| 1. |
- Andersson, Anna, et al.
(författare)
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Gene expression signatures in childhood acute leukemias are largely unique and distinct from those of normal tissues and other malignancies.
- 2010
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Ingår i: BMC Medical Genomics. - : Springer Science and Business Media LLC. - 1755-8794. ; 3
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Childhood leukemia is characterized by the presence of balanced chromosomal translocations or by other structural or numerical chromosomal changes. It is well know that leukemias with specific molecular abnormalities display profoundly different global gene expression profiles. However, it is largely unknown whether such subtype-specific leukemic signatures are unique or if they are active also in non-hematopoietic normal tissues or in other human cancer types. METHODS: Using gene set enrichment analysis, we systematically explored whether the transcriptional programs in childhood acute lymphoblastic leukemia (ALL) and myeloid leukemia (AML) were significantly similar to those in different flow-sorted subpopulations of normal hematopoietic cells (n = 8), normal non-hematopoietic tissues (n = 22) or human cancer tissues (n = 13). RESULTS: This study revealed that e.g., the t(12;21) [ETV6-RUNX1] subtype of ALL and the t(15;17) [PML-RARA] subtype of AML had transcriptional programs similar to those in normal Pro-B cells and promyelocytes, respectively. Moreover, the 11q23/MLL subtype of ALL showed similarities with non-hematopoietic tissues. Strikingly however, most of the transcriptional programs in the other leukemic subtypes lacked significant similarity to approximately 100 gene sets derived from normal and malignant tissues. CONCLUSIONS: This study demonstrates, for the first time, that the expression profiles of childhood leukemia are largely unique, with limited similarities to transcriptional programs active in normal hematopoietic cells, non-hematopoietic normal tissues or the most common forms of human cancer. In addition to providing important pathogenetic insights, these findings should facilitate the identification of candidate genes or transcriptional programs that can be used as unique targets in leukemia.
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| 2. |
- Andreasson, Ulrika, et al.
(författare)
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Identification of uniquely expressed transcription factors in highly purified B-cell lymphoma samples.
- 2010
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Ingår i: American Journal of Hematology. - : Wiley. - 0361-8609 .- 1096-8652. ; 85:6, s. 418-425
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Tidskriftsartikel (refereegranskat)abstract
- Transcription factors (TFs) are critical for B-cell differentiation, affecting gene expression both by repression and transcriptional activation. Still, this information is not used for classification of B-cell lymphomas (BCLs). Traditionally, BCLs are diagnosed based on a phenotypic resemblance to normal B-cells; assessed by immunohistochemistry or flow cytometry, by using a handful of phenotypic markers. In the last decade, diagnostic and prognostic evaluation has been facilitated by global gene expression profiling (GEP), providing a new powerful means for the classification, prediction of survival, and response to treatment of lymphomas. However, most GEP studies have typically been performed on whole tissue samples, containing varying degrees of tumor cell content, which results in uncertainties in data analysis. In this study, global GEP analyses were performed on highly purified, flow-cytometry sorted tumor-cells from eight subgroups of BCLs. This enabled identification of TFs that can be uniquely associated to the tumor cells of chronic lymphocytic leukemia (CLL), diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), hairy cell leukemia (HCL), and mantle cell lymphoma (MCL). The identified transcription factors influence both the global and specific gene expression of the BCLs and have possible implications for diagnosis and treatment.
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| 3. |
- Fjordén, Karin, et al.
(författare)
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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
- 2012
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Ingår i: Leukemia & Lymphoma. - : Informa UK Limited. - 1042-8194 .- 1029-2403. ; 53:9, s. 1764-1768
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 4. |
- Kalderstam, Jonas, et al.
(författare)
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Ensembles of genetically trained artificial neural networks for survival analysis
- 2013
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Ingår i: ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. - 9782874190810 ; , s. 333-338
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Konferensbidrag (refereegranskat)abstract
- We have developed a prognostic index model for survival data based on an ensemble of artificial neural networks that optimizes directly on the concordance index. Approximations of the c-index are avoided with the use of a genetic algorithm, which does not require gradient information. The model is compared with Cox proportional hazards (COX) and three support vector machine (SVM) models by Van Belle et al. [10] on two clinical data sets, and only with COX on one artificial data set. Results indicate comparable performance to COX and SVM models on clinical data and superior performance compared to COX on non-linear data.
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| 5. |
- Kalderstam, Jonas, et al.
(författare)
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Training artificial neural networks directly on the concordance index for censored data using genetic algorithms.
- 2013
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Ingår i: Artificial Intelligence in Medicine. - : Elsevier BV. - 1873-2860 .- 0933-3657. ; 58:2, s. 125-132
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: The concordance index (c-index) is the standard way of evaluating the performance of prognostic models in the presence of censored data. Constructing prognostic models using artificial neural networks (ANNs) is commonly done by training on error functions which are modified versions of the c-index. Our objective was to demonstrate the capability of training directly on the c-index and to evaluate our approach compared to the Cox proportional hazards model. METHOD: We constructed a prognostic model using an ensemble of ANNs which were trained using a genetic algorithm. The individual networks were trained on a non-linear artificial data set divided into a training and test set both of size 2000, where 50% of the data was censored. The ANNs were also trained on a data set consisting of 4042 patients treated for breast cancer spread over five different medical studies, 2/3 used for training and 1/3 used as a test set. A Cox model was also constructed on the same data in both cases. The two models' c-indices on the test sets were then compared. The ranking performance of the models is additionally presented visually using modified scatter plots. RESULTS: Cross validation on the cancer training set did not indicate any non-linear effects between the covariates. An ensemble of 30 ANNs with one hidden neuron was therefore used. The ANN model had almost the same c-index score as the Cox model (c-index=0.70 and 0.71, respectively) on the cancer test set. Both models identified similarly sized low risk groups with at most 10% false positives, 49 for the ANN model and 60 for the Cox model, but repeated bootstrap runs indicate that the difference was not significant. A significant difference could however be seen when applied on the non-linear synthetic data set. In that case the ANN ensemble managed to achieve a c-index score of 0.90 whereas the Cox model failed to distinguish itself from the random case (c-index=0.49). CONCLUSIONS: We have found empirical evidence that ensembles of ANN models can be optimized directly on the c-index. Comparison with a Cox model indicates that near identical performance is achieved on a real cancer data set while on a non-linear data set the ANN model is clearly superior.
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| 6. |
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| 7. |
- Nordström, Lena, et al.
(författare)
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SOX11 and TP53 add prognostic information to MIPI in a homogenously treated cohort of mantle cell lymphoma : a Nordic Lymphoma Group study
- 2014
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Ingår i: British Journal of Haematology. - : Wiley. - 0007-1048 .- 1365-2141. ; 166:1, s. 98-108
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Tidskriftsartikel (refereegranskat)abstract
- Mantle cell lymphoma (MCL) is an aggressive B cell lymphoma, where survival has been remarkably improved by use of protocols including high dose cytarabine, rituximab and autologous stem cell transplantation, such as the Nordic MCL2/3 protocols. In 2008, a MCL international prognostic index (MIPI) was created to enable stratification of the clinical diverse MCL patients into three risk groups. So far, use of the MIPI in clinical routine has been limited, as it has been shown that it inadequately separates low and intermediate risk group patients. To improve outcome and minimize treatment-related morbidity, additional parameters need to be evaluated to enable risk-adapted treatment selection. We have investigated the individual prognostic role of the MIPI and molecular markers including SOX11, TP53 (p53), MKI67 (Ki-67) and CCND1 (cyclin D1). Furthermore, we explored the possibility of creating an improved prognostic tool by combining the MIPI with information on molecular markers. SOX11 was shown to significantly add prognostic information to the MIPI, but in multivariate analysis TP53 was the only significant independent molecular marker. Based on these findings, we propose that TP53 and SOX11 should routinely be assessed and that a combined TP53/MIPI score may be used to guide treatment decisions.
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| 8. |
- Teles, José, et al.
(författare)
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Transcriptional regulation of lineage commitment - a stochastic model of cell fate decisions.
- 2013
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Ingår i: PLoS Computational Biology. - : Public Library of Science (PLoS). - 1553-7358. ; 9:8
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Tidskriftsartikel (refereegranskat)abstract
- Molecular mechanisms employed by individual multipotent cells at the point of lineage commitment remain largely uncharacterized. Current paradigms span from instructive to noise-driven mechanisms. Of considerable interest is also whether commitment involves a limited set of genes or the entire transcriptional program, and to what extent gene expression configures multiple trajectories into commitment. Importantly, the transient nature of the commitment transition confounds the experimental capture of committing cells. We develop a computational framework that simulates stochastic commitment events, and affords mechanistic exploration of the fate transition. We use a combined modeling approach guided by gene expression classifier methods that infers a time-series of stochastic commitment events from experimental growth characteristics and gene expression profiling of individual hematopoietic cells captured immediately before and after commitment. We define putative regulators of commitment and probabilistic rules of transition through machine learning methods, and employ clustering and correlation analyses to interrogate gene regulatory interactions in multipotent cells. Against this background, we develop a Monte Carlo time-series stochastic model of transcription where the parameters governing promoter status, mRNA production and mRNA decay in multipotent cells are fitted to experimental static gene expression distributions. Monte Carlo time is converted to physical time using cell culture kinetic data. Probability of commitment in time is a function of gene expression as defined by a logistic regression model obtained from experimental single-cell expression data. Our approach should be applicable to similar differentiating systems where single cell data is available. Within our system, we identify robust model solutions for the multipotent population within physiologically reasonable values and explore model predictions with regard to molecular scenarios of entry into commitment. The model suggests distinct dependencies of different commitment-associated genes on mRNA dynamics and promoter activity, which globally influence the probability of lineage commitment.
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