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Background Many patients receiving adjuvant endocrine therapy (ET) for breast cancer experience side effects and reduced quality of life (QoL) and discontinue ET. We sought to describe these issues and develop a prediction model of early discontinuation of ET. Methods Among patients with hormone receptor–positive and HER2-negative stage I-III breast cancer of the Cancer Toxicities cohort (NCT01993498) who were prescribed adjuvant ET between 2012 and 2017, upon stratification by menopausal status, we evaluated adjuvant ET patterns including treatment change and patient-reported discontinuation and ET-associated toxicities and impact on QoL. Independent variables included clinical and demographic features, toxicities, and patient-reported outcomes. A machine-learning model to predict time to early discontinuation was trained and evaluated on a held-out validation set. Results Patient-reported discontinuation rate of the first prescribed ET at 4 years was 30% and 35% in 4122 postmenopausal and 2087 premenopausal patients, respectively. Switching to a new ET was associated with higher symptom burden, poorer QoL, and higher discontinuation rate. Early discontinuation rate of adjuvant ET before treatment completion was 13% in postmenopausal and 15% in premenopausal patients. The early discontinuation model obtained a C index of 0.62 in the held-out validation set. Many aspects of QoL, most importantly fatigue and insomnia (European Organization for Research and Treatment of Cancer QoL questionnaire 30), were associated with early discontinuation. Conclusion Tolerability and adherence to ET remains a challenge for patients who switch to a second ET. An early discontinuation model using patient-reported outcomes identifies patients likely to discontinue their adjuvant ET. Improved management of toxicities and novel more tolerable adjuvant ETs are needed for maintaining patients on treatment. [ABSTRACT FROM AUTHOR]

Motivation The principle of Breiman's random forest (RF) is to build and assemble complementary classification trees in a way that maximizes their variability. We propose a new type of random forest that disobeys Breiman's principles and involves building trees with no classification errors in very large quantities. We used a new type of decision tree that uses a neuron at each node as well as an in-innovative half Christmas tree structure. With these new RFs, we developed a score, based on a family of ten new statistical information criteria, called Nguyen information criteria (NICs), to evaluate the predictive qualities of features in three dimensions. Results The first NIC allowed the Akaike information criterion to be minimized more quickly than data obtained with the Gini index when the features were introduced in a logistic regression model. The selected features based on the NICScore showed a slight advantage compared to the support vector machines—recursive feature elimination (SVM-RFE) method. We demonstrate that the inclusion of artificial neurons in tree nodes allows a large number of classifiers in the same node to be taken into account simultaneously and results in perfect trees without classification errors. Availability and implementation The methods used to build the perfect trees in this article were implemented in the 'ROP' R package, archived at https://cran.r-project.org/web/packages/ROP/index.html. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

'Breast cancer gene-expression miner' (bc-GenExMiner) is a breast cancer–associated web portal (http://bcgenex.ico.unicancer.fr). Here, we describe the development of a new statistical mining module, which permits several differential gene expression analyses, i.e. 'Expression' module. Sixty-two breast cancer cohorts and one healthy breast cohort with their corresponding clinicopathological information are included in bc-GenExMiner v4.5 version. Analyses are based on microarray or RNAseq transcriptomic data. Thirty-nine differential gene expression analyses, grouped into 13 categories, according to clinicopathological and molecular characteristics ('Targeted' and 'Exhaustive') and gene expression ('Customized'), have been developed. Output results are visualized in four forms of plots. This new statistical mining module offers, among other things, the possibility to compare gene expression in healthy (cancer-free), tumour-adjacent and tumour tissues at once and in three triple-negative breast cancer subtypes (i.e. C1: molecular apocrine tumours; C2: basal-like tumours infiltrated by immune suppressive cells and C3: basal-like tumours triggering an ineffective immune response). Several validation tests showed that bioinformatics process did not alter the pathobiological information contained in the source data. In this work, we developed and demonstrated that bc-GenExMiner 'Expression' module can be used for exploratory and validation purposes. Database URL : http://bcgenex.ico.unicancer.fr [ABSTRACT FROM AUTHOR]

Background Breast Cancer Trials of Oral Everolimus 2 (BOLERO-2), a phase III study in postmenopausal women with estrogen receptor–positive breast cancer progressing despite nonsteroidal aromatase inhibitor therapy, showed statistically significant benefits with adding everolimus to exemestane. Moreover, in preclinical studies, mammalian target of rapamycin inhibition was associated with decreased osteoclast survival and activity. Exploratory analyses in BOLERO-2 evaluated the effect of everolimus on bone marker levels and progressive disease in bone. Methods Patients were treated with exemestane (25mg/day) and randomized (2:1) to everolimus (10mg/day; combination) or placebo (exemestane only). Exploratory endpoints included changes in bone turnover marker levels vs baseline and progressive disease in bone, defined as unequivocal progression of a preexisting bone lesion or the appearance of a new bone lesion. Results Baseline disease characteristics were well balanced between arms (N = 724); baseline bisphosphonate use was not (43.9% combination vs 54.0% exemestane only). At a median of 18 months of follow-up, median progression-free survival (primary endpoint) was statistically significantly longer with the combination vs exemestane only (Cox proportional hazard ratio = 0.45, 95% confidence interval = 0.38 to 0.54; log-rank, 1-sided P < .0001). Bone marker levels at 6 and 12 weeks increased with exemestane only, as expected, but decreased with the combination. The cumulative incidence rate of progressive disease in bone was lower in the combination arm. Bone-related adverse events occurred with similar frequency in both arms (3.3% combination vs 4.2% exemestane only). Conclusion These exploratory analyses suggest that everolimus has beneficial effects on bone turnover and progressive disease in bone in patients receiving exemestane for hormone receptor–positive breast cancer progressing during/after nonsteroidal aromatase inhibitor therapy. [ABSTRACT FROM PUBLISHER]

We recently developed a user-friendly web-based application called bc-GenExMiner (http://bcgenex.centregauducheau.fr), which offered the possibility to evaluate prognostic informativity of genes in breast cancer by means of a 'prognostic module'. In this study, we develop a new module called 'correlation module', which includes three kinds of gene expression correlation analyses. The first one computes correlation coefficient between 2 or more (up to 10) chosen genes. The second one produces two lists of genes that are most correlated (positively and negatively) to a 'tested' gene. A gene ontology (GO) mining function is also proposed to explore GO 'biological process', 'molecular function' and 'cellular component' terms enrichment for the output lists of most correlated genes. The third one explores gene expression correlation between the 15 telomeric and 15 centromeric genes surrounding a 'tested' gene. These correlation analyses can be performed in different groups of patients: all patients (without any subtyping), in molecular subtypes (basal-like, HER2+, luminal A and luminal B) and according to oestrogen receptor status. Validation tests based on published data showed that these automatized analyses lead to results consistent with studies' conclusions. In brief, this new module has been developed to help basic researchers explore molecular mechanisms of breast cancer. [ABSTRACT FROM AUTHOR]