Assessing Associations between the AURKA-HMMR-TPX2-TUBG1 Functional Module and Breast Cancer Risk in BRCA1/2 Mutation Carriers

While interplay between BRCA1 and AURKA-RHAMM-TPX2-TUBG1 regulates mammary epithelial polarization, common genetic variation in HMMR (gene product RHAMM) may be associated with risk of breast cancer in BRCA1 mutation carriers. Following on these observations, we further assessed the link between the AURKA-HMMR-TPX2-TUBG1 functional module and risk of breast cancer in BRCA1 or BRCA2 mutation carriers. Forty-one single nucleotide polymorphisms (SNPs) were genotyped in 15,252 BRCA1 and 8,211 BRCA2 mutation carriers and subsequently analyzed using a retrospective likelihood approach. The association of HMMR rs299290 with breast cancer risk in BRCA1 mutation carriers was confirmed: per-allele hazard ratio (HR) = 1.10, 95% confidence interval (CI) 1.04-1.15, p = 1.9 x 10(-4) (false discovery rate (FDR)-adjusted p = 0.043). Variation in CSTF1, located next to AURKA, was also found to be associated with breast cancer risk in BRCA2 mutation carriers: rs2426618 per-allele HR = 1.10, 95% CI 1.03-1.16, p = 0.005 (FDR-adjusted p = 0.045). Assessment of pairwise interactions provided suggestions (FDR-adjusted pinteraction values > 0.05) for deviations from the multiplicative model for rs299290 and CSTF1 rs6064391, and rs299290 and TUBG1 rs11649877 in both BRCA1 and BRCA2 mutation carriers. Following these suggestions, the expression of HMMR and AURKA or TUBG1 in sporadic breast tumors was found to potentially interact, influencing patients' survival. Together, the results of this study support the hypothesis of a causative link between altered function of AURKA-HMMR-TPX2-TUBG1 and breast carcinogenesis in BRCA1/2 mutation carriers

Assessing associations between the AURKAHMMR-TPX2-TUBG1 functional module and breast cancer risk in BRCA1/2 mutation carriers

While interplay between BRCA1 and AURKA-RHAMM-TPX2-TUBG1 regulates mammary epithelial polarization, common genetic variation in HMMR (gene product RHAMM) may be associated with risk of breast cancer in BRCA1 mutation carriers. Following on these observations, we further assessed the link between the AURKA-HMMR-TPX2-TUBG1 functional module and risk of breast cancer in BRCA1 or BRCA2 mutation carriers. Forty-one single nucleotide polymorphisms (SNPs) were genotyped in 15,252 BRCA1 and 8,211 BRCA2 mutation carriers and subsequently analyzed using a retrospective likelihood appr

The tumor-associated antigen RHAMM (HMMR/CD168) is expressed by monocyte-derived dendritic cells and presented to T cells

We formerly demonstrated that vaccination with Wilms' tumor 1 (WT1)-loaded autologous monocyte-derived dendritic cells (mo-DCs) can be a well-tolerated effective treatment in acute myeloid leukemia (AML) patients. Here, we investigated whether we could introduce the receptor for hyaluronic acid-mediated motility (RHAMM/HMMR/CD168), another clinically relevant tumor-associated antigen, into these mo-DCs through mRNA electroporation and elicit RHAMM-specific immune responses. While RHAMM mRNA electroporation significantly increased RHAMM protein expression by mo-DCs, our data indicate that classical mo-DCs already express and present RHAMM at sufficient levels to activate RHAMM-specific T cells, regardless of electroporation. Moreover, we found that RHAMM-specific T cells are present at vaccination sites in AML patients. Our findings implicate that we and others who are using classical mo-DCs for cancer immunotherapy are already vaccinating against RHAMM

Determinants of University Spin-Offs’ Growth: Do Socioeconomic Networks and Support Matter?

University spin-offs (USOs), as a type of entrepreneurial firms, face the challenge of obtaining sufficient resources to realize perceived business opportunities. USOs are vulnerable to many obstacles in this endeavor, particularly obstacles related to a lack of entrepreneurial knowledge (skills). Support such as office facilities, loan, and business coaching provided by incubator organizations, may help USOs to overcome obstacles. On the other hand, USOs may also overcome the lack of resources by participating in networks of supportive relationships. Social networking by USOs, including its spatial dimension, is not well understood. For instance, it is still not known how universities as a main source of knowledge contribute to the knowledge needs of nearby USOs; similarly, the spatial layout of knowledge relations of USOs has remained virtually unknown. This paper attempts to fill this knowledge gap. Our conceptual model of early growth of USOs, in terms of knowledge needs and fulfilment, is based on resource-based theory and social network theory. In this paper, we assume that USOs’ embeddedness in a network of ties is an important source of variation in the acquisition of knowledge resources. We argue that, aside from support from incubation organizations, USOs that maintain networks rich in bridging or boundary-spanning ties with knowledge institutions/actors are better-off compared with USOs that don’t employ such ties. We focus on the role of local institutions, particularly the university, as a source of knowledge. Our assumptions are tested on the basis of a sample of academic spin-offs of TU Delft, the Netherlands. The results from regression modeling are expected to support the embeddedness hypothesis and to produce new insights about the link between USOs’ social networks, the acquisition of knowledge and survival and growth.

An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression

Using supervised machine learning approaches to recognize human activities from on-body wearable accelerometers generally requires a large amount of labelled data. When ground truth information is not available, too expensive, time consuming or difficult to collect, one has to rely on unsupervised approaches. This paper presents a new unsupervised approach for human activity recognition from raw acceleration data measured using inertial wearable sensors. The proposed method is based upon joint segmentation of multidimensional time series using a Hidden Markov Model (HMM) in a multiple regression context. The model is learned in an unsupervised framework using the Expectation-Maximization (EM) algorithm where no activity labels are needed. The proposed method takes into account the sequential appearance of the data. It is therefore adapted for the temporal acceleration data to accurately detect the activities. It allows both segmentation and classification of the human activities. Experimental results are provided to demonstrate the efficiency of the proposed approach with respect to standard supervised and unsupervised classification approache

decodeRNA-predicting non-coding RNA functions using guilt-by-association

Although the long non-coding RNA (lncRNA) landscape is expanding rapidly, only a small number of lncRNAs have been functionally annotated. Here, we present decodeRNA (http://www.decoderna.org), a database providing functional contexts for both human lncRNAs and microRNAs in 29 cancer and 12 normal tissue types. With state-of-the-art data mining and visualization options, easy access to results and a straightforward user interface, decodeRNA aims to be a powerful tool for researchers in the ncRNA field

Learning from life-logging data by hybrid HMM: a case study on active states prediction

In this paper, we have proposed employing a hybrid classifier-hidden Markov model (HMM) as a supervised learning approach to recognize daily active states from sequential life-logging data collected from wearable sensors. We generate synthetic data from real dataset to cope with noise and incompleteness for training purpose and, in conjunction with HMM, propose using a multiobjective genetic programming (MOGP) classifier in comparison of the support vector machine (SVM) with variant kernels. We demonstrate that the system with either algorithm works effectively to recognize personal active states regarding medical reference. We also illustrate that MOGP yields generally better results than SVM without requiring an ad hoc kernel

Telomere erosion in NF1 tumorigenesis

Neurofibromatosis type 1 (NF1; MIM# 162200) is a familial cancer syndrome that affects 1 in 3,500 individuals worldwide and is inherited in an autosomal dominant fashion. Malignant Peripheral Nerve Sheath Tumors (MPNSTs) represent a significant cause of morbidity and mortality in NF1 and currently there is no treatment or definite prognostic biomarkers for these tumors. Telomere shortening has been documented in numerous tumor types. Short dysfunctional telomeres are capable of fusion and it is considered that the ensuing genomic instability may facilitate clonal evolution and the progression to malignancy. To evaluate the potential role of telomere dysfunction in NF1-associated tumors, we undertook a comparative analysis of telomere length in samples derived from 10 cutaneous and 10 diffused plexiform neurofibromas, and 19 MPNSTs. Telomere length was determined using high-resolution Single Telomere Length Analysis (STELA). The mean Xp/Yp telomere length detected in MPNSTs, at 3.282 kb, was significantly shorter than that observed in both plexiform neurofibromas (5.793 kb; [p = 0.0006]) and cutaneous neurofibromas (6.141 kb; [p = 0.0007]). The telomere length distributions of MPNSTs were within the length-ranges in which telomere fusion is detected and that confer a poor prognosis in other tumor types. These data indicate that telomere length may play a role in driving genomic instability and clonal progression in NF1-associated MPNSTs