The Cambridge post-mastectomy radiotherapy (C-PMRT) index : a practical tool for patient selection

BACKGROUND AND PURPOSE: Post mastectomy radiotherapy (PMRT) reduces loco-regional recurrence (LRR) and has been associated with survival benefit. It is recommended for patients with T3/T4 tumours and/or ⩾ 4 positive lymph nodes (LN). The role of PMRT in 1-3 positive LN and LN negative patients is contentious. The C-PMRT index has been designed for selecting PMRT patients, using independent prognostic factors for LRR. This study reports a 10 year experience using this index. MATERIALS AND METHODS: The C-PMRT index was constructed using the following prognostic factors (a) number of positive LN/lymphovascular invasion, (b) tumour size (c) margin status and (d) tumour grade. Patients were categorised as high (H) risk, intermediate (I) risk and low (L) risk. PMRT was recommended for H and I risk patients. The LRR, distant metastasis and overall survival (OS) rates were measured from the day of mastectomy. RESULTS: From 1999 to 2009, 898 invasive breast cancers in 883 patients were treated by mastectomy (H: 323, I: 231 and L: 344). At a median follow up of 5.2 years, 4.7% (42/898) developed LRR. The 5-year actuarial LRR rates were 6%, 2% and 2% for the H, I and L risk groups, respectively. 1.6% (14/898) developed isolated LRR (H risk n = 4, I risk group n = 0 and L risk n = 10). The 5-year actuarial overall survival rates were 67%, 77% and 90% for H, I and L risk groups, respectively. CONCLUSION: Based on published literature, one would have expected a higher LRR rate in the I risk group without adjuvant RT. We hypothesise that the I risk group LRR rates have been reduced to that of the L risk group by the addition of RT. Apart from LN status and tumour size, other prognostic factors should also be considered in selecting patients for PMRT. This pragmatic tool requires further validation

PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer.

INTRODUCTION: The aim of this study was to develop and validate a prognostication model to predict overall and breast cancer specific survival for women treated for early breast cancer in the UK. METHODS: Using the Eastern Cancer Registration and Information Centre (ECRIC) dataset, information was collated for 5,694 women who had surgery for invasive breast cancer in East Anglia from 1999 to 2003. Breast cancer mortality models for oestrogen receptor (ER) positive and ER negative tumours were derived from these data using Cox proportional hazards, adjusting for prognostic factors and mode of cancer detection (symptomatic versus screen-detected). An external dataset of 5,468 patients from the West Midlands Cancer Intelligence Unit (WMCIU) was used for validation. RESULTS: Differences in overall actual and predicted mortality were <1% at eight years for ECRIC (18.9% vs. 19.0%) and WMCIU (17.5% vs. 18.3%) with area under receiver-operator-characteristic curves (AUC) of 0.81 and 0.79 respectively. Differences in breast cancer specific actual and predicted mortality were <1% at eight years for ECRIC (12.9% vs. 13.5%) and <1.5% at eight years for WMCIU (12.2% vs. 13.6%) with AUC of 0.84 and 0.82 respectively. Model calibration was good for both ER positive and negative models although the ER positive model provided better discrimination (AUC 0.82) than ER negative (AUC 0.75). CONCLUSIONS: We have developed a prognostication model for early breast cancer based on UK cancer registry data that predicts breast cancer survival following surgery for invasive breast cancer and includes mode of detection for the first time. The model is well calibrated, provides a high degree of discrimination and has been validated in a second UK patient cohort.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Inclusion of KI67 significantly improves performance of the PREDICT prognostication and prediction model for early breast cancer.

BACKGROUND: PREDICT (http://www.predict.nhs.uk) is a prognostication and treatment benefit tool for early breast cancer (EBC). The aim of this study was to incorporate the prognostic effect of KI67 status in a new version (v3), and compare performance with the Predict model that includes HER2 status (v2). METHODS: The validation study was based on 1,726 patients with EBC treated in Nottingham between 1989 and 1998. KI67 positivity for PREDICT is defined as >10% of tumour cells staining positive. ROC curves were constructed for Predict models with (v3) and without (v2) KI67 input. Comparison was made using the method of DeLong. RESULTS: In 1274 ER+ patients the predicted number of events at 10 years increased from 196 for v2 to 204 for v3 compared to 221 observed. The area under the ROC curve (AUC) improved from 0.7611 to 0.7676 (p=0.005) in ER+ patients and from 0.7546 to 0.7595 (p=0.0008) in all 1726 patients (ER+ and ER-). CONCLUSION: Addition of KI67 to PREDICT has led to a statistically significant improvement in the model performance for ER+ patients and will aid clinical decision making in these patients. Further studies should determine whether other markers including gene expression profiling provide additional prognostic information to that provided by PREDICT.SEARCH was funded through a programme grant from Cancer Research UK (C490/A10124) and this work is supported by the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge.This is the final version of the article. It first appeared from BioMed Central via http://dx.doi.org/10.1186/1471-2407-14-90

Solution structure of polyglutamine tracts in GST-polyglutamine fusion proteins

AbstractAggregation of expanded polyglutamine (polyQ) seems to be the cause of various genetic neurodegenerative diseases. Relatively little is known as yet about the polyQ structure and the mechanism that induces aggregation. We have characterised the solution structure of polyQ in a proteic context using a model system based on glutathione S-transferase fusion proteins. A wide range of biophysical techniques was applied. For the first time, nuclear magnetic resonance was used to observe directly and selectively the conformation of polyQ in the pathological range. We demonstrate that, in solution, polyQs are in a random coil conformation. However, under destabilising conditions, their aggregation behaviour is determined by the polyQ length

Little House in the Mountains? A small Mesolithic structure from the Cairngorm Mountains, Scotland

This paper describes a small Mesolithic structure from the Cairngorm Mountains, Scotland. Excavations at Caochanan Ruadha identified a small oval structure (c. 3 m×2.2 m) with a central fire setting, in an upland valley (c.540 m asl). The site was occupied at c. 8200 cal BP and demonstrates hunter-gatherer use of the uplands during a period of significant climatic deterioration. The interpretation of the structure is primarily based on the distribution of the lithic assemblage, as the heavily podsolised soils have left no trace of light structural features. The lithic assemblage is specialised, dominated by microlith fragments, and functional analysis has identified different uses of different areas inside the structure. The identification of small, specialised Mesolithic sites is unusual and this paper will discuss the evidence for the presence of the structure and its use, compare it to other Mesolithic structures in Britain and highlight some methodological implications

An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation

BACKGROUND PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in 'step' changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. METHODS Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. RESULTS In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age of 40. CONCLUSIONS The PREDICT v2 is an improved prognostication and treatment benefit model compared with v1. The online version should continue to aid clinical decision making in women with early breast cancer

Hyaluronan in mesenchymal stromal cell lineage differentiation from human pluripotent stem cells:application in serum free culture

BACKGROUND: Hyaluronan (HA) is an extracellular glycosaminoglycan polysaccharide with widespread roles throughout development and in healthy and neoplastic tissues. In pluripotent stem cell culture it can support both stem cell renewal and differentiation. However, responses to HA in culture are influenced by interaction with a range of cognate factors and receptors including components of blood serum supplements, which alter results. These may contribute to variation in cell batch production yield and phenotype as well as heighten the risks of adventitious pathogen transmission in the course of cell processing for therapeutic applications. MAIN: Here we characterise differentiation of a human embryo/pluripotent stem cell derived Mesenchymal Stromal Cell (hESC/PSC-MSC)-like cell population by culture on a planar surface coated with HA in serum-free media qualified for cell production for therapy. Resulting cells met minimum criteria of the International Society for Cellular Therapy for identification as MSC by expression of. CD90, CD73, CD105, and lack of expression for CD34, CD45, CD14 and HLA-II. They were positive for other MSC associated markers (i.e.CD166, CD56, CD44, HLA 1-A) whilst negative for others (e.g. CD271, CD71, CD146). In vitro co-culture assessment of MSC associated functionality confirmed support of growth of hematopoietic progenitors and inhibition of mitogen activated proliferation of lymphocytes from umbilical cord and adult peripheral blood mononuclear cells, respectively. Co-culture with immortalized THP-1 monocyte derived macrophages (Mɸ) concurrently stimulated with lipopolysaccharide as a pro-inflammatory stimulus, resulted in a dose dependent increase in pro-inflammatory IL6 but negligible effect on TNFα. To further investigate these functionalities, a bulk cell RNA sequence comparison with adult human bone marrow derived MSC and hESC substantiated a distinctive genetic signature more proximate to the former.CONCLUSION: Cultivation of human pluripotent stem cells on a planar substrate of HA in serum-free culture media systems is sufficient to yield a distinctive developmental mesenchymal stromal cell lineage with potential to modify the function of haematopoietic lineages in therapeutic applications.</p

Spatiotemporal integration of molecular and anatomical data in virtual reality using semantic mapping

We have developed a computational framework for spatiotemporal integration of molecular and anatomical datasets in a virtual reality environment. Using two case studies involving gene expression data and pharmacokinetic data, respectively, we demonstrate how existing knowledge bases for molecular data can be semantically mapped onto a standardized anatomical context of human body. Our data mapping methodology uses ontological representations of heterogeneous biomedical datasets and an ontology reasoner to create complex semantic descriptions of biomedical processes. This framework provides a means to systematically combine an increasing amount of biomedical imaging and numerical data into spatiotemporally coherent graphical representations. Our work enables medical researchers with different expertise to simulate complex phenomena visually and to develop insights through the use of shared data, thus paving the way for pathological inference, developmental pattern discovery and biomedical hypothesis testing