Does chemotherapy-induced neutropaenia result in a postponement of adjuvant or neoadjuvant regimens in breast cancer patients? Results of a retrospective analysis

In 2005, 224 patients received adjuvant/neoadjuvant chemotherapy for breast cancer in a single institution according to daily practices. Regimens consisted of epirubicin-based chemotherapy (FEC100, four or six cycles), or three cycles of FEC100 followed by three cycles of docetaxel. An absolute blood count was carried out every 3 weeks, 1–3 days before planned chemotherapy cycle. Overall, 1238 cycles were delivered. An absolute neutrophil count (ANC) <1.5 × 109 l−1 before planned chemotherapy was found in 171 cycles. Of these, 130 cycles (76%) were delivered as planned regardless of whether ANC levels recovered, and 41 (24%) were delayed. None of these patients developed a febrile neutropaenia. Haematopoietic support (granulocyte colony-stimulating factor (G-CSF)) was required in 12 cycles. We found that the majority of patients with an ANC <1.5 × 109 l−1 before planned chemotherapy received planned doses, without complications and need for G-CSF

Observational and Physical Classification of Supernovae

This chapter describes the current classification scheme of supernovae (SNe). This scheme has evolved over many decades and now includes numerous SN Types and sub-types. Many of these are universally recognized, while there are controversies regarding the definitions, membership and even the names of some sub-classes; we will try to review here the commonly-used nomenclature, noting the main variants when possible. SN Types are defined according to observational properties; mostly visible-light spectra near maximum light, as well as according to their photometric properties. However, a long-term goal of SN classification is to associate observationally-defined classes with specific physical explosive phenomena. We show here that this aspiration is now finally coming to fruition, and we establish the SN classification scheme upon direct observational evidence connecting SN groups with specific progenitor stars. Observationally, the broad class of Type II SNe contains objects showing strong spectroscopic signatures of hydrogen, while objects lacking such signatures are of Type I, which is further divided to numerous subclasses. Recently a class of super-luminous SNe (SLSNe, typically 10 times more luminous than standard events) has been identified, and it is discussed. We end this chapter by briefly describing a proposed alternative classification scheme that is inspired by the stellar classification system. This system presents our emerging physical understanding of SN explosions, while clearly separating robust observational properties from physical inferences that can be debated. This new system is quantitative, and naturally deals with events distributed along a continuum, rather than being strictly divided into discrete classes. Thus, it may be more suitable to the coming era where SN numbers will quickly expand from a few thousands to millions of events.Comment: Extended final draft of a chapter in the "SN Handbook". Comments most welcom

Uncertainties in steric sea level change estimation during the satellite altimeter era: concepts and practices

This article presents a review of current practice in estimating steric sea level change, focussed on the treatment of uncertainty. Steric sea level change is the contribution to the change in sea level arising from the dependence of density on temperature and salinity. It is a significant component of sea level rise and a reflection of changing ocean heat content. However tracking these steric changes remains still a significant challenge for the scientific community. We review the importance of understanding the uncertainty in estimates of steric sea level change. Relevant concepts of uncertainty are discussed and illustrated with the example of observational uncertainty propagation from a single profile of temperature and salinity measurements to steric height. We summarise and discuss the recent literature on methodologies and techniques used to estimate steric sea level in the context of the treatment of uncertainty. Our conclusions are that progress in quantifying steric sea level uncertainty will benefit from: greater clarity and transparency in published discussions of uncertainty, including exploitation of international standards for quantifying and expressing uncertainty in measurement; and the development of community ‘recipes’ for quantifying the error covariances in observations and from sparse sampling, and for estimating and propagating uncertainty across spatio-temporal scales

Can Survival Prediction Be Improved By Merging Gene Expression Data Sets?

BACKGROUND:High-throughput gene expression profiling technologies generating a wealth of data, are increasingly used for characterization of tumor biopsies for clinical trials. By applying machine learning algorithms to such clinically documented data sets, one hopes to improve tumor diagnosis, prognosis, as well as prediction of treatment response. However, the limited number of patients enrolled in a single trial study limits the power of machine learning approaches due to over-fitting. One could partially overcome this limitation by merging data from different studies. Nevertheless, such data sets differ from each other with regard to technical biases, patient selection criteria and follow-up treatment. It is therefore not clear at all whether the advantage of increased sample size outweighs the disadvantage of higher heterogeneity of merged data sets. Here, we present a systematic study to answer this question specifically for breast cancer data sets. We use survival prediction based on Cox regression as an assay to measure the added value of merged data sets. RESULTS:Using time-dependent Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) and hazard ratio as performance measures, we see in overall no significant improvement or deterioration of survival prediction with merged data sets as compared to individual data sets. This apparently was due to the fact that a few genes with strong prognostic power were not available on all microarray platforms and thus were not retained in the merged data sets. Surprisingly, we found that the overall best performance was achieved with a single-gene predictor consisting of CYB5D1. CONCLUSIONS:Merging did not deteriorate performance on average despite (a) The diversity of microarray platforms used. (b) The heterogeneity of patients cohorts. (c) The heterogeneity of breast cancer disease. (d) Substantial variation of time to death or relapse. (e) The reduced number of genes in the merged data sets. Predictors derived from the merged data sets were more robust, consistent and reproducible across microarray platforms. Moreover, merging data sets from different studies helps to better understand the biases of individual studies and can lead to the identification of strong survival factors like CYB5D1 expression