Hormone Receptor and ERBB2 Status in Gene Expression Profiles of Human Breast Tumor Samples

The occurrence of large publically available repositories of human breast tumor gene expression profiles provides an important resource to discover new breast cancer biomarkers and therapeutic targets. For example, knowledge of the expression of the estrogen and progesterone hormone receptors (ER and PR), and that of the ERBB2 in breast tumor samples enables choice of therapies for the breast cancer patients that express these proteins. Identifying new biomarkers and therapeutic agents affecting the activity of signaling pathways regulated by the hormone receptors or ERBB2 might be accelerated by knowledge of their expression levels in large gene expression profiling data sets. Unfortunately, the status of these receptors is not invariably reported in public databases of breast tumor gene expression profiles. Attempts have been made to employ a single probe set to identify ER, PR and ERBB2 status, but the specificity or sensitivity of their prediction is low. We enquired whether estimation of ER, PR and ERBB2 status of profiled tumor samples could be improved by using multiple probe sets representing these three genes and others with related expression

The Overlap of Lung Tissue Transcriptome of Smoke Exposed Mice with Human Smoking and COPD

© 2018, The Author(s). Genome-wide mRNA profiling in lung tissue from human and animal models can provide novel insights into the pathogenesis of chronic obstructive pulmonary disease (COPD). While 6 months of smoke exposure are widely used, shorter durations were also reported. The overlap of short term and long-term smoke exposure in mice is currently not well understood, and their representation of the human condition is uncertain. Lung tissue gene expression profiles of six murine smoking experiments (n = 48) were obtained from the Gene Expression Omnibus (GEO) and analyzed to identify the murine smoking signature. The “human smoking” gene signature containing 386 genes was previously published in the lung eQTL study (n = 1,111). A signature of mild COPD containing 7 genes was also identified in the same study. The lung tissue gene signature of “severe COPD” (n = 70) contained 4,071 genes and was previously published. We detected 3,723 differentially expressed genes in the 6 month-exposure mice datasets (FDR <0.1). Of those, 184 genes (representing 48% of human smoking) and 1,003 (representing 27% of human COPD) were shared with the human smoking-related genes and the COPD severity-related genes, respectively. There was 4-fold over-representation of human and murine smoking-related genes (P = 6.7 × 10−26) and a 1.4 fold in the severe COPD -related genes (P = 2.3 × 10−12). There was no significant enrichment of the mice and human smoking-related genes in mild COPD signature. These data suggest that murine smoke models are strongly representative of molecular processes of human smoking but less of COPD

Prevalence and characteristics of progressive fibrosing interstitial lung disease in a prospective registry

Rationale Progressive fibrosing interstitial lung disease (PF-ILD) is characterized by progressive physiologic, symptomatic, and/or radiographic worsening. The real-world prevalence and characteristics of PF-ILD remain uncertain. Methods Patients were enrolled from the Canadian Registry for Pulmonary Fibrosis between 2015-2020. PF-ILD was defined as a relative forced vital capacity (FVC) decline ≥10%, death, lung transplantation, or any 2 of: relative FVC decline ≥5 and &lt;10%, worsening respiratory symptoms, or worsening fibrosis on computed tomography of the chest, all within 24 months of diagnosis. Time-to-event analysis compared progression between key diagnostic subgroups. Characteristics associated with progression were determined by multivariable regression. Results Of 2,746 patients with fibrotic ILD (mean age 65±12 years, 51% female), 1,376 (50%) met PFILD criteria in the first 24 months of follow-up. PF-ILD occurred in 427 (59%) patients with idiopathic pulmonary fibrosis (IPF), 125 (58%) with fibrotic hypersensitivity pneumonitis (HP), 281 (51%) with unclassifiable ILD (U-ILD), and 402 (45%) with connective tissue diseaseassociated ILD (CTD-ILD). Compared to IPF, time to progression was similar in patients with HP (hazard ratio [HR] 0.96, 95% confidence interval, CI 0.79-1.17), but was delayed in patients with U-ILD (HR 0.82, 95% CI 0.71-0.96) and CTD-ILD (HR 0.65, 95% CI 0.56-0.74). Background treatment varied across diagnostic subtypes with 66% of IPF patients receiving antifibrotic therapy, while immunomodulatory therapy was utilized in 49%, 61%, and 37% of patients with CHP, CTD-ILD, and U-ILD respectively. Increasing age, male sex, gastroesophageal reflux disease, and lower baseline pulmonary function were independently associated with progression. Interpretation Progression is common in patients with fibrotic ILD, and is similarly prevalent in HP and IPF. Routinely collected variables help identify patients at risk for progression and may guide therapeutic strategie

Gene expression and in situ protein profiling of candidate SARS-CoV-2 receptors in human airway epithelial cells and lung tissue

In December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)emerged, causing the coronavirus disease 2019 (COVID-19) pandemic. SARS-CoV, the agent responsible for the 2003 SARS outbreak, utilises angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) host molecules for viral entry. ACE2 and TMPRSS2 have recently been implicated in SARS-CoV-2 viral infection. Additional host molecules including ADAM17, cathepsin L, CD147 and GRP78 may also function as receptors for SARS-CoV-2.To determine the expression and in situ localisation of candidate SARS-CoV-2 receptors in the respiratory mucosa, we analysed gene expression datasets from airway epithelial cells of 515 healthy subjects, gene promoter activity analysis using the FANTOM5 dataset containing 120 distinct sample types, single cell RNA sequencing (scRNAseq) of 10 healthy subjects, proteomic datasets, immunoblots on multiple airway epithelial cell types, and immunohistochemistry on 98 human lung samples.We demonstrate absent to lowACE2promoter activity in a variety of lung epithelial cell samples andlowACE2gene expression in both microarray and scRNAseq datasets of epithelial cell populations.Consistent with gene expression, rare ACE2 protein expression was observed in the airway epithelium and alveoli of human lung, confirmed with proteomics. We present confirmatory evidence for the presence ofTMPRSS2, CD147 and GRP78 protein in vitro in airway epithelial cells and confirm broad in situ protein expression of CD147 and GRP78 in the respiratory mucosa. Collectively, our data suggest the presence of a mechanism dynamically regulating ACE2 expression inhuman lung, perhaps in periods of SARS-CoV-2 infection, and also suggest that alternative receptors forSARS-CoV-2 exist to facilitate initial host cell infection

Disease-free (DFS) and overall survival (OS) of patients with tumors belonging to Luminal-like subtype compared to other subtypes.

<p>Comparisons with corrected p-values<0.05 are marked in bold and Hazard Ratios (HR) and their 95% Confidence Intervals are provided in brackets.</p

Identification of a Novel Luminal Molecular Subtype of Breast Cancer

<div><p>The molecular classification of human breast tumors has afforded insights into subtype specific biological processes, patient prognosis and response to therapies. However, using current methods roughly one quarter of breast tumors cannot be classified into one or another molecular subtype. To explore the possibility that the unclassifiable samples might comprise one or more novel subtypes we employed a collection of publically available breast tumor datasets with accompanying clinical information to assemble 1,593 transcript profiles: 25% of these samples could not be assigned to one of the current molecular subtypes of breast cancer. All of the unclassifiable samples could be grouped into a new molecular subtype, which we termed “luminal-like”. We also identified the luminal-like subtype in an independent collection of tumor samples (NKI295). We found that patients harboring tumors of the luminal-like subtype have a better prognosis than those with basal-like breast cancer, a similar prognosis to those with ERBB2+, luminal B or claudin-low tumors, but a worse prognosis than patients with luminal A or normal-like breast tumors. Our findings suggest the occurrence of another molecular subtype of breast cancer that accounts for the vast majority of previously unclassifiable breast tumors.</p></div

Correlation of 397 unclassified samples with 7 subtypes.

<p>A. Distribution of samples based on highest correlation coefficient (excluding the correlation to “Luminal-like” centroid) B. Comparison of the same correlation coefficients across subtypes. The basal-like subtype showed lower coefficients than the luminal A and luminal B subtypes and claudin-low subtype showed lower coefficients than luminal B subtype (Kruskal-Wallis (p-value = 0.0004, post-hoc Dunn’s multiple comparison test)).</p

Identification of the novel cluster.

<p>A. Distribution of Spearman Rank Correlation Coefficients. Distribution of correlation coefficients is shown in the form of a histogram. Gaussian distributions fitted by using the EM (Expectation-Maximalization) algorithm are shown in blue, the sum of these distributions is shown in green. Correlation coefficients lower than than 0.3 are marked in cyan; correlation coefficients higher than 0.3 are marked in red. B. Cophenetic coefficient obtained from NMF clustering. Optimal number of clusters established by this method is indicated by black arrow. C. Principal Component Analysis performed on 1,593 tumor samples by using 710 genes (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103514#pone.0103514.s002" target="_blank">Table S1</a>). Samples are colored by the subtype they were assigned to. D. Principal Component Analysis performed on 1,593 tumor samples by using 710 genes (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103514#pone.0103514.s002" target="_blank">Table S1</a>). Samples are colored by the dataset of their origin.</p