| 1. |
- Cook, Daniel John, 1986, et al.
(författare)
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Molecular natural history of breast cancer: Leveraging transcriptomics to predict breast cancer progression and aggressiveness
- 2020
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Ingår i: Cancer Medicine. - : Wiley. - 2045-7634. ; 9:10, s. 3551-3562
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Tidskriftsartikel (refereegranskat)abstract
- Cancer Medicine published by John Wiley & Sons Ltd. Background: Characterizing breast cancer progression and aggressiveness relies on categorical descriptions of tumor stage and grade. Interpreting these categorical descriptions is challenging because stage convolutes the size and spread of the tumor and no consensus exists to define high/low grade tumors. Methods: We address this challenge of heterogeneity in patient-specific cancer samples by adapting and applying several tools originally created for understanding heterogeneity and phenotype development in single cells (specifically, single-cell topological data analysis and Wanderlust) to create a continuous metric describing breast cancer progression using bulk RNA-seq samples from individual patient tumors. We also created a linear regression-based method to predict tumor aggressiveness in vivo from bulk RNA-seq data. Results: We found that breast cancer proceeds along three convergent phenotype trajectories: luminal, HER2-enriched, and basal-like. Furthermore, 31 296 genes (for luminal cancers), 17 827 genes (for HER2-enriched), and 18 505 genes (for basal-like) are dynamically differentially expressed during breast cancer progression. Across progression trajectories, our results show that expression of genes related to ADP-ribosylation decreased as tumors progressed (while PARP1 and PARP2 increased or remained stable), suggesting the potential for a differential response to PARP inhibitors based on cancer progression. Additionally, we developed a 132-gene expression regression equation to predict mitotic index and a 23-gene expression regression equation to predict growth rate from a single breast cancer biopsy. Conclusion: Our results suggest that breast cancer dynamically changes during disease progression, and growth rate of the cancer cells is associated with distinct transcriptional profiles.
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| 2. |
- Einarsson, Rasmus, 1988, et al.
(författare)
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Nitrogen flows on organic and conventional dairy farms: a comparison of three indicators
- 2018
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Ingår i: Nutrient Cycling in Agroecosystems. - : Springer Science and Business Media LLC. - 1385-1314 .- 1573-0867. ; 110:1
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Tidskriftsartikel (refereegranskat)abstract
- This paper analyzes nitrogen (N) flows on organic and conventional dairy farms in Sweden, and compares three indicators for the N pollution associated with the milk: (1) the farm-gate N surplus, (2) the chain N surplus, and (3) the N footprint. We find that, compared to indicators based on N surplus, the N footprint is a more understandable indicator for the N pollution associated with a product. However, the N footprint is not a replacement for the often-used farm-gate N surplus per unit area, since the two indicators give different information. An uncertainty analysis shows that, despite the large dataset, 1566 conventional and 283 organic farms, there is substantial uncertainty in the indicator values, of which a large part is due to possible bias in estimates of biological N fixation (BNF). Hence, although the best estimate is that conventional milk has 10–20% higher indicator values than organic, it is conceivable that improved estimates of BNF will change that conclusion. All three indicators simplify reality by aggregating N flows over time and space, and of different chemical forms. Thus, they hide many complexities with environmental relevance, which means that they can be misleading for decision-makers. This motivates further research on the relation between N surpluses and N footprints, and actual environmental damages.
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| 3. |
- Kallus, Jonatan, 1985
(författare)
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Network modeling and integrative analysis of high-dimensional genomic data : Nätverksmodellering och integrativ analys av högdimensionell genomikdata
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Genomic data describe biological systems on the molecular level and are, due to the immense diversity of life, high-dimensional. Network modeling and integrative analysis are powerful methods to interpret genomic data. However, network modeling is limited by the requirement to select model complexity and due to a bias towards biologically unrealistic network structures. Furthermore, there is a need to be able to integratively analyze data sets describing a wider range of different biological aspects, studies and groups of subjects. This thesis aims to address these challenges by using resampling to control the false discovery rate (FDR) of edges, by combining resampling-based network modeling with a biologically realistic assumption on the structure and by increasing the richness of data sets that can be accommodated in integrative analysis, while facilitating the interpretation of results. In paper I, a statistical model for the number of times each edge is included in network estimates across resamples is proposed, to allow for estimation of how the FDR is affected by sparsity. Accuracy is improved compared to state-of-the-art methods, and in a network estimated for cancer data all hub genes have documented cancer-related functions. In paper II, a new method for integrative analysis is proposed. The method, based on matrix factorization, introduces a versatile objective function that allows for the study of more complex data sets and easier interpretation of results. The power of the method as an explorative tool is demonstrated on a set of genomic data. In paper III, network estimation across resamples is combined with repeated community detection to compensate for the structural bias inherent in common network estimation methods. For estimation of the regulatory network in human cancer, this compensation leads to an increased overlap with a database of gene interactions. Software implementations of the presented methods have been published. The contributed methods further the understanding that can be gained from high-dimensional genomic data, and may thus help to devise new treatments and diagnostics for cancer and other diseases.
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| 4. |
- Kallus, Jonatan, 1985
(författare)
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Resampling in network modeling of high-dimensional genomic data
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Network modeling is an effective approach for the interpretation of high-dimensional data sets for which a sparse dependence structure can be assumed. Genomic data is a challenging and important example. In genomics, network modeling aids the discovery of biological mechanistic relationships and therapeutic targets. The usefulness of methods for network modeling is improved when they produce networks that are accompanied by a reliability estimate. Furthermore, for methods to produce reliable networks they need to have a low sensitivity to occasional outlier observations. In this thesis, the problem of robust network modeling with error control in terms of the false discovery rate (FDR) of edges is studied. As a background, existing types of genomic data are described and the challenges of high-dimensional statistics and multiple hypothesis testing are explained. Methods for estimation of sparse dependency structures in single samples of genomic data are reviewed. Such methods have a regularization parameter that controls sparsity of estimates. Methods that are based on a single sample are highly sensitive to outlier observations and to the value of the regularization parameter. We introduce the method ROPE, resampling of penalized estimates, that makes robust network estimates by using many data subsamples and several levels of regularization. ROPE controls edge FDR at a specified level by modeling edge selection counts as coming from an overdispersed beta-binomial mixture distribution. Previously existing resampling based methods for network modeling are reviewed. ROPE was evaluated on simulated data and gene expression data from cancer patients. The evaluation shows that ROPE outperforms state-of-the-art methods in terms of accuracy of FDR control and robustness. Robust FDR control makes it possible to make a principled decision of how many network links to use in subsequent analysis steps.
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