Breast tumors expressing estrogen receptor-alpha (ER) tend to be less responsive to cytotoxic chemotherapy than ER-negative breast cancers; advanced ER+ breast cancer remains largely incurable. Moreover, this breast cancer subtype frequently exhibits marked dormancy, conferring upon patients a high risk of experiencing a late recurrence (emergence from dormancy) that persists for decades after their initial diagnosis and treatment. Why ER+ breast is the most strongly associated with dormancy is unknown and represents a critical barrier to progress in the field. Since 70% of newly diagnosed breast cancers are ER+ but ~50% of these eventually recur, many doing so years after cessation of an otherwise apparently successful initial intervention, significance of the dormant phenotype is clear. Using transcriptome data from pretreatment tumors from women with ER+ breast cancer who were subsequently treated with tamoxifen (TAM) as their only systemic therapy, we derived an initial molecular classifier that robustly separates early (d 3 yrs) from late (e 5 yrs; emergence from dormancy) distant recurrences in both training and independent datasets. We then identified novel features of the early (E) vs. late (L) signaling network's topology in thse datasets. We also developed an adaptation of the DMBA-induced rat mammary tumor model and show that it represents E, L, and not (N) recurring tumors, molecular features of which are present in human breast tumors and cell lines. Mechanistically, our data implicate a rewiring (including epigenetic events) of the signaling from the unfolded protein response (UPR) and autophagy, and adaptations in cellular metabolism, as the molecular events that may enable cells to maintain, and later escape from, dormancy. Our central hypothesis is that dormancy reflects the growth arrest induced by endocrine therapies in both residual tumors (in-breast recurrences) and micro-metastases (distant recurrences), and that the signaling maintaining dormancy after systemic therapy ends is epigenetically regulated. Indeed, prolonged hormone therapy may well work by extending dormancy. Individual cells can emerge from dormancy as their metabolic capacity becomes sufficient to support both survival and replication, and the integrated balance between autophagy (prosurvival) and apoptosis (prodeath) signaling enables more cells in a tumor to proliferate and the tumor population size to grow. We propose an integrated, multidisciplinary research program that, if successful, will generate innovative insights into the molecular drivers of dormancy in ER+ breast cancers, develop new in silico signaling models, identify predictors of patient risk to experience a late recurrence, and explore mechanisms of dormancy with the longer term goal of discovering potentially novel therapeutic interventions.

Public Health Relevance

Breast tumors expressing estrogen receptor-alpha (ER) comprise 70% of all newly diagnosed invasive breast cancers. ER+ breast tumors represent the breast cancer subtype most strongly associated with tumor dormancy, conferring upon these patients a high risk of experiencing a late recurrence (emergence from dormancy) that can persist for decades after initial diagnosis and treatment. Unfortunately, very little is known abou the molecular drivers that determine the onset, maintenance, and emergence from dormancy. Our multidisciplinary team will apply state-of-the-art technologies to several unique, independent, human breast cancer data sets (including matched primary and recurrent tumors from the same individuals) and a rat mammary tumor model to address two fundamental aspects of dormancy; i.e., we will (i) accurately and robustly identify those breast cancer patients at greatest risk of experiencing a late recurrence (e 5 yrs after initial diagnosis) and (i) define the molecular events driving dormancy to help develop new treatment strategies that can be targeted to these patients.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZCA1-SRLB-X (J2))
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Couch, Jennifer A
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Georgetown University
Internal Medicine/Medicine
Schools of Medicine
United States
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Wärri, Anni; Cook, Katherine L; Hu, Rong et al. (2018) Autophagy and unfolded protein response (UPR) regulate mammary gland involution by restraining apoptosis-driven irreversible changes. Cell Death Discov 4:40
Shi, Xu; Wang, Xiao; Wang, Tian-Li et al. (2018) SparseIso: a novel Bayesian approach to identify alternatively spliced isoforms from RNA-seq data. Bioinformatics 34:56-63
Shajahan-Haq, Ayesha N; Boca, Simina M; Jin, Lu et al. (2017) EGR1 regulates cellular metabolism and survival in endocrine resistant breast cancer. Oncotarget 8:96865-96884
Chen, Xi; Shi, Xu; Hilakivi-Clarke, Leena et al. (2017) PSSV: a novel pattern-based probabilistic approach for somatic structural variation identification. Bioinformatics 33:177-183
Shi, Xu; Banerjee, Sharmi; Chen, Li et al. (2017) CyNetSVM: A Cytoscape App for Cancer Biomarker Identification Using Network Constrained Support Vector Machines. PLoS One 12:e0170482
Zhang, Xiyuan; Cook, Katherine L; Warri, Anni et al. (2017) Lifetime Genistein Intake Increases the Response of Mammary Tumors to Tamoxifen in Rats. Clin Cancer Res 23:814-824
Shajahan-Haq, Ayesha N; Boca, Simina M; Jin, Lu et al. (2017) EGR1 regulates cellular metabolism and survival in endocrine resistant breast cancer. Oncotarget :
Wang, Xiao; Gu, Jinghua; Hilakivi-Clarke, Leena et al. (2017) DM-BLD: differential methylation detection using a hierarchical Bayesian model exploiting local dependency. Bioinformatics 33:161-168
Sumis, Allison; Cook, Katherine L; Andrade, Fabia O et al. (2016) Social isolation induces autophagy in the mouse mammary gland: link to increased mammary cancer risk. Endocr Relat Cancer 23:839-56
Chen, Xi; Jung, Jin-Gyoung; Shajahan-Haq, Ayesha N et al. (2016) ChIP-BIT: Bayesian inference of target genes using a novel joint probabilistic model of ChIP-seq profiles. Nucleic Acids Res 44:e65

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