Clinical and molecular characteristics of estrogen receptor-positive ultralow risk breast cancer tumors identified by the 70-gene signature.

The metastatic potential of estrogen receptor (ER)-positive breast cancers is heterogeneous and distant recurrences occur months to decades after primary diagnosis. We have previously shown that patients with tumors classified as ultralow risk by the 70-gene signature have a minimal long-term risk of fatal breast cancer. Here, we evaluate the previously unexplored underlying clinical and molecular characteristics of ultralow risk tumors in 538 ER-positive patients from the Stockholm tamoxifen randomized trial (STO-3). Out of the 98 ultralow risk tumors, 89% were luminal A molecular subtype, whereas 26% of luminal A tumors were of ultralow risk. Compared to other ER-positive tumors, ultralow risk tumors were significantly (Fisher's test, P < .05) more likely to be of smaller tumor size, lower grade, progesterone receptor (PR)-positive, human epidermal growth factor 2 (HER2)-negative and have low Ki-67 levels (proliferation-marker). Moreover, ultralow risk tumors showed significantly lower expression scores of multi-gene modules associated with the AKT/mTOR-pathway, proliferation (AURKA), HER2/ERBB2-signaling, IGF1-pathway, PTEN-loss and immune response (IMMUNE1 and IMMUNE2) and higher expression scores of the PIK3CA-mutation-associated module. Furthermore, 706 genes were significantly (FDR < 0.001) differentially expressed in ultralow risk tumors, including lower expression of genes involved in immune response, PI3K/Akt/mTOR-pathway, histones, cell cycle, DNA repair, apoptosis and higher expression of genes coding for epithelial-to-mesenchymal transition and homeobox proteins, among others. In conclusion, ultralow risk tumors, associated with minimal long-term risk of fatal disease, differ from other ER-positive tumors, including luminal A molecular subtype tumors. Identification of these characteristics is important to improve our prediction of nonfatal vs fatal breast cancer.

Assessment of Long-term Distant Recurrence-Free Survival Associated With Tamoxifen Therapy in Postmenopausal Patients With Luminal A or Luminal B Breast Cancer.

IMPORTANCE: Patients with estrogen receptor (ER)-positive breast cancer have a long-term risk for fatal disease. However, the tumor biological factors that influence the long-term risk and the benefit associated with endocrine therapy are not well understood. OBJECTIVE: To compare the long-term survival from tamoxifen therapy for patients with luminal A or luminal B tumor subtype. DESIGN, SETTING, AND PARTICIPANTS: Secondary analysis of patients from the Stockholm Tamoxifen (STO-3) trial conducted from 1976 to 1990, which randomized postmenopausal patients with lymph node-negative breast cancer to receive adjuvant tamoxifen or no endocrine therapy. Tumor tissue sections were assessed in 2014 using immunohistochemistry and Agilent microarrays. Only patients with luminal A or B subtype tumors were evaluated. Complete long-term follow-up data up to the end of the STO-3 trial on December 31, 2012, were obtained from the Swedish National registers. Data analysis for the secondary analysis was conducted in 2017 and 2018. INTERVENTIONS: Patients were randomized to receive at least 2 years of tamoxifen therapy or no endocrine therapy; patients without recurrence who reconsented were further randomized to 3 additional years of tamoxifen therapy or no endocrine therapy. MAIN OUTCOMES AND MEASURES: Distant recurrence-free interval (DRFI) by luminal A and luminal B subtype and trial arm was assessed by Kaplan-Meier analyses and time-dependent flexible parametric models to estimate time-varying hazard ratios (HRs) that were adjusted for patient and tumor characteristics. RESULTS: In the STO-3 treated trial arm, 183 patients had luminal A tumors and 64 patients had luminal B tumors. In the untreated arm, 153 patients had luminal A tumors and 62 had luminal B tumors. Age at diagnosis ranged from 45 to 73 years. A statistically significant difference in DRFI by trial arm was observed (log rank, P < .001 [luminal A subtype, n = 336], P = .04 [luminal B subtype, n = 126]): the 25-year DRFI for luminal A vs luminal B subtypes was 87% (95% CI, 82%-93%) vs 67% (95% CI, 56%-82%) for treated patients, and 70% (95% CI, 62%-79%) vs 54% (95% CI, 42%-70%) for untreated patients, respectively. Patients with luminal A tumors significantly benefited from tamoxifen therapy for 15 years after diagnosis (HR, 0.57; 95% CI, 0.35-0.94), and those with luminal B tumors benefited from tamoxifen therapy for 5 years (HR, 0.38; 95% CI, 0.24-0.59). CONCLUSIONS AND RELEVANCE: Patients with luminal A subtype tumors had a long-term risk of distant metastatic disease, which was reduced by tamoxifen treatment, whereas patients with luminal B tumors had an early risk of distant metastatic disease, and tamoxifen benefit attenuated over time.

Acute doses of caffeine shift nervous system cell expression profiles toward promotion of neuronal projection growth.

Caffeine is a widely consumed psychoactive substance, but little is known about the effects of caffeine stimulation on global gene expression changes in neurons. Here, we conducted gene expression profiling of human neuroepithelial stem cell-derived neurons, stimulated with normal consumption levels of caffeine (3 µM and 10 µM), over a period of 9 h. We found dosage-dependent activation of immediate early genes after 1 h. Neuronal projection development processes were up-regulated and negative regulation of axon extension processes were down-regulated at 3 h. In addition, genes involved in extracellular matrix organization, response for wound healing, and regulation of immune system processes were down-regulated by caffeine at 3 h. This study identified novel genes within the neuronal projection guidance pathways that respond to acute caffeine stimulation and suggests potential mechanisms for the effects of caffeine on neuronal cells.

Tamoxifen therapy benefit for patients with 70-gene signature high and low risk.

BACKGROUND: Breast cancer molecular prognostic tools that predict recurrence risk have mainly been established on endocrine-treated patients and thus are not optimal for the evaluation of benefit from endocrine therapy. The Stockholm tamoxifen (STO-3) trial which randomized postmenopausal node-negative patients to 2-year tamoxifen (followed by an optional randomization for an additional 3-year tamoxifen vs nil), versus no adjuvant treatment, provides a unique opportunity to evaluate long-term 20-year benefit of endocrine therapy within prognostic risk classes of the 70-gene prognosis signature that was developed on adjuvantly untreated patients. METHODS: We assessed by Kaplan-Meier analysis 20-year breast cancer-specific survival (BCSS) and 10-year distant metastasis-free survival (DMFS) for 538 estrogen receptor (ER)-positive, STO-3 trial patients with retrospectively ascertained 70-gene prognosis classification. Multivariable analysis of long-term (20 years) BCSS by STO-3 trial arm in the 70-gene high-risk and low-risk subgroups was performed using Cox proportional hazard modeling adjusting for classical patient and tumor characteristics. RESULTS: Tamoxifen-treated, 70-gene low- and high-risk patients had 20-year BCSS of 90 and 83%, as compared to 80 and 65% for untreated patients, respectively (log-rank p < 0.0001). Notably, there is equivalent tamoxifen benefit in both high (HR 0.42 (0.21-0.86), p = 0.018) and low (HR 0.46 (0.25-0.85), p = 0.013) 70-gene risk categories even after adjusting for clinico-pathological factors for BCSS. Limited tamoxifen exposure as given in the STO-3 trial provides persistent benefit for 10-15 years after diagnosis in a time-varying analysis. 10-year DMFS was 93 and 85% for low- and high-risk tamoxifen-treated, versus 83 and 70% for low- and high-risk untreated patients, respectively (log-rank p < 0.0001). CONCLUSIONS: Patients with ER-positive breast cancer, regardless of high or low 70-gene risk classification, receive significant survival benefit lasting over 10 years from adjuvant tamoxifen therapy, even when given for a relatively short duration.

Pseudomonas Genome Database: improved comparative analysis and population genomics capability for Pseudomonas genomes.

Pseudomonas is a metabolically-diverse genus of bacteria known for its flexibility and leading free living to pathogenic lifestyles in a wide range of hosts. The Pseudomonas Genome Database (http://www.pseudomonas.com) integrates completely-sequenced Pseudomonas genome sequences and their annotations with genome-scale, high-precision computational predictions and manually curated annotation updates. The latest release implements an ability to view sequence polymorphisms in P. aeruginosa PAO1 versus other reference strains, incomplete genomes and single gene sequences. This aids analysis of phenotypic variation between closely related isolates and strains, as well as wider population genomics and evolutionary studies. The wide range of tools for comparing Pseudomonas annotations and sequences now includes a strain-specific access point for viewing high precision computational predictions including updated, more accurate, protein subcellular localization and genomic island predictions. Views link to genome-scale experimental data as well as comparative genomics analyses that incorporate robust genera-geared methods for predicting and clustering orthologs. These analyses can be exploited for identifying putative essential and core Pseudomonas genes or identifying large-scale evolutionary events. The Pseudomonas Genome Database aims to provide a continually updated, high quality source of genome annotations, specifically tailored for Pseudomonas researchers, but using an approach that may be implemented for other genera-level research communities.

PSORTdb--an expanded, auto-updated, user-friendly protein subcellular localization database for Bacteria and Archaea.

The subcellular localization (SCL) of a microbial protein provides clues about its function, its suitability as a drug, vaccine or diagnostic target and aids experimental design. The first version of PSORTdb provided a valuable resource comprising a data set of proteins of known SCL (ePSORTdb) as well as pre-computed SCL predictions for proteomes derived from complete bacterial genomes (cPSORTdb). PSORTdb 2.0 (http://db.psort.org) extends user-friendly functionalities, significantly expands ePSORTdb and now contains pre-computed SCL predictions for all prokaryotes--including Archaea and Bacteria with atypical cell wall/membrane structures. cPSORTdb uses the latest version of the SCL predictor PSORTb (version 3.0), with higher genome prediction coverage and functional improvements over PSORTb 2.0, which has been the most precise bacterial SCL predictor available. PSORTdb 2.0 is the first microbial protein SCL database reported to have an automatic updating mechanism to regularly generate SCL predictions for deduced proteomes of newly sequenced prokaryotic organisms. This updating approach uses a novel sequence analysis we developed that detects whether the microbe being analyzed has an outer membrane. This identification of membrane structure permits appropriate SCL prediction in an auto-updated fashion and allows PSORTdb to serve as a practical resource for genome annotation and prokaryotic research.

PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes.

MOTIVATION: PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. However, the recall needs to be improved and no accurate SCL predictors yet make predictions for archaea, nor differentiate important localization subcategories, such as proteins targeted to a host cell or bacterial hyperstructures/organelles. Such improvements should preferably be encompassed in a freely available web-based predictor that can also be used as a standalone program. RESULTS: We developed PSORTb version 3.0 with improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories. It is the first SCL predictor specifically geared for all prokaryotes, including archaea and bacteria with atypical membrane/cell wall topologies. It features an improved standalone program, with a new batch results delivery system complementing its web interface. We evaluated the most accurate SCL predictors using 5-fold cross validation plus we performed an independent proteomics analysis, showing that PSORTb 3.0 is the most accurate but can benefit from being complemented by Proteome Analyst predictions. AVAILABILITY: http://www.psort.org/psortb (download open source software or use the web interface). CONTACT: psort-mail@sfu.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.