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Ductal carcinoma in situ (DCIS) is characterized by inter-tumor heterogeneity that poses a therapeutic challenge due to its unpredictable recurrence and progression to invasive breast cancer (IBC). Recent publications have implicated the crucial role of the tumor microenvironment particularly stromal differences between DCIS patients who progress to IBC (progressors) and those that do not (non-progressors). However, spatial regulation of the collagen proteome has yet to be investigated in the context of disease progression in DCIS. In this study, we hypothesized that the collagen proteome was significantly altered between DCIS and IBC and that differentiating collagen peptide signatures could be related to clinical outcomes. Our initial studies investigated collagen peptide signatures in lumpectomies (n=13) annotated as DCIS, DCIS and invasive ductal carcinoma (IDC), or IDC only. We leveraged our previously published method for spatial imaging of collagen proteomics on tissue to report collagen types and collagen post-translational modifications including 40 other extracellular matrix (ECM) proteins involved in the regulation of collagen fibers. Over 1000 peaks were found to be linked to annotated pathologies or adjacent regions. Initial comparison of DCIS to IDC lesions demonstrated 63 differentially expressed peaks between these regions by unpaired, two-tailed t-test (p< 0.001). Image segmentation of the 315,541 pixels demonstrated 16 high-level hierarchical groups designating unique spatially localized ECM proteomic groups. Notably, these groups overlaid with histopathological features and pathological annotations. Next, we investigated collagen peptide signatures in a subset of DCIS samples from the Resource of Archival Breast Tissue (RAHBT) (n=37). Samples were histologically diverse within the tissue microarrays, with cribriform, micropapillary, papillary, solid, and comedo necrosis architectural patterns. In our preliminary analysis, we found two peptide peaks that could distinguish the solid subtype (n=22) from comedo necrosis (n=4) and one peak that could discriminate between the cribriform (n=8) and solid subtype (n=22) by area under the receiver operating curve (AUROC)≥0.75 and Wilson/Brown t-test (p< 0.05). Evaluated per clinical outcome, four ECM peptides showed significantly different peak intensities in progressors with IBC recurrence (n=7) compared to non-progressors (n=26) (AUROC≥0.75; Wilson/Brown t-test p< 0.05). One peptide had significantly different peak intensities between progressors with contralateral IBC recurrence and DCIS recurrence (n=5) and non-progressors (n=26) (AUROC≥0.75; Wilson/Brown t-test p< 0.05). Overall, the data suggest that unique collagen signatures in DCIS could be useful for understanding recurrence and progression to IBC. Further investigation of the spatial distribution of the collagen proteome within DCIS pathologies and relative to clinical outcome is warranted. Citation Format: Taylor S. Hulahan, Elizabeth N. Wallace, Siri H. Strand, Graham A. Colditz, E Shelley Hwang, Robert West, Laura Spruill, Jeffrey Marks, Richard R. Drake, Peggi M. Angel. Unique Collagen Peptide Signatures between Ductal Carcinoma in Situ and Invasive Breast Cancer by Mass Spectrometry Tissue Imaging [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 P2-21-03.

PURPOSE To examine incremental values of estrogen receptor (ER) status, body mass index (BMI), menopausal status, and a previously reported multi-gene classifier over commonly used clinical factors (i.e. age, tumor grade, comedonecrosis, surgical margins, and treatment) in predicting risk of any ipsilateral recurrence (IR) event within five years after DCIS diagnosis. METHODS A derivation cohort consisted of participants in the Translational Breast Cancer Research Consortium (TBCRC) 038, a retrospective multicenter cohort study in women undergoing surgical resection for DCIS between 01/01/1998 and 02/29/2016 (n=216). The validation cohort, the Repository of Archival Human Breast Tissue (RAHBT) at Washington University School of Medicine, provided cases meeting the same eligibility criteria as TBCRC038 (n=97). Participants in both cohorts had RNA-seq data and either developed IR 1-5y after initial DCIS diagnosis or were free from subsequent breast events for at least five years. The previously reported 812-gene classifier had been developed from a subset of the TBCRC038 samples using a negative-binomial regression model to identify differentially expressed genes in the primary tumor associated with subsequent recurrence events. This classifier has been shown to be highly correlated with 5-year invasive, DCIS, and all breast cancer events, and validated in the RAHBT cohort. Cox proportional hazards regression was used to estimate hazard ratios (HRs) of IR in the TBCRC038 cohort (76 with IR). The clinical score was developed using clinical predictors (aforementioned clinical factors and ER) and their regression coefficients from the model with the maximum predictive accuracy (e.g. c-index) and the minimum number of predictors; the summary score integrated the clinical score and multi-gene classifier. Predictive performance of both clinical and summary scores was validated in the RAHBT cohort (20 with IR). RESULTS In the TBCRC cohort derivation set, we used a multivariable model based on clinical factors alone (clinical score) and found that ER status, but not BMI or menopausal status, was independently associated with a higher IR risk (HR=2.06, 95% CI 1.18-3.58). Adding the multi-gene classifier to the clinical factors-based model (summary score) in the TBCRC038 test set increased predictive accuracy (c-index 0.68 to 0.70), with the genomic classifier-adjusted HR of 14.96 (95% CI 8.64-25.91). The summary score had higher predictive performance for IR risk than clinical score alone (c-index 0.82 vs. 0.70). In the RAHBT validation samples, model performance was similarly improved using summary scores clinical factors-based model plus multigene classifier as compared to clinical scores along (c-index 0.74 vs. 0.58). CONCLUSION Combining clinical factors and a multigene classifier provided more accurate risk estimates of IR within five years after excision of DCIS than clinical factors alone. Figure 1. Observed and predicted recurrence-free survival in the first five years after initial DCIS diagnosis in the RAHBT validation cohort, by risk groups defined by clinical scores (left) and clinical score plus multigene classifier (right). Citation Format: Ying Liu, Siri H. Strand, Lorraine King, Bryan Harmon, Fergus J. Couch, Kristalyn Gallagher, Mark Kilgore, Shi Wei, Angela DeMichele, Tari King, Priscilla F. McAuliffe, Jeffrey Marks, Carlo Maley, Robert West, E Shelley Hwang, Graham A. Colditz. Using clinical characteristics and molecular markers to predict the risk of subsequent ipsilateral breast events after excision of DCIS [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 P1-07-02.

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.

Ductal carcinoma in situ; Tumor microenvironment; Whole genome sequencing Carcinoma ductal in situ; Microambiente tumoral; Secuenciación del genoma completo Carcinoma ductal in situ; Microambient tumoral; Seqüenciació del genoma complet Ductal carcinoma in situ (DCIS) is the most common precursor of invasive breast cancer (IBC), with variable propensity for progression. We perform multiscale, integrated molecular profiling of DCIS with clinical outcomes by analyzing 774 DCIS samples from 542 patients with 7.3 years median follow-up from the Translational Breast Cancer Research Consortium 038 study and the Resource of Archival Breast Tissue cohorts. We identify 812 genes associated with ipsilateral recurrence within 5 years from treatment and develop a classifier that predicts DCIS or IBC recurrence in both cohorts. Pathways associated with recurrence include proliferation, immune response, and metabolism. Distinct stromal expression patterns and immune cell compositions are identified. Our multiscale approach employed in situ methods to generate a spatially resolved atlas of breast precancers, where complementary modalities can be directly compared and correlated with conventional pathology findings, disease states, and clinical outcome. This publication is part of the HTAN (Human Tumor Atlas Network) Consortium paper package. A list of HTAN members is available at humantumoratlas.org/htan-authors/. R01 CA185138-01 (E.S.H.); U2C CA-17-035 Pre-Cancer Atlas (PCA) Research Centers (E.S.H., R.B.W., C.M., K.P., G.A.C., K.O.); UO1 CA214183 (J.R.M.); DOD BC132057 (E.S.H., C.M.); BCRF 19-074 (E.S.H.); BCRF 19-028 (G.A.C.); PRECISION CRUK Grand Challenge (E.S.H.); R01CA193694 (R.B.W., G.A.C.), BCRF PPI-18-006 (R.B.W.). AEI RYC2019- 026576-I, "LaCaixa" Foundation LCF/PR/PR17/51120011 (J.A.S.). S.H.S. was supported by the Lundbeck Foundation (R288-2018-35) and the Danish Cancer Society (R229-A13616). K.E.H. was supported by a CIHR Banting Postdoctoral Fellowship. TBCRC 038 was conducted by the TBCRC, which receives major funding support from The Breast Cancer Research Foundation and Susan G. Komen. Some results in this paper are based upon data generated by the TCGA Research Network.

Purpose: To examine incremental values of estrogen receptor (ER) status, body mass index (BMI), menopausal status, and a multi-gene classifier over commonly used clinical factors (i.e. age, tumor grade, comedonecrosis, surgical margins, and treatment) in predicting risk of ipsilateral recurrence (IR) within five years after DCIS diagnosis. Methods: A derivation cohort consisted of participants in the discovery cohort, a retrospective multicenter cohort study in women undergoing surgical resection for DCIS between 01/01/1998 and 02/29/2016. The validation cohort provided cases meeting the same eligibility criteria as the discovery cohort. This analysis includes 313 participants with RNA-seq data who either developed IR 1-5 years after initial DCIS diagnosis or were free from subsequent breast events at least five years. Cox proportional hazards regression was used to estimate hazard ratios (HRs) of IR in 216 TBCRC participants (76 with IR). We developed the clinical score using clinical predictors (aforementioned clinical factors and ER) and their regression coefficients from the model with the maximum predictive accuracy (e.g. c-index) and the minimum number of predictors, and the summary score integrating the clinical score and multi-gene classifier. Predictive performance of clinical and summary scores was validated in 97 RAHBT patients (20 with IR). Results: In the discovery cohort, ER negativity, but not BMI or menopausal status, was independently associated with a higher IR risk (HR=2.06, 95% CI 1.18-3.58), and adding ER to the clinical factors-based model increased predictive accuracy (c-index 0.68 to 0.70). The clinical score-adjusted HR was 14.96 (95% CI 8.64-25.91) for multi-gene classifier. Summary scores were better in predicting IR risk than clinical scores (c-index 0.82 vs. 0.70). Compared with their low-risk counterparts, the HR was 4.79 (95% CI 2.55-8.98) in the clinical score-defined high-risk group and 29.02 (95% CI 14.23-59.15) in the summary score-defined high-risk group. In the validation cohort, model performance was improved using summary scores as compared to clinical scores (c-index 0.74 vs. 0.58). Conclusion: Combination of clinical factors and multigene classifier provided more accurate risk estimates of IR within five years after excision of DCIS than clinical factors alone. Citation Format: Graham A. Colditz, Ying Liu, Siri H. Strand, Lorraine King, Jeff Marks, Carlo Maley, Robert B. West, E. Shelley Hwang. Using clinical characteristics and molecular markers to predict the risk of subsequent ipsilateral breast events after excision of 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 A012.

Identifying distinct biomarkers of DCIS and determining the invasive potential of DCIS are of great clinical importance. N-linked glycans present on the cell surface of DCIS lesions and surrounding stroma are potential biomarkers of disease and tissue, but remain largely unexplored. An N-glycan targeted imaging mass spectrometry (IMS) approach was initiated to identify N-glycans associated with DCIS and progression in clinical FFPE tissues. A cohort of pathologist annotated DCIS and IDC tissues with a range of disease severity as well as the RAHBT TMA DCIS cohort were evaluated. The RAHBT cohort contains primary DCIS lesions with known long-term outcomes and serves as a good sample set to identify early markers indicating progressive potential, while the DCIS and IDC large tissue biopsies provide information on distinct glycan profiles when DCIS has progressed to IDC. Initially, tissue samples containing DCIS alone or both DCIS and IDC were processed for N-glycan MALDI-IMS analysis, the goal being to establish a consensus panel of each analyte and determine distinct profiles of DCIS when alone vs. when IDC was present. For N-glycans, a peak list of 54 N-glycans was selected for evaluation of each DCIS only tissue, as well as the mixed DCIS/IDC tissues. By highest intensity, the major DCIS-associated glycans were in the high-mannose and paucimannose structure categories. Additionally, a series of tri- and tetra-antennary multi-fucosylated glycans were also detected specifically in the DCIS lesions. In tissues containing both IDC and DCIS lesions, the high mannose glycans were also detected in the IDC regions, and GlcNAc-bisected glycans were seen elevated in these as compared to DCIS-only tissues. For samples in the RAHBT TMA cohort (progressors N=43; non-progressors N=78), the bisected N-glycans seen elevated with IDC were also seen to be significantly increased in the RAHBT samples that would eventually progress to IDC. Additional analyses to evaluate N-glycan isomer distributions for fucosylated and sialylated glycan species are also ongoing. The cumulative N-glycan data will be assessed with the extensive spatial genomic, transcriptomic and immunohistological data already generated for the same samples in the RAHBT cohort. This novel combination of multi-enzymatic digests with histopathology annotations represents an extensive multi-dimensional profile of DCIS and a novel tissue biomarker discovery approach. Citation Format: Elizabeth N. Wallace, Grace Grimsley, Siri H. Strand, Robert Michael Angelo, Graham Colditz, E. Shelley Hwang, Robert West, Jeffrey R. Marks, Peggi M. Angel, Richard R. Drake. Characterizing N-glycan profiles of DCIS progression using tissue imaging MALDI mass spectrometry [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 PR010.

Ductal carcinoma in situ (DCIS) is characterized by inter-tumor heterogeneity that poses a therapeutic challenge due to its unpredictable recurrence and progression to invasive breast cancer (IBC). In recent publications, collagen stromal differences have been reported between patients that progressed to IBC (progressors) and those that did not (non-progressors). However, details on the role spatial regulation of the collagen proteome might play in this progression have yet to be studied. Here, we investigated the pathological distribution of collagen post-translational modifications in a cohort of patients classified as progressors with ipsilateral IBC recurrence compared to non-progressors. Previously published methods for collagen proteomics by targeted tissue mass spectrometry imaging were used. The method reports collagen types and post-translational modifications within the collagen triple-helical region as well as approximately 40 other extracellular matrix (ECM) proteins involved in the regulation of collagen fibers. Initial studies investigated collagen variation in lumpectomies (n=7) with DCIS, DCIS plus invasive ductal carcinoma (IDC) or IDC only. Over 590 peptides were found to be linked to annotated pathologies. A preliminary comparison of DCIS (n=2392 spectra) to IBC (n=4696 spectra) using area under the receiver operating curve (AUROC) ≥0.85 demonstrated that 47 peptides could individually discriminate between DCIS and IBC in this limited cohort. Image segmentation of the 405,652 pixels demonstrated 11 high-level hierarchical groups designating unique spatially localized ECM proteomic groups; these groups overlaid with histopathological features and pathological annotations. A total of 87 samples from the Resource of Archival Breast Tissue (RAHBT) matched with clinical characteristics were also investigated. Cores were histologically diverse within the tissue microarrays, with cribriform, micropapillary, papillary, solid, and comedo necrosis architectural patterns. Initial results suggest certain peptides may differentiate between non-progressors and progressors with ipsilateral IBC recurrence. Our current work focuses on correlating collagen signatures to mixed pathologies and the cellular content of cores. Further investigation of the collagen proteome is warranted. Overall, the data suggest unique collagen signatures in DCIS that could be useful for understanding recurrence and progression to IBC. Citation Format: Taylor S. Hulahan, Elizabeth N. Wallace, Siri H. Strand, Robert Michael Angelo, Graham Colditz, Eun-Sil Shelley Hwang, Robert West, Laura Spruill, Jeffrey R. Marks, Richard R. Drake, Peggi M. Angel. Discrete regulation of the collagen proteome among pathological features in DCIS and invasive breast cancer by mass spectrometry tissue imaging [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 B019.

SUMMARY Ductal carcinoma in situ (DCIS) is a pre-invasive lesion that is thought to be a precursor to invasive breast cancer (IBC). To understand the changes in the tumor microenvironment (TME) accompanying transition to IBC, we used multiplexed ion beam imaging by time of flight (MIBI-TOF) and a 37-plex antibody staining panel to interrogate 79 clinically annotated surgical resections using machine learning tools for cell segmentation, pixel-based clustering, and object morphometrics. Comparison of normal breast with patient-matched DCIS and IBC revealed coordinated transitions between four TME states that were delineated based on the location and function of myoepithelium, fibroblasts, and immune cells. Surprisingly, myoepithelial disruption was more advanced in DCIS patients that did not develop IBC, suggesting this process could be protective against recurrence. Taken together, this HTAN Breast PreCancer Atlas study offers insight into drivers of IBC relapse and emphasizes the importance of the TME in regulating these processes., In brief A spatial imaging atlas of patient-matched ductal carcinoma in situ and invasive breast cancer depicts coordinated changes in the tumor microenviroment associated with invasive relapse, suggesting a potential protective role of myoepithelial disruption against invasive progression., Graphical Abstract