McAuliffe, Priscilla F.

Mixed invasive ductal and lobular carcinoma (MDLC) is a rare histologic subtype of breast cancer displaying both E-cadherin positive ductal and E-cadherin negative lobular morphologies within the same tumor, posing challenges with regard to anticipated clinical management. It remains unclear whether these distinct morphologies also have distinct biology and risk of recurrence. Our spatially resolved transcriptomic, genomic, and single-cell profiling revealed clinically significant differences between ductal and lobular tumor regions including distinct intrinsic subtype heterogeneity – e.g., MDLC with triple-negative breast cancer (TNBC) or basal ductal and estrogen receptor positive (ER+) luminal lobular regions, distinct enrichment of cell cycle arrest/senescence and oncogenic (ER and MYC ) signatures, genetic and epigenetic CDH1 inactivation in lobular but not ductal regions, and single-cell ductal and lobular subpopulations with unique oncogenic signatures further highlighting intraregional heterogeneity. Altogether, we demonstrated that the intratumoral morphological/histological heterogeneity within MDLC is underpinned by intrinsic subtype and oncogenic heterogeneity which may result in prognostic uncertainty and therapeutic dilemma.

Abstract 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.

Abstract 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.