Biomedical subjects

Matti Nykter

Publications and source records attributed to Matti Nykter.

3 recordsLinked to original sources

PubMed2006

Differential gene expression in non-malignant tumour microenvironment is associated with outcome in follicular lymphoma patients treated with rituximab and CHOP.

Rituximab in combination with chemotherapy (immunochemotherapy) is one of the most effective treatments available for follicular lymphoma (FL). This study aimed to determine whether differences in gene expression in FL tissue correlate with outcome in response to rituximab and CHOP (cyclophosphamide, doxorubicin, vincristine, prednisone) chemotherapy (R-CHOP). We divided 24 patients into long- [time to treatment failure (TTF) >35 months] and short-term (TTF <23 months) responders, and analysed the gene expression profiles of lymphoma tissue using oligonucleotide microarrays. We used a supervised learning technique to identify genes correlating with outcome, and confirmed the expression of selected genes with quantitative polymerase chain reaction (qPCR) and immunohistochemistry. Among the transcripts with a high correlation between microarray and qPCR analyses, we identified EPHA1, a tyrosine kinase involved in transepithelial migration, SMAD1, a transcription factor and a mediator of bone morphogenetic protein and transforming growth factor-beta signalling, and MARCO, a scavenger receptor on macrophages. According to Kaplan-Meier estimates, high EPHA1, and low SMAD1 and MARCO expression were associated with better progression-free survival (PFS). Immunohistochemistry showed that EphA1 was primarily localised in granulocytes. In addition, both EphA1 and Smad1 were expressed in vascular endothelia. However, no difference in vasculature was detected between long- and short-term responders. In a validation set of 40 patients, a trend towards a better PFS was observed among patients with high EphA1 expression. We conclude that gene expression in non-malignant cells contributes to clinical outcome in R-CHOP-treated FL patients.

Adult↗

PubMed2006

Simulation of microarray data with realistic characteristics.

BACKGROUND: Microarray technologies have become common tools in biological research. As a result, a need for effective computational methods for data analysis has emerged. Numerous different algorithms have been proposed for analyzing the data. However, an objective evaluation of the proposed algorithms is not possible due to the lack of biological ground truth information. To overcome this fundamental problem, the use of simulated microarray data for algorithm validation has been proposed. RESULTS: We present a microarray simulation model which can be used to validate different kinds of data analysis algorithms. The proposed model is unique in the sense that it includes all the steps that affect the quality of real microarray data. These steps include the simulation of biological ground truth data, applying biological and measurement technology specific error models, and finally simulating the microarray slide manufacturing and hybridization. After all these steps are taken into account, the simulated data has realistic biological and statistical characteristics. The applicability of the proposed model is demonstrated by several examples. CONCLUSION: The proposed microarray simulation model is modular and can be used in different kinds of applications. It includes several error models that have been proposed earlier and it can be used with different types of input data. The model can be used to simulate both spotted two-channel and oligonucleotide based single-channel microarrays. All this makes the model a valuable tool for example in validation of data analysis algorithms.

Algorithms↗

PubMed2006

Unsupervised analysis uncovers changes in histopathologic diagnosis in supervised genomic studies.

Human gastrointestinal stromal tumors (GIST) have recently emerged as a distinct mesenchymal tumor type that has a unique phenotype characterized by a gain of function mutations in c-kit. In contrast, leiomyosarcomas (LMS) of the gastrointestinal tract or retroperitoneum, which were previously classified together with GISTs as gastrointestinal sarcomas, have much less frequent mutations of c-kit. We performed microarray analyses to gain a comprehensive understanding of the difference between the two types of soft-tissue sarcomas at the level of gene expression. Microarray experiments were performed on 30 GISTs and 30 LMSs that were collected at the time of surgical resection. These tumors were categorized based on the histopathologic diagnosis recorded in our institutional database. Prior to our search for genes that are differentially expressed between these two types of cancers, we first carried out an unsupervised analysis using multidimensional scaling (MDS) to determine whether the two groups have marked overall differences in gene expression. Initially, the MDS did not reveal a good separation between the two groups. We then re-reviewed the histopathology of these tumors and realized that some of the cases included in our study were acquired 10 years ago when the diagnosis of gastrointestinal sarcoma was made according to histopathologic criteria alone without immunohistochemistry for c-kit. An experienced pathologist reviewed all of the specimens and this revealed that a number of the GIST cases were classified as LMS in the clinical database. Correction of the histopathologic diagnosis and relabeling of the samples resulted in a much more pronounced separation of GIST and LMS in the MDS analysis. This study underscores the need to re-review histopathology as reclassification occurs. While updating the clinical database may be desired, this is usually impractical. For molecular studies that use archival samples, it is critical to have the archival samples re-reviewed by a pathologist. Further, unsupervised analysis often proves to be a critical quality control step in identifying structural problems that may exist. Finally, MDS analysis further supports that GIST is a distinct type of sarcoma.

Biomarkers, Tumor↗