The prognostic and predictive value of selected biomarkers in colorectal cancer

Tissue-based biomarkers are studied to receive information about the pathologic processes and cancer outcome, and to enable development of patient-tailored treatments. The aim of this study was to investigate the potential prognostic and/or predictive value of selected biomarkers in colorectal cancer (CRC). Group IIA secretory phospholipase A2 (IIA PLA2) expression was assessed in 114 samples presenting different phases of human colorectal carcinogenesis. Securin, Ki-67, CD44 variant 6 (CD44v6), aldehyde dehydrogenase 1 (ALDH1) and β-catenin were studied in a material including 227 rectal carcinoma patients treated with short-course preoperative radiotherapy (RT), long-course preoperative (chemo)RT (CRT) or surgery only. Epidermal growth factor receptor (EGFR) gene copy number (GCN), its heterogeneity in CRC tissue, and association with response to EGFR-targeted antibodies cetuximab and panitumumab were analyzed in a cohort of 76 metastatic CRC. IIA PLA2 expression was decreased in invasive carcinomas compared to adenomas, but did not relate to patient survival. High securin expression after long-course (C)RT and high ALDH1 expression in node-negative rectal cancer were independent adverse prognostic factors, ALDH1 specifically in patients treated with adjuvant chemotherapy. The lack of membranous CD44v6 in the rectal cancer invasive front associated with infiltrative growth pattern and the risk of disease recurrence. Heterogeneous EGFR GCN increase predicted benefit from EGFR-targeted antibodies, also in the chemorefractory patient population. In summary, high securin and ALDH1 protein expression independently relate to poor outcome in subgroups of rectal cancer patients, potentially because of resistance to conventional chemotherapeutics. Heterogeneous increase in EGFR GCN was validated to be a promising predictive factor in the treatment of metastatic CRC.

On Bayesian predictive modeling : with applications on the analysis of chromatography data in forensic investigations

Med prediktion avses att man skattar det framtida värdet på en observerbar storhet. Kännetecknande för det bayesianska paradigmet är att osäkerhet gällande okända storheter uttrycks i form av sannolikheter. En bayesiansk prediktiv modell är således en sannolikhetsfördelning över de möjliga värden som en observerbar, men ännu inte observerad storhet kan anta. I de artiklar som ingår i avhandlingen utvecklas metoder, vilka bl.a. tillämpas i analys av kromatografiska data i brottsutredningar. Med undantag för den första artikeln, bygger samtliga metoder på bayesiansk prediktiv modellering. I artiklarna betraktas i huvudsak tre olika typer av problem relaterade till kromatografiska data: kvantifiering, parvis matchning och klustring. I den första artikeln utvecklas en icke-parametrisk modell för mätfel av kromatografiska analyser av alkoholhalt i blodet. I den andra artikeln utvecklas en prediktiv inferensmetod för jämförelse av två stickprov. Metoden tillämpas i den tredje artik eln för jämförelse av oljeprover i syfte att kunna identifiera den förorenande källan i samband med oljeutsläpp. I den fjärde artikeln härleds en prediktiv modell för klustring av data av blandad diskret och kontinuerlig typ, vilken bl.a. tillämpas i klassificering av amfetaminprover med avseende på produktionsomgångar.

Stability and predictive ability of beta

The purpose of this paper is to examine the stability and predictive abilities of the beta coefficients of individual equities in the Finnish stock market. As beta is widely used in several areas of finance, including risk management, asset pricing and performance evaluation among others, it is important to understand its characteristics and find out whether its estimates can be trusted and utilized.