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- Pozzoli, Susanna, et al.
(författare)
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Domain expertise–agnostic feature selection for the analysis of breast cancer data*
- 2020
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Ingår i: Artificial Intelligence in Medicine. - : Elsevier B.V.. - 0933-3657 .- 1873-2860. ; 108
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Tidskriftsartikel (refereegranskat)abstract
- Progress in proteomics has enabled biologists to accurately measure the amount of protein in a tumor. This work is based on a breast cancer data set, result of the proteomics analysis of a cohort of tumors carried out at Karolinska Institutet. While evidence suggests that an anomaly in the protein content is related to the cancerous nature of tumors, the proteins that could be markers of cancer types and subtypes and the underlying interactions are not completely known. This work sheds light on the potential of the application of unsupervised learning in the analysis of the aforementioned data sets, namely in the detection of distinctive proteins for the identification of the cancer subtypes, in the absence of domain expertise. In the analyzed data set, the number of samples, or tumors, is significantly lower than the number of features, or proteins; consequently, the input data can be thought of as high-dimensional data. The use of high-dimensional data has already become widespread, and a great deal of effort has been put into high-dimensional data analysis by means of feature selection, but it is still largely based on prior specialist knowledge, which in this case is not complete. There is a growing need for unsupervised feature selection, which raises the issue of how to generate promising subsets of features among all the possible combinations, as well as how to evaluate the quality of these subsets in the absence of specialist knowledge. We hereby propose a new wrapper method for the generation and evaluation of subsets of features via spectral clustering and modularity, respectively. We conduct experiments to test the effectiveness of the new method in the analysis of the breast cancer data, in a domain expertise–agnostic context. Furthermore, we show that we can successfully augment our method by incorporating an external source of data on known protein complexes. Our approach reveals a large number of subsets of features that are better at clustering the samples than the state-of-the-art classification in terms of modularity and shows a potential to be useful for future proteomics research.
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| 2. |
- Landberg, Göran, 1963, et al.
(författare)
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Patient-derived scaffolds uncover breast cancer promoting properties of the microenvironment
- 2020
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Ingår i: Biomaterials. - : Elsevier Ltd. - 0142-9612 .- 1878-5905. ; 235
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Tidskriftsartikel (refereegranskat)abstract
- Tumor cells interact with the microenvironment that specifically supports and promotes tumor development. Key components in the tumor environment have been linked to various aggressive cancer features and can further influence the presence of subpopulations of cancer cells with specific functions, including cancer stem cells and migratory cells. To model and further understand the influence of specific microenvironments we have developed an experimental platform using cell-free patient-derived scaffolds (PDSs) from primary breast cancers infiltrated with standardized breast cancer cell lines. This PDS culture system induced a series of orchestrated changes in differentiation, epithelial-mesenchymal transition, stemness and proliferation of the cancer cell population, where an increased cancer stem cell pool was confirmed using functional assays. Furthermore, global gene expression profiling showed that PDS cultures were similar to xenograft cultures. Mass spectrometry analyses of cell-free PDSs identified subgroups based on their protein composition that were linked to clinical properties, including tumor grade. Finally, we observed that an induction of epithelial-mesenchymal transition-related genes in cancer cells growing on the PDSs were significantly associated with clinical disease recurrences in breast cancer patients. Patient-derived scaffolds thus mimics in vivo-like growth conditions and uncovers unique information about the malignancy-inducing properties of tumor microenvironment. © 2019 The Authors
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| 3. |
- Landberg, Göran, 1963, et al.
(författare)
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Characterization of cell-free breast cancer patient-derived scaffolds using liquid chromatography-mass spectrometry/mass spectrometry data and RNA sequencing data
- 2020
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Ingår i: Data in Brief. - : Elsevier BV. - 2352-3409. ; 31
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Tidskriftsartikel (refereegranskat)abstract
- Patient-derived scaffolds (PDSs) generated from primary breast cancer tumors can be used to model the tumor microenvironment in vitro . Patient-derived scaffolds are generated by repeated detergent washing, removing all cells. Here, we analyzed the protein composition of 15 decellularized PDSs using liquid chromatography-mass spectrometry/mass spectrometry. One hundred forty-three proteins were detected and their relative abundance was calculated using a reference sample generated from all PDSs. We performed heatmap analysis of all the detected proteins to display their expression patterns across different PDSs together with pathway enrichment analysis to reveal which processes that were connected to PDS protein composition. This protein dataset together with clinical information is useful to investigators studying the microenvironment of breast cancers. Further, after repopulating PDSs with either MCF7 or MDA-MB-231 cells, we quantified their gene expression profiles using RNA sequencing. These data were also compared to cells cultured in conventional 2D conditions, as well as to cells cultured as xenografts in immune-deficient mice. We investigated the overlap of genes regulated between these different culture conditions and performed pathway enrichment analysis of genes regulated by both PDS and xenograft cultures compared to 2D in both cell lines to describe common processes associated with both culture conditions. Apart from our described analyses of these systems, these data are useful when comparing different experimental model systems. Downstream data analyses and interpretations can be found in the research article "Patient-derived scaffolds uncover breast cancer promoting properties of the microenvironment" [1] . (C) 2020 The Authors. Published by Elsevier Inc.
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