| 1. |
- Andersson, Anna, et al.
(författare)
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Microarray-based classification of a consecutive series of 121 childhood acute leukemias: prediction of leukemic and genetic subtype as well as of minimal residual disease status.
- 2007
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Ingår i: Leukemia. - : Springer Science and Business Media LLC. - 1476-5551 .- 0887-6924. ; 21:6, s. 1198-1203
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Tidskriftsartikel (refereegranskat)abstract
- Gene expression analyses were performed on 121 consecutive childhood leukemias (87 B-lineage acute lymphoblastic leukemias (ALLs), 11 T-cell ALLs and 23 acute myeloid leukemias (AMLs)), investigated during an 8-year period at a single center. The supervised learning algorithm k-nearest neighbor was utilized to build gene expression predictors that could classify the ALLs/AMLs according to clinically important subtypes with high accuracy. Validation experiments in an independent data set verified the high prediction accuracies of our classifiers. B-lineage ALLs with uncharacteristic cytogenetic aberrations or with a normal karyotype displayed heterogeneous gene expression profiles, resulting in low prediction accuracies. Minimal residual disease status (MRD) in T-cell ALLs with a high (40.1%) MRD at day 29 could be classified with 100% accuracy already at the time of diagnosis. In pediatric leukemias with uncharacteristic cytogenetic aberrations or with a normal karyotype, unsupervised analysis identified two novel subgroups: one consisting mainly of cases remaining in complete remission (CR) and one containing a few patients in CR and all but one of the patients who relapsed. This study of a consecutive series of childhood leukemias confirms and extends further previous reports demonstrating that global gene expression profiling provides a valuable tool for genetic and clinical classification of childhood leukemias.
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| 2. |
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| 3. |
- Niméus, Emma, et al.
(författare)
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Gene expression profilers and conventional clinical markers to predict distant recurrences for premenopausal breast cancer patients after adjuvant chemotherapy.
- 2006
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Ingår i: European Journal of Cancer. - : Elsevier BV. - 1879-0852 .- 0959-8049. ; 42:16, s. 2729-2737
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Tidskriftsartikel (refereegranskat)abstract
- A large proportion of breast cancer patients are treated with adjuvant chemotherapy after the primary operation, but some will recur in spite of this treatment. In order to achieve an improved and more individualised therapy, our knowledge in mechanisms for drug resistance needs to be increased. We have investigated to what extent cDNA microarray measurements could distinguish the likelihood of recurrences after adjuvant CMF (cyclophosphamide, methotrexate and 5-fluorouracil) treatment of premenopausal, lymph node positive breast cancer patients, and have also compared this with the corresponding performance when using conventional clinical variables. We tried several gene selection strategies, and built classifiers using the resulting gene lists. The best performing classifier with odds ratio (OR) = 6.5 (95% confidence interval (CI) = 1.4-62) did not outperform corresponding classifiers based on clinical variables. For the clinical variables, calibrated on the samples, either using all the clinical parameters or the Nottingham Prognostic Index (NPI) parameters, the areas under the receiver operating characteristics (ROC) curve were 0.78 and 0.79, respectively. The ORs at 90% sensitivity were 15 (95% CI = 3.1-140) and 10 (95% CI = 2.1-97), respectively. Our data have provided evidence for a comparable prediction of clinical outcome in CMF-treated breast cancer patients using conventional clinical variables and gene expression based markers. (c) 2006 Elsevier Ltd. All rights reserved.
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| 4. |
- Ritz, Cecilia, et al.
(författare)
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Accounting for one-channel depletion improves missing value imputation in 2-dye microarray data
- 2008
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Ingår i: BMC Genomics. - : Springer Science and Business Media LLC. - 1471-2164. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Abstract in UndeterminedBackground: For 2-dye microarray platforms, some missing values may arise from an un-measurably low RNA expression in one channel only. Information of such "one-channel depletion" is so far not included in algorithms for imputation of missing values.Results: Calculating the mean deviation between imputed values and duplicate controls in five datasets, we show that KNN-based imputation gives a systematic bias of the imputed expression values of one-channel depleted spots. Evaluating the correction of this bias by cross-validation showed that the mean square deviation between imputed values and duplicates were reduced up to 51%, depending on dataset.Conclusion: By including more information in the imputation step, we more accurately estimate missing expression values.
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| 5. |
- Ritz, Cecilia
(författare)
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Classification and Computational Methods in Gene Expression Data Analysis
- 2007
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The technology of cDNA microarrays has given us the possibility to monitor the state of cells by measuring the activity of thousands of genes simultaneously. This high-throughput techniqe has in cancer research allowed exploratory studies of molecular mechanisms behind for example metastasis and response to therapy. This increased knowledge can hopefully result in new therapies and improved prognostic and predictive tools. These tools however have to be properly validated in large cohorts and must be subjected to large-scale trials before use in the clinic. One aim of this thesis is to evaluate the performance of classifiers of clinical outcome for breast cancer based on gene expression data as compared to conventional clinical markers. Additionally, we develop computational methods for analysis and classification using gene expression data. Our results suggests that clinical markers and molecular profiling have similar power in breast cancer prognosis. Further studies using larger cohorts are thus needed to validate and refine molecular prognostic profiles. We have also performed multicategory classification of leukemia into genetic subtypes and have predicted response to therapy in a subgroup. The main contribution to the computational analysis is our development of a method for improvement of missing value imputation of 2-dye cDNA microarray data. Recognizing that some categories of missing values are over- or underestimated in a kNN-based imputation method, we suggest a linear model that corrects for this bias and improves imputation of these spots.
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