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Featured researches published by Laila Stordrange.


Chemometrics and Intelligent Laboratory Systems | 2000

The morphological score and its application to chemical rank determination

Hailin Shen; Laila Stordrange; Rolf Manne; Olav M. Kvalheim; Yi-Zeng Liang

Abstract Most spectra with structural information are smooth. Making use of this feature, a new procedure is proposed for automatic chemical rank determination. In order to avoid accumulation of noise, key spectra instead of the full matrix are analyzed in the procedure. A simplified morphological factor, morphological score (MS), is proposed. The noise level of morphological analysis is established with the help of F -test. The proposed procedure was successfully applied in the study of self-association behavior of alcohol. Results show that two classes of aggregates contribute to near infrared (NIR) spectra besides the monomer. Data sets produced by other techniques were also tested.


Clinical Cancer Research | 2007

Increased Expression of SIM2-s Protein Is a Novel Marker of Aggressive Prostate Cancer

Ole J. Halvorsen; Kari Rostad; Anne Margrete Øyan; Hanne E. Puntervoll; Trond Hellem Bø; Laila Stordrange; Sue Olsen; Svein A. Haukaas; Leroy Hood; Inge Jonassen; Karl-Henning Kalland; Lars A. Akslen

Purpose: The human SIM2 gene is located within the Downs syndrome critical region of chromosome 21 and encodes transcription factors involved in brain development and neuronal differentiation. SIM2 has been assigned a possible role in the pathogenesis of solid tumors, and the SIM2-short isoform (SIM2-s) was recently proposed as a molecular target for cancer therapy. We previously reported SIM2 among the highly up-regulated genes in 29 prostate cancers, and the purpose of our present study was to examine the expression status of SIM2 at the transcriptional and protein level as related to outcome in prostate cancer. Experimental Design: By quantitative PCR, mRNA in situ hybridization, and immunohistochemistry, we evaluated the expression and significance of SIM2 isoforms in 39 patients with clinically localized prostate cancer and validated the expression of SIM2-s protein in an independent cohort of 103 radical prostatectomies from patients with long and complete follow-up. Results: The SIM2 isoforms (SIM2-s and SIM2-l) were significantly coexpressed and increased in prostate cancer. Tumor cell expression of SIM2-s protein was associated with adverse clinicopathologic factors like increased preoperative serum prostate-specific antigen, high histologic grade, invasive tumor growth with extra-prostatic extension, and increased tumor cell proliferation by Ki-67 expression. SIM2-s protein expression was significantly associated with reduced cancer-specific survival in multivariate analyses. Conclusions: These novel findings indicate for the first time that SIM2 expression might be important for clinical progress of human cancer and support the recent proposal of SIM2-s as a candidate for targeted therapy in prostate cancer.


BMC Cancer | 2009

Genes of cell-cell interactions, chemotherapy detoxification and apoptosis are induced during chemotherapy of acute myeloid leukemia

Anne Margrete Øyan; Nina Ånensen; Trond Hellem Bø; Laila Stordrange; Inge Jonassen; Øystein Bruserud; Karl-Henning Kalland; Bjørn Tore Gjertsen

BackgroundThe molecular changes in vivo in acute myeloid leukemia cells early after start of conventional genotoxic chemotherapy are incompletely understood, and it is not known if early molecular modulations reflect clinical response.MethodsThe gene expression was examined by whole genome 44 k oligo microarrays and 12 k cDNA microarrays in peripheral blood leukocytes collected from seven leukemia patients before treatment, 2–4 h and 18–24 h after start of chemotherapy and validated by real-time quantitative PCR. Statistically significantly upregulated genes were classified using gene ontology (GO) terms. Parallel samples were examined by flow cytometry for apoptosis by annexin V-binding and the expression of selected proteins were confirmed by immunoblotting.ResultsSignificant differential modulation of 151 genes were found at 4 h after start of induction therapy with cytarabine and anthracycline, including significant overexpression of 31 genes associated with p53 regulation. Within 4 h of chemotherapy the BCL2/BAX and BCL2/PUMA ratio were attenuated in proapoptotic direction. FLT3 mutations indicated that non-responders (5/7 patients, 8 versus 49 months survival) are characterized by a unique gene response profile before and at 4 h. At 18–24 h after chemotherapy, the gene expression of p53 target genes was attenuated, while genes involved in chemoresistance, cytarabine detoxification, chemokine networks and T cell receptor were prominent. No signs of apoptosis were observed in the collected cells, suggesting the treated patients as a physiological source of pre-apoptotic cells.ConclusionPre-apoptotic gene expression can be monitored within hours after start of chemotherapy in patients with acute myeloid leukemia, and may be useful in future determination of therapy responders. The low number of patients and the heterogeneity of acute myeloid leukemia limited the identification of gene expression predictive of therapy response. Therapy-induced gene expression reflects the complex biological processes involved in clinical cancer cell eradication and should be explored for future enhancement of therapy.


Journal of Near Infrared Spectroscopy | 2003

A comparison of techniques for modelling data with non-linear structure

Laila Stordrange; Olav M. Kvalheim; Per A. Hassel; Dick Malthe-Sørenssen; Fred O. Libnau

Partial least squares (PLS) is a powerful tool for multivariate linear regression. But what if the data show a non-linear structure? Near infrared spectra from a pharmaceutical process were used as a case study. An ANOVA test revealed that the data are well described by a 2nd order polynomial. This work investigates the application of regression techniques that account for slightly non-linear data. The regression techniques investigated are: linearising data by applying transformations, local PLS, i.e. splitting of data, and quadratic PLS. These models were compared with ordinary PLS and principal component regression (PCR). The predictive ability of the models was tested on an independent data set acquired a year later. Using the knowledge of non-linear pattern and important spectral regions, simpler models with better predictive ability can be obtained.


Haematologica | 2007

Subclassification of patients with acute myelogenous leukemia based on chemokine responsiveness and constitutive chemokine release by their leukemic cells

Øystein Bruserud; Anita Ryningen; Astrid Marta Olsnes; Laila Stordrange; Anne Margrete Øyan; Karl-Henning Kalland; Bjørn Tore Gjertsen


International Journal of Oncology | 2005

Gene expression profiles in prostate cancer: Association with patient subgroups and tumour differentiation

Ole J. Halvorsen; Anne Margrete Øyan; Trond Hellem Bø; Sue Olsen; Kari Rostad; Svein A. Haukaas; August Bakke; Bruz Marzolf; Krassen Dimitrov; Laila Stordrange; Biaoyang Lin; Inge Jonassen; Leroy Hood; Lars A. Akslen; Karl-Henning Kalland


International Journal of Oncology | 2007

ERG upregulation and related ETS transcription factors in prostate cancer.

Kari Rostad; Monica Mannelqvist; Ole J. Halvorsen; Anne Margrete Øyan; Trond Hellem Bø; Laila Stordrange; Sue Olsen; Svein A. Haukaas; Biaoyang Lin; Leroy Hood; Inge Jonassen; Lars A. Akslen; Karl-Henning Kalland


Journal of Physical Chemistry A | 2002

Study of the Self-Association of Alcohols by Near-Infrared Spectroscopy and Multivariate 2D Techniques

Laila Stordrange; Alfred A. Christy; Olav M. Kvalheim; Hailin Shen; Yi-Zeng Liang


Journal of Chemometrics | 2002

Feasibility study of NIR for surveillance of a pharmaceutical process, including a study of different preprocessing techniques

Laila Stordrange; Fred O. Libnau; Dick Malthe-Sørenssen; Olav M. Kvalheim


Chemometrics and Intelligent Laboratory Systems | 2004

Multiway methods to explore and model NIR data from a batch process

Laila Stordrange; Tarja Rajalahti; Fred O. Libnau

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Ole J. Halvorsen

Haukeland University Hospital

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