict response, but to gain insight and learn now to potentiate agents and combinations. Citation Format: Michael J. Campbell, Christina Yau, Jennifer Bolen, Scott Vandenberg, Clifford Hoyt, Lamorna Brown-Swigart, Gillian Hirst, Rita Nanda, Minetta Liu, Smita Asare, Laura van'tVeer, Douglas Yee, Angie DeMichele, Don Berry, Laura Esserman, I SPY 2 TRIAL Investigators. Analysis of immune cell infiltrates as predictors of response to the checkpoint inhibitor pembrolizumab in the neoadjuvant I-SPY 2 TRIAL [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr CT003.