Follow-up Routines Matter for Adherence to Endocrine Therapy in the Adjuvant Setting of Breast Cancer.

Background: Endocrine therapy (ET) adherence leads to increased survival in breast cancer (BC). How follow-up should be done to maximize adherence is not known. Objectives: To assess adherence to ET, factors favouring adherence to ET and effects on survival in a population-based cohort of BC patients in western Sweden. Design: This is a retrospective study. Methods: We included 358 patients operated for oestrogen receptor-positive BC and recommended 5 years of ET, in Region Halland, Sweden, year 2015 to 2016. Demographical, clinical and pathological data and use of ET were retrieved from the electronic medical records. Patients were considered adherent if taking ET for 5 years or during the full extent of the follow-up, until termination of ET due to BC relapse or death and where renewals of prescriptions of ET covered ⩾80% of the ordinated dose. Two follow-up routines were employed, ie, routine A where patients were contacted annually by nurses and a more passive follow-up routine B where patients were only contacted by nurses at 2 years and 5 years following start of ET. Results: Medication persistence for 4 years and more was good and similar between patients initiating aromatase inhibitor (AI) and tamoxifen (75.7% and 72.0%, respectively, P = .43). More patients initiating AIs changed ET due to side effects compared with patients initiating tamoxifen (24.3% vs 9.9%, respectively, P < .0001). Endocrine therapy adherence was better for follow-up routine B than for follow-up routine A (hazard ratio [HR] = 2.71 [1.44-5.09], P = .0027). Conclusions: Adherence to ET in BC is high in Western Sweden. Less regular nurse-initiated contacts between BC patients and nursesled surprisingly to a better adherence than a more regular nurse-initiated contact.

Follow-up routines are important for adherence to anti-hormonal therapy after breast cancer surgery In this study conducted in western Sweden, researchers looked at how well breast cancer (BC) patients followed their prescribed endocrine therapy (ET) for 5 years, which is crucial for their survival. They studied 358 patients diagnosed with oestrogen receptor-positive BC between 2015 and 2016. The study compared two follow-up routines: one where patients were contacted annually by nurses (routine A) and another where patients were contacted only at 2 years and 5 years after starting ET (routine B). Surprisingly, patients in routine B, with less frequent nurse contacts, were more likely to adhere with their ET compared with those in routine A. The study also found that patients taking aromatase inhibitors (AIs) were more likely to switch their ET due to side effects compared with those taking tamoxifen, but overall, adherence rates were similar between the 2 groups. In summary, the study showed that BC patients in western Sweden generally followed their prescribed ET well. In addition, having less frequent nurse-initiated contacts surprisingly improved patient adherence with their treatment.

How Reliable Are Gene Expression-Based and Immunohistochemical Biomarkers Assessed on a Core-Needle Biopsy? A Study of Paired Core-Needle Biopsies and Surgical Specimens in Early Breast Cancer.

In early breast cancer, a preoperative core-needle biopsy (CNB) is vital to confirm the malignancy of suspected lesions and for assessing the expression of treatment predictive and prognostic biomarkers in the tumor to choose the optimal treatments, emphasizing the importance of obtaining reliable results when biomarker status is assessed on a CNB specimen. This study aims to determine the concordance between biomarker status assessed as part of clinical workup on a CNB compared to a medically untreated surgical specimen. Paired CNB and surgical specimens from 259 patients that were part of the SCAN-B cohort were studied. The concordance between immunohistochemical (IHC) and gene expression (GEX) based biomarker status was investigated. Biomarkers of interest included estrogen receptor (ER; specifically, the alpha variant), progesterone receptor (PgR), Ki67, HER2, and tumor molecular subtype. In general, moderate to very good correlation in biomarker status between the paired CNB and surgical specimens was observed for both IHC assessment (83-99% agreement, kappa range 0.474-0.917) and GEX assessment (70-97% agreement, kappa range 0.552-0.800), respectively. However, using IHC, 52% of cases with low Ki67 status in the CNB shifted to high Ki67 status in the surgical specimen (McNemar's p = 0.011). Similarly, when using GEX, a significant shift from negative to positive ER (47%) and from low to high Ki67 (16%) was observed between the CNB and surgical specimen (McNemar's p = 0.027 and p = 0.002 respectively). When comparing biomarker status between different techniques (IHC vs. GEX) performed on either CNBs or surgical specimens, the agreement in ER, PgR, and HER2 status was generally over 80% in both CNBs and surgical specimens (kappa range 0.395-0.708), but Ki67 and tumor molecular subtype showed lower concordance levels between IHC and GEX (48-62% agreement, kappa range 0.152-0.398). These results suggest that both the techniques used for collecting tissue samples and analyzing biomarker status have the potential to affect the results of biomarker assessment, potentially also impacting treatment decisions and patient survival outcomes.

Classification of protein-protein interaction full-text documents using text and citation network features.

We participated (as Team 9) in the Article Classification Task of the Biocreative II.5 Challenge: binary classification of full-text documents relevant for protein-protein interaction. We used two distinct classifiers for the online and offline challenges: 1) the lightweight Variable Trigonometric Threshold (VTT) linear classifier we successfully introduced in BioCreative 2 for binary classification of abstracts and 2) a novel Naive Bayes classifier using features from the citation network of the relevant literature. We supplemented the supplied training data with full-text documents from the MIPS database. The lightweight VTT classifier was very competitive in this new full-text scenario: it was a top-performing submission in this task, taking into account the rank product of the Area Under the interpolated precision and recall Curve, Accuracy, Balanced F-Score, and Matthew's Correlation Coefficient performance measures. The novel citation network classifier for the biomedical text mining domain, while not a top performing classifier in the challenge, performed above the central tendency of all submissions, and therefore indicates a promising new avenue to investigate further in bibliome informatics.

Uncovering protein interaction in abstracts and text using a novel linear model and word proximity networks.

BACKGROUND: We participated in three of the protein-protein interaction subtasks of the Second BioCreative Challenge: classification of abstracts relevant for protein-protein interaction (interaction article subtask [IAS]), discovery of protein pairs (interaction pair subtask [IPS]), and identification of text passages characterizing protein interaction (interaction sentences subtask [ISS]) in full-text documents. We approached the abstract classification task with a novel, lightweight linear model inspired by spam detection techniques, as well as an uncertainty-based integration scheme. We also used a support vector machine and singular value decomposition on the same features for comparison purposes. Our approach to the full-text subtasks (protein pair and passage identification) includes a feature expansion method based on word proximity networks. RESULTS: Our approach to the abstract classification task (IAS) was among the top submissions for this task in terms of measures of performance used in the challenge evaluation (accuracy, F-score, and area under the receiver operating characteristic curve). We also report on a web tool that we produced using our approach: the Protein Interaction Abstract Relevance Evaluator (PIARE). Our approach to the full-text tasks resulted in one of the highest recall rates as well as mean reciprocal rank of correct passages. CONCLUSION: Our approach to abstract classification shows that a simple linear model, using relatively few features, can generalize and uncover the conceptual nature of protein-protein interactions from the bibliome. Because the novel approach is based on a rather lightweight linear model, it can easily be ported and applied to similar problems. In full-text problems, the expansion of word features with word proximity networks is shown to be useful, although the need for some improvements is discussed.

Transcriptional regulatory networks via gene ontology and expression data.

Transcriptional regulatory network (TRN) discovery using information from a single source does not seem feasible due to lack of sufficient information, resulting in the construction of spurious or incomplete TRNs. A methodology, TRND, that integrates a preliminary TRN, gene expression data and gene ontology is developed to discover TRNs. The method is applied to a comprehensive set of expression data on B cell and a preliminary TRN that included 1,335 genes, 443 transcription factors (TFs) and 4032 gene/TF interactions. Predictions were obtained for 443 TFs and 9,589 genes. 14,616 of 4,247,927 possible gene/TF interactions scored higher than the imposed threshold. Results for three TFs, E2F-4, p130 and c-Myc, were examined in more detail to assess the accuracy of the integrated methodology. Although the training sets for E2F-4 and p130 were rather limited, the activities of these two TFs were found to be highly correlated and a large set of coregulated genes is predicted. These predictions were confirmed with published experimental results not used in the training set. A similar test was run for the c-Myc TF using the comprehensive resource www.myccancergene.org. In addition, correlations between expression of genes that encode TFs and TF activities were calculated and showed that the assumption of TF activity correlates with encoding gene expression might be misleading. The constructed B cell TRN, and scores for individual methodologies and the integrated approach are available at systemsbiology.indiana.edu/trndresults.

Transcriptional regulatory network discovery via multiple method integration: application to e. coli K12.

Transcriptional regulatory network (TRN) discovery from one method (e.g. microarray analysis, gene ontology, phylogenic similarity) does not seem feasible due to lack of sufficient information, resulting in the construction of spurious or incomplete TRNs. We develop a methodology, TRND, that integrates a preliminary TRN, microarray data, gene ontology and phylogenic similarity to accurately discover TRNs and apply the method to E. coli K12. The approach can easily be extended to include other methodologies. Although gene ontology and phylogenic similarity have been used in the context of gene-gene networks, we show that more information can be extracted when gene-gene scores are transformed to gene-transcription factor (TF) scores using a preliminary TRN. This seems to be preferable over the construction of gene-gene interaction networks in light of the observed fact that gene expression and activity of a TF made of a component encoded by that gene is often out of phase. TRND multi-method integration is found to be facilitated by the use of a Bayesian framework for each method derived from its individual scoring measure and a training set of gene/TF regulatory interactions. The TRNs we construct are in better agreement with microarray data. The number of gene/TF interactions we discover is actually double that of existing networks.