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Background: Invasive lobular carcinoma (ILC) represents the second most common subtype of breast cancer after invasive breast cancer of no special type (NST). In this retrospective analysis of the MINDACT trial, we aimed at identifying/refining the transcriptomic differences between: 1) estrogen receptor positive/HER2-negative (ER+/HER2-) ILC versus ER+/HER2- NST, 2) classic and non-classic ER+/HER2- ILC, and, 3) recurring and non-recurring ER+/HER2- ILC in the subgroup of patients with a low clinical and low genomic (cL/gL) risk (as defined by a modified version of Adjuvant Online! and the 70-gene signature). Patients and methods: Central pathology review was performed for histological subtype, grade and Ki67 (G.V.) for 5929/6693 (88.6%) of the patients included in the MINDACT trial (NCT00433589). Analysis of transcriptomic data adjusted for age and grade was performed using the R/Bioconductor package ‘limma’ to identify differentially expressed genes (DEGs). DEGs having absolute log-fold change (logFC)≥ 0.2 and FDR-adjusted p-value (q-value) < 0.05 were considered. Gene set enrichment analyses (GSEA) of MSigDB hallmark gene sets were performed. Adjusted Cox regression models were used to evaluate the association of these hallmarks with disease free survival (DFS) and distant recurrence free survival (DRFS). Results: After central pathological review, 464 patients with ER+/HER2- ILC and 3798 patients with ER+/HER2- NST were identified. Patients with ILC were significantly older at diagnosis, had larger tumors, less axillary nodal involvement, more grade 2 tumors than patients with NST. At the transcriptomic level, we observed a high number of DEGs between these 2 subgroups, confirming their distinct phenotype. CDH1, the gene coding for E-cadherin, was as expected the most highly overexpressed gene in NST versus ILC. We further observed an increased expression of leptin (LEP), leptin receptor (LEPR), lipoprotein lipase (LPL), and the fatty acid transporter CD36 in ILC. This could suggest that ILC relied on increased lipid uptake thanks to the increased contact of ILC tumor cells with the adipocytes. IGF1 was also overexpressed in ILC versus NST, as a potential consequence of high LEP and high LEPR expression. Differences were also evident with regard to the extracellular matrix (ECM), with many collagens, matrix metalloproteinases (MMPs) and other key enzymes (e.g. LOXL1) being differentially expressed. We confirmed a decreased ER-signaling and increased PI3K/Akt signaling in ILC versus NST. Out of the 464 ER+/HER2- ILC tumors, 253 (55%) were classic ILC and 211 (45%) non-classic ILC. There were more grade 3 tumors, more highly proliferative tumors and more nodal involvement in patients with non-classic versus classic ILC. At the transcriptomic level, differences were subtler than the differences seen above. Still, a significant enrichment of the hallmarks related to cell cycle in the non-classic ILC, and of the hallmarks related to epithelial-to-mesenchymal transition, hypoxia, adipogenesis and IL6/JAK/STAT3 signaling in classic ILC was observed. Finally, 216/464 patients with ER+/HER2- ILC (47%) were assigned to the cL/gL risk group and did not receive chemotherapy. 28/216 of these patients (13%) relapsed (DFS, median FU: 8.7 years). Enrichment of hallmarks related to apoptosis, inflammatory response, hypoxia and oncogenic signaling (PI3K/Akt, Ras, c-Myc) was associated with worse survival. Conclusion: This represents, to the best of our knowledge, the largest set of gene expression data for patients with ILC, issued from a clinical trial where histology was reviewed centrally. These results could be used to personalize treatment for patients with ILC. This project is funded by the Breast Cancer Research Foundation. Citation Format: Christine Desmedt, Ha-Linh Nguyen, François Richard, Sabine Linn, Otto Metzger, Coralie Poncet, Jelle Wesseling, Florentine Hilbers, Kim Aalders, Mauro Delorenzi, Suzette Delaloge, Jean-Yves Pierga, Etienne Brain, Suzan Vrijaldenhoven, Peter A Neijenhuis, Karen Van Baelen, Marion Maetens, Emiel Rutgers, Martine Piccart, Laura Van ’t Veer, Giuseppe Viale, Fatima Cardoso. Transcriptomic insights into lobular breast cancer biology: a retrospective analysis of the MINDACT clinical trial [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-14-01.

Background: Breast cancer with low HER2 expression (HER2-low) is of high clinical relevance because of new therapeutic options with antibody-drug conjugates. We have recently shown in a large cohort from neoadjuvant clinical trials that HER2-low breast cancer has different molecular characteristics as well as different clinical outcomes compared to HER2-zero. Considering the positive correlation between HER2-low expression and hormone receptor positivity observed consistently in many investigations, we have extended our analysis to HR+ tumors from the post-neoadjuvant PenelopeB trial. In PenelopeB, patients with HR+ breast cancer and residual disease after neoadjuvant chemotherapy (NACT) were randomized to post-neoadjuvant palbociclib versus placebo in addition to endocrine therapy. We evaluated the molecular phenotype and clinical outcomes of HER2-low compared to HER2-zero patients. Methods: A total of 1250 patients were randomized, HER2 status was available for 1151 tumors from pretherapeutic core biopsy, determined mainly by local pathology, and from 1213 tumors from the post-NACT sample, determined as part of central pathology. For 1119 patients a paired HER2-status was both available. HER2-zero was defined as IHC0 and HER2-low-positive was defined as IHC1+ or IHC2+/ISH-. Gene expression analysis of 2549 genes using the HTG oncology biomarker panel was performed in 620 pretherapeutic biopsies and 780 post-NACT residual tumor samples, with 539 paired gene expression samples. Breast cancer subtypes were determined using the AIMS approach. Results: In pretherapeutic biopsies, 695 tumors (60%) were HER2-low and 457 (40%) were HER2-zero. A HER2-low status in the biopsy was significantly linked to improved iDFS (HR 0.76 (0.60-0.96; p=0.02). In residual tumors, 632 tumors (60%) were HER2-low and 581 (40%) were HER2-zero, without any prognostic impact of HER2 low status. In addition, a shift of HER2-low-status comparing core biopsy and residual tumor was observed in 415 (37%) of 1119 tumors. 161 (14%) had a shift from HER2-zero to HER2-low and 254 (23%) shifted from HER2-low to HER2-zero. A shift from HER2-zero to HER2-low in the post-NACT samples was significantly linked to reduced iDFS (HR 1.43 [95%CI 1.01-2.01]), p=0.04), compared to HER2-low group, while a shift from HER2-low to HER2-zero was associated with better iDFS compared to HER2-zero group, although not statistically significant (p=0.17). We did not observe a significant correlation of HER2-low status and AIMS molecular subtypes. In particular, the HER2-enriched (HER2E) subtype was assigned to only 4.3% of HER2-zero and 3.1% of HER2-low tumors. Significant iDFS differences were observed for HER2-low-status in combination with AIMS subtypes (lumB/basal/HER2E vs. lumA/normL; overall p-value < 0.0001) for both pretherapeutic biopsies and residual tumor. Patients with post-NACT HER2-low tumors had an improved survival in the subgroups of aggressive AIMS subtypes (lumB/basal/HER2E), but not in the less aggressive AIMs subtypes (lumA/normL), with a positive test for interaction (p=0.02). For the pre-NACT samples a similar, but non-significant trend was observed. We evaluated a total of 620 core biopsies for differences in gene expression comparing HER2-low and HER2-zero tumors. A total of 417 genes were statistically significantly different, but in a hierarchical clustering there was no clear separation of HER2-low and HER2-zero tumors. Conclusions: In the PenelopeB cohort of HR+ tumors, a HER2-low status in pretherapeutic core biopsies is related to improved disease-free survival, especially for those tumors that have a more aggressive intrinsic subtype. A shift of HER2-low status was observed before and after chemotherapy, indicating an adaptation of the pathway activity to therapy-induced stress, which might become relevant for future diagnostic and therapeutic approaches. Citation Format: Carsten Denkert, Miguel Martín, Michael Untch, Hervé R. Bonnefoi, Erik S. Knudsen, Seock-Ah Im, Angela DeMichele, Agnieszka Witkiewicz, Laura Van ’t Veer, Sung-Bae Kim, Harry D. Bear, Nicole McCarthy, Karen Gelmon, Frederik Marmé, José Ángel García-Sáenz, Nicholas Turner, Federico Rojo, Martin Filipits, Lesley-Ann Martin, Peter A. Fasching, Christian Schem, Catherine M. Kelly, Toralf Reimer, Masakazu Toi, Hope Rugo, Michael Gnant, Andreas Makris, Yuan Liu, Karsten Weber, Sivaramakrishna Rachakonda, Sibylle Loibl. HER2-06 Outcome analysis of HER2-zero or HER2-low hormone receptor-positive (HR+) breast cancer patients - characterization of the molecular phenotype in combination with molecular subtyping [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr HER2-06.

Background: Breast cancer (BC) is one of the cancer types recognized as an obesity-associated disease. Current understandings of molecular mechanisms underlying the BC-obesity connection however largely came from experimental models while systematic investigation of the impact of obesity on BC biology in large patient series is still lacking. The purpose of this study is to discover changes in the transcriptomic profile of primary BC according to patients’ body mass index (BMI). Data and Methods: Bulk and single-cell gene expression data from treatment-naïve primary breast tumors from non-underweight patients were retrieved from the MINDACT trial (NCT00433589; N = 1481) and the pre-treatment cohort of the BioKey trial (NCT03197389, N = 36), respectively. Three categories were considered for BMI: lean, overweight and obese. The main analyses focused on the invasive carcinoma of no special type (NST) estrogen receptor-positive/HER2-negative (ER+/HER2-, N_bulk = 735, N_single-cell(sc) = 10) and NST ER-/HER2- (N_bulk = 118, N_sc = 15) subgroups. The bulk expression data was subjected to differential gene expression analyses according to BMI which was adjusted for menopausal status and tumor grade, then followed by gene set enrichment analyses. Clustering and cluster annotation were performed on the single-cell profiling data before differentially expressed genes according to BMI were identified for each of the present cell types. Results: Obesity-associated differences in the transcriptomic profile of breast tumors, which were subtle but potentially indicative of a biological relationship, were revealed by the bulk data. In both investigated subgroups, tumors from obese patients were shown to be enriched in cell cycle hallmarks. In ER-/HER2- tumors, adiposity further increased MYC signaling. We also observed different obesity-associated changes according to the ER status. Among ER+/HER2- tumors, those from obese patients were enriched in hallmarks related to inflammatory response compared to those from lean patients. In contrast, these hallmarks appeared to be enriched in the ER-/HER2- tumors from lean patients. Our investigation of the single-cell data further revealed shifts in the cell composition of tumor tissue and cell type-specific transcriptomic differences according to BMI which were more pronounced than those detected from the bulk data. ER+/HER2- tumors from obese patients have a higher frequency of immunosuppressive and pro-tumoral cell subpopulations such as dendritic cells (DC) enriched in immunoregulatory molecules (p = .03), LYVE1+ macrophages (p = .02) and myofibroblasts (p = .03) than those from lean patients. Overexpression of Cyclin D1 and CD24 was found in cancer cells in ER+/HER2- tumors from obese patients. A reduction in anti-tumor immune responses was evident with downregulation of multiple interferons in CD8+ and CD4+ T cells as well as B cells. We observed in the ER-/HER2- subgroup increased infiltration of plasmacytoid DC (p = .01), CCL2+ macrophages (p = .01) in tumors from obese versus lean patients, while fibroblasts showed an opposite tendency. Additionally, significant obesity-associated downregulation of major histocompatibility complex (MHC) molecules class I in cancer cells and MHC class II molecules in B cells could be suggestive of deficient antigen presentation and activation of cytotoxic and helper T cells. Conclusion: We highlighted the impact of obesity on the remodeling of tumor and tumor microenvironment which might generally lead to a suppression of anti-tumor immune responses, albeit potentially via diverse axes according to the ER status. Although investigation on a larger cohort is warranted, our current results suggest that obesity-associated transcriptomic changes in BC could be highly cell type-specific, hence we recommend single-cell approaches in addition to spatial multi-omics analysis to further elucidate the interplay between obesity and BC. Citation Format: Ha-Linh Nguyen, Tatjana Geukens, Marion Maetens, Karen Van Baelen, Maxim De Schepper, Coralie Poncet, Mauro Delorenzi, Marjanka K. Schmid, Emiel Rutgers, Laura Van ’t Veer, Martine Piccart, Fatima Cardoso, Giuseppe Viale, Ayse Bassez, Hanne Vos, Patrick Neven, Ines Nevelsteen, Kevin Punie, Hans Wildiers, Giuseppe Floris, Diether Lambrechts, Ann Smeets, Elia Biganzoli, François Richard, Christine Desmedt. PD4-06 Obesity-associated changes in transcriptomic profile and immune landscape of primary breast cancer revealed by bulk and single-cell gene expression data [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD4-06.

Worldwide, there is a growing proportion of women who are overweight or obese. While obesity has been associated with an increased risk of developing breast cancer (BC) and worse prognosis in BC patients, yet the impact of adiposity (abnormal or excess body fat) on BC biology remains understudied in humans. This retrospective study aimed to investigate how the biology of primary BC would differ according to patients’ body mass index (BMI). We examined clinicopathological data (including BMI at the time of diagnosis) and molecular data (including genomic, bulk and single-cell transcriptomic data) of treatment-naïve (early stage) BC patients from five cohorts (N = 2071). We identified several genomic alterations considered actionable or of potential clinical relevance which had a different prevalence in overweight or obese patients compared to lean patients, for instances, less PIK3CA gene mutations, and more CCND1, CCNE1 and IGFR1 amplifications. Moreover, we found evidence supporting an ageing accelerating effect of obesity at the genetic level, through its association with an age-associated mutational signature. We showed that BMI-associated differences in transcriptomic profile were subtle at the bulk resolution while single cell profiling allowed detection of more pronounced changes in different cell compartments. Investigation at the single cell resolution revealed an elevated and unresolved inflammation of the BC tumor microenvironment (TME) associated with obesity, which had distinct characteristics contingent on the estrogen receptor status. Collectively, analyses at both genomic and transcriptomic levels implied that obesity is associated with an inflammaging-like phenotype of the TME. Our results indicate that patient adiposity might play a significant role in the heterogeneity of BC and should be considered in the context of precision medicine.

HER2 is a predictive biomarker for HER2-targeted therapeutics. For antibody–drug conjugates (ADCs; e.g., trastuzumab emtansine (T-DM1)), HER2 is utilized as a transport gate for cytotoxic agents into the cell. ADC biomarkers may therefore be more complex, also reflecting the intracellular drug transport. Here we report on a positive correlation between the early endosome marker RAB5A and T-DM1 sensitivity in five HER2-positive cell lines. Correlation between RAB5A expression and T-DM1 sensitivity is confirmed in breast cancer patients treated with trastuzumab emtansine/pertuzumab in the I-SPY2 trial (NCT01042379), but not in the trastuzumab/paclitaxel control arm. The clinical correlation is further verified in patients from the KAMILLA trial (NCT01702571). In conclusion, our results suggest RAB5A as a predictive biomarker for T-DM1 response and outline proteins involved in endocytic trafficking as predictive biomarkers for ADCs., Antibody Drug Conjugates (ADCs) are emerging in the field of precision cancer medicine. Here, the authors suggest using endocytosis as a predictive biomarker for response

Introduction: One of the principal limiting factors to achieving cures in patients with cancer is drug resistance. Drug repositioning is the application of FDA-approved drug compounds for novel indications beyond the scope of the drug’s original intended use. This approach offers advantages over traditional drug development by reducing development costs and providing shorter paths to approval, as drug safety has already been established during the drug’s original regulatory process. One approach for computational drug repositioning involves generating a disease gene expression signature and then identifying a drug that can reverse this disease signature. In this study, we extracted drug resistance signatures from the I-SPY 2 TRIAL by comparing gene expression profiles of responder and non-responder patients stratified by treatment and molecular subtype. We then applied our drug repositioning pipeline to predict compounds that can reverse the gene expression profiles of these drug resistance signatures. We hypothesize that reversing these drug resistance signatures will resensitize tumors to treatment and improve patient outcome. Methods: We first generated the drug resistance signatures by performing differential expression between responders (RCB 0/I) and non-responders (RCB III) within treatment arms and molecular subtypes. An optimal log fold-change cutoff was selected for each signature by identifying the cutoff that best separates the responder and non-responder samples using k-means clustering. We then applied our drug repositioning pipeline to identify compounds that significantly reverse these signatures using the drug perturbation profiles generated in a breast cancer cell line in the Connectivity Map v2 dataset. Briefly, the pipeline uses a non- parametric, rank-based pattern-matching strategy based on the Kolmogorov-Smirnov (KS) statistic to assess the enrichment of resistance genes in a ranked drug gene expression list. Significance of each prediction is estimated from a null distribution of scores generated from random gene signatures. Results: We found that few individual genes are shared among the resistance signatures across the treatment arms and molecular subtypes, with the most common genes present in only 5/17 of the treatment arm and molecular subtype groups. At the pathway-level, however, we found that immune-related pathways are generally enriched among the responders and estrogen-response pathways are generally enriched among the non-responders. Although most of our drug predictions are unique to treatment arms and molecular subtypes, our drug repositioning pipeline identified the selective estrogen receptor degrader (SERD) fulvestrant as a compound that can potentially reverse resistance across a majority of the treatment arms and molecular subtypes. Conclusion: We applied our drug repositioning pipeline to identify novel agents to sensitize drug-resistant tumors in the I-SPY 2+ clinical trial and identified a SERD, fulvestrant, as a potential candidate for multiple molecular subtypes and treatment arms. Citation Format: Katharine Yu, Amrita Basu, Christina Yau, Denise Wolf, Gillian Hirst, Laura Sit, Nicholas O’Grady, Thelma Brown, I-SPY 2 TRIAL Investigators, Angela DeMichele, Don Berry, Nola Hylton, Doug Yee, Laura Esserman, Laura van 't Veer, Marina Sirota. Computational drug repositioning for the identification of new agents to sensitize drug-resistant breast tumors across treatment arms and molecular subtypes [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS11-04.

Background: I-SPY 2 is a neoadjuvant platform trial open to patients with locally advanced, molecular high-risk breast cancer. In a concerted pursuit of mid-therapy response biomarkers, we evaluated inter-regimen biopsies, to identify patients who may be candidates for treatment de-escalation. In a pilot study, we observed that absence of carcinoma in an inter-regimen biopsy may predict pathologic complete response (pCR). In this expanded study of 100 participants, we sought to confirm that finding and assess pathologic features of the inter-regimen biopsy as predictors of tumor response to neoadjuvant therapy. Methods: Digital H&E images of 100 inter-regimen (12 week) image-guided breast biopsies +/- ancillary immunohistochemistry (p63 and/or cytokeratin) were reviewed by 9 I-SPY affiliated pathologists to record 1) tumor bed and 2) presence/absence of residual invasive carcinoma (IC) (with tumor cellularity scored as 0-100%). The data set included 393 cores (mean 3.9 (2-4) cores/biopsy). Fisher’s exact t-test was used for association of presence/absence of IC with pCR, and tumoral hormone receptor (HR) and HER2 status. Association between biopsy tumor cellularity and residual cancer burden (RCB) indices used Pearson’s correlation. Results: In the biopsy set, 84 (84%) had ≥80% inter-observer diagnostic agreement on both 1) presence of tumor bed and 2) presence/absence of IC (53 IC+ /31 IC-). IC+/IC- biopsies had equal numbers of evaluable tissue cores. The primary tumors were 63% HR+/37% HR-. The presence of IC in the biopsy correlated with tumoral HR/HER2 status (p=0.0014: 74%: HR+HER2-; 62%: TN; 60%: HR+HER2+; 10%: HR-HER2+). Of 31 patients with IC- biopsies, 25 (80%) went on to pCR, whereas only 7/53 (13%) of patients with IC+ biopsies had pCR, conferring an odds ratio for pCR of 26, Fisher p=7.5E-10. Overall, IC- biopsies had a positive predictive value (PPV) for pCR of 81%, with a PPV for HR- tumors of 94% vs. 67% for HR+ tumors (Table 1). In the 6 IC- biopsies from patients with non-pCR (“false-negatives”), most were HR+ (5/6, Table 1), and tumor bed size in the resection specimen was smaller than for IC+ biopsies with non-pCR: 276 mm2 (0.4-1000 mm2) vs. 1166 mm2 (1-11960 mm2). In contrast, the 46/53 IC+ biopsies in patients with non-pCR had a PPV for predicting non-pCR of 86%, (PPV for HR+ tumors: 94% vs. PPV for HR- tumors: 66%. Tumor cellularity in the biopsy (mean 37%, [2.5-93%]) did not correlate with RCB index (p=0.57) or RCB breast-only index (p = 0.17) at resection. Conclusion: In this 100 biopsy set from the I-SPY2 trial, the absence of residual carcinoma in inter-regimen biopsies was highly predictive of pCR, particularly for HR- tumors. The “false-negative” biopsies (IC-/non-pCR) were predominantly HR+ tumors with small residual tumor beds at resection. Conversely, the presence of carcinoma in inter-regimen biopsies was highly predictive of non-pCR, particularly for HR+ tumors. These data demonstrate the utility, and the limitations, of the inter-regimen biopsy as one tool to identify patients who may benefit from therapeutic de-escalation. Table 1: PPV for pCR/non-PCR by Inter-regimen Biopsy StatusInter-regimen biopsy with or without Invasive carcinoma (IC+/-)pCRnon-pCRPPV (Sensitivity) for pCR(IC- Biopsies)PPV (Sensitivity) for non-pCR(IC+ biopsies)IC- biopsiesAll25681% (78%)-HR+10567% (83%)-HR-15194% (75%)-IC+ biopsiesAll746-86% (88%)HR+236-94% (88%)HR-510-66% (91%) Citation Format: Jodi M Carter, Molly E Klein, Sara J Venters, Kimmie Rabe, I Tolgay Ocal, Kamaljeet Singh, Denise M Wolf, Sunati Sahoo, Shuko Harada, Laila Khazai, Malini Harigopal, Alexander D Borowsky, Gregor Krings, Ronald Balassanian, Yunn-Yi Chen, Kimberley Cole, Sonal Shad, Amy Delson, Lamorna Brown-Swigart, I-SPY 2 TRIAL Consortium, Laura Esserman, Laura van ‘t Veer, W Fraser Symmans. Pathologic features of the inter-regimen biopsy predict response to neoadjuvant therapy in the I-SPY 2 TRIAL [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS4-09.

Background: The I-SPY 2 TRIAL, open to patients with locally advanced, molecular high-risk breast cancer, aims to bring each patient to pathologic complete response (pCR) with a minimum of toxicity. Here we test the hypothesis that imaging (MR volume predictors) combined with core biopsy may be used to accurately select candidates who show early response and provide an option of treatment de-escalation at mid-therapy (12 weeks). Methods: Of 100 I-SPY 2 patients with pathologist-assessed core biopsies at the inter-regimen time point (~12 weeks through treatment) and pCR data, 87 also had serial MR images and were considered in this study. Eleven I-SPY 2 TRIAL pathologists independently provided a digital assessment of the presence or absence of residual invasive cancer from H&E stained, and any requested ancillary IHC, images from imaging-guided core biopsies. Pathology predicts pCR if there is a consensus of no invasive residual disease. We generated predictions for all (55) unique pairs over the 11 pathologists, where pCR is predicted if both pathologists find no invasive cells. MRI pCR prediction models were previously developed on an independent dataset of ~990 I-SPY 2 patients, and applied to this cohort. Volume-based prediction models were previously optimized within each subtype and predicted probability thresholds were selected over a range of positive predictive value (PPV). In this study, MR predicts pCR (positive test) if the predicted probability is above a threshold that yields a given PPV value. For each pathologist pair, we combined pathology-based and MR-based predictors into a predictive-RCB (pre-RCB); and pre-RCB predicts a patient as pCR (RCB0) if both MR and pathology predicts pCR. Predictive performance is assessed by calculating the mean and range of PPV and sensitivity.Results: 39% (34/87) of the patients in this study achieved pCR. Over all pairs of pathologists, on average 80% of pathology-only predicted pCRs were true pCRs (mean PPV = 80% [range: 69-92%]), and 74% of patients who achieved pCR were predicted pCR by pathology alone (mean sensitivity = 74% [65-82%]). We assessed combinations with MR probability thresholds at PPV levels 50%-70%; and observed the best balance of PPV and sensitivity for the pre-RCB when MR thresholds were set at 50% PPV level. At this threshold setting, the pre-RCB achieved a PPV = 92% [83-100%], meaning on average 92% of predicted pCRs were true pCRs, and this improvement in positive predictive performance over pathology alone is achieved with a lower but still-reasonable 53% sensitivity [33-62%]. Conclusion: Pre-RCB, which predicts a patient as pCR if both MR and inter-regimen pathology predicts pCR, provides clinically actionable accuracy for treatment de-escalation for early responders (PPV>90%). Adding a final MR review at the time of early surgery may further improve performance. Resulting from data presented in this abstract, the pre-RCB algorithm, including the final MR review, has been operationalized and will be used prospectively to identify patients who are highly likely to have already achieved pCR by the inter-regimen timepoint. Citation Format: Sara J Venters, Wen Li, Denise M Wolf, Jodi M Carter, Molly E Klein, Kamaljeet Singh, Kimmie Rabe, I Tolgay Ocal, David Newitt, Christina Yau, Natsuko Onishi, Jessica Gibbs, Sunati Sahoo, Shuko Harada, Laila Khazai, Malini Harigopal, Alexander D Borowsky, Gregor Krings, Ronald Balassanian, Yunn-Yi Chen, Kimberley Cole, Sonal Shad, Barbara LeStage, Amy Delson, Sandra Finestone, Lamorna Brown-Swigart, I-SPY 2 Imaging Working Group, I-SPY 2 TRIAL Consortium, Laura Esserman, Laura van ‘t Veer, W Fraser Symmans, Nola M Hylton. Serial MRI and pathology combined to select candidates for therapy de-escalation in the I-SPY 2 TRIAL [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS4-10.

Background: Disseminated tumor cells (DTCs) in bone marrow detected after treatment may represent occult residual disease. We enumerated DTCs after neoadjuvant chemotherapy (NACT) in patients (pts) diagnosed with high-risk early stage breast cancer and examined the relationship of these cells with response and survival. Methods: I-SPY SURMOUNT is a sub-study of the I-SPY 2 TRIAL (NCT01042379). Pts enrolled on I-SPY 2, who signed consent for this sub-study, had bone marrow aspirates (BMA) collected after NACT at the time of surgery. DTCs were isolated and enumerated from BMA using immunomagnetic enrichment/flow cytometry (IE/FC). DTCs were defined as EPCAM-positive and CD45-negative nucleated cells. Samples were considered positive using a predetermined threshold of >4 DTCs per mL (Magbanua et al, unpublished data). Pathologic response was assessed using the residual cancer burden (RCB) method at local sites, and pts underwent standard adjuvant therapy if indicated and follow up for recurrence events and death. Relationship of DTCs with clinicopathologic variables was examined using Chi-squared test. Group means were compared using t tests. The log-rank test was used to compare survival curves. Results: A total of 73 patients were enrolled, 51 of whom had successful DTC assessment. The median DTC per mL was 4 (interquartile range 1.2-11.6). 24/51 (47%) were DTC-positive. Clinical characteristics by DTC status are shown in the table. DTC-positive pts were significantly younger (p=0.02) and had larger pretreatment tumors (longest diameter by magnetic resonance imaging) compared to DTC-negative pts (p=0.032). DTCs were not associated with receptor subtype. Thirty pts (41%) achieved a pathologic complete response (pCR). DTCs were not associated with pCR (p= 0.166); however, DTC-positive patients were significantly more likely to have residual cancer (RCB-II/III) after NACT compared to DTC-negative patients (OR 3.3, p=0.037). Median follow up of this cohort was 2.8 years (range: 0.9-4.8). Interim survival analysis showed that DTCs were not significantly correlated with EFS (p=0.6) or DRFS (p=0.41). Conclusions: Detection of DTCs at surgery after NACT is significantly more common in young patients, those with larger tumors, and those with residual disease at surgery. While these associations suggest higher risk for later recurrence, larger studies and longer follow up are necessary to determine if DTCs add prognostic value over pathologic evaluation alone for pts receiving NACT. Citation Format: Mark Jesus M Magbanua, Laura van 't Veer, Amy Clark, A. Jo Chien, Judy Boughey, Heather Han, Anne Wallace, Heather Beckwith, Minetta Liu, Christina Yau, E. Paul Wileyto, Lamorna Brown Swigart, Jane Perlmutter, Lauren Bayne, Shannon Deluca, Stephanie Yee, Erica Carpenter, Laura Esserman, John Park, Lewis Chodosh, Angela DeMichele. Outcomes associated with disseminated tumor cells at surgery after neoadjuvant chemotherapy in high-risk early stage breast cancer: The I-SPY SURMOUNT study [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS2-07.

Background: The Athena Breast Health Network is a multi-site effort aimed at integrating cutting-edge research and clinical care for breast cancer screening and treatment. At the University of California, San Francisco (UCSF) Breast Care Center (BCC), an Athena-based electronic Health Questionnaire System (eHQS) is distributed to all new patients. This questionnaire assesses self-reported personal and familial health history, comorbidities, and Patient-Reported Outcome Measurement and Information System (PROMIS) quality of life domains. Based on these responses, patients are proactively triaged to genetic counseling, psycho-oncology, onco-fertility, smoking cessation, peer support, nutritional counseling, behavioral sleep, and social work. These referrals are based on pre-defined thresholds, such as Ashkenazi Jewish Ancestry (referral to Genetic Counseling), or PROMIS Depression and Anxiety scores greater than or equal to 9 and 11, respectively. This analysis aims to assess the eHQS as a means to refer patients to supportive care services and assess if high-need patients (those with the highest symptom burden as assessed by quality of life PROMIS scores) are referred to services more frequently. Patients and Methods: Since Athena’s launch in 2013, over 6,000 patients have completed the online intake questionnaire at the UCSF BCC, 4,984 of whom have consented to research. 3,627 patients completed all relevant data to this study and were used in our analysis. A review of patient referrals was conducted to assess the number of referrals as a result of eHQS completion. PROMIS T-Scores in 8 domains (depression, anxiety, fatigue, sleep-related impairment and disturbance, cognitive function, applied cognition and physical function) were calculated with the Health Measures system and compared across patients stratified by age and cancer type and against the US general population. Results: When compared to the US population as a whole, PROMIS scores indicate that UCSF BCC patients have impaired quality of life in every assessed PROMIS domain except for depression. As age increases, PROMIS scores across multiple domains (depression, anxiety, sleep disturbance, cognitive function) increase, signifying lower symptom burden. Importantly, high symptom burden in the anxiety PROMIS domain was suggestive of lower quality of life scores in 6 out of the other 7 assessed PROMIS domains. 39% (1408 patients) of all analyzed BCC patients received at least one referral to a supportive care service. 721 of 1450 (50%) patients with an Invasive Breast Cancer (IBC) diagnosis and 246 of 571 (43%) patients with a DCIS diagnosis triggered at least one referral. 31% of all patients received a referral to genetic counseling, 16.3% to psycho-oncology, 11% to behavioral sleep, 8% to nutritional counseling, 7.6% to social work, 2.4% to smoking cessation, and 1.6% to onco-fertility. When compared to patients with DCIS or without a cancer diagnosis, patients with IBC are most likely to receive a referral to genetic counseling and psychosocial services. Referrals to supportive care services are less frequent as age increases. Conclusion: The Athena online intake form at the UCSF BCC effectively triages patients with high levels of anxiety (PROMIS > 8) and depression (PROMIS >10) to supportive care services. Preliminary results regarding the correlation between anxiety and overall poor quality of life suggest that referrals to psycho-oncology may be most efficacious in decreasing symptom burden. Using these analyses, we aim to refine our use of the eHQS to tailor the most relevant and effective referrals to the highest-need patients. Citation Format: Madeline Matthys, André Dempsey, Amrita Basu, Emily Wong, Nickolas Dreher, Irene Acerbi, Suzanne Ackerson, Laura Esserman, Athena Breast Health Network Investigators and Advocate Partners, Michelle Melisko, Laura van 't Veer. Triaging breast care center patients to supportive care services based on the Athena Breast Health Network online intake form and the patient-reported outcomes measurement information system (PROMIS) [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-12-08.

Background: A variety of investigational HER2-inhibitor agents/combinations have been tested in I-SPY 2, including neratinib (N), TDM1 combined with pertuzumab (P) (TDM1/P), and trastuzumab (H) combined with pertuzumab (H/P; prior to this combination becoming standard of care), all with trastuzumab as control (Ctr). All three experimental arms graduated, showing improved efficacy over control in one or more receptor subsets (HR+HER2+, HR-HER2+, or/and HER2+). Here we assess 10 biomarkers in the HER2, ER/PR, and proliferation pathways on multiple levels of resolution (expression, protein, phospho-protein) as predictors of response in these four arms, hypothesizing that highly HER2-activated, proliferative tumors may be more sensitive to HER2-inhibition than those that are more luminal and quiescent. Methods: 192 HER2+ patients were considered in this analysis: (31 Ctr, 65 N, 52 TDM1/P, and 44 H/P). 10 biomarkers relating to HER2, ER, or proliferation were evaluated from pre-treatment biopsies: HER2 IHC (n=145), 3 expression signatures (n=192), BluePrint subtype (n=192), and 5 protein/phospho-protein analytes by RPPA (n=175). Each biomarker was tested for association with pCR in the whole population and within each arm using a logistic model. This analysis was adjusted for HR status and treatment arm as covariates, and performed within receptor subtypes. This analysis does not adjust for multiplicities of other biomarkers. Results: In the population as a whole, HER2 and HER2-signaling biomarkers, evaluated at multiple levels of resolution: IHC, total-/phospho-protein by RPPA, and mRNA (HER2 amplicon module) - are highly correlated (rho=0.8 [0.65-0.92]). Higher HER2 levels and activity are associated with response: HER2 IHC 3+ status (LR p=0.00032), total quantitative ERBB2 protein by RPPA (LR p=5.4E-09), ERBB2 activation levels (LR p=6.58E-06 (pERBB2 (Y1248)) and 9.95E-06 (pEGFR Y1173)), and the ERBB2 amplicon expression signature (LR p=2.38E-08). In contrast, higher average ER/PR expression associates with non-response to HER2-targeted therapy (LR p=4.28E-08). Both HER2 and ER/PR signaling phenotypes are captured by BluePrint subtyping; and consistent with the individual pathway markers, tumors classified Luminal-type had a lower pCR rate relative to those classified as Her2-type (or Basal-type) (LR p=4.84E-11). These associations all retain significance in a model adjusting for HR status and treatment arm, and in the HR+HER2+ subset. In addition, we quantitatively assessed proliferation markers at the total protein (RPPA: Ki67), phospho-protein (pAURKA) and mRNA (proliferation signature Module11_Prolif) levels. All three proliferation biomarkers predict response overall; but this association is strongest within the HR+HER2+ subset (LR p=0.0012 (Module11_prolif), 0.0036 (pAURK), and 0.045 (Ki67)). None of the biomarkers tested were associated with response in the HR-HER2+ subset. Numbers are small within individual arms. Within the HR+HER2+ subtype, higher HER2 and lower ER/PR is observed in responders in all experimental arms; but the proliferation markers Module11_Prolif (LR p=0.0031) and Ki67 total protein (LR p=0.0029) are associated with response to TDM1/P but not H/P or N. Conclusion: High HER2 signaling at the expression, protein, and phospho-protein levels, and low ER signaling, all predict response to HER2-inhibition across treatment arms. Proliferation markers may be useful for prioritizing therapies in the HR+HER2+ subset. Citation Format: Denise M Wolf, Christina Yau, Julia Wulfkuhle, Lamorna Brown-Swigart, Smita M. Asare, Gillian L. Hirst, Laura Sit, Jane Perlmutter, I-SPY 2 TRIAL Consortium, Minetta Liu, John Park, Angela DeMichele, Douglas Yee, Don Berry, Laura Esserman, Emanuel Petricoin, Laura van t' Veer. HER2 signaling, ER, and proliferation biomarkers predict response to multiple HER2-targeted agents/combinations plus standard neoadjuvant therapy in the I-SPY 2 trial [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-10-02.

Background: While new treatments and improved subtyping schemas are anticipated to improve treatment response in triple-negative breast cancer (TNBC) patients, therapeutic resistance remains a significant challenge. Moreover, there is an urgent need for additional research model systems to study resistance and residual disease in breast cancer, including aggressive subtypes of breast cancer. Organoid culture is a promising technology used for growing breast cancer cells with high efficiency; however, the extent to which treatment resistance can be modeled using this system is unknown. This research used patient-derived organoid cultures in the context of computational analyses of large molecular and clinical datasets to study resistance mechanisms, biomarkers, and alternative treatment strategies to overcome drug resistance in early-stage TNBC. Methods: Organoid cultures were derived from breast tumor samples (taken from lumpectomy, mastectomy, or core biopsy samples), digested to the organoid level using collagenase, and grown in three dimensional cultures using a basement membrane extract and a fully-defined organoid medium (Dekkers et al. Nat Protoc 2021). An evaluation of all available I-SPY2 biomarker data (Wolf et al. Cancer Cell 2022) was performed focusing on genes, proteins, and pathways associated with resistance. These were then used to study whether resistance biomarkers identified in patient tumors are also present in organoids propagated from breast cancer post-treatment residual disease. To this end, bulk RNA sequencing data of organoids were normalized and merged with the TCGA dataset (Hoadley et al. Cell 2018) to enable analysis in a larger context, and immunofluorescence staining of organoids was performed. A high-throughput 386 anti-cancer drug compound screen and subsequent synergy testing with the most promising compounds were performed to identify and predict alternative treatment strategies. Additional assays to explore kinome activity in this organoid model are in progress. Results: A TNBC organoid biobank was established (n=31), which was enriched for inflammatory breast cancer (IBC; n=15), an aggressive form of breast cancer. Most organoids were derived from residual disease after neoadjuvant therapy. Bulk RNA sequencing analysis performed on 10 TNBC organoids revealed 3 subsets that were characterized predominantly by either normal-like/luminal androgen receptor or basal-like features or expressed distinct gene expression profiles, with IBC cases present in all 3 subsets. Intriguingly, the IBC organoids were characterized by higher expression of a number of immune-related signatures, despite an absence of clear immune cells in culture. A residual disease IBC/TNBC organoid resistant to chemotherapy was used to perform the 386-drug compound screen. The organoid model showed resistance to veliparib-cisplatin, consistent with the expression of gene/protein biomarkers predictive of drug resistance found in I-SPY2 (low PARPi7 levels and high pFOXO1 and pMEK1/2 expression). In addition, the screen identified multiple classes of inhibitors as promising synergistic/additive candidates for overcoming resistance to cisplatin. Conclusion: In this proof-of-principle study, we demonstrated the utility of matching I-SPY2 resistance biomarkers and signatures to residual disease tumor organoid cultures. We show that tumor organoid cultures modeling drug resistance states are a useful complement to existing research models of breast cancer and can be used for compound testing. We have developed a pipeline to propagate residual tumors from patients enrolled in I-SPY2 into organoid cultures to create a broader platform for preclinical drug testing informed by tumor biology with the ultimate goal of enabling faster, more successful translational studies and increased treatment options for resistant disease. Citation Format: Tam Binh V. Bui, Denise M. Wolf, Kaitlin Moore, Isaac S. Harris, Pravin Phadatare, Christina Yau, Lamorna A. Brown Swigart, Laura J. Esserman, Jean-Philippe Coppe, Julia Wulfkuhle, Emanuel F. Petricoin, Michael Campbell, Laura M. Selfors, Deborah A. Dillon, Beth Overmoyer, Filipa Lynce, Laura Van ’t Veer, Jennifer Rosenbluth. PD5-02 An Organoid Model System to Study Resistance Mechanisms, Predictive Biomarkers, and New Strategies to Overcome Therapeutic Resistance in Early-Stage Triple-Negative Breast Cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD5-02.

BACKGROUND WISDOM (Women Informed to Screen Depending on Measures of risk) is a pragmatic trial comparing the safety, efficacy, cost, and patient acceptability of personalized versus annual breast cancer screening in women ages 40-74 years with no personal history of breast cancer. Cancer registry reporting is considered the gold standard for ascertaining cancer diagnosis data; however, the time lag between diagnosis and reporting (1-2 years or more) by the California Cancer Registry (CCR) presents challenges. The WISDOM study must quickly ascertain and report accurate cancer diagnoses to the Data Safety Monitoring Board; delays could lead to preventable harm, unnecessary costs, and unwarranted early trial termination. To ascertain cancer diagnoses in a timely manner, we used multiple data collection methods and compared self-reported with Electronic Health Records (EHR) information, and in some cases verified with chart review. Specifically, we conducted a procedural study with WISDOM participants who sought care at a University of California (UC) health system by extracting their data from the University of California Data Warehouse (UCDW) to 1) verify self-reported breast cancer diagnoses in WISDOM; 2) ensure that all on-study breast cancer diagnoses have been captured within the WISDOM study cohort; and 3) assess the accuracy and reliability of self-reported data. The UCDW provides harmonized EHR data from the six University of California (UC) health systems and constitutes ~120 billion data points from 7.8 million patient records. METHODS We provided the UCDW with a list of WISDOM participants who indicated they had sought care from a UC health system. Participants with recorded breast cancer diagnostic code (ICD-10CM C50.) were matched with the WISDOM self-reported cancer dataset to uncover participants who had no recorded breast cancer diagnosis in the study system. For those individuals with no recorded self-report diagnosis, coordinators conducted manual chart review to determine whether the participants had been diagnosed with breast cancer. RESULTS This cohort included 11,314 enrolled WISDOM participants who self-reported care at a UC and had at least one diagnostic or procedural breast care code. Among these participants, 160 had a ICD-10-CM C50 breast cancer diagnostic code of which 132 breast cancer cases were confirmed: 111 were already self-reported through WISDOM and 21 additional confirmed breast cancer cases were identified through the UCDW. As a standard process, only WISDOM self-reported cancer cases that were confirmed by chart review are entered into the study database. The percentage of confirmed UCDW-identified cases that were not also self-reported was greater for recent diagnoses (16% overall and 6% for period prior to June 2020). Among the 11,314 participants in our cohort, the CCR identified 61 pre-June 2020 cancer diagnoses. Of these 61 diagnoses, 92% (56) were identified by the UCDW procedural process. If the UCDW had been used as the single source for cancer diagnosis, 21 participants without cancer and 7 participants with unclear status (unlocated or inaccessible records) would have been classified as cancer cases. DISCUSSION/CONCLUSION Self-reported data provides quick ascertainment with relative accuracy compared to cancer registry. Cancer ascertainment can be further improved by combining self-reported data with EHR data from a health system data warehouse registry, particularly for self-reported questionnaire issues such as timing and lack of response. Accuracy of self-reported cancer diagnosis from annually distributed questionnaires improves over time. Identifying cancer diagnosis discordance between data sources can inform processes to improve self-reported study accuracy. Citation Format: Katherine Leggat-Barr, Rita H. Ryu, Allison Stover Fiscalini, Tomiyuri Lewis, Rohini S. Bulusu, Samrrah A. Raouf, Hannah Lui Park, Alyssa N. Rocha, Liliana Johansen, Laura Van ’t Veer, Laura J. Esserman, Michael A. Hogarth. The WISDOM Study: Early Ascertainment of Breast Cancer Diagnoses by Utilizing Self-Reported Questionnaires and Data Warehouse Electronic Health Records [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-03-24.

Background: I-SPY2 is a multicenter, phase 2 trial using response-adaptive randomization within biomarker subtypes defined by hormone-receptor (HR), HER2, and MammaPrint (MP) status to evaluate novel agents as neoadjuvant therapy for high-risk breast cancer. The primary endpoint is pathologic complete response (pCR). Cemiplimab (Cemi) is a PD-1 inhibitor approved for the treatment of NSCLC, cutaneous basal, and squamous cell cancer. Here, we report current efficacy rates of Cemi in combination with paclitaxel followed by AC. Methods: Women with tumors ≥ 2.5cm were eligible for screening. Only HER2 negative (HER2-) patients were eligible for this treatment; HR positive (HR+) patients had to be MP high risk. Treatment included paclitaxel 80 mg/m2 IV weekly x 12 and Cemi 350 mg IV given q3weeks x 4, followed by doxorubicin/cyclophosphamide (AC) every 2 weeks x 4. The control arm was weekly paclitaxel x 12 followed by AC every 2-3 weeks x 4. All patients undergo serial MRI imaging; and imaging response (at 3 weeks, 12 weeks and prior to surgery) were used along with accumulating pCR data to continuously update and estimate pCR rates for trial arms. Analysis was modified intent to treat. Patients who switched to non-protocol therapy count as non-pCR. The goal is to identify (graduate) regimens with ≥85% Bayesian predictive probability of success (i.e. demonstrating superiority to control) in a future 300-patient phase 3 neoadjuvant trial with a pCR endpoint within responsive signatures. Cemi was eligible to graduate in 3 pre-defined signatures: HER2-, HR-HER2-, and HR+HER2-. To adapt to changing standard of care, we constructed “dynamic controls” comprising ‘best’ alternative therapies using I-SPY 2 and external data and estimated the probability of Cemi being superior to the dynamic control. Results: 60 HER2- patients (28 HR+ and 32 HR-) received Cemi arm treatment. The control group included 357 patients with HER2- tumors (201 HR+ and 156 HR-) enrolled since March 2010. Cemi graduated in HR-/HER2- signature. Estimated pCR rates (as of June 2022) are summarized in the table. Immune-related endocrine disorders include: hypothyroid (14.5%), adrenal insufficiency (10%), hyperthyroid (4.8%),) and thyroiditis (3.2%). Only one grade 3 adrenal insufficiency was observed. All immune related AE’s were manageable. Additional biomarker analyses are ongoing and will be presented at the meeting. Response predictive subtypes (Immune+ vs Immune-) and additional predictive biomarkers were assessed. Associations with pCR will be presented at SABCS. Conclusion: The I-SPY 2 study aims to assess the probability that investigational regimens will be successful in a phase 3 neoadjuvant trial. Anti-PD-1 therapy with Cemi resulted in a higher predicted pCR rate in HR-/HER2- 55 rate% disease compared to control at 29%. Immune-mediated AE’s were observed. This data is consistent with previously published data using check point inhibitors in early-stage HR-/HER2- breast cancer. Estimated pCR rates Citation Format: Erica Stringer-Reasor, Rebecca A. Shatsky, Jo Chien, Anne Wallace, Judy C. Boughey, Kathy S. Albain, Hyo S. Han, Rita Nanda, Claudine Isaacs, Kevin Kalinsky, Zahi Mitri, Amy S. Clark, Christos Vaklavas, Alexandra Thomas, Meghna S. Trivedi, Janice Lu, Smita Asare, Ruixiao Lu, Maria Pitsouni, Amy Wilson, Jane Perlmutter, Hope Rugo, Richard Schwab, W. Fraser Symmans, Nola M. Hylton, Laura Van ’t Veer, Douglas Yee, Angela DeMichele, Donald Berry, Laura J. Esserman, I-SPY Investigators. PD11-01 Evaluation of the PD-1 Inhibitor Cemiplimab in early-stage, high-risk HER2-negative breast cancer: Results from the neoadjuvant I-SPY 2 TRIAL [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD11-01.

Background: In previous work we leveraged the I-SPY2 trial to create treatment response predictive subtypes (RPS) incorporating tumor biology beyond clinical HR/HER2, to better predict drug responses in an expanded treatment landscape that includes platinum agents, dual HER2-targeting regimens and immunotherapy [1]. We showed that best performing schemas incorporate Immune, DRD and HER2/Luminal phenotypes, and that treatment allocation based on these would increase the overall pCR rate to 63% from 51% using HR/HER2-based treatment selection. The RPS schema has been selected for prospective evaluation in I-SPY2. Using the RPS, one would prioritize platinum-based therapy for HER2-/Immune-/DRD+, immunotherapy for HER2-/Immune+, and dual-anti-HER2 for HER2+ that are not luminal. HER2+/Luminal patients have low response rates to dual-anti-HER2 therapy but may respond better to anti-AKT. However, there is still a considerable ‘biomarker-negative’ group of resistant cancers (HER2-/Immune-/DRD-) with very low pCR rates to all tested agents, that require a new therapeutic approach. Here we characterize the protein signaling architecture of these tumors to identify new target candidates. Methods: 987 I-SPY 2 patients from 10 arms of the trial were considered for this analysis. All have gene expression, pCR and RPS; 944 have distant recurrence free survival (DRFS) data; and 736 have reverse phase protein array (RPPA) data from laser capture microdissected tumor epithelium. These data – known collectively as the I-SPY2-990 mRNA/RPPA Data Resource - were recently made public on NCBI’s Gene Expression Omnibus [GEO: GSE196096]. We focus on HER2-/Immune-/DRD- tumors, applying Wilcoxon and t-tests to identify phosphoproteins that differ between HR+HER2-/Immune-/DRD- and other HR+HER2- tumors; and between TN/Immune-/DRD- and other TNs. The Benjamini-Hochberg (BH) method is used to adjust p-values for multiple hypothesis testing. In addition, the Kaplan-Meier method is used to estimate DRFS. Results: 201/736 I-SPY 2 patients with RPPA data are classified HER2-/Immune-/DRD- (HR+HER2-: n=138; TN: n=63). Of these, 8.5% (17/201) achieved pCR. Non-responding HER2-/Immune-DRD- had worse outcomes than responders (~75% vs. ~95% DRFS at 5 years). 60/139 phospho-proteins differ significantly between HR+HER2-/Immune-/DRD- and other HR+HER2- tumors (n=122). These tumors are relatively ‘cold’, in that 90% (54/60) of the phosphoprotein activities characterizing this group are at lower levels than in the overall HR+HER2- population; including immune (e.g. pPDL1, pJAK/STAT) and proliferation (e.g., Ki67, CyclinB1, pAURK) endpoints. Phosphoproteins showing higher levels in this subset include ERBB2 (BH p=1.7E-06), Cyclin D1 (BH p=1.4E-05), pAR (BH p=1.4E-05), and ER (BH p=3E-04). Within the TN subset, only 3/139 phospho-proteins differed significantly between TN/Immune-/DRD- and other TN tumors (n=189). These were all immune-related (pPDL1, pSTAT1, and HLA DR), with lower expression in the TN/Immune-/DRD- group. Conclusion: HR+HER2- and TN patients who are Immune-Low and DRD-Low have very low pCR rates to all tested therapeutics in I-SPY2 including standard chemotherapy, platinum, and immunotherapy. Senolytics (possibly targeting Cyclin D1), HER2low agents, and AR modulators may overcome resistance in HR+HER2-/Immune-/DRD-, whereas an immune activator beyond checkpoint inhibition is suggested for TN/Immune-/DRD- patients. [1] Wolf et. al., Redefining Breast Cancer Subtypes to Guide Treatment Prioritization and Maximize Response: Predictive Biomarkers across 10 Cancer Therapies. Cancer Cell 2022 Citation Format: Denise M. Wolf, Christina Yau, Julia Wulfkuhle, Rosa I. Gallagher, Lamorna A. Brown Swigart, Gillian L. Hirst, Jean-Philippe Coppe, Mark Jesus M. Magbanua, Rosalyn Sayaman, I-SPY2 Investigators, Laura Sit, Nola M. Hylton, Angela DeMichele, Donald A. Berry, Lajos Pusztai, Douglas Yee, Laura J. Esserman, Emanuel F. Petricoin, Laura Van ’t Veer. PD5-04 Characterizing the HER2-/Immune-/DNA repair (DRD-) response predictive breast cancer subtype: the hunt for new protein targets in a high-needs population with low response to all I-SPY2 agents [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD5-04.

Background: The detection of circulating tumor DNA (ctDNA) may serve as an early predictor of response and recurrence. In this study, we used a tumor-informed ctDNA test to monitor clinical outcomes in patients with high-risk hormone receptor-positive HER2-negative (HR+HER2-) tumors who received neoadjuvant chemotherapy (NAC) on the I-SPY 2 trial (NCT01042379). Methods: We collected blood samples at pretreatment, during (at 3 and 12 weeks after initiation of paclitaxel-based treatment with or without an investigational drug), after NAC prior to surgery, 4 weeks after surgery, and annually until clinical diagnosis of recurrence. Cell-free DNA was isolated from plasma (N=329 samples) and ctDNA was detected using a personalized, tumor-informed multiplex polymerase chain reaction next generation sequencing-based test (SignateraTM). All patients were at high risk for recurrence by MammaPrint. The response endpoints were pathologic complete response (pCR) and residual cancer burden (RCB), and the survival endpoint was event-free survival (EFS). Results: This analysis included 66 patients with HR+HER2- breast cancer who had blood samples collected before, during, after NAC and had at least one blood sample after surgery with sufficient plasma for analysis. 57.1% (32/56) had grade III disease; 72.4% (42/58) were node-positive; 36.2% (21/58) had T3/T4 disease; and 33.3% (22/66) were MammaPrint High 2. The percent ctDNA positivity rates at pretreatment, after NAC prior to surgery, and 4 weeks after surgery were 79.7% (47/59), 6.5% (4/62), and 2% (1/50), respectively. Significantly higher ctDNA positivity rates at pretreatment were observed in patients with larger tumors (95% in T3/T4 vs. 69% in T1/T2, Fisher’s exact p=0.0387), higher grade tumors (94% in Grade III vs. 67% in Grade I/II, p=0.0147) and by MammaPrint score (100% in High 2 vs. 71% in High 1, p=0.0052). In this high-risk HR+/HER2- cohort, 10/66 (15.2%) achieved pCR/RCB 0, who were all ctDNA-negative at surgery. 56/66 (84.8%) had no-PCR, with RCB I (limited residual cancer), II (moderate) and III (extensive) in 7 (10.6%), 31 (47.0%) and 18 (27.3%), respectively. ctDNA-positivity after paclitaxel-based treatment was significantly associated with RCB II/III status (Fisher’s exact p=0.01). All patients in this cohort with persistent ctDNA subsequently had RCB II or III at surgery. 47 patients had paired samples collected after NAC prior to surgery and at 4 weeks after surgery. Of the 47, 91.5% (43/47) were ctDNA-negative at both time points and 8.5% (4/47) were discordant; 1 was ctDNA-negative and later tested ctDNA-positive, while 3 were ctDNA-positive and later tested ctDNA-negative. 61/66 patients had EFS data with a median of 1.6 years of follow up (range: 0.6 to 5.6). 5 tested ctDNA-positive in at least one time point after surgery. Of these, 2 experienced a recurrence (one local relapse and one distant metastasis) and both tested positive at the time of recurrence. For the patient who developed a distant recurrence it was the only blood sample available at a follow-up time point; for the patient who developed a local recurrence, blood from two earlier follow-up time points had tested negative. To date, no recurrences have been observed in those whose test(s) after surgery were negative for ctDNA. Conclusions: The persistence of ctDNA during neoadjuvant therapy is associated with the extent of residual disease in a cohort of patients with HR+HER2- breast cancer in the I-SPY 2 trial and thus may be useful in identifying patients who are not having an optimal response to therapy. I-SPY 2.2 will test whether ctDNA has utility in redirecting therapy to improve surgical outcome and subsequent prognosis. Citation Format: Mark Jesus M. Magbanua, Hope Rugo, Lamorna A. Brown Swigart, Ziad Ahmed, Gillian L. Hirst, Denise M. Wolf, Ruixiao Lu, Ekaterina Kalashnikova, Derrick Renner, Angel Rodriguez, Minetta C. Liu, Christina Yau, Laura J. Esserman, Laura Van ’t Veer, Angela DeMichele. Monitoring for response and recurrence in neoadjuvant-treated hormone receptor-positive HER2-negative breast cancer by personalized circulating tumor DNA testing [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-05-05.

Background: Aggressive breast cancers have increased proliferation or metastatic potential and portend a poor prognosis. The ability to identify women at elevated risk of aggressive cancers could have major implications for screening and prevention, yet there are no available tools for predicting aggressive cancer risk. We sought to construct a polygenic risk score (PRS) for aggressive breast cancers by leveraging the associations of single nucleotide polymorphisms (SNPs) with tumor gene expression. We used as our measure of aggressiveness the risk of recurrence score weighted on proliferation (ROR-P), a validated tumor prognostic signature. We hypothesized that known breast cancer susceptibility SNPs would have differential associations with ROR-P, which could then be used to construct a PRS for ROR-P. Methods: We developed our PRS in a case-only analysis of 3 studies containing SNP genotypes and tumor gene expression: The Cancer Genome Atlas, METABRIC, and the I-SPY 2 TRIAL (total n=2,363). We used linear regression models to evaluate individual SNP associations with ROR-P, adjusted for genetic ancestry and study. We then constructed PRS using varying p-value thresholds and used cross-validation to identify the PRS with highest model r2. To assess whether the ROR-P PRS was associated with poor prognosis, we performed survival analysis in two longitudinal cohorts of breast cancer patients: the UK Biobank (women with incident invasive cancers only) and the Pathways Study. These studies included 10,196 total cases with 785 deaths. We built Cox proportional hazards models to evaluate the association between the ROR-P PRS (adjusted for genetic ancestry) and breast cancer-specific survival (BCSS) in both studies. We then performed meta-analysis of the Cox model results. We also constructed joint models containing the ROR-P PRS and a PRS representing the case-case risk of ER-negative vs. ER-positive cancer, PRSER-/ER+. Results: We tested the associations between 226 breast cancer susceptibility SNPs and ROR-P. The best-performing PRS contained 76 SNPs and had a cross-validated r2 of 0.051. In the UK Biobank and Pathways Study, higher ROR-P PRS was associated with worse BCSS, with nearly identical effects observed in each study, HR per standard deviation of 1.13 (95% CI 1.05-1.21, p=9.0x10-4) in meta-analysis. The ROR-P PRS’s effect was minimally attenuated when adjusted for PRSER-/ER+, suggesting that the ROR-P PRS was providing additional prognostic information beyond ER status. Conclusions: We used breast cancer susceptibility SNPs to construct a PRS for ROR-P, a prognostic signature recapitulating aggressiveness, and found the ROR-P PRS to be associated with worse BCSS. Our findings represent an improvement on current PRS for overall breast cancer risk, which preferentially predict cancers with favorable prognosis. Given that aggressive cancers are more likely to present as advanced cancers even among women undergoing routine screening, our findings could potentially identify women who may benefit from more intensive screening. Citation Format: Yiwey Shieh, Jacquelyn Roger, Christina Yau, Denise Wolf, Gillian Hirst, Lamorna Swigart, Scott Huntsman, Donglei Hu, Jovia Nierenberg, Pooja Middha, Rachel Heise, Linda Kachuri, Qianqian Zhu, Song Yao, Christine Ambrosone, Marilyn Kwan, Bette Caan, John Witte, Lawrence Kushi, Laura van ’T. Veer, Laura Esserman, Elad Ziv. Development and testing of a polygenic risk score for breast cancer. Aggressiveness. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr PR008.

Mapping protein interactions driving cancer Cancer is a genetic disease, and much cancer research is focused on identifying carcinogenic mutations and determining how they relate to disease progression. Three papers demonstrate how mutations are processed through networks of protein interactions to promote cancer (see the Perspective by Cheng and Jackson). Swaney et al . focus on head and neck cancer and identify cancer-enriched interactions, demonstrating how point mutant–dependent interactions of PIK3CA, a kinase frequently mutated in human cancers, are predictive of drug response. Kim et al . focus on breast cancer and identify two proteins functionally connected to the tumor-suppressor gene BRCA1 and two proteins that regulate PIK3CA. Zheng et al . developed a statistical model that identifies protein networks that are under mutation pressure across different cancer types, including a complex bringing together PIK3CA with actomyosin proteins. These papers provide a resource that will be helpful in interpreting cancer genomic data. —VV

Importance Residual cancer burden (RCB) distributions may improve the interpretation of efficacy in neoadjuvant breast cancer trials. Objective To compare RCB distributions between randomized control and investigational treatments within subtypes of breast cancer and explore the relationship with survival. Design, Setting, and Participants The I-SPY2 is a multicenter, platform adaptive, randomized clinical trial in the US that compares, by subtype, investigational agents in combination with chemotherapy vs chemotherapy alone in adult women with stage 2/3 breast cancer at high risk of early recurrence. Investigational treatments graduated in a prespecified subtype if there was 85% or greater predicted probability of higher rate of pathologic complete response (pCR) in a confirmatory, 300-patient, 1:1 randomized, neoadjuvant trial in that subtype. Evaluation of a secondary end point was reported from the 10 investigational agents tested in the I-SPY2 trial from March 200 through 2016, and analyzed as of September 9, 2020. The analysis plan included modeling of RCB within subtypes defined by hormone receptor (HR) andERBB2 status and compared control treatments with investigational treatments that graduated and those that did not graduate. Interventions Neoadjuvant paclitaxel plus/minus 1 of several investigational agents for 12 weeks, then 12 weeks of cyclophosphamide/doxorubicin chemotherapy followed by surgery. Main Outcomes and Measures Residual cancer burden (pathological measure of residual disease) and event-free survival (EFS). Results A total of 938 women (mean [SD] age, 49 [11] years; 66 [7%] Asian, 103 [11%] Black, and 750 [80%] White individuals) from the first 10 investigational agents were included, with a median follow-up of 52 months (IQR, 29 months). Event-free survival worsened significantly per unit of RCB in every subtype of breast cancer (HR-positive/ERBB2-negative: hazard ratio [HZR], 1.75; 95% CI, 1.45-2.16; HR-positive/ERBB2-positive: HZR, 1.55; 95% CI, 1.18-2.05; HR-negative/ERBB2-positive: HZR, 2.39; 95% CI, 1.64-3.49; HR-negative/ERBB2-negative: HZR, 1.99; 95% CI, 1.71-2.31). Prognostic information from RCB was similar from treatments that graduated (HZR, 2.00; 95% CI, 1.57-2.55; 254 [27%]), did not graduate (HZR, 1.87; 95% CI, 1.61-2.17; 486 [52%]), or were control (HZR, 1.79; 95% CI, 1.42-2.26; 198 [21%]). Investigational treatments significantly lowered RCB in HR-negative/ERBB2-negative (graduated and nongraduated treatments) andERBB2-positive subtypes (graduated treatments), with improved EFS (HZR, 0.61; 95% CI, 0.41-0.93) in the exploratory analysis. Conclusions and Relevance In this randomized clinical trial, the prognostic significance of RCB was consistent regardless of subtype and treatment. Effective neoadjuvant treatments shifted the distribution of RCB in addition to increasing pCR rate and appeared to improve EFS. Using a standardized quantitative method to measure response advances the interpretation of efficacy. Trial Registration ClinicalTrials.gov Identifier:NCT01042379

We investigated whether serial measurements of circulating tumor DNA (ctDNA) and functional tumor volume (FTV) by magnetic resonance imaging (MRI) can be combined to improve prediction of pathologic complete response (pCR) and estimation of recurrence risk in early breast cancer patients treated with neoadjuvant chemotherapy (NAC). We examined correlations between ctDNA and FTV, evaluated the additive value of ctDNA to FTV-based predictors of pCR using area under the curve (AUC) analysis, and analyzed the impact of FTV and ctDNA on distant recurrence-free survival (DRFS) using Cox regressions. The levels of ctDNA (mean tumor molecules/mL plasma) were significantly correlated with FTV at all time points (p p p = 0.25). In contrast, ctDNA-positivity after NAC provided significant additive value to FTV in identifying patients with increased risk of metastatic recurrence and death (p = 0.004). In this pilot study, we demonstrate that ctDNA and FTV were correlated measures of tumor burden. Our preliminary findings based on a limited cohort suggest that ctDNA at surgery improves FTV as a predictor of metastatic recurrence and death. Validation in larger studies is warranted.

Additional file 1: Supplemental Figure S1. Approximately Unbiased (AU) and Bootstrap Probability (BP) p-values for hierarchical clustering of the binarized response dataset (Fig. 2). Based on multiscale bootstrap resampling, clusters that are highly supported by the data will have large AU values. Blue squares (AU = 0.9) and red lines (AU = 0.95) represent clusters highly supported by the data. Supplemental Figure S2. A) Waterfall plot showing the number of agents with response that can be predicted (y axis; Fisher test, uncorrected p 1, p 0.05. Supplemental Figure S3. Approximately Unbiased (AU) and Bootstrap Probability (BP) p-values for hierarchical clustering of the OR matrix model of history-dependent response (Fig. 3b). Based on multiscale bootstrap resampling, clusters that are highly supported by the data will have large AU values. Blue squares (AU = 0.9) and red lines (AU = 0.95) represent clusters highly supported by the data. Supplemental Figure S4. Heatmap showing the odds ratios (log (OR)) of responding to one drug given knowledge of (intrinsic/prior) response of another drug, for only those drug pairs that are significantly associated after multiple testing correction (BH p 1, BH p 0.05. Supplemental Figure S5. A) Heatmap showing the odds ratios (log (OR))) of responding to one drug given knowledge of (intrinsic/prior) response of another drug in luminal (n = 17) and basal or claudin-low (n = 18), and ERBB2-amp molecular subtypes (n = 10). Red: positive association between drugs A and B (OR > 1, p 0.05. Top panel: shown for all drugs (uncorrected p

Background: Identification of women at elevated risk for highly proliferative, poor prognosis breast cancers could have important implications for screening and prevention. Genome-wide association studies (GWAS) have found >200 single nucleotide polymorphisms (SNPs) associated with breast cancer risk, with many SNPs differentially associated with ER status or intrinsic subtype. We hypothesized that some of these SNPs are preferentially associated with more proliferative tumors, while others are preferentially associated with less proliferative tumors. In this study, we aimed to build a polygenic risk score (PRS) predictive of proliferative tumors, using the GWAS-identified SNPs. Methods: We used data from 3 studies that included array-based SNP genotyping and tumor transcriptomic data: The Cancer Genome Atlas, METABRIC, and the I-SPY 2 Trial (total n=2,467). Our outcome was the risk of recurrence score weighted on proliferation (ROR-P), a validated tumor prognostic signature. Using the breast cancer risk SNPs, we built respective linear regression models to predict ROR-P, with genetic ancestry, study, and ER status as covariates. We performed 5-fold cross-validation and used the model r2 to identify the optimal p-value threshold for including SNPs in the PRS. To decrease uncertainty of our estimates, we performed 100 repeats across the pooled datasets. To test whether this model predicted poor prognosis breast cancers, we first used it to impute ROR-P among genotyped breast cancer cases in UK Biobank (UKB). We then examined the association between “genetically predicted” ROR-P and breast cancer-specific survival using Cox proportional hazards models adjusted for genetic ancestry and age at diagnosis. Results: Associations between 224 breast cancer SNPs and ROR-P were tested. The best-performing model in cross-validation contained 96 SNPs, each associated with ROR-P at p < 0.45, with model r2 0.054. The SNPs with the strongest positive correlations with ROR-P included those discovered in GWAS for HER2-positive and ER-negative cancers, both of which tend to be highly proliferative. Among 7,247 incident cancers in UKB, higher genetically predicted ROR-P was associated with shorter survival, with a per-standard deviation hazard ratio of 1.14 (95% CI 1.05-1.24, p = 0.002). Conclusions: We used breast cancer susceptibility SNPs to construct a PRS fitted to ROR-P, a prognostic signature recapitulating tumor proliferation. This PRS was associated with worse clinical outcomes in breast cancer cases from UKB. Our results suggest that correlations between SNPs and tumor gene expression can be used to “tune” PRS to tumor phenotype, e.g. proliferation. Highly proliferative tumors are more likely to present as advanced cancers even among women getting routine screening. If replicated in other datasets, our findings could be used to identify women who may especially benefit from tailored screening and prevention. Citation Format: Yiwey Shieh, Jacquelyn Roger, Scott Huntsman, Donglei Hu, Jovia L. Nierenberg, Pooja Middha Kapoor, Christina Yau, Gillian Hirst, Laura van 't Veer, Laura Esserman. Development and testing of a polygenic risk score for proliferative breast cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5883.

Background: We compared the predictive and prognostic value of ctDNA dynamics in high-risk hormone receptor-positive/HER2-negative (HR+/HER2-) and triple negative breast cancer (TNBC) receiving neoadjuvant chemotherapy (NAC) enrolled in the I-SPY 2 trial (NCT01042379). To our knowledge, this is the largest ctDNA study in breast cancer in the neoadjuvant setting. Methods: Blood samples were collected at pre-treatment (T0), during treatment (T1 at 3 weeks, and T2 at 12 weeks) and after NAC (T3 at 24 weeks) from 106 HR+/HER2- and 97 TNBC patients. Plasma samples (n=734) were analyzed using a personalized and tumor-informed mPCR NGS-based ctDNA test (SignateraTM). Patients, all high risk for recurrence by MammaPrint, received paclitaxel-based treatment +/- experimental therapy followed by anthracycline. The median follow-up was 3.0 years (0.5 to 6.5). The predictive and prognostic value of ctDNA dynamics and status at different timepoints were examined. Our analysis is exploratory and does not adjust for other biomarkers. Results: Pretreatment ctDNA positivity (Fisher p Conclusions: The predictive value of ctDNA for prediction of pCR and RCB differed between subtypes (HR+/HER2- vs. TNBC), while similar prognostic value was observed. In TNBC, early clearance of ctDNA at 3 weeks was a significant predictor of favorable response to NAC. Compared to patients who were ctDNA-positive after NAC, ctDNA-negative status in both subtypes was associated with improved DRFS even in patients with residual cancer (no pCR or RCB-II/III). These findings could inform on the design of future studies that seek to demonstrate the utility of ctDNA in the curative setting. Predictive and prognostic significance of ctDNA in early breast cancer in the neoadjuvant setting HR+HER2- (n=106) TNBC (n=97) Predictive value for prediction of pCR and RCB Fisher p-value Fisher p-value Early ctDNA clearance (between T0 and T1) and pCR 0.4521 Citation Format: Mark Jesus Mendoza Magbanua, Lamorna Brown Swigart, Derrick Renner, Svetlana Shchegrova, Gillian L. Hirst, Christina Yau, Denise M. Wolf, Hsin-Ta Wu, Ekaterina Kalashnikova, Amy L. Delson, A. Jo Chien, Debu Tripathy, Smita Asare, Raheleh Salari, Angel Rodriguez, Bernhard Zimmermann, Himanshu Sethi, Alexey Aleshin, Paul Billings, Rita Nanda, Hope S. Rugo, Laura J. Esserman, Minetta C. Liu, Angela DeMichele, Laura van 't Veer. Comparison of the predictive and prognostic significance of circulating tumor DNA in patients with high risk HER2-negative breast cancer receiving neoadjuvant chemotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB111.

563 Background: A polygenic risk score (PRS) consisting of 313 single nucleotide polymorphisms (PRS313) is associated with risk of breast cancer and contralateral breast cancer. One of the most promising clinical applications for the use of PRS is to provide a personalized risk assessment to individualize breast cancer screening programs. This study aimed to evaluate the association of the PRS313 with clinical-pathological characteristics and survival of breast cancer. Methods: Women of European ancestry with invasive breast cancer were included, 98,397 women from the Breast Cancer Association Consortium (BCAC) and 683 women from the MINDACT trial. Associations between PRS313 (continuous, per SD) and clinical-pathological characteristics, including the 70-gene signature for patients included in MINDACT, were evaluated with logistic regression analyses. Associations of PRS313 with overall survival (OS), breast cancer-specific survival (BCSS) and distant metastasis-free interval (DMFI) were evaluated with Cox regression analyses, adjusted for clinical-pathological characteristics and treatment. Results: The PRS313 was associated with favorable tumor characteristics. In BCAC, increasing PRS313 was mostly associated with lower grade, hormone receptor-positive tumors, and with smaller tumor size. In MINDACT, PRS313 was associated with a low risk 70-gene signature, but the association was attenuated after adjustment for clinical-pathological characteristics. In BCAC, univariable analyses showed an association of PRS313 with better survival, hazard ratio (HR) per unit SD increase of PRS313 for OS: 0.96 (95% confidence interval (CI): 0.94-0.97), for BCSS HR: 0.96 (95% CI: 0.94-0.98) and for DMFI HR: 0.98 (95% CI: 0.96-1.00). The association in the unadjusted analysis was explained by differences in clinical-pathological characteristics (and treatment) and disappeared after adjustment: OS HR: 1.01 (95% CI: 0.98-1.05), BCSS HR: 1.02 (95% CI: 0.98-1.07) and DMFI HR: 1.03 (95% CI: 0.99-1.07). Conclusions: An increased PRS313 is associated with favorable tumor characteristics but was not independently associated with prognosis. This information is crucial input for modelling effective stratified screening programs, especially in the current era of optimized (systemic) treatments. Given the significant increase in breast cancer risk associated with increasing PRS313, absolute breast cancer mortality will still be higher for women with higher PRS313.

The AKT inhibitor MK2206 (M) was evaluated in I-SPY 2 and graduated in the HER2+, HR−, and HR− HER2+ signatures. We hypothesized that AKT signaling axis proteins/genes may specifically predict response to M and tested 26 phospho-proteins and 10 genes involved in AKT-mTOR-HER signaling; in addition, we tested 9 genes from a previous study in the metastatic setting. One hundred and fifty patients had gene expression data from pretreatment biopsies available for analysis (M: 94, control: 56) and 138 had protein data (M: 87, control: 51). Logistic modeling was used to assess biomarker performance in pre-specified analysis. In general, phospho-protein biomarkers of activity in the AKT-mTOR-HER pathway appeared more predictive of response to M than gene expression or total protein biomarkers in the same pathway; however, the nature of the predictive biomarkers differed in the HER2+ and TN groups. In the HER2+ subset, patients achieving a pCR in M had higher levels of multiple AKT kinase substrate phospho-proteins (e.g., pmTOR, pTSC2). In contrast, in the TN subset responding patients had lower levels of AKT pathway phospho-proteins, such as pAKT, pmTOR, and pTSC2. Pathway mutations did not appear to account for these associations. Additional exploratory whole-transcriptome analysis revealed immune signaling as strongly associated with response to M in the HER2+ subset. While our sample size is small, these results suggest that the measurement of particular AKT kinase substrate phospho-proteins could be predictive of MK2206 efficacy in both HER2+ and TN tumors and that immune signaling may play a role in response in HER2+ patients.

Background Transcriptomic immune-related gene signatures have been associated with achievement of pathologic complete response (pCR) and prognosis in the neoadjuvant setting. I-SPY 2 is a multicenter, phase 2 platform trial using response-adaptive randomization within subtypes defined by receptor status (HR/HER2) and MammaPrint (MP) risk to evaluate novel agents as neoadjuvant therapy for women with high-risk breast cancer. Given racial disparities in mortality from breast cancer and the paucity of racial demographic data from clinical trials, we aimed to evaluate the association between racial groups and baseline characteristics, including expression-based subtypes and immune signatures, treatment response, and prognosis of patients enrolled in the I-SPY 2 TRIAL. Methods Our study population included 990 I-SPY 2 patients. 15 patients identified as part of a racial group with Citation Format: Beverly Kyalwazi, Christina Yau, Olufunmilayo Olopade, A. Jo Chien, Anne Wallace, Andres Forero-Torres, Lajos Pusztai, Erin Ellis, Kathy Albain, Anne Blaes, Barbara Haley, Judy Boughey, Anthony Elias, Amy Clark, Claudine Isaacs, Rita Nanda, Hyo Han, Rachel Yung, Debu Tripathy, Kristen Edmiston, Rebecca Viscusi, Donald Northfelt, Qamar Khan, Ashish Sanil, Scott Berry, Smita Asare, Amy Wilson, Gillian Hirst, Nola Hylton, Michelle Melisko, Jane Perlmutter, Hope Rugo, Fraser Symmans, Laura van ‘t Veer, Donald Berry, Laura Esserman. Analysis of clinical outcomes and expression-based immune signatures by race in the I-SPY 2 trial [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr GS4-02.

Background: Liquid biopsies have emerged as effective diagnostic tools in disease monitoring and minimal residual disease detection. Circulating tumor DNA (ctDNA) was recently shown to be a predictor of poor response and recurrence in breast cancer. However, ctDNA shedding from breast tumors can rapidly decrease during treatment, resulting in reduced sensitivity in measuring early changes in tumor response or residual cancer burden (RCB) after neoadjuvant chemotherapy (NAC). We recently reported the discovery of orphan non-coding RNAs (oncRNAs), a class of small RNAs that are not present in healthy cells, but emerge from cancer cells. Similar to ctDNA, tumor-released oncRNAs can be used to detect the presence of an underlying tumor; however, since they are actively released by cancer cells, their abundance in the cell-free compartment is substantially higher than ctDNA. Therefore, we hypothesized that monitoring circulating oncRNAs in blood permits a more sensitive approach to measuring treatment response (i.e., pathologic complete response, or pCR) and estimating RCB. Patients and Methods: Cell-free RNA (cfRNA) was extracted from ~1 ml sera of 72 breast cancer patients treated in the neoadjuvant I-SPY 2 TRIAL with NAC alone or combined with MK-2206 (AKT inhibitor) treatment. For each patient, treatment-naïve samples (T0) were compared with samples from post-treatment and prior to surgery (T3) time-point. RNA samples were subjected to small RNA sequencing (SMARTer), and the presence and abundance of cell-free oncRNA species were then determined by identifying and counting the reads that map to oncRNA loci across samples. Notably, oncRNAs species were pre-annotated from the Cancer Genome Atlas (TCGA), and our approach does not require bespoke personalized assays. We used a machine-learning model to compare abundance of cfRNA species before and after treatment (i.e., T3-T0) to predict pCR and RCB. For this, we split our cohort into a training and a testing set (48 and 24) and trained a model to simultaneously learn the presence of residual disease (pCR vs. no pCR) and its extent (RCB). We then measured the performance of our model on the held-out test data and the entire dataset. To confirm the robustness of our model, we also employed a leave-one-out strategy, whereby pCR and RCBIndex of each patient was predicted using a model that was trained on the other patients in the cohort. Finally, to assess the ability of our oncRNA-based model to risk-stratify patients who fail to achieve pCR (without having been explicitly trained on relapse data), we used the model’s oncRNA score to predict patients at the highest risk of distant recurrence (n=8 out of 36) and performed a multivariate Cox analysis, controlling for HR/Her2 status (median follow-up time was 4.8 years). Results: The model’s accuracy for predicting pCR—based on changes in circulating oncRNA species between T3 and T0—was 85% for the training data and 79% for the held-out test data (positive predictive value of 75% and negative predictive value of 83%) with combined accuracy of 83%; precision 86% and recall 83%; Pearson R=0.5 for RCB. A leave-one-out strategy showed similar performance (area under ROC of 0.77 versus 0.81 in train-test split). Finally, among the patients who failed to achieve pCR, we observed a significantly higher risk of distant recurrence in those with the highest scores (DRFS: hazard-ratio = 8.4, ANOVA P Citation Format: Hani Goodarzi, Albertas Navickas, Jefferey Wang, Kristle Garcia, Mark J Magbanua, Lisa Fish, Lamorna Brown Swigart, Gillian Hirst, Denise Wolf, Christina Yau, Jo Chien, Carol Simmons, Amy Delson, Laura Esserman, Laura van 't Veer. Tumor-released circulating orphan non-coding RNAs reflect treatment response and survival in breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD9-04.

Background: Invasive lobular carcinoma (ILC) is the second most common type of breast cancer after invasive ductal carcinoma (IDC). ILC has unique features, such as a diffuse growth pattern due to characteristic loss of E cadherin, and a different pattern of disease metastasis compared to IDC. Prior investigators have shown increased numbers of circulating tumor cells (CTCs) in patients with metastatic ILC versus IDC. We explored the distribution of CTCs and disseminated tumor cells (DTCs) in early stage ILC versus IDC. Methods: We performed a secondary data analysis of the TIPPING study, an institutional review board approved study that pre-operatively collected blood and bone marrow samples from 655 treatment-naïve early-stage breast cancer patients. We analyzed data from 284 patients who had CTCs and DTC enumerated by an EPCAM-based method involving immunomagnetic enrichment and flow cytometry (IE/FC) (61 patients with ILC and 223 patients with IDC). We compared CTC and DTC counts by histology using the Welch Two Sample t-test, linear regression models, as well as ANOVA tests. Multivariate Cox regression analyses were performed to assess association between levels of CTCs/DTCs and clinical outcomes (distant recurrence-free survival [DRFS] and breast cancer-specific survival [BCSS]). Results: ILC tumors were lower grade than IDCs and had a higher proportion of HR+HER2- subtypes (92.00% vs. 75.30%; p Citation Format: Silver Alkhafaji, Denise Wolf, Mark Magbanua, Laura Van 't Veer, John Park, Laura J. Esserman, Rita A. Mukhtar. Differences in levels of circulating tumor cells (CTC) and disseminated tumor cells (DTC) in early-stage lobular versus ductal breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-02-02.

Cancers have been associated with a diverse array of genomic alterations, many of which are rare with unknown significance. To understand the cellular mechanisms impacted by such alterations in breast invasive carcinoma, we have applied affinity-purification mass spectrometry to delineate comprehensive biophysical interaction networks for 40 frequently altered breast cancer proteins across three human breast cell lines, providing a novel resource of context-specific and shared protein-protein interaction networks in breast cancer cells. These networks interconnect and enrich for common and rare cancer mutations, and are substantially rewired by mutations. Our analysis identifies novel PIK3CA-interacting proteins which repress AKT signaling, and UBE2N emerges as a BRCA1 interactor predictive of clinical response to PARP inhibition. We also show that Spinophilin interacts with and dephosphorylates BRCA1 to promote DNA double-strand break repair. Thus, cancer protein interaction landscapes provide a framework for recognizing oncogenic drivers and drug vulnerabilities. Citation Format: Minkyu Kim, Jisoo Park, Mehdi Bouhaddou, Kyumin Kim, Ajda Rojc, Maya Modak, Margaret Soucheray, Patrick O'Leary, Denise Wolf, Dominique C. Mitchell, Fan Zheng, John D. Gordan, Jean-Philippe Coppé, Danielle L. Swaney, Laura van' t Veer, Alan Ashworth, Trey Ideker, Nevan J. Krogan. The protein interaction landscape of breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2308.

Precision medicine aims to use patient genomic, epigenomic, specific drug dose, and other data to define disease patterns that may potentially lead to an improved treatment outcome. Personalized dosing regimens based on tumor drug penetration can play a critical role in this approach. State-of-the-art techniques to measure tumor drug penetration focus on systemic exposure, tissue penetration, cellular or molecular engagement, and expression of pharmacological activity. Using in silico methods, this information can be integrated to bridge the gap between the therapeutic regimen and the pharmacological link with clinical outcome. These methodologies are described, and challenges ahead are discussed. Supported by many examples, this review shows how the combination of these techniques provides enhanced patient-specific information on drug accessibility at the tumor tissue level, target binding, and downstream pharmacology. Our vision of how to apply tumor drug penetration measurements offers a roadmap for the clinical implementation of precision dosing., SCOPUS: re.j, info:eu-repo/semantics/published

587 Background: Residual cancer burden (RCB) is a continuous score that captures the amount of residual cancer after neoadjuvant chemotherapy and predicts disease recurrence and survival across all breast cancer subtypes. RCB score 0 corresponds to pathological complete response (pCR; ypT0, ypN0). We hypothesize that comparison of the distributions of RCB scores between randomized treatment arms of a trial could predict treatment effect on recurrence free survival better than comparison of pCR rates only. Methods: The cancer Treatment Efficacy Score (TES) compares efficacies of two treatments using non-continuous RCB results. We examined (i) area between cumulative distribution (ABC) functions; (ii) density ratio of RCB scores; and (iii) density difference of RCB scores from two treatments, to select the most efficient metric to compute TES. A random permutation procedure was used to estimate the p-value from each test. These methods were applied to data from the durvalumab/olaparib arm and corresponding controls of the I-SPY2 trial, separately by molecular subtype. In subsampling and simulation experiments we assessed robustness of results including power and false positive rate control under variable sample sizes to select the most robust TES metric. The other 11 experimental arms of I-SPY2 were used to assess the performance of the final metric. We calculated correlation between TES and (i) pCR rate difference, and 3- and 5-year (ii) event-free (EFS) and (iii) distant recurrence free survivals (DRFS). Results: RCB scores are multimodal and do not follow normal distribution.In simulated data ABC provided more stable results than the other methods, had good power, performed well with small sample sizes, resulted in low false positive rate, required the least computational time, and therfore was selected as the TES metric for validation in 11 arms of I-SPY2. We found a high correlation between difference in pCR rate and TES value across all molecular subtypes in each of the 11 trial arms (r = 0.92, p = 1.7e-8). There was also significant linear relationship between TES and survival estimates in EFS (r = 0.58, p = 9.3e-3 for 3-years survival; r = 0.62, p = 4.8e-3 for 5-years survival) and DRFS (r = 0.56, p = 1.2e-2 for 3-years survival; r = 0.54, p = 1.8e-2 for 5-years survival). Statistically significant TES score correlated significantly with higher benefit in 3-years survival (p = 9.7e-4 for EFS; p = 5.7e-3 for DRFS) and 5-years survival (p = 9.7e-4 for EFS; p = 3.0e-3 for DRFS). In most instances, this correlation with survival was higher than seen with pCR difference. Conclusions: TES is a novel more optimal metric to identify the more effective cytotoxic neoadjuvant regimen from the entire distribution of pathologic response that significantly correlates with event and recurrence free survival and may serve as a better surrogate than pCR rate difference.

500 Background: Gene signatures have proven successful in identifying patients with a low risk of distant recurrence who could forego chemotherapy (CT) and are currently included in international treatment guidelines for breast cancer. For the 70-gene signature (MammaPrint) an additional threshold was established within the low risk category to identify patients with an ultralow risk of distant recurrence. In independent cohorts, these patients had excellent breast cancer specific survival at 15 years, suggesting that ultralow risk cancers represent indolent disease (Esserman, JAMA Oncol 2017, Delahaye, BC Res Treat 2017). Here we evaluate survival of patients with an ultralow risk 70-gene signature who participated in the randomized phase 3 MINDACT trial (Piccart, Lancet Oncol 2021). Methods: Of the 6,693 patients enrolled in the MINDACT trial (EORTC 10041/BIG 3-04) between 2007-2011, profiling revealed an ultralow risk 70-gene signature in 1,000 patients (15%). We assessed 5- and 8-year distant metastasis free interval (DMFI) and breast cancer specific survival (BCSS) in patients stratified by 70-gene signature result (high, low, ultralow), and within the ultralow risk group stratified by clinical risk. For these exploratory analyses, we used Kaplan-Meier estimates for time to event endpoints and Cox-regression models to calculate hazard ratio’s (HR). Results: Median follow-up was 8.7 years. Among the ultralow risk patients (n = 1,000), 67% were ≥50 years, 81% had tumors < 2cm, 80% were lymph node negative, 96% had grade 1 or 2 tumors and 99% were ER-positive. Systemic therapy was received by 83% of patients (69% endocrine therapy (ET), 14% ET + CT) and 16% received no adjuvant systemic treatment (AST). Survival estimates for all endpoints are shown in the table; 8-year DMFI was 97.0% (95% CI 95.8-98.1) for ultralow risk. The 8-year DMFI in ultralow risk patients who received no AST or ET only was 97.8% (95% CI 95.3-100) and 97.4% (95% CI 96.1-98.7), respectively. The HR for DMFI was 0.66 (95% CI 0.46-0.95) for ultralow vs low risk, after adjusting for tumor and treatment characteristics (preliminary results). Conclusions: In this prospective study, patients with an ultralow risk 70-gene signature have an excellent prognosis with 8-year BCSS above 99% regardless of clinical risk status, and with an 8-year DMFI of 95-98%.[Table: see text]

502 Background: The 70-gene MammaPrint (MP) assay predicts risk of distant recurrence (DR) in hormone-receptor positive early-stage breast cancer and classifies cancers as Low Risk or High Risk. NSABP B-42 evaluated ELT in patients (pts) who had completed 5 yrs of adjuvant endocrine therapy (tx). The primary objective was to determine the utility of MP to identify pts enrolled in NSABP B-42 who are likely to benefit from ELT. Methods: A total of 1,866 pts from B-42 had available MP results. Primary endpoint is DR. Secondary endpoints are disease-free survival (DFS) and breast cancer-free interval (BCFI). For the primary analysis, pts were classified as High Risk (MP-H) (MP score ≤0.000) or Low Risk (MP-L) (MP score > 0.000). Exploratory analyses were performed for MP-L subcategories: MP Ultralow Risk (MP-UL) (MP score > 0.355) and MP-L but not MP-UL (MP-LNUL) (MP score > 0.000, ≤0.355). Likelihood ratio test based on stratified Cox proportional hazards (PH) model was used for treatment by risk group interaction. Stratified log-rank test was used to compare treatment groups. Hazard ratios and 95% CI were computed based on the stratified Cox PH model. Results: Among 1,866 pts, 706 (38%) were MP-H and 1,160 (62%) were MP-L. Of the MP-L, 252 (22%) were MP-UL. There were no significant differences in the distribution of patient and tumor characteristics between the MP group and the rest of the B-42 cohort, except for HER2 status. ELT effect was more pronounced in the MP cohort than in the overall B-42 population. For DR, there was statistically significant ELT benefit in MP-L (HR = 0.43, 95% CI 0.25-0.74, p = 0.002), but not MP-H (HR = 0.65, 0.34-1.24, p = 0.19) (interaction p = 0.38). For DFS, there was statistically significant ELT benefit in MP-L, but not MP-H (interaction p = 0.015). Similar findings were observed for BCFI (interaction p = 0.006). Within subcategories of MP-L, there was statistically significant ELT benefit in MP-LNUL, but not in MP-UL for all three endpoints, however the power in MP-UL was limited due to low number of pts (Table). Clinical trial information: 00382070. Conclusions: Statistically significant ELT benefit was observed for MP-L, but not MP-H. The treatment by risk group interaction was not statistically significant for DR, but it was for DFS and BCFI. The benefit appears to be stronger in MP-LNUL than in MP-UL. NCT: 00382070. Support: U10CA180868, -180822, U24CA196067; Novartis; Agendia.[Table: see text]

519 Background: About one third of patients with hormone-receptor-positive (HR+), HER2‐ primary breast cancer with residual invasive disease after neoadjuvant chemotherapy will relapse despite adjuvant endocrine therapy. Therapeutic inhibition of cyclin-dependent kinase 4 and 6 (CDK 4/6) by palbociclib combined with endocrine therapy demonstrated highly relevant efficacy in metastatic breast cancer. The phase III PENELOPE-B (NCT01864746) study did not show a significant benefit from palbociclib in women with centrally confirmed HR+, HER2- primary breast cancer without a pathological complete response after taxane‐containing neoadjuvant chemotherapy and at high-risk of relapse (CPS‐EG score ≥3 or 2 and ypN+) for the primary endpoint (Loibl et al. JCO 2021). Methods: After completion of neoadjuvant chemotherapy and locoregional therapy, PENELOPE-B patients were randomized (1:1) to receive 13 cycles (1 year) of palbociclib 125mg daily or placebo on days 1-21 in a 28d cycle in addition to standard endocrine therapy. Analysis of the primary endpoint of invasive disease-free survival (iDFS) was planned after 290 events. Secondary objective included iDFS in luminal-B group by treatment. Gene expression in post-neoadjuvant surgical residual tumor tissue samples was profiled using the HTG EdgeSeq Oncology Biomarker Panel targeting 2559 genes (HTG Molecular Diagnostics Inc.). Based on 91 genes of this panel the AIMS subtype (Paquet & Hallett, JNCI 2014) was calculated. Results: Gene expressions were measured in tumors from 906 of 1250 (72%) PENELOPE-B patients; 663 had LumA subtype, 64 LumB, 135 NormL, 16 BasalL, and 28 HER2E. Compared to LumA the LumB patients were older, had higher post-neoadjuvant Ki-67, higher risk status (CPS-EG), and higher grade; no significant correlation was found for the region of participating sites, cT, ypT, and ypN. Patients with LumB tumors had an estimated 3-year iDFS of 71.9% with palbociclib vs 44.8% with placebo HR = 0.50 (0.24-1.05); outcome was similar in patients with LumA tumors (3-year iDFS 83.9% vs 79.5%, HR = 0.93 (0.68-1.28), interaction p = 0.132); this was confirmed in multivariable analyses. Ki-67 by IHC and proliferation biomarkers from the HTG panel also showed no significant interaction with treatment. Conclusions: PENELOPE-B did not show a benefit from the addition of 1 year palbociclib to endocrine therapy compared to placebo in the total enrolled high-risk primary breast cancer population. However, the small group of luminal-B tumors (n = 64) derived benefit from palbociclib, although without a statistically significant interaction. Further investigation is required in a larger cohort to validate a palbociclib benefit that might be confined to this group.

Background: In the I-SPY 2 TRIAL, the addition of P to standard NAC resulted in more than doubling of the pathologic complete response (pCR) rates for both hormone receptor-positive (HR+)/HER2- and triple-negative (TN) early breast cancer (EBC) patients (pts) compared to NAC only (Nanda et al, JAMA Oncol, 2020). At 3 years, distant recurrence-free survival (DRFS) rates in pts with pCR following NAC+P was >95%. We hypothesized that ctDNA can serve as a predictive biomarker of response and survival in pts treated with NAC. Methods: A personalized ctDNA test (Signatera) was performed on 511 serial plasma samples from 138 pts with high-risk HR+/HER2- (n=77) or TN (n=61) stage II/III EBC. Pts received P with paclitaxel (Tx) followed by AC (P arm, n=42) or standard NAC only (n=96), an exploratory subset of pts evaluated for P efficacy. Plasma was collected; pretreatment (T0), 3 weeks after treatment initiation (T1), between Tx+/-P and AC regimens (T2), and prior to surgery (T3). ctDNA was deemed positive with a minimum of 2 of the pt specific tumor mutation fragments detected in cfDNA. Association of ctDNA with response and survival was analyzed using logistic and Cox regressions with pCR and DRFS as endpoints. Median follow-up was 2.8 years. Results: Detection of ctDNA decreased over time (P arm: T0-81%, T1-50%, T2-19%, T3-3%) and NAC only: T0-82%, T1-65%, T2-26%, T3-10%). ctDNA data at T0 and T1 was available for 96% (132/138) of pts in P arm or NAC only (Table). Among ctDNA+ patients at baseline, clearance at T1 was significantly associated with pCR (OR=1.92, ctDNA+/-; OR=0.27, ctDNA+/+; LR p ctDNA data at T0, T1, and T2 was available for 86% (118/138) pts. (Table). Among all ctDNA+ pts at baseline, dynamics through T2 was associated with pCR (OR=1.44, ctDNA+/-/-; OR=0.33, ctDNA+/+/-, OR=0.12, ctDNA+/+/+; LR p=0.0011). This association remained significant when adjusted for HR status and treatment (LR p All pts who achieved pCR were ctDNA- at T3 (n=34). Among those who failed to achieve pCR (n=81), DRFS was significantly better in ctDNA- (n=72/81; 20 in P and 52 in NAC) versus ctDNA+ pts (n=9/81; 1 in P and 8 in NAC) (adjusted HR 0.13; 95% CI 0.05-0.37). Conclusions: These exploratory results align with our previous findings that early clearance of ctDNA during NAC treatment was significantly associated with increased likelihood of achieving pCR. Additionally, we show that ctDNA clearance can be an early surrogate marker for therapy response assessment. Residual ctDNA after neoadjuvant treatment was a significant predictor of metastatic recurrence and death. Personalized monitoring of ctDNA during the course of NAC is feasible and provides information that can be combined with imaging and pathology, and may help to optimize decision making for de-escalation or escalation of therapy. Larger studies are ongoing. ctDNA dynamics and pCRctDNA status at T0 and T1 (n=132)ctDNA status at T0, T1, and T2 (n=118)ctDNA-/-ctDNA+/-ctDNA+/+ctDNA-/-/-ctDNA+/-/-ctDNA+/+/-ctDNA+/+/+Total, n (%)24 (18)28 (21)80 (61)22 (19)24 (20)43 (36)27 (23)pCR, n (%)9 (38)15 (54)11 (14)9 (41)12 (50)8 (19)2 (7)No pCR, n (%)15 (63)13 (46)69 (86)13 (59)12 (50)35 (81)25 (93) Citation Format: Mark Jesus M Magbanua, Denise Wolf, Derrick Renner, Svetlana Shchegrova, Lamorna Brown Swigart, Christina Yau, Gillian Hirst, Hsin-Ta Wu, Ekaterina Kalashnikova, Antony Tin, Amy Delson, Douglas Yee, Angela DeMichele, Raheleh Salari, Angel Rodriguez, Bernhard Zimmermann, Himanshu Sethi, Alexey Aleshin, Paul Billings, Laura Esserman, Minetta Liu, Rita Nanda, Laura van ‘t Veer, I-SPY 2 Investigators. Personalized ctDNA as a predictive biomarker in high-risk early stage breast cancer (EBC) treated with neoadjuvant chemotherapy (NAC) with or without pembrolizumab (P) [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD9-02.

Background: The goal of I-SPY 2 is to rapidly test novel therapies in addition to standard of care in high-risk breast cancer patients. It has resulted in increasing response rates, where pCR rates in TNBC and HR-HER2+ subsets have reached ~60% and ~75%, respectively. Yet, there remains a sizeable subset of non-responders, especially among HR+ patients. Identification of ‘universal’ resistance mechanisms may guide rational selection of agents to improve these patient's outcomes. Thus, we analyzed reverse phase protein array (RPPA) based quantitative protein/phosphoprotein data across arms to assess whether there are common mechanisms rendering these cancers resistant to all agent classes tested to date. Methods: 736 patients (260 HR+HER2-, 252 TN, 142 HR+HER2-, and 82 HR-HER2+; over 8 arms: 194 Ctr, 105 neratinib (N), 63 veliparib/carboplatin (VC), 128 AMG386 (anti-ANG1/2), 87 MK2206 (anti-AKT), 43 TH/pertuzumab (P), 49 TDM1/P, and 67 pembrolizumab (Pembro)) with pCR and RPPA data at the pre-treatment time point were considered for this analysis. 141 RPPA endpoints representing key cancer pathways were assessed for association with pCR using logistic regression modeling, with HR, HER2 and treatment arm as covariates (likelihood ratio test; p Citation Format: Julia Wulfkuhle, Denise Wolf, Christina Yau, Lamorna Brown-Swigart, Rosa I Gallagher, Gillian Hirst, Laura Sit, Smita Asare, I-SPY 2 TRIAL Investigators, Nola Hylton, Angela DeMichele, Douglas Yee, Jo Chien, Hope Rugo, John Park, Kathy Albain, Rita Nanda, Debu Tripathy, Richard Schwab, Don Berry, Laura Esserman, Laura van t' Veer, Emanual Petricoin, III. Identification of biomarkers associated with therapeutic resistance: Quantitative protein/phosphoprotein analysis of ~750 patients across 8 arms of the neoadjuvant I-SPY 2 TRIAL for high-risk early stage breast cancer [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD9-04.

There is a growing body of literature demonstrating that immune-related expression signatures predict breast cancer prognosis and chemo-/targeted-therapy responsiveness. However, it is unclear whether these signatures correlate with each other or represent distinct immune-related signals. We evaluated 57 published immune-related expression signatures in four public breast cancer datasets totaling 3295 samples. For each dataset, we used consensus clustering to group signatures together based on their co-expression pattern. Signatures that were in the same consensus cluster across all four datasets were used to define immune modules. Tumors were then classified into immune subtypes based on their module scores using consensus clustering. Survival analysis was conducted using Cox proportional hazards modeling. Consensus clustering consistently yields four distinct co-expression modules across the datasets. These modules appear to represent distinct immune components and signals, where constituent signatures relate to (1) T-cells and/or B-cells (T/B-cell), (2) interferon (IFN), (3) transforming growth factor beta (TGFB), and (4) core–serum response, dendritic cells, and/or macrophages (CSR). Subtyping of tumors based on these co-expression modules consistently yields subsets that fall into five major immune subtypes: T/B-cell/IFN High, IFN/CSR High, CSR High, TGFB High, and Immune Low. Basal and/or triple-negative breast cancer patients with CSR High tumors have significantly worse outcome relative to those within the T/B-cell/IFN High subtype. Our exploratory study identified four distinct immune co-expression modules (T/B-cell, IFN, TGFB, or CSR) from published immune signatures. Using these modules, we identified five immune subtypes with significant outcome differences in basal breast cancers.

MammaPrint is an FDA-cleared microarray-based test that uses expression levels of the 70 MammaPrint genes to assess distant recurrence risk in early-stage breast cancer. The prospective RASTER study proved that MammaPrint Low Risk patients can safely forgo chemotherapy, which is further subject of the prospective randomized MINDACT trial. While MammaPrint diagnostic results are obtained from mini-arrays, clinical trials may be performed on whole-genome arrays. Here we demonstrate the equivalence and reproducibility of the MammaPrint test. MammaPrint indices were collected for breast cancer samples: (i) on both customized certified array types (n = 1,897 sample pairs), (ii) with matched fresh and FFPE tissues (n = 552 sample pairs), iii) for control samples replicated over a period of 10 years (n = 11,333), and iv) repeated measurements (n = 280). The array type indicated a near perfect Pearson correlation of 0.99 (95 % CI: 0.989–0.991). Paired fresh and FFPE samples showed an excellent Pearson correlation of 0.93 (95 % CI 0.92–0.94), in spite of the variability introduced by intratumoral tissue heterogeneity. Control samples showed high consistency over 10 year's time (overall reproducibility of 97.4 %). Precision and repeatability are overall 98.2 and 98.3 %, respectively. Results confirm that the combination of the near perfect correlation between array types, excellent equivalence between tissue types, and a very high stability, precision, and repeatability demonstrate that results from clinical trials (such as MINDACT and I-SPY 2) are equivalent to current MammaPrint FFPE and fresh diagnostics, and can be used interchangeably. Electronic supplementary material The online version of this article (doi:10.1007/s10549-016-3764-5) contains supplementary material, which is available to authorized users.