Reprogramming SREBP1-dependent lipogenesis and inflammation in high-risk breast with licochalcone A: a novel path to cancer prevention.

Background: Anti-estrogens have had limited impact on breast cancer (BC) prevention. Novel agents with better tolerability, and efficacy beyond estrogen receptor (ER) positive BC are needed. We studied licochalcone A (LicA) for ER-agnostic BC prevention.

Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system.

Introduction: The rapid development of artificial intelligence (AI) in healthcare has exposed the unmet need for growing a multidisciplinary workforce that can collaborate effectively in the learning health systems. Maximizing the synergy among multiple teams is critical for Collaborative AI in Healthcare.

Methods: We have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare.

Breast cancer prevention with liquiritigenin from licorice through the inhibition of aromatase and protein biosynthesis in high-risk women's breast tissue.

Breast cancer risk continues to increase post menopause. Anti-estrogen therapies are available to prevent postmenopausal breast cancer in high-risk women. However, their adverse effects have reduced acceptability and overall success in cancer prevention.

Lipid exposure activates gene expression changes associated with estrogen receptor negative breast cancer.

Improved understanding of local breast biology that favors the development of estrogen receptor negative (ER-) breast cancer (BC) would foster better prevention strategies. We have previously shown that overexpression of specific lipid metabolism genes is associated with the development of ER- BC. We now report results of exposure of MCF-10A and MCF-12A cells, and mammary organoids to representative medium- and long-chain polyunsaturated fatty acids.

Progesterone receptor antagonists reverse stem cell expansion and the paracrine effectors of progesterone action in the mouse mammary gland.

Background: The ovarian hormones estrogen and progesterone (EP) are implicated in breast cancer causation. A specific consequence of progesterone exposure is the expansion of the mammary stem cell (MSC) and luminal progenitor (LP) compartments. We hypothesized that this effect, and its molecular facilitators, could be abrogated by progesterone receptor (PR) antagonists administered in a mouse model.

Selective progesterone receptor blockade prevents BRCA1-associated mouse mammary tumors through modulation of epithelial and stromal genes.

Pharmacological approaches to breast cancer risk-reduction for BRCA1 mutation carriers would provide an alternative to mastectomy. BRCA1-deficiency dysregulates progesterone signaling, promoting tumorigenesis. Selective progesterone receptor (PR) modulators (SPRMs) are therefore candidate prevention agents.

Polygenic risk score for the prediction of breast cancer is related to lesser terminal duct lobular unit involution of the breast.

Terminal duct lobular units (TDLUs) are the predominant anatomical structures where breast cancers originate. Having lesser degrees of age-related TDLU involution, measured as higher TDLUs counts or more epithelial TDLU substructures (acini), is related to increased breast cancer risk among women with benign breast disease (BBD). We evaluated whether a recently developed polygenic risk score (PRS) based on 313-common variants for breast cancer prediction is related to TDLU involution in the background, normal breast tissue, as this could provide mechanistic clues on the genetic predisposition to breast cancer.

Somatic genetic aberrations in benign breast disease and the risk of subsequent breast cancer.

It is largely unknown how the development of breast cancer (BC) is transduced by somatic genetic alterations in the benign breast. Since benign breast disease is an established risk factor for BC, we established a case-control study of women with a history of benign breast biopsy (BBB). Cases developed BC at least one year after BBB and controls did not develop BC over an average of 17 years following BBB.

BRCA1 mutation influences progesterone response in human benign mammary organoids.

Background: Women, who carry a germline BRCA1 gene mutation, have a markedly increased risk of developing breast cancer during their lifetime. While BRCA1 carriers frequently develop triple-negative, basal-like, aggressive breast tumors, hormone signaling is important in the genesis of BRCA1 mutant breast cancers. We investigated the hormone response in BRCA1-mutated benign breast tissue using an in vitro organoid system.

Selective Progesterone Receptor Modulators in Early-Stage Breast Cancer: A Randomized, Placebo-Controlled Phase II Window-of-Opportunity Trial Using Telapristone Acetate.

Purpose: Selective progesterone receptor modulators (SPRMs) show preclinical activity against hormone-sensitive breast cancer, but have not been tested in patients with early, treatment-naïve tumors.

Patients And Methods: In a double-blind presurgical window trial of oral telapristone acetate (TPA) 12 mg daily versus placebo, 70 patients with early-stage breast cancer were randomized 1:1 (stratified by menopause) and treated for 2 to 10 weeks. The primary endpoint was change in Ki67 between diagnostic biopsy and surgical specimens.

Cancer classification and pathway discovery using non-negative matrix factorization.

Objectives: Extracting genetic information from a full range of sequencing data is important for understanding disease. We propose a novel method to effectively explore the landscape of genetic mutations and aggregate them to predict cancer type.

Design: We applied non-smooth non-negative matrix factorization (nsNMF) and support vector machine (SVM) to utilize the full range of sequencing data, aiming to better aggregate genetic mutations and improve their power to predict disease type.

Identifying Breast Cancer Distant Recurrences from Electronic Health Records Using Machine Learning.

Accurately identifying distant recurrences in breast cancer from the Electronic Health Records (EHR) is important for both clinical care and secondary analysis. Although multiple applications have been developed for computational phenotyping in breast cancer, distant recurrence identification still relies heavily on manual chart review. In this study, we aim to develop a model that identifies distant recurrences in breast cancer using clinical narratives and structured data from EHR.

Using natural language processing and machine learning to identify breast cancer local recurrence.

Background: Identifying local recurrences in breast cancer from patient data sets is important for clinical research and practice. Developing a model using natural language processing and machine learning to identify local recurrences in breast cancer patients can reduce the time-consuming work of a manual chart review.

Methods: We design a novel concept-based filter and a prediction model to detect local recurrences using EHRs.

Mechanism of Telapristone Acetate (CDB4124) on Progesterone Receptor Action in Breast Cancer Cells.

Progesterone is a steroid hormone that plays an important role in the breast. Progesterone exerts its action through binding to progesterone receptor (PR), a transcription factor. Deregulation of the progesterone signaling pathway is implicated in the formation, development, and progression of breast cancer.

Contralateral Breast Cancer Event Detection Using Nature Language Processing.

To facilitate the identification of contralateral breast cancer events for large cohort study, we proposed and implemented a new method based on features extracted from narrative text in progress notes and features from numbers of pathology reports for each side of breast cancer. Our method collects medical concepts and their combinations to detect contralateral events in progress notes. In addition, the numbers of pathology reports generated for either left or right side of breast cancer were derived as additional features.

Near-infrared remotely triggered drug-release strategies for cancer treatment.

Remotely controlled, localized drug delivery is highly desirable for potentially minimizing the systemic toxicity induced by the administration of typically hydrophobic chemotherapy drugs by conventional means. Nanoparticle-based drug delivery systems provide a highly promising approach for localized drug delivery, and are an emerging field of interest in cancer treatment. Here, we demonstrate near-IR light-triggered release of two drug molecules from both DNA-based and protein-based hosts that have been conjugated to near-infrared-absorbing Au nanoshells (SiO core, Au shell), each forming a light-responsive drug delivery complex.

"Big Data" for Breast Cancer: Where to look and what you will find.

Accessing the massive amount of breast cancer data that is currently publically available may seem daunting to the brand new graduate student embarking on his/her first project or even to the seasoned lab leader, who may wish to explore a new avenue of investigation. In this review, we provide an overview of data resources focusing on high-throughput data and on cancer-related data resources. While not intended as an exhaustive list, the information included in this review will provide a jumping off point with descriptions of and links to the various data resources of interest.

Optical dosimetry probes to validate Monte Carlo and empirical-method-based NIR dose planning in the brain: publisher's note.

This note points out additional funding information that was not added to [Appl. Opt.55, 9875 (2016)APOPAI0003-693510.

Motivations for contralateral prophylactic mastectomy as a function of socioeconomic status.

Background: Despite no demonstrated survival advantage for women at average risk of breast cancer, rates of contralateral prophylactic mastectomy (CPM) continue to increase. Research reveals women with higher socioeconomic status (SES) are more likely to select CPM. This study examines how indicators of SES, age, and disease severity affect CPM motivations.

Understanding Resonant Light-Triggered DNA Release from Plasmonic Nanoparticles.

Nanoparticle-based platforms for gene therapy and drug delivery are gaining popularity for cancer treatment. To improve therapeutic selectivity, one important strategy is to remotely trigger the release of a therapeutic cargo from a specially designed gene- or drug-laden near-infrared (NIR) absorbing gold nanoparticle complex with NIR light. While there have been multiple demonstrations of NIR nanoparticle-based release platforms, our understanding of how light-triggered release works in such complexes is still limited.

Optical dosimetry probes to validate Monte Carlo and empirical-method-based NIR dose planning in the brain.

A three-dimensional photon dosimetry in tissues is critical in designing optical therapeutic protocols to trigger light-activated drug release. The objective of this study is to investigate the feasibility of a Monte Carlo-based optical therapy planning software by developing dosimetry tools to characterize and cross-validate the local photon fluence in brain tissue, as part of a long-term strategy to quantify the effects of photoactivated drug release in brain tumors. An existing GPU-based 3D Monte Carlo (MC) code was modified to simulate near-infrared photon transport with differing laser beam profiles within phantoms of skull bone (B), white matter (WM), and gray matter (GM).

Peer-facilitated discrete trial training for children with autism spectrum disorder.

In 2 studies, we evaluated the feasibility and efficacy of peer-mediated, school-based discrete trial training (DTT) for students with autism spectrum disorder (ASD). In the first, 6 typically developing elementary-age students were trained to use DTT procedures to teach target academic skills to 3 students with ASD who had been educated in a self-contained setting. A multiple probe-across-tutors design was applied to evaluate the accuracy with which the tutors implemented the DTT protocol.

Association between breast cancer genetic susceptibility variants and terminal duct lobular unit involution of the breast.

Terminal duct lobular units (TDLUs) are the predominant source of future breast cancers, and lack of TDLU involution (higher TDLU counts, higher acini count per TDLU and the product of the two) is a breast cancer risk factor. Numerous breast cancer susceptibility single nucleotide polymorphisms (SNPs) have been identified, but whether they are associated with TDLU involution is unknown. In a pooled analysis of 872 women from two studies, we investigated 62 established breast cancer SNPs and relationships with TDLU involution.