Diffuse large B-cell lymphoma - tumour characteristics on RNA and protein level associated with prognosis

Diffuse large B-cell lymphoma (DLBCL) is the most frequent lymphoma subtype. In Sweden 450 new cases are diagnosed annually. With modern anthracycline-containing chemotherapy DLBCL is potentially curable, with an estimated overall cure rate of approximately 50% for patients with advanced stage disease. Through molecular profiling of DLBCL the ?cell-of-origin concept? has been established: patients with tumours expressing genes characteristic of germinal center B-cells, ?GC-profile? has a significantly better survival than patients with tumors expressing genes normally induced during in vitro activation of peripheral blood B-cells, ?ABC-profile?. The first study (n=125) aimed to identify a protein pattern that could be used for discriminating germinal center derived (GC) and activated B-cell like (ABC)/non-GC DLBCL, using immunohistochemistry (IHC). BCL6, CD10 and CD40 were chosen as markers of a GC-phenotype, CD23 as a marker of pre/early GC-origin and CD138 as a marker of post-GC origin (i.e non-GC). No prognostically different subgroups, corresponding to GC or ABC (non-GC) could be identified. A new finding was the positive prognostic impact of CD23 and CD40 expression. In the second study (n=125) the prognostic effect of CD40, but not CD23, was confirmed. The effect of CD40 effect could not be explained by association with the GC-phenotype or by an enhanced autologous tumour response, as detected by tumour infiltrating helper and cytotoxic T-lymphocytes. The prognostic effect of a GC versus non-GC phenotype according to Hans et al (Blood 2004) was confirmed. The third study (n=122) identified the tissue microarray technique to be unreliable for immunohistochemical detection in GC vs. non-GC phenotypes, mostly due to difficulties interpreting BCL6 status. In the fourth study tumours from patients with cured (n=24) versus primary chemotherapy-refractory DLBCL (n=13), were investigated with respect to gene expression profiles, using spotted 55K oligonucleotide arrays produced in Lund. The genes that most differed between chemotherapy sensitive and refractory tumours mainly coded for proteins expressed by cells in the tumour microenvironment, and not by the tumour cells themselves. Confirmative IHC showed that the frequency of tumour infiltrating lymphocytes, macrophages and reactive cells expressing proteolytic and pro-inflammatory proteins were higher in the chemo-sensitive cohort, indicating that the microenvironment has an impact on the response to chemotherapy in DLBCL

Precision-based analysis of traffic prediction with seasonal ARIMA modeling.

Intelligenta Transportsystem (ITS) utgör idag en central del i arbetet att försöka höja kvaliteten i transportnätverken, genom att exempelvis ge stöd i arbetet att leda trafik i realtid och att ge trafikanter större möjlighet att ta informerade beslut gällandes sin körning. Kortsiktig prediktion av trafikdata, däribland trafikvolym, spelar en central roll för de tjänster ITS-systemen levererar. Den starka teknologiska utvecklingen de senaste decennierna har bidragit till en ökad möjlighet till att använda datadriven modellering för att utföra kortsiktiga prediktioner av trafikdata. Säsongsbaserad ARIMA (SARIMA) är en av de vanligaste datadrivna modellerna för modellering och predicering av trafikdata, vilken använder mönster i historisk data för att predicera framtida värden. Vid modellering med SARIMA behöver en mängd beslut tas gällandes de data som används till modelleringen. Exempel på sådana beslut är hur stor mängd träningsdata som ska användas, vilka dagar som ska ingå i träningsmängden och vilket aggregationsintervall som ska användas. Därtill utförs nästintill enbart enstegsprediktioner i tidigare studier av SARIMA-modellering av trafikdata, trots att modellen stödjer predicering av flera steg in i framtiden. Besluten gällandes de parametrar som nämnts saknar ofta teoretisk motivering i tidigare studier, samtidigt som det är högst troligt att dessa beslut påverkar träffsäkerheten i prediktionerna. Därför syftar den här studien till att utföra en känslighetsanalys av dessa parametrar, för att undersöka hur olika värden påverkar precisionen vid prediktion av trafikvolym. I studien utvecklades en modell, med vilken data kunde importeras, preprocesseras och sedan modelleras med hjälp av SARIMA. Studien använde trafikvolymdata som insamlats under januari och februari 2014, med hjälp av kameror placerade på riksväg 40 i utkanten av Göteborg. Efter differentiering av data används såväl autokorrelations- och partiell autokorrelationsgrafer som informationskriterier för att definiera lämpliga SARIMA-modeller, med vilka prediktioner kunde göras. Med definierade modeller genomfördes ett experiment, där åtta unika scenarion testades för att undersöka hur prediktionsprecisionen av trafikvolym påverkades av olika mängder träningsdata, vilka dagar som ingick i träningsdata, längden på aggregationsintervallen och hur många tidssteg in i framtiden som predicerades. För utvärdering av träffsäkerheten i prediktionerna användes MAPE, RMSE och MAE. Resultaten som experimentet visar är att definierade SARIMA-modeller klarar att predicera aktuell data med god precision oavsett vilka värden som sattes för de variabler som studerades. Resultaten visade dock indikationer på att en träningsvolym omfattande fem dagar kan generera en modell som ger mer träffsäkra prediktioner än när volymer om 15 eller 30 dagar används, något som kan ha stor praktisk betydelse vid realtidsanalys. Därtill indikerar resultaten att samtliga veckodagar bör ingå i träningsdatasetet när dygnsvis säsongslängd används, att SARIMA-modelleringen hanterar aggregationsintervall om 60 minuter bättre än 30 eller 15 minuter samt att enstegsprediktioner är mer träffsäkra än när horisonter om en eller två dagar används. Studien har enbart fokuserat på inverkan av de fyra parametrarna var för sig och inte om en kombinerad effekt finns att hitta. Det är något som föreslås för framtida studier, liksom att vidare utreda huruvida en mindre träningsvolym kan fortsätta att generera mer träffsäkra prediktioner även för andra perioder under året.Intelligent Transport Systems (ITS) today are a key part of the effort to try to improve the quality of transport networks, for example by supporting the real-time traffic management and giving road users greater opportunity to take informed decisions regarding their driving. Short-term prediction of traffic data, including traffic volume, plays a central role in the services delivered by ITS systems. The strong technological development has contributed to an increased opportunity to use data-driven modeling to perform short-term predictions of traffic data. Seasonal ARIMA (SARIMA) is one of the most common models for modeling and predicting traffic data, which uses patterns in historical data to predict future values. When modeling with SARIMA, a variety of decisions are required regarding he data used. Examples of such decisions are the amount of training data to be used, the days to be included in training data and the aggregation interval to be used. In addition, one-step predictions are performed most often in previous studies of SARIMA modeling of traffic data, although the model supports multi-step prediction into the future. Often, in previous studies, decisions are made concerning mentioned variables without theoretical motivation, while it is highly probable that these decisions affect the accuracy of the predictions. Therefore, this study aims at performing a sensitivity analysis of these parameters to investigate how different values affect the accuracy of traffic volume prediction. The study developed a model with which data could be imported, preprocessed and then modeled using a SARIMA model. Traffic volume data was used, which was collected during January and February 2014, using cameras located on highway 40 on the outskirts of Gothenburg. After differentiation of data, autocorrelation and partial autocorrelation graphs as well as information criteria are used to define appropriate SARIMA models, with which predictions could be made. With defined models, an experiment was conducted in which eight unique scenarios were tested to investigate how the prediction accuracy of traffic volume was influenced by different amount of exercise data, what days was included in training data, length of aggregation intervals, and how many steps into the future were predicted. To evaluate the accuracy of the predictions, MAPE, RMSE and MAE were used. The results of the experiment show that developed SARIMA models are able to predict current data with good precision no matter what values were set for the variables studied. However, the results showed indications that a training volume of five days can generate a model that provides more accurate predictions than when using 15 or 30-day volumes, which can be of great practical importance in real-time analysis. In addition, the results indicate that all weekdays should be included in the training data set when daily seasonality is used, SARIMA modeling handles aggregation intervals of 60 minutes better than 30 or 15 minutes, and that one-step predictions are more accurate than when one or two days horizons are used. The study has focused only on the impact of the four parameters separately and not if a combined effect could be found. Further research is proposed for investigating if combined effects could be found, as well as further investigating whether a lesser training volume can continue to generate more accurate predictions even for other periods of the year

Varför minskar söktrycket & vad kan göras åt detta? : En kvalitativ fallstudie av universitetsprogrammet Music & Event Management

This essay is a qualitative case study about the Swedish university programme Music & Event Management (MEM). The programme is a part of the Linneaus University (LNU) and the education takes place at two different locations, the first half of the programme in the small town of Hultsfred and the remaining time in the city of Kalmar. The programme has existed since the year 2001. Since 2013 the number of applying students has decreased and this resulted in the decision that the programme will not accept any applications the year 2019. This study is of an abductive nature. Nine people from MEM and other similar programmes have been interviewed. Results have been made by analyzing and coding the empirical data collected in the interviews along with presented theories.   The purpose of this case study is to identify and analyze the possible causes behind the decreasing number of applying students and to find out which factors the programme and university can work with in order to turn this negative trend around. By doing so we hope to identify factors that other higher creative educational programmes, nished for a specific industry, can have in mind to avoid a decreasing number of applying students or when having this problem.   Our findings suggests that there are a number of different factors that can have an impact on the number of applications for this kind of education. Some of these factors are possible to avoid to some extent and some are not. We found that the world in which the education operates within can affect the attractivity of the programme, such as the economical situation and the ever changing industry that the programme is nished to. The educational content of the programme can also have great effect on whether or not students find the programme suitable for eventually working in the music industry, there is a choice of being a practical or academically focused programme. We also identified that the marketing is a crucial factor regarding the attractivity, how the programme presents itself for the students as a brand and how available the education is. We hope that our findings in this case study can help MEM and other programmes of this kind to prevent the problem of decreasing applications

CD40 is a potential marker of favorable prognosis in patients with diffuse large B-cell lymphoma treated with immunochemotherapy.

We have previously shown that expression of CD40 has a favorable prognostic impact in diffuse large B-cell lymphoma (DLBCL) after anthracycline-based chemotherapy. Here we examined the prognostic value of immunohistochemically defined CD40 expression in 95 patients with DLBCL treated with both anthracycline-based chemotherapy and rituximab. Using a 10% cut-off level, 77% of the patients had CD40-positive tumors and showed a superior overall survival (p = 0.02 log-rank, hazard ratio 0.35, 95% CI 0.14-0.88, p = 0.03 Cox regression). When adjusted for International Prognostic Index in multivariate analysis, CD40 was not an independent prognostic factor (hazard ratio 0.39, 95% CI 0.15-1.04, p = 0.06 Cox regression). However, even after the introduction of immunochemotherapy, CD40 has a potential prognostic impact in DLBCL. Additional and larger studies are necessary, regarding the immunohistochemical robustness of CD40 and the biological mechanisms that contribute to the superior prognosis in CD40-expressing DLBCL

A Subset of CD5- Diffuse Large B-Cell Lymphomas Expresses Nuclear Cyclin D1 With Aberrations at the CCND1 Locus.

In 231 diffuse large B-cell lymphomas, the expression of cyclin D1 and CD5 was evaluated. All cases were CD5-. Ten (4.3%) were positive for cyclin D1 and were subjected to fluorescence in situ hybridization at the CCND1 locus. One case showed the t(11;14). In another case, the telomeric probe signal for cyclin D1 was lost in most tumor cells, and in a small proportion of the cells, there were fluorescence signals indicative of the t(11;14). Two other cases displayed additional cyclin D1 signals in the absence of the t(11;14). All cases but 1 were positive for bcl-6 or MUM1, disfavoring the possibility of misdiagnosed blastoid variants of CD5- mantle cell lymphomas. Thus, contrary to the current view, there seems to exist a certain number of cyclin D1+ and CD5- diffuse large B-cell lymphomas, some of which have structural aberrations at the CCND1 locus, including the t(11;14)

Inhibition of geranylgeranylation mediates sensitivity to CHOP-induced cell death of DLBCL cell lines.

Prenylation is a post-translational hydrophobic modification of proteins, important for their membrane localization and biological function. The use of inhibitors of prenylation has proven to be a useful tool in the activation of apoptotic pathways in tumor cell lines. Rab geranylgeranyl transferase (Rab GGT) is responsible for the prenylation of the Rab family. Overexpression of Rab GGTbeta has been identified in CHOP refractory diffuse large B cell lymphoma (DLBCL). Using a cell line- based model for CHOP resistant DLBCL, we show that treatment with simvastatin, which inhibits protein farnesylation and geranylgeranylation, sensitises DLBCL cells to cytotoxic treatment. Treatment with the farnesyl transferase inhibitor, FTI-277, or the geranylgeranyl transferase I inhibitor, GGTI-298, indicates that the reduction in cell viability was restricted to inhibition of geranylgeranylation. In addition, treatment with BMS1, a combined inhibitor of farnesyl transferase and Rab GGT, resulted in a high cytostatic effect in WSU-NHL cells, demonstrated by reduced cell viability and decreased proliferation. Co-treatment of BMS1 or GGTI-298 with CHOP showed synergistic effects with regard to markers of apoptosis. We propose that inhibition of protein geranylgeranylation together with conventional cytostatic therapy is a potential novel strategy for treating patients with CHOP refractory DLBCL

Gene expression profiling indicates that immunohistochemical expression of CD40 is a marker of an inflammatory reaction in the tumor stroma of diffuse large B-cell lymphoma

Immunohistochemical expression of CD40 is seen in 60-70% of diffuse large B-cell lymphoma (DLBCL) and is associated with a superior prognosis. By using gene expression profiling we aimed to further explore the underlying mechanisms for this effect. Ninety-eight immunohistochemically defined CD40 positive or negative DLBCL tumors, 63 and 35 respectively, were examined using spotted 55K oligonucleotide arrays. CD40 expressing tumors were characterized by up-regulated expression of genes encoding proteins involved in cell-matrix interactions: collagens, integrin a V, proteoglycans and proteolytic enzymes, and antigen presentation. Immunohistochemistry confirmed that CD40 positive tumors co-express the proinflammatory proteoglycan biglycan (p = 0.005), which in turn correlates with the amount of infiltrating macrophages and CD4 and CD8 positive T-cells. We postulate that immunohistochemical expression of CD40 mainly reflects the inflammatory status in tumors. A high intratumoral inflammatory reaction may correlate with an increased autologous tumor response, and thereby a better prognosis