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PURPOSE Chemotherapy has not demonstrated benefit over adjuvant endocrine therapy alone for postmenopausal patients with node-positive breast cancer with a 21-gene breast recurrence score (RS) of 25 or below (RS ≤ 25). We tested whether combined results from RS and the sensitivity to endocrine therapy (SET2,3) index of endocrine-related transcription (SETER/PR) adjusted for baseline prognostic index (BPI) improve prognostic assessment, and whether SET2,3 predicted benefit from anthracycline-based chemotherapy. METHODS A blinded retrospective clinical validation of SET2,3 in two randomized treatment arms from the SWOG S8814 trial comparing adjuvant anthracycline-based chemotherapy followed by tamoxifen endocrine therapy for 5 years, versus tamoxifen alone. SET2,3 assay was calibrated and measured using whole-transcriptome RNA sequence of tumor samples already tested for RS. The primary end point was disease-free survival (DFS). RESULTS There were 106 events in 283 patients over a median follow-up of 8.99 years. Proportional hazards assumptions were met during the first 5 years only. SET2,3 index and RS were not correlated (r = –0.04) and were independently prognostic (SET2,3: hazard ratio [HR], 0.48 per unit; 95% CI, 0.34 to 0.68; P < .001; RS: HR, 1.28 per 10 units; 95% CI, 1.14 to 1.44; P < .001). SET2,3 index did not predict chemotherapy benefit (interaction P = .77). SET2,3 was high in 93/175 (53%) patients with RS ≤ 25 (concordant low-risk), with 5-year DFS 97%. SET2,3 was low in 55/108 (51%) patients with RS > 25 (concordant high-risk), with 5-year DFS 53%. Both components of SET2,3 index were prognostic after adjustment for RS: SETER/PR (HR, 0.65; 95% CI, 0.46 to 0.92) and BPI (HR, 0.45; 95% CI, 0.31 to 0.64). CONCLUSION SET2,3 index was not correlated with RS, demonstrated additive prognostic performance, and was not chemopredictive in this subset of patients from S8814. The SETER/PR and BPI components of SET2,3 each added prognostic information to RS.

Background/Purpose: Superparamagnetic iron oxide (SPIO)-based techniques like Magseed and Magtrace offer alternatives for breast lesion and sentinel lymph node (SNL) localization. Magseeds placed under ultrasound guidance offer logistical and safety advantages compared to traditional wire-localization or radioactive seed placement. Further, Magtrace injection is a viable SNL-localization alternative that overcomes radiation safety, skin necrosis, and allergic reactions associated with traditional 99mTc colloid and Methylene blue/Isosulfan blue dyes. To evaluate the effectiveness of these techniques in surgical practice, we report the implementation of Magseed and Magtrace at Baylor St. Luke’s Medical Center. Methods: We retrospectively reviewed prospectively collected data from the 128 female patients undergoing Magseed/Magtrace procedures from October 2019 to December 2020. Demographics and clinical characteristics were summarized, and surgical outcomes analyzed including margin status, re-excision rates, post-operative breast and axilla complications, successful localization and detection rates. Results: Patients presented with varied diagnoses including 98 (77%) with invasive carcinoma, 21 (16%) with DCIS, and 9 (7%) with fibroadenoma, papillary, or other lesions. 50 patients (39%) underwent neoadjuvant therapy. Mean BMI at surgery was 27.33 kg/m2 (range, 17.80-48.65) and mean age was 57.26 years (range, 27-86). Breast density varied, 5 (4%) categorized as almost entirely fatty, 69 (54%) as heterogeneously dense, 12 (9%) as extremely dense, and 39 (30%) with fibroglandular density. For breast size there were 2 (2%) A cups, 13 (10%) B cups, 16 (13%) C cups, 10 (8%) D cups, 8 (6%) DD cups, 2 (2%) DDD cups, 1 (.8%) F cup, and 12 (9%) with in between breast size (A-DDD). 108 patients (84%) had on-table Magtrace injection and 52 (41%) had Magseed localization. 18 (14%) patients had Methylene blue/Isosulfan blue dye or Technetium-99 used with Magseed and/or Magtrace. There was a 100% (52/52) success rate of lesion localization using Magseed and a 94% (101/108) success rate of SLN detection using Magtrace. Magtrace brown skin staining occurred in 12/108 patients (11%). Breast complications occurred in 43 patients (34%), with bruising/hematoma in 30/43 (70%), seroma in 7/43 (16%), and infection in 4/43 (9%). Axilla complications occurred in 11 patients (9%): 5/11 (45%) had bruising/hematoma, 6/11 (55%) had seroma, and 1/11 (9%) had infection. Positive margins were seen in 11 (9%) patients with re-excision performed in 8 women (6%). Conclusions: Magseed and Magtrace are comparable to standard practices in terms of practicality, surgical implementation, and risk of surgical complications and re-excision, providing a rationale for the adoption of SPIO-based techniques. By putting these surgical techniques into practice and evaluating their success in excising a wide range of lesions and monitoring complications, there is possibility of introducing new, innovative techniques into a standardized clinical setting within surgical oncology. Magseed/Magtrace Procedures at BSLMCRaw NumberPercentageTechniquesMagtrace Only6047%Magseed Only1915%Magtrace + Magseed3124%Isotope1713%Blue Dye11%Skin Staining Post-Op.1211%Breast Complication4334%Wound Complication921%Infection49%Bruising/Hematoma3070%Seroma716%Patients with >1 complication49%Axilla Complication119%Wound Complication00%Infection19%Bruising/Hematoma545%Seroma655%Patients with >1 complication19%Re-excision Rate86%Histological TypesER+, HER2-7055%ER+10078%HER2+2016%TNB1310% Citation Format: Jessica Montalvan, Logan Healy, Huma Javaid, Ivan Marin, Brian Menegaz, Cary Hsu, Eric Silberfein, Elizabeth Bonefas, Stacey Carter, Alastair Thompson. Magseed/magtrace-guided surgery in breast cancer- an institutional perspective [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 P3-20-06.

Background. DCIS consists of a molecularly heterogeneous group of premalignant lesions, with variable risk of invasive progression. Understanding biomarkers for invasive progression could help individualize treatment recommendations based upon tumor biology. As part of the NCI Human Tumor Atlas Network (HTAN), we conducted comprehensive genomic analyses on two large DCIS case-control cohorts. Methods. We performed smart3-seq and low-pass whole genome sequencing on two independent, retrospective, longitudinally sampled DCIS case-control cohorts. TBCRC 038 was a multicenter cohort diagnosed with DCIS between 1998 and 2016 at one of the Translational Breast Cancer Research sites; the RAHBT (Resource of Archival Human Breast Tissue) cohort included women identified through the St. Louis Breast Tissue Repository, and the Women’s Health Repository diagnosed between 1997 and 2001. We studied the spectrum of molecular changes present and sought genomic predictors of subsequent ipsilateral breast events (iBEs: DCIS recurrence or invasive progression) in both DCIS epithelium and stroma in formalin fixed paraffin embedded tissue. We generated de novo tumor and stroma-centric subtypes for DCIS that represents fundamental transcriptomic organization. Copy number analysis was performed using low-pass DNA sequencing. Non-negative matrix factorization (NMF) was applied to the RNA expression of all coding genes to identify clusters. A negative-binomial regression model was used to identify differentially expressed genes. Results. We analyzed 677 DCIS samples from 481 patients with 7.1 years median follow-up. In TBCRC samples, we identified three clusters via NMF in TBCRC referred to as ER low, quiescent, and ER high. The ER-low cluster had significantly higher levels of ERBB2 and lower levels of ESR1 compared to quiescent and ER-high clusters. Quiescent cluster lesions were less proliferative and less metabolically active than ER high and ER low subtypes. These findings were replicated in the RAHBT cohort. Focusing on the stromal component of DCIS from laser capture microdissection in RAHBT samples, we identified four distinct DCIS-associated stromal clusters. A “normal-like” stromal cluster with ECM organization and PI3K-AKT signaling; a “collagen-rich” stromal cluster; a “desmoplastic” stromal cluster with high fibroblast and total myeloid abundance, mostly associated with macrophages and myeloid dendritic cells (mDC); and an “immune-dense” stromal cluster. Further, we compared differentially expressed genes in patients with or without subsequent iBEs within 5 years of diagnosis. Hypothesizing that the resulting 812 DE genes (DESeq2) represent multiple routes to subsequent iBEs, we leveraged NMF to identify paths to progression. In both TBCRC and RAHBT cohorts, poor outcome groups exhibited increased ER, MYC signaling, and oxidative phosphorylation, supporting that these pathways are important for DCIS recurrence and progression. Conclusion. Comprehensive genomic profiling in two independent DCIS cohorts with longitudinal outcomes shows distinct DCIS stromal expression patterns and immune cell composition. RNA expression profiles reveal underlying tumor biology that is associated with later iBEs in both cohorts. These studies provide new insight into DCIS biology and will guide the design of diagnostic strategies to prevent invasive progression. Citation Format: Siri H Strand, Belén Rivero-Gutiérrez, Kathleen E Houlahan, Jose A Seoane, Lorraine M King, Tyler Risom, Lunden Simpson, Sujay Vennam, Aziz Khan, Timothy Hardman, Bryan E Harmon, Fergus J Couch, Kristalyn Gallagher, Mark Kilgore, Shi Wei, Angela DeMichele, Tari King, Priscilla F McAuliffe, Julie Nangia, Joanna Lee, Jennifer Tseng, Anna Maria Storniolo, Alastair Thompson, Gaorav Gupta, Robyn Burns, Deborah J Veis, Katherine DeSchryver, Chunfang Zhu, Magdalena Matusiak, Jason Wang, Shirley X Zhu, Jen Tappenden, Daisy Yi Ding, Dadong Zhang, Jingqin Luo, Shu Jiang, Sushama Varma, Cody Straub, Sucheta Srivastava, Christina Curtis, Rob Tibshirani, Robert Michael Angelo, Allison Hall, Kouros Owzar, Kornelia Polyak, Carlo Maley, Jeffrey R Marks, Graham A Colditz, E Shelley Hwang, Robert B West. The Breast PreCancer Atlas DCIS genomic signatures define biology and correlate with clinical outcomes: An analysis of TBCRC 038 and RAHBT cohorts [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 GS4-07.

Background and Purpose: There is currently lack of recognized imaging criteria for prediction of treatment response to NAST in breast cancer patients. And early identification of treatment response to neoadjuvant systemic therapy (NAST) in Triple Negative Breast Cancer (TNBC) patients is important for appropriate treatment selection and response monitoring. A novel MRI sequence, Magnetic Resonance Image Compilation (MagIC) is capable of simultaneous quantitation of several tissue water properties including longitudinal (T1), transverse (T2) relaxation times, and proton density (PD). In this study we evaluated the ability of a radiomic model extracted from a novel MagIC sequence acquired early during NAST to predict pathologic complete response to NAST in TNBC. Materials and Methods: This IRB approved prospective ARTEMIS trial (NCT02276443) included 184 women (122 training dataset, 62 testing dataset) diagnosed with stage I-III TNBC. All patients were scanned with MagIC on a 3T MRI scanner at baseline (184 patients), and after 4 cycles (156 Patients) of NAST. T1, T2 and PD maps were generated from the source images using SyMRI (SyntheticMR, Linkoping, Sweden). Histopathology at surgery was used to determine pathologic complete response (pCR) which was defined as absence of the invasive cancer in the breast and axillary lymph nodes. 3D contouring of the tumors was performed using an in-house toolbox. 310 (10 first-order, 300 GLCM) textural features were extracted from each map, with total of 930 features/patient. Radiomic features were compared between pCR and non-pCR using Wilcoxon Rank Sum test and Fisher’s exact test. To build a multivariate, predictive model, logistic regression with elastic net regularization was performed for texture feature selection. The tuning parameter was optimized using 5-fold cross-validation based on the average area under curve (AUC) of each fold of a cross-validation using training data. Then the testing data were used to compare model’s performance by AUC. Results: Univariate analysis found 23 PD, 17 T1 and 10 T2 radiomic features at C4 time point to be able to predict pCR status with AUC >70% in both training and testing cohort. The top performing radiomic features were Entropy, Variance, Homogeneity and Energy (Tables1-2). Multivariate radiomics models from C4-PD, and C4-T1 maps showed best performance during both cross validation and independent testing. The radiomic signature of C4-T1 map that included 27features had best performance, with an AUC of 0.77, 0.70 (95% CI: 0.571-0.868) in training and testing cohort respectively. C4-PD map radiomic signature that included 6features was able to predict the pCR status with AUC of 0.73, 0.72 (95% CI: 0.571-0.868) in training and testing cohort respectively. Conclusion: Our data found that MagIC-based radiomics signature could potentially predict pathologic complete response in TNBC early during NAST. This data shows the potential application of MagIC radiomic model for improvement of response assessment in TNBC. Table 1.Best performing radiomic features from PD map after 4 cycles of NAST in TNBC patients.FeatureTraining CohortTraining CohortTraining CohortTesting CohortTesting CohortTesting CohortNAUC95% CINAUC95% CIP-valuePD-mapAngular Variance of Sum entropy1060.73820.6437-0.8328500.73240.5895-0.8752 Table 2.Best performing radiomic features from T1-T2 maps after 4 cycles of NAST in TNBC patients.FeatureTraining CohortTraining CohortTraining CohortTesting CohortTesting CohortTesting CohortNAUC95% CINAUC95% CIP-valueT1-mapAngular Variance of Sum entropy1060.76530.6762-0.8544500.70510.5524-0.8579 Citation Format: Nabil Elshafeey, Ken-Pin Hwang, Beatriz Elena Adrada, Rosalind Pitpitan Candelaria, Medine Boge, Rania M Mahmoud, Huiqin Chen, Jia Sun, Wei Yang, Aikaterini Kotrotsou, Benjamin C Musall, Jong Bum Son, Gary J Whitman, Jessica Leung, Huong Le-Petross, Lumarie Santiago, Deanna Lynn Lane, Marion Elizabeth Scoggins, David Allen Spak, Mary Saber Guirguis, Miral Mahesh Patel, Frances Perez, Abeer H Abdelhafez, Jason B White, Lei Huo, Elizabeth Ravenberg, Wei Peng, Alastair Thompson, Senthil Damodaran, Debu Tripathy, Stacey L Moulder, Clinton Yam, Mark David Pagel, Jingfei Ma, Gaiane Margishvili Rauch. Radiomics model based on magnetic resonance image compilation (MagIC) as early predictor of pathologic complete response to neoadjuvant systemic therapy in triple-negative 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 PD11-06.

Background/objectives The natural history of microinvasive carcinoma of the breast (one or more foci of invasion of 1mm or less) and its optimal management remain controversial. We analysed the frequency and outcome of patients with DCIS plus microinvasion within a large prospective cohort of screen-detected DCIS. Methods Patients with screen-detected DCIS diagnosed between 2003 and 2012 with or without microinvasion in the final surgical specimen were identified from the UK Sloane Project database. Comprehensive imaging, surgical, pathology, oncology and subsequent outcome data were collected with a median follow-up of 9 years. Results Among 11285 DCIS patients diagnosed in the UK, microinvasion was reported in 521 (4.6%). The frequency of reported microinvasion varied considerably among screening units (0-25%) but overall decreased from 7% in 2003/04 to 3% in 2011/12.As reported elsewhere, microinvasion was significantly associated with high grade DCIS (5.9% of 7182 cases) compared to 2.9% of intermediate grade and 40mm had microinvasive foci reported. Microinvasion was also associated with the presence of comedo necrosis (p Citation Format: Abeer M Shaaban, Bridget Hilton, Karen Clements, David Dodwell, Nisha Sharma, Cliona Kirwan, Elinor Sawyer, Anthony Maxwell, Matthew Wallis, Hilary Stobart, Senthurun Mylvaganam, Janet.Litherland Litherland, Samantha Brace-Mcdonnell, Joanne Dulson-Cox, Olive Kearins, Elena Provenzano, Sarah Pinder, Alastair Thompson. The diagnosis and prognosis of ductal carcinoma in situ (DCIS) with microinvasion - Results from the United Kingdom Sloane project [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 P3-12-36.

Background The variable natural history of ductal carcinoma in situ (DCIS) is poorly understood. The aim of this retrospective cohort study was to determine the outcomes of women who were diagnosed through the English National Health Service Breast Screening Programme (NHS BSP) and had no surgery for screen-detected DCIS. Method English NHS BSP databases were searched for women diagnosed with DCIS without invasive cancer on needle biopsy between 1 April 2001 and 31 March 2018 inclusive, who had no record of surgery within 6 months of diagnosis. These were cross-referenced with cancer registry data for further treatment, event information and mortality records. Details of potentially eligible women were sent to the relevant breast screening units for verification and for completion of data forms detailing clinical, radiological and pathological findings, non-surgical treatment, subsequent clinical course and outcomes. Results Data for 311 eligible women (median age 62 years) were included, with median follow-up (primary DCIS diagnosis to either surgery, development of invasive disease, death or last known to be alive) of 4.1 years (range 0.5 to 17.4 years). The median age at diagnosis was 64 years (range 47 to 90 years). Sixty (19%) women developed invasive breast cancer, 56 ipsilateral and 4 contralateral. Twenty-two women (7%) underwent surgery for DCIS (>6 months after diagnosis) and 86 (28%) died of other causes. Women with high or intermediate grade DCIS were significantly more likely to develop ipsilateral invasive breast cancer (iIBC) than those with low grade DCIS. 28/123 (23%) with high grade DCIS, 21/105 (20%) with intermediate grade DCIS, 5/76 (7%) with low grade DCIS and 2/7 (29%) with an unknown grade of DCIS developed iIBC. Ipsilateral invasion risk increased approximately linearly with time for at least 10 years. Women who developed iIBC were significantly younger than those who did not. This was driven primarily by a strong association among those with high grade DCIS. There was no significant difference in age between those with intermediate and low grade DCIS respectively who did or did not develop iIBC. The median baseline size of DCIS in women who developed iIBC was higher than in women who did not develop iIBC, both overall and in each of the three grade categories (median sizes - high grade 38 mm vs 29 mm, p=0.19; intermediate grade 35 mm vs 18 mm, p=0.31; low grade 18 mm vs 15 mm, p=0.38). This reached statistical significance for all grades combined: 37 mm (range 3-95) v 20 mm (range 3-200), p=0.02. Of the 262 women with a known microinvasion status, 8/25 (32%) with definite or possible microinvasion were subsequently diagnosed with ipsilateral invasive cancer compared to 38/237 (16%) without microinvasion (p=0.027). There was no significant association on univariable analysis between risk of iIBC and microcalcification as the predominant radiological feature (iIBC and microcalcification 47/258 vs iIBC and other radiological feature 7/46; p=0.83), the presence of histological necrosis (iIBC and necrosis, 14/89 vs iIBC and no necrosis, 31/157; p=0.49) or the use of ET (iIBC and ET 11/67 vs iIBC and no ET 45/244; p=0.86). Of the 51 invasive cancers that developed with a known grade, 46 (90%) were grade 2 or 3. Conclusion The findings suggest that women diagnosed with high or intermediate grade DCIS and those with microinvasion should continue to be offered surgery. For those with low grade DCIS there is a need for shared decision-making in the choice of surgery or active surveillance based on a discussion of the risks and benefits of the options as currently understood, and in the light of the low reproducibility of DCIS grading. Citation Format: Karen Clements, Anthony Maxwell, Bridget Hilton, Matthew Wallis, Cliona Kirwan, Hilary Stobart, Elena Provenzano, Nisha Sharma, Janet Litherland, Abeer Shaaban, David Dodwell, Joanne Dulson-Cox, Elinor Sawyer, Senthurun Mylvaganam, Olive Kearins, Samantha Brace-McDonnell, Sarah Pinder, Alastair Thompson. A longitudinal cohort study of outcomes in 311 women with unresected ductal carcinoma in situ detected through the English breast screening programme [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 P1-22-06.

Background: TNBC constitutes an aggressive and heterogeneous group of tumors with variable response to neoadjuvant therapy (NAT) that currently lacks clinically available profiling strategies for prediction. We aimed to develop an integrated model based on imaging, pathological and clinical data capable to predict NAT response in TNBC early during therapy. METHOD AND MATERIALS:125 Stage I-III TNBC patients enrolled in an IRB approved prospective clinical trial (NCT02276433) who had DCE-MRI at baseline (BL) and post 2 cycles (C2) of NAT, and had surgery were included in this analysis. Tumor volume was calculated using 3D measurements at BL and C2 time points DCE-MRI. Percent tumor volume reduction (TVR) between BL and C2 was calculated. Demographic, clinical, and pathological data (age, T and N stage, histology, androgen receptor expression, Ki-67, stromal tumor infiltrating lymphocytes level (sTIL), and PD-L1 expression), and treatment response at surgery (pCR vs non-pCR) were documented. Recursive partitioning was used to identify TVR cutoff value. Multivariate logistic regression and ROC analysis were used to assess associations and build and evaluate predictive models. RESULTS: 61 (49%) TNBC pts showed pCR at surgery, and 64 (51%) non-pCR. Recursive partitioning analysis identified ≥ 55% TVR as the optimal cutoff values for pCR prediction at C2. TVR, N stage and sTIL were significantly associated with pCR in the multivariate analyses (p Citation Format: Gaiane Margishvili Rauch, Rosalind P. Candelaria, Mary Saber Guirguis, Medine Boge, Rania M. M. Mohamed, Nabil Elshafeey, Jia Sun, Gary J Whitman, Jessica Leung, Huong C Le-Petross, Lumarie Santiago, Deanna Lane, Marion Scoggins, David Spak, Miral M Patel, Frances Perez, Jason B. White, Elizabeth Ravenberg, Wei Peng, Debu Tripathy, Vicente Valero, Jennifer Litton, Lei Huo, Clinton Yam, Alastair Thompson, Jingfei Ma, Stacy L. Moulder, Wei Yang, Beatriz E. Adrada. Integrated model for early prediction of neoadjuvant systemic therapy response in triple negative 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 PD11-07.

Background: High body mass index (BMI) is an established risk factor for developing breast cancer (BC), especially estrogen receptor (ER)-positive, and also has been associated with adverse survival. Still, patients with BC are currently treated independently of their BMI given limited understandings of the association between BC biology and patient adiposity. In this study, using retrospective data retrieved from two large BC studies, we aimed to identify genomic alterations of primary BC that are associated with BMI in the most common histological BC subtype - invasive carcinoma of no special type (NST). Patients, Data and Methods: Clinicopathological and genomic alteration data were retrieved from two study cohorts: METABRIC (Pereira et al. 2016) and ICGC (Nik-Zainal et al. 2016), with BMI recorded at the time of diagnosis and represented as either a continuous variable or a categorical variable of three categories - lean, overweight and obese. Stratification according to ER and HER2 status resulted in two focused subgroups: NST ER+/HER2- (n=392) and NST ER-/HER2- (n=152). Mutations classified as oncogenic using a set of predefined criteria were used to determine gene-level mutation status. Copy number alteration (CNA) calls were distinguished into three event types: amplification, hemizygous deletion and homozygous deletion. We used multivariable Firth’s logistic regression models with the presence of a genomic alteration as the response variable, BMI as the predicting variable of interest, and data cohort (METABRIC vs ICGC), age group (≤50 vs >50) and tumor grade (I & II vs III) as covariates, to assess the associations between BMI and recurrent gene-level genomic alterations, including gene mutations and CNAs. In a similar manner, we performed multivariable linear regression analysis, adjusting for age and tumor grade, to evaluate the associations of BMI with mutational signatures (MS) and tumor mutational burden in the ICGC NST subsets where these data are available. Results: Considering BMI as a categorical variable, we observed in the NST ER+/HER2- subgroup that PIK3CA was significantly less frequently mutated in obese compared to lean patients (33% vs 46%, odds ratio (OR) = 0.57 (95% confidence interval = (0.33, 0.97)), p = .039), while PTEN and TBX3 showed an increased frequency in overweight (6% vs 1%, OR = 4.14 (1.1, 22.34), p = .034) and obese (8% vs 1%, OR = 7.41 (1.82, 70.65), p = .008) patients, respectively. Regression analyses with BMI as a continuous variable revealed an increased prevalence of mutations in CDH1 and TBX3 genes as BMI increases by 1kg/m2 (OR = 1.14 (1.05, 1.24), p = .002, and OR = 1.13 (1.04, 1.22), p = .005, respectively) in patients with NST ER+/HER2- BC. No associations between BMI and oncogenic mutations was observed in the NST ER-/HER2- subgroup. Interrogation of gene-level CNAs in both subgroups demonstrated differences according to BMI in the prevalence of CNAs affecting a number of genes, many of which are known or have been presented with evidence to be involved in regulation of or regulated by hallmark pathways of BC, such as the MAPK/ERK, JAK/STAT and Wnt/β-catenin signaling pathways. We report a strong positive association between the single-base substitution signature 1 (SBS1), an age-correlated MS, and both continuous (coefficient (coef) = 18.3 (7.7, 28.9), p < .001) and categorical BMI (obese vs lean, coef = 336.3 (187.9, 484.8), p < .001) in the ICGC NST ER+/HER2- subgroup. Conclusion: This exploratory retrospective study suggests that the genomic profiles of primary BC may differ according to BMI. Clinical implications of these differences, especially the decreased prevalence of PIK3CA mutations in obese patients in the context of alpelisib, warrant further investigation. These results however indicate that patient adiposity should be taken into account in the era of personalized medicine. Citation Format: Ha-Linh Nguyen, Tatjana Geukens, Marion Maetens, Karen Van Baelen, Maxim De Schepper, Sophia Leduc, Edoardo Isnaldi, Sam Aparicio, Ake Borg, Jane Brock, Annegien Broeks, Carlos Caldas, Andrew Green, Hazem Khout, Eyfjörð Jórunn, Stian Knappskog, Savitri Krishnamurthy, Sunil Lakhani, Anita Langerod, John WM Martens, Leigh Murphy, Serena Nik-Zainal, Colin Purdie, Emad Rakha, Andrea Richardson, Anne Salomon, Peter Simpson, Christos Sotiriou, Paul Span, Benita Kiat-Tee Tan, Alastair Thompson, Stefania Tommasi, Marc Van de Vijver, Steven Van Laere, Alain Viari, Giuseppe Floris, Elia Biganzoli, François Richard, Christine Desmedt. The association between genomic alterations and body mass index in patients with early 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 P3-09-18.

Clinico-pathologic characteristics of patients according to NGS results (success/failure).

Weighted Kaplan-Meier (KM) estimates of breast cancer-free interval (BCFI) and distant recurrence-free interval (DRFI) according to tumor subtype, separately by PIK3CA mutational status (mutated, MUT vs wild-type, WT). The 5-year event-free estimates (95% CI) are provided. Hazard ratio (HR) estimates (95% CI) and log-rank tests from weighted univariate models.

Purpose:Women with hormone receptor–positive early breast cancers have a persistent risk of relapse and biomarkers for late recurrence are needed. We sought to identify tumor genomic aberrations associated with increased late-recurrence risk.Experimental Design:In a secondary analysis of Study of Letrozole Extension trial, a case-cohort–like sampling selected 598 primary breast cancers for targeted next-generation sequencing analysis of gene mutations and copy-number gains (CNGs). Correlations of genomic aberrations with clinicopathologic factors and breast and distant recurrence-free intervals (BCFIs and DRFIs) were analyzed using weighted Cox models.Results:Analysis of mutations and CNGs was successfully performed for 403 and 350 samples, including 148 and 134 patients with breast cancer recurrences (median follow-up time, 5.2 years), respectively. The most frequent alterations were PIK3CA mutations (42%) and CNGs of CCND1 (15%), ERBB2 (10%), FGFR1 (8%), and MYC (8%). PIK3CA mutations and MYC CNGs were associated with lower (P = 0.03) and higher (P = 0.004) tumor grade, respectively; a higher Ki-67 was seen in tumor with CCND1, ERBB2, and MYC CNGs (P = 0.01, P < 0.001, and P = 0.03, respectively). FGFR1 CNG was associated with an increased risk of late events in univariate analyses [17/29 patients; BCFI: HR, 3.2; 95% confidence interval (CI), 1.48–6.92; P = 0.003 and DRFI: HR, 3.5; 95% CI, 1.61–7.75; P = 0.002) and in multivariable models adjusted for clinicopathologic factors.Conclusions:Postmenopausal women with hormone receptor–positive early breast cancer harboring FGFR1 CNG had an increased risk of late recurrence despite extended therapy. FGFR1 CNG may represent a useful prognostic biomarker for late recurrence and a therapeutic target.

Substitution signature exposures estimated in 640 breast cancer WGS and the ovarian sample (AOCS-166).

Mismatch repair (MMR)–deficient cancers have been discovered to be highly responsive to immune therapies such as PD-1 checkpoint blockade, making their definition in patients, where they may be relatively rare, paramount for treatment decisions. In this study, we utilized patterns of mutagenesis known as mutational signatures, which are imprints of the mutagenic processes associated with MMR deficiency, to identify MMR-deficient breast tumors from a whole-genome sequencing dataset comprising a cohort of 640 patients. We identified 11 of 640 tumors as MMR deficient, but only 2 of 11 exhibited germline mutations in MMR genes or Lynch Syndrome. Two additional tumors had a substantially reduced proportion of mutations attributed to MMR deficiency, where the predominant mutational signatures were related to APOBEC enzymatic activity. Overall, 6 of 11 of the MMR-deficient cases in this cohort were confirmed genetically or epigenetically as having abrogation of MMR genes. However, IHC analysis of MMR-related proteins revealed all but one of 10 samples available for testing as MMR deficient. Thus, the mutational signatures more faithfully reported MMR deficiency than sequencing of MMR genes, because they represent a direct pathophysiologic readout of repair pathway abnormalities. As whole-genome sequencing continues to become more affordable, it could be used to expose individually abnormal tumors in tissue types where MMR deficiency has been rarely detected, but also rarely sought. Cancer Res; 77(18); 4755–62. ©2017 AACR.

Mutations in MMR genes

Detection of MMR deficient breast cancers in whole exome sequencing data (WES)

Details of 11 MMR deficient breast cancers.

This file contains the following Supplementary figures and Supplementary Material and Methods: Supplementary Figure 1: Contribution of base substitution signatures and indels in 640 breast cancer WGS. Supplementary Figure 2: Whole genome plots for 11 MMR deficient breast cancers Supplementary Figure 3: Genome plots for the two samples with germline PMS2 mutations. Supplementary Figure 4: Scatter plot demonstrating the relationship between total number of substitutions associated with the three MMR signatures 6, 20, and 26 and number of indels. Supplementary Materials and Methods 1. Mutational signatures background 2. Detection of MMR deficient cancers in other data sets 2.1 Detection of a genomic profile characteristic of PMS2 deficient tumours in Ovarian cancer. 2.2 Detection of MMR deficient breast cancers in WES

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.

PURPOSE Circulating tumor cells (CTCs) are strongly prognostic for overall survival (OS) in metastatic breast cancer although additional prognostic biomarkers are needed. We evaluated the complementary prognostic value of tumor‐derived extracellular vesicles (tdEVs) next to CTCs. METHODS We applied the open-source ACCEPT software to archived CellSearch images from the prospective clinical trial SWOG0500 to enumerate CTCs and tumor-derived extracellular vesicles (tdEVs) before and after one cycle of chemotherapy. RESULTS CTCs enumerated by ACCEPT were strongly correlated with classical ocular enumeration (correlation r = 0.98). OS was worse with elevated tdEVs (median OS for high/medium/low groups: 17.1 v 29.0 v 43.3 months; P < .0001). In patients with longer OS by CTC counts (< 5 CTC/7.5 mL blood), elevated tdEV levels were independently associated with poorer OS (multivariable analysis P < .001). OS was also longer for patients with low tdEVs after one cycle of chemotherapy (median OS for high/medium/low group: 10.8 v 17.8 v 26.7; P < .0001). CONCLUSION This study highlights the complementary prognostic significance of tdEVs in metastatic breast cancer before and after one cycle of chemotherapy.

Ductal-carcinoma-in-situ (DCIS) is the most common early-stage breast cancer and a non-obligate precursor to invasive breast cancer. However our understanding of the spatial microenvironment and the reprogramming of cell types in DCIS remains limited. To address this question, we developed a Spatial Nucleus Barcoding (SNuBar) method that utilizes a single oligonucleotide adaptor to preserve spatial information followed by performing single nucleus RNA sequencing. We validated the accuracy and scalability of SNuBar in cell lines and applied this method to investigate the spatial microenvironment of 4 normal breast tissues, 7 normal adjacent DCIS tissues, and 8 DCIS cancers. The analysis of single cells isolated from 596 macrodissected spatial regions identified 13 major cell types and 43 cell states, that co-localized in different combinations across four main topographic areas. Spatial analysis revealed that vascular and myoepithelial cells were reprogrammed in localized zones near the premalignant cells, while fibroblasts, T-cells and myeloid cells were reprogrammed across broader fields. Our data shows that many diverse cell types are reprogrammed in spatially-defined areas in the DCIS microenvironment, relative to the normal breast tissue regions. Citation Format: Kaile Wang, Zhenna Xiao, Yiyun Lin, Runmin Wei, Jie Ye, Rui Ye, Min Hu, Shanshan Bai, Emi Sei, Aatish Thennavan, Bora Lim, Andrew Futreal, Alastair Thompson, Savitri Krishnamurthy, Nicholas Navin. Reprogramming of the preinvasive breast microenvironment identified by spatial nucleus barcoding [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 76.

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer that lacks the expression of estrogen receptor (ER), progesterone receptor (PR) and HER2 and therefore have limited hormonal treatment options. Neoadjuvant chemotherapy (NAC) is backbone of treatment for TNBC, and about 50% of patients respond well leading to pathological complete response (pCR). However, the remaining patients develop resistance to NAC and progress to metastatic disease and poor survival in 1-2 years after the initial treatment. Previous studies have performed bulk RNA expression profiling of TNBC patients and identified 5-6 subgroups of patients, however these studies could not resolve expression programs at single cell resolution to distinguish between the tumor cells and different components of the tumor microenvironment (TME). Here we performed scRNA-seq of pre-treatment fresh core biopsy tissue samples from TNBC patients in the ARTEMIS clinical trial and compared these data between pCR and non-pCR patients to identify programs associated with response to NAC. We also compared these data to scRNA-seq data from patients with disease-free breast tissue to understand the basic biology of TNBC and identify cell types that are reprogrammed in malignant disease. Using the single cell tumor cell data, we identified 4 archetypes of TNBC which represent patient-level intertumor expression programs: luminal secretory-like (LS), basal/luminal-like (BL), immunoregulatory (IM), and luminal androgen receptor (LAR). Notably, the archetype BL was associated with non-pCR, while IM was associated with pCR. We further identified 13 metatraits, which are unique intratumoral expression programs that are shared across patients. Across the cancer cells, we identified 13 metatraits such as cell cycling, stress, hypoxia, interferon response, HLA, partial epithelial-mesenchymal transition, and endoplasmic reticulum stress, many of which corresponded to NAC response. In the immune compartment, we found 15 myeloid cell states, 14 T/NK cell states, and 6 B cell states, several of which corresponded to pCR/non-pCR. Similarly, in the stromal compartment, there were 4 fibroblast cell states, 4 pericyte cell states, and 7 endothelial cell subtypes, of which several cell states were associated with NAC response. Overall, these data report the natural biology of TNBC patients and malignant cell states that are reprogrammed in malignant disease, as well as their correspondence to NAC response, providing new data to predict which TNBC patients are likely to respond to chemotherapy. Citation Format: Tapsi Kumar, Yiyun Lin, Yun Yan, Shanshan Bai, Jianzhuo Li, Tuan Tran, Min Hu, Elizabeth Ravenberg, Maia Rauch, Alyson Clayborn, Alastair Thompson, Lei Huo, Stacy Moulder, Clinton Yam, Nicholas Navin. Decoding the natural biology of triple-negative breast cancer and response to chemotherapy by single-cell transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2147.

Early assessment of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) is critical for patient care in order to avoid the unnecessary toxicity of an ineffective treatment. We assessed functional tumor volumes (FTVs) from dynamic contrast-enhanced (DCE) MRI after 2 cycles (C2) and 4 cycles (C4) of NAST as predictors of response in TNBC. A group of 100 patients with stage I-III TNBC who underwent DCE MRI at baseline, C2, and C4 were included in this study. Tumors were segmented on DCE images of 1 min and 2.5 min post-injection. FTVs were measured using the optimized percentage enhancement (PE) and signal enhancement ratio (SER) thresholds. The Mann–Whitney test was used to compare the performance of the FTVs at C2 and C4. Of the 100 patients, 49 (49%) had a pathologic complete response (pCR) and 51 (51%) had a non-pCR. The maximum area under the receiving operating characteristic curve (AUC) for predicting the treatment response was 0.84 (p < 0.001) for FTV at C4 followed by FTV at C2 (AUC = 0.82, p < 0.001). The FTV measured at baseline was not able to discriminate pCR from non-pCR. FTVs measured on DCE MRI at C2, as well as at C4, of NAST can potentially predict pCR and non-pCR in TNBC patients.

Purpose: To develop and validate a convolutional neural network (CNN) to identify regions of interest (ROIs) suspicious for ductal carcinoma in situ (DCIS) and residual malignancy in lumpectomy margins using wide-field optical coherence tomography (WF-OCT). Background: WF-OCT is the optical analog of high-frequency ultrasound and produces high-resolution intraoperative imaging in real time, with a tissue penetration depth up to 2 mm. Multi-reader studies of WF-OCT have demonstrated the ability to differentiate normal breast parenchyma from neoplasms with greater than 85% sensitivity and specificity. Intraoperative evaluation of lumpectomy specimens using WF-OCT may aid in achieving negative margins at the time of primary surgery and avoid re-excisions. CNNs, a form of artificial intelligence (AI), can be trained to spot ROIs in WF-OCT images of margins suspicious for DCIS and, more generally, residual malignancy. Methods: Lumpectomy margins from 126 patients with ductal malignancy were imaged using WF-OCT, compared to permanent histology (PH), and annotated by board-certified breast pathologists to create a training set of 25,000 control ROIs. A CNN algorithm was developed with 3 convolutional layers, a 3x3 kernel, and 3 fully connected layers to perform binary classification of images as either “suspicious” or “non-suspicious” for malignancy. A weighted loss function was implemented to balance the training data available for non-suspicious vs. suspicious images and to tune sensitivity and specificity. Once trained and properly weighted, the CNN was tested in a prospective study using WF-OCT images of margins from 29 lumpectomy specimens from 29 patients with biopsy-proven DCIS, invasive ductal carcinoma (IDC), or both. The CNN results were compared to PH. Results: Patients were 61.5 ± 7.3 years old, 100% female, with Stage 0-1 disease. Disease types included DCIS (n=27), atypical ductal hyperplasia (n=24), IDC (n=20), invasive lobular carcinoma (n=2), mixed (n=74), and benign findings including usual ductal hyperplasia (n=35), atypical lobular hyperplasia (n=19), duct ectasia (n=17), lymphatic invasion (n=13), and lobular carcinoma in situ (n=12). Following primary surgery, fresh margins were scanned using WF-OCT and approximately 1.9M ROIs were analyzed by the CNN, yielding 15,136 as suspicious for malignancy. Overall, four hundred and ten (410) ROIs were correctly identified, yielding a 74% true positive and 0.8% false positive detection rate; sensitivity and specificity were 74.4% and 99.2%, respectively. Specific to DCIS, the CNN demonstrated a 73% true and 0.5% false positive rate; sensitivity and specificity were 73.0% and 99.5%, respectively. Conclusions: Automated analysis of WF-OCT images of lumpectomy specimens, using a trained CNN to identify ROIs suspicious for malignancy is feasible, demonstrating high concordance with PH. Specific to DCIS, the CNN demonstrated equivalent utility with a lower false positive rate. A prospective trial is needed to evaluate specimens in real time to determine improvement in re-excision rates. Citation Format: David Rempel, Andrew Berkeley, Alastair Thompson, Savitri Krishnamurthy, Beryl Augustine, Kelly Hunt, Ismail Jatoi, Alia Nazarullah, Chandandeep Nagi, Yanir Levy. Development and validation of a convolutional neural network to identify ductal carcinoma in situ in lumpectomy margins using wide field optical coherence tomography [abstract]. In: Proceedings of the AACR Special Conference on Rethinking DCIS: An Opportunity for Prevention?; 2022 Sep 8-11; Philadelphia, PA. Philadelphia (PA): AACR; Can Prev Res 2022;15(12 Suppl_1): Abstract nr A004.

Introduction: Treatment options for women diagnosed with DCIS require careful consideration of the potential risks and benefits. An interactive decision support tool (DST) was developed to provide information about these options, including their potential long-term risk. The DST was implemented through the website www.dcisoptions.org in collaboration with the AFT-25 Comparing an Operation to Monitoring, with or without Endocrine Therapy (COMET), for low-risk DCIS study. Methods: The DST provides personalized prediction of the potential clinical impact of six different treatment options over a 10-year period. Women were asked to select one or more option, and to complete two surveys - one prior to interacting with the DST and one following interaction. Chi-square tests were used to compare the distribution of age group and DCIS grade among women who completed both surveys and those who completed the pre-tool survey only. Mean age was compared using the t-test and median age was compared using the Wilcoxon-Mann-Whitney test. The signed-rank test was used to compare the median age. The cohort that answered both surveys was analyzed for potential differences in response (pre- versus post-tool). The McNemar test was used to compare percentage distributions and the paired t-test was used to compare mean responses for questions using the Likert scale. A signed rank test was used to compare median changes from pre- to post-tool. Statistical significance was defined as P Citation Format: Thomas Lynch, Ann Partridge, E. Shelley Hwang, Alastair Thompson, Elizabeth Frank, Donna Pinto, Deborah Collyar, Desiree Basila, Terry Hyslop, Marc Ryser, Anna Weiss, Anna Rapperport, Rinaa Punglia, Elissa Ozanne. Effectiveness of an online decision support tool in communicating information about treatment options and related risks for ductal carcinoma in situ (DCIS) [abstract]. In: Proceedings of the AACR Special Conference on Rethinking DCIS: An Opportunity for Prevention?; 2022 Sep 8-11; Philadelphia, PA. Philadelphia (PA): AACR; Can Prev Res 2022;15(12 Suppl_1): Abstract nr A011.

Introduction: Ductal carcinoma in situ (DCIS) is considered a non-obligate precursor of invasive ductal carcinoma. With the aim of preventing a subsequent invasive cancer, all DCIS lesions are currently treated with surgical excision often supplemented with radiotherapy (RT). To prevent DCIS over- or undertreatment, a reliable marker of DCIS invasiveness risk is urgently needed. Methods: We studied two large DCIS cohorts: the Sloane cohort, a prospective breast screening cohort from the UK (median follow-up of 11 years), and a Dutch population-based cohort (NKI, median follow-up of 13 years). FFPE tissue specimens from patients with pure primary DCIS after breast-conserving surgery (BCS) +/- RT that did develop a subsequent ipsilateral event (DCIS or invasive) were considered as cases, whereas patients that did not develop any form of recurrence up to the last follow-up or death were considered as controls. We performed copy number analysis (CNA) and RNAseq analysis on 229 cases (80 DCIS only recurrences) and 344 controls. Results: DCIS was classified into the PAM50 subtypes using RNAseq data which revealed an enrichment of luminal A phenotype in DCIS that did not recur (P = 0.01, Fisher Exact test). No single copy number aberration was more common in cases compared to controls. RNAseq data did not reveal any genes significantly over/under-expressed in cases versus controls after FDR correction. However, by limiting the analysis to samples that had not had RT and excluding pure DCIS recurrences, we could develop a penalized Cox model from RNAseq data. The model was trained on weighted samples (to correct for the biased sampling of the case-control dataset) from the NKI series with double loop cross-validation. The genes were selected using the Elastic net framework of penalization. Using this predicted hazard ratio, the samples were split into high, medium, and low-risk quantiles, with a recurrence risk of 23%, 7% and 2%, respectively at 5 years (p = 10-10, Wald test). The NKI-trained predictor was independently validated in the Sloane No RT no DCIS recurrence cohort (p = 0.02, Wald test). GSEA analysis revealed proliferation hallmarks enriched in the recurrence predictor (FDR = 0.058). The RNAseq predictor was more predictive of recurrence than PAM50, clinical features (Grade, Her2 and ER) and the 12-gene Oncotype DCIS score (p < 0.001, permutation test using the Wald statistic) in both the NKI and Sloane series. Conclusion: Genomic profiling of two independent series of DCIS with outcome data did not reveal any clear associations with recurrence until analysis was limited to a set of samples who had not had radiotherapy and DCIS recurrences were excluded. We then identified an RNAseq-based classifier that could differentiate primary DCIS in low-, medium-, and high-risk groups, and validated it in an independent cohort. This classifier, if validated in other datasets, will allow us to identify women who do not need intensive treatment for their DCIS. Citation Format: Maria Roman Escorza, Michael Sheinman, Tycho Bismeijer, Ahmed A. Ahmed, Vandna Shah, Jeffrey R. Marks, Lorraine M. King, Anargyros Megalios, Lindy L. Visser, Marlous Hoogstrat, Helen R. Davies, Tapsi Kumar, Deborah Collyar, Hilary Stobart, Sarah Pinder, Nicholas N. Navin, Andrew Futreal, Serena Nik-Zainal, E. Shelley Hwang, Esther H. Lips, Alastair Thompson, Lodewyk F.A. Wessels, Jelle Wesseling, Elinor J. Sawyer. Genomic predictor can discriminate between high- and low-risk DCIS [abstract]. In: Proceedings of the AACR Special Conference on Rethinking DCIS: An Opportunity for Prevention?; 2022 Sep 8-11; Philadelphia, PA. Philadelphia (PA): AACR; Can Prev Res 2022;15(12 Suppl_1): Abstract nr PR002.

Ductal carcinoma in situ (DCIS) is early-stage non-malignant breast cancer that tumor cells are confined to the lumens of ducts. Over 50,000 DCIS patients are diagnosed each year in the United States, however, less than half of DCIS will develop into invasive breast cancer (IBC), suggesting a risk of overtreatment for some DCIS patients. A comprehensive investigation of the molecular mechanisms of DCIS initiation and progression is urgently needed that can facilitate us stratifying the risk of DCIS invasion and design better intervention approaches. In this study, we conducted single-cell RNA-seq and spatial transcriptomics (ST) on DCIS and IBC samples (most estrogen receptor positive). We first inferred copy number aberrations (CNA) in these samples and found that DCIS and IBC shared the most copy number events while only marginal differences were identified. Tumor CNA subclones colocalized in different ductal regions from the ST data. Further, we applied a sample-wise non-negative matrix factorization (NMF) to identify robust gene expression metaprograms across tumor samples. On the ST data, we found these metaprograms also displayed distinct spatial patterns. For non-tumor cells in the TME, we sub-clustering them into cell states and identified that some cell states showed a gradual change along breast cancer initiation and progression. To recapitulate the tumor ecosystems, we also conducted an ecotype analysis in DCIS samples and identified that some ecotypes showed an increasing/decreasing trend in IBC patients. Further, tumor and TME ecotypes also showed spatially colocalizations based on our ST data. In summary, these results demonstrated that genomic signatures could hardly predict the DCIS progression while tumor metaprograms and TME ecotypes might serve as a potential risk stratification approach for DCIS patients. Citation Format: Runmin Wei, Siyuan He, Shanshan Bai, Emi Sei, Min Hu, Chandandeep Nagi, Brian Menegaz, Huma Javaid, Jelle Wesseling, Andrew Futreal, Alastair Thompson, Savitri Krishnamurthy, Nicholas Navin. A single-cell and spatial investigation of tumor and TME for DCIS [abstract]. In: Proceedings of the AACR Special Conference on Rethinking DCIS: An Opportunity for Prevention?; 2022 Sep 8-11; Philadelphia, PA. Philadelphia (PA): AACR; Can Prev Res 2022;15(12 Suppl_1): Abstract nr IA013.

Single-cell RNA sequencing methods can profile the transcriptomes of single cells but cannot preserve spatial information. Conversely, spatial transcriptomics assays can profile spatial regions in tissue sections, but do not have single-cell resolution. Here, we developed a computational method called CellTrek that combines these two datasets to achieve single-cell spatial mapping through coembedding and metric learning approaches. We benchmarked CellTrek using simulation and in situ hybridization datasets, which demonstrated its accuracy and robustness. We then applied CellTrek to existing mouse brain and kidney datasets and showed that CellTrek can detect topological patterns of different cell types and cell states. We performed single-cell RNA sequencing and spatial transcriptomics experiments on two ductal carcinoma in situ tissues and applied CellTrek to identify tumor subclones that were restricted to different ducts, and specific T cell states adjacent to the tumor areas. Our data show that CellTrek can accurately map single cells in diverse tissue types to resolve their spatial organization.

Background: The early detection of breast cancer (BrC) is associated with improved survival. We describe a blood-based breast cancer detection test based on functional enrichment of breast-adenocarcinoma-associated circulating tumor cells (BrAD-CTCs) and their identification via multiplexed fluorescence immunocytochemistry (ICC) profiling for GCDFP15, GATA3, EpCAM, PanCK, and CD45 status. Methods: The ability of the test to differentiate BrC cases (N = 548) from healthy women (N = 9632) was evaluated in a case–control clinical study. The ability of the test to differentiate BrC cases from those with benign breast conditions was evaluated in a prospective clinical study of women (N = 141) suspected of BrC. Results: The test accurately detects BrAD-CTCs in breast cancers, irrespective of age, ethnicity, disease stage, grade, or hormone receptor status. Analytical validation established the high accuracy and reliability of the test under intended use conditions. The test detects and differentiates BrC cases from healthy women with 100% specificity and 92.07% overall sensitivity in a case–control study. In a prospective clinical study, the test shows 93.1% specificity and 94.64% overall sensitivity in differentiating breast cancer cases (N = 112) from benign breast conditions (N = 29). Conclusion: The findings reported in this manuscript support the clinical potential of this test for blood-based BrC detection.

Single cell RNA sequencing (scRNA-seq) methods can profile the transcriptomes of single cells but cannot preserve spatial information. Conversely, spatial transcriptomics (ST) assays can profile spatial regions in tissue sections but do not have single cell genomic resolution. To address this issue, computational approaches (e.g., cell2location, RCTD) have been designed to deconvolute ST spots into proportions of different cell types. However, the spatial deconvolution method has the following limitations, 1) it can only infer cell type proportions of each spot and cannot reach higher granular "cell states" mapping; 2) it only predict categorical labels and cannot infer continuous cell information (e.g., lineage trajectories, gene signatures, continuous phenotypes) at a spatial resolution. Here, we developed a computational approach called CellTrek that combines these two datasets to achieve single cell spatial mapping. Using a machine learning-based metric learning approach, CellTrek learned a cell-spot graph and then transfer spatial coordinates to cells. The CellTrek toolkit also provides two downstream analysis modules, including SColoc for spatial colocalization analysis and SCoexp for spatial co-expression analysis. We benchmarked CellTrek using a simulation and two in situ datasets. We then applied CellTrek to reconstruct cellular spatial structures in existing datasets from normal mouse brain and kidney tissues. We also performed scRNA-seq and ST experiments on two ductal carcinoma in situ (DCIS) tissues and applied CellTrek to identify subclones that were restricted to different ducts, and specific T cell states adjacent to the tumor areas. Our data shows that CellTrek can accurately map single cells in diverse tissue types to resolve their spatial organization into cellular neighborhoods and tissue structures. This method provides a new paradigm that is distinct from ST deconvolution, enabling a more flexible and direct investigation of single cell data with spatial topography. Citation Format: Runmin Wei, Siyuan He, Shanshan Bai, Emi Sei, Min Hu, Alastair Thompson, Ken Chen, Savitri Krishnamurthy, Nicholas Navin. Spatial charting of single cell transcriptomes in tissues [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 2129.

Background: Optical coherence tomography (OCT) is a high-resolution tissue-imaging modality that enables real-time imaging up to 2mm deep. The Perimeter B-Series OCT System combines wide field-OCT (WF-OCT) with an artificial intelligence system, ImgAssist, to assist clinicians in the detection of lesions suspicious for breast cancer. The objective of this investigational device trial is to assess adjunctive Perimeter B-Series OCT System use compared to standard care in identifying and addressing positive margins intraoperatively. Design: Prospective, multicenter, randomized, double-arm trial in females with biopsy-confirmed cancer undergoing breast conservation surgery (BCS) (NCT05113927). Following lumpectomy and standard-of-care intraoperative margin assessment participants will be randomized to a device or control arm (2:1 schema). Controls may undergo intraoperative pathology or frozen section analysis, per routine. WF-OCT will be done on all device arm specimens, with an opportunity to excise tissue from the lumpectomy cavity post-analysis. The new margin will be imaged with WF-OCT and the surgeon may take up to a maximum of 6 total shaves, including up to 2 shaves in each orientation. Eligibility: Adult females undergoing breast conservation surgery for biopsy-confirmed Stage 0-III invasive ductal and/or DCIS are eligible, including post-neoadjuvant therapy. Pregnant or lactating, those with Stage IV cancer, lobular carcinoma as primary diagnosis, previous ipsilateral breast surgery, multi-centric or bilateral disease, use of cryolocalization, or any treatment affecting margin integrity will be excluded. Specific Aim: The study hypothesis is that use of the Perimeter B-Series OCT System in BCS will reduce the proportion of subjects with at least 1 unaddressed positive margin. The primary endpoint is occurrence of aat least 1 unaddressed positive margin for a subject. The secondary endpoint is the number of unaddressed positive margins per subject. Safety includes Adverse Events; false-positive shaves per subject (device arm); and BREAST-Q Satisfaction with Breasts subscale. Other outcomes are total excised tissue volume, initial and all procedures including repeat surgeries; margin-level effectiveness (sensitivity, specificity, NPV and PPV); and operative time. Statistical Methods: The study was powered via the primary endpoint, assuming a 15% unaddressed margin rate, 90% power, 2:1 device-to-control ratio, and 10% loss of subjects due to screen or device failure. Both the primary and secondary effectiveness endpoints are based on within-subject comparison of the 2 treatments in the device arm (SOC+WF-OCT). Planned Accrual: 309 subjects (206 device, 103 control) will be enrolled at 8 centers over approximately 12 months. Citation Format: David Rempel, Andrew Berkeley, David Moos, Allison A. DiPasquale, Maryam Elmi, Richard E. Fine, Marie Lee, Bridget O’Brien, John F. Turner, Lee G. Wilke, Alastair Thompson. A prospective, multi-center, randomized, double-arm trial to determine the impact of the Perimeter B-Series OCT System on positive margin rates in breast conservation surgery [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 CT230.

With the widespread adoption of breast cancer screening the incidence of pure ductal carcinoma in situ (DCIS) has increased. As DCIS is considered a non-obligate precursor of invasive ductal carcinoma most women with pure DCIS are treated with breast conserving surgery (BCS) +/- radiotherapy. However, for many this is likely to be overtreatment as only a minority will develop a subsequent ipsilateral recurrence. Studies also show that only ~60% of these ipsilateral recurrences are invasive disease with the remainder being pure DCIS. To predict which women are most likely to benefit from interventions, there is a need to identify biomarkers that are associated with invasive recurrence. Our aim was to assess whether copy number aberrations (CNAs) could be used to identify DCIS that was likely to recur as invasive disease or remain recurrence-free during long-time follow up. We performed somatic copy number profiling on 309 pure DCIS samples that had not developed an ipsilateral event (controls), 198 that had developed subsequent ipsilateral invasive disease (INV-cases) and 58 that had developed subsequent ipsilateral pure DCIS (DCIS-cases). The samples were obtained from two large nation-wide cohorts: the Sloane cohort, a prospective breast screening cohort from the UK with a median follow up of 12.5 years and a Dutch population based cohort, with a median follow up of 13 years. CNAs were assessed using the CytoSNP array or low pass whole genome sequencing and analyzed using GISTIC. Integrative cluster (IntClust) subtyping revealed that only 5 subtypes were well represented in DCIS compared to 10 in invasive disease and the distribution of clusters between INV-cases and controls was similar with the exception of IntClust 4, which was significantly more common in controls (P= 0.025, Fishers exact test). IntClust 4 is characterized to have low levels of genomic instability and a CNA-devoid. INV-cases were globally more aberrant than controls (P = 0.006, Wilcoxon test) as assessed by the chromosomal instability index (CIN) score. GISTIC identified 17 recurrent amplifications, 21 recurrent gains and 22 recurrent losses in the whole cohort. Six of these regions were more common in INV-cases compared to controls: amplifications at 17q24.1 and 8p11.23, losses at 1p36.13 and 11q23.2 ​and gains at 17q21.33​ and 16p (Nominal P < 0.05 and FDR < 0.1, Fishers exact test). Subgroup analysis of ER+, Her2- INV-cases versus controls revealed an additional differential CNA, amplification at 11q13.3 more common in cases. DCIS-cases had similar CNAs to INV-cases and were more aberrant than controls in terms of CIN score (P < 0.037, Wilcoxon test) but not as aberrant as INV-cases. In conclusion, we have identified potential CNAs that are associated with invasive recurrence. Further analysis will integrate gene expression with copy number data to identify which genes are being targeted by these CNAs in order to identify pathways important in progression of DCIS. Citation Format: Ahmed A. Ahmed, Maria Roman-Escorza, Tycho Bismeijer, Michael Sheinman, Vandna Shah, Rana Shami, Jeffrey R. Marks, Lorraine M. King, Anargyros Megalios, Lindy L. Visser, Marlous Hoogstraat, Helen R. Davies, Tapsi Kumar, Deborah Collyar, Hilary Stobart, Sarah Pinder, Nicholas N. Navin, Andrew Futreal, Serena Nik-Zainal, E. Shelley Hwang, Lodewyk F. Wessels, Esther H. Lips, Alastair Thompson, Jelle Wesseling, Elinor J. Sawyer. Copy number analysis of pure DCIS and association with recurrence [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 5108.