Showing papers by "Jens Lagergren published in 2018"

Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity

Abstract: Intra-tumor heterogeneity is one of the biggest challenges in cancer treatment today. Here we investigate tissue-wide gene expression heterogeneity throughout a multifocal prostate cancer using the spatial transcriptomics (ST) technology. Utilizing a novel approach for deconvolution, we analyze the transcriptomes of nearly 6750 tissue regions and extract distinct expression profiles for the different tissue components, such as stroma, normal and PIN glands, immune cells and cancer. We distinguish healthy and diseased areas and thereby provide insight into gene expression changes during the progression of prostate cancer. Compared to pathologist annotations, we delineate the extent of cancer foci more accurately, interestingly without link to histological changes. We identify gene expression gradients in stroma adjacent to tumor regions that allow for re-stratification of the tumor microenvironment. The establishment of these profiles is the first step towards an unbiased view of prostate cancer and can serve as a dictionary for future studies.

Evolutionary history of metastatic breast cancer reveals minimal seeding from axillary lymph nodes

Abstract: Metastatic breast cancers are still incurable. Characterizing the evolutionary landscape of these cancers, including the role of metastatic axillary lymph nodes (ALNs) in seeding distant organ metastasis, can provide a rational basis for effective treatments. Here, we have described the genomic analyses of the primary tumors and metastatic lesions from 99 samples obtained from 20 patients with breast cancer. Our evolutionary analyses revealed diverse spreading and seeding patterns that govern tumor progression. Although linear evolution to successive metastatic sites was common, parallel evolution from the primary tumor to multiple distant sites was also evident. Metastatic spreading was frequently coupled with polyclonal seeding, in which multiple metastatic subclones originated from the primary tumor and/or other distant metastases. Synchronous ALN metastasis, a well-established prognosticator of breast cancer, was not involved in seeding the distant metastasis, suggesting a hematogenous route for cancer dissemination. Clonal evolution coincided frequently with emerging driver alterations and evolving mutational processes, notably an increase in apolipoprotein B mRNA-editing enzyme, catalytic polypeptide-like-associated (APOBEC-associated) mutagenesis. Our data provide genomic evidence for a role of ALN metastasis in seeding distant organ metastasis and elucidate the evolving mutational landscape during cancer progression.

Charting Tissue Expression Anatomy by Spatial Transcriptome Deconvolution

Abstract: We create data-driven maps of transcriptomic anatomy with a probabilistic framework for unsupervised pattern discovery in spatial gene expression data. Convolved negative binomial regression is used to find patterns which correspond to cell types, microenvironments, or tissue components, and that consist of gene expression profiles and spatial activity maps. Expression profiles quantify how strongly each gene is expressed in a given pattern, and spatial activity maps reflect where in space each pattern is active. Arbitrary covariates and prior hierarchies can be specified to support and leverage complex experimental designs. Transcriptomic patterns in mouse brain and olfactory bulb correspond to neuroanatomically distinct cell layers. Moreover, batch effects are successfully addressed, leading to consistent pattern inference for multi-sample analyses. On this basis, we identify known and uncharacterized genes that are spatially differentially expressed in the hippocampal field between Ammon's horn and the dentate gyrus.

SCuPhr: A Probabilistic Framework for Cell Lineage Tree Reconstruction

Abstract: Reconstruction of cell lineage trees, from single-cell DNA sequencing data, has the potential to become a fundamental tool in studies of development and disease, in particular cancer. For cells without copy number alterations that has not been exposed to specific marking techniques, that is normal cells, lineage tracing is naturally based on somatic point mutations. Current single cell sequencing techniques applicable to such cells require an amplification step, which introduces errors, and still often suffer from so-called allelic dropout. We present a detailed model of current technologies, which is likely to capture also any near future technical improvements, for the purpose of estimating the distance between cells without copy number changes, based on single-cell DNA sequencing data. The model is equipped with prior probabilities for key parameters and allows for a Bayesian inference of the distance between two cells, simultaneously taking all the other cells into account. In particular, the model contains variables associated with pairs of loci, of which one is homozygous and the other heterozygous, and has the capacity to perform Bayesian probabilistic read phasing. By applying a fast distance based method, such as FNJ, to the estimated distance, a cell lineage tree can be obtained. In contrast to MCMC based methods, FNJ can easily handle data sets with tens of thousands of taxa. The high accuracy of the so obtained method, called SCuPhr (Single Cell Urn PHased Read), is shown in studies of several synthetic data set.