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.
Identifying the interrelations among cancer driver genes and the patterns in which the driver genes get mutated is critical for understanding cancer. In this paper, we study cross-sectional data from ...cohorts of tumors to identify the cancer-type (or subtype) specific process in which the cancer driver genes accumulate critical mutations. We model this mutation accumulation process using a tree, where each node includes a driver gene or a set of driver genes. A mutation in each node enables its children to have a chance of mutating. This model simultaneously explains the mutual exclusivity patterns observed in mutations in specific cancer genes (by its nodes) and the temporal order of events (by its edges). We introduce a computationally efficient dynamic programming procedure for calculating the likelihood of our noisy datasets and use it to build our Markov Chain Monte Carlo (MCMC) inference algorithm, ToMExO. Together with a set of engineered MCMC moves, our fast likelihood calculations enable us to work with datasets with hundreds of genes and thousands of tumors, which cannot be dealt with using available cancer progression analysis methods. We demonstrate our method's performance on several synthetic datasets covering various scenarios for cancer progression dynamics. Then, a comparison against two state-of-the-art methods on a moderate-size biological dataset shows the merits of our algorithm in identifying significant and valid patterns. Finally, we present our analyses of several large biological datasets, including colorectal cancer, glioblastoma, and pancreatic cancer. In all the analyses, we validate the results using a set of method-independent metrics testing the causality and significance of the relations identified by ToMExO or competing methods.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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.
Cell lineage tree reconstruction methods are developed for various tasks, such as investigating the development, differentiation, and cancer progression. Single-cell sequencing technologies enable ...more thorough analysis with higher resolution. We present Scuphr, a distance-based cell lineage tree reconstruction method using bulk and single-cell DNA sequencing data from healthy tissues. Common challenges of single-cell DNA sequencing, such as allelic dropouts and amplification errors, are included in Scuphr. Scuphr computes the distance between cell pairs and reconstructs the lineage tree using the neighbor-joining algorithm. With its embarrassingly parallel design, Scuphr can do faster analysis than the state-of-the-art methods while obtaining better accuracy. The method's robustness is investigated using various synthetic datasets and a biological dataset of 18 cells.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Here, both mutations ... and BLACK SQUARE must occur before ... and finally diamonds can occur. ...the model encodes two mutational pathways, namely ... right arrow BLACK SQUARE right arrow ... ...right arrow diamonds and BLACK SQUARE right arrow ... right arrow ... right arrow diamonds, and each tumor would follow exactly one of these. Various learning algorithms have been proposed, including exact combinatorial optimization techniques, local optimization using the structural expectation-maximization (EM) algorithm, heuristic search strategies, and Bayesian inference using Markov chain Monte Carlo (MCMC) 2,75,76.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Identification of mutations of the genes that give cancer a selective advantage is an important step towards research and clinical objectives. As such, there has been a growing interest in developing ...methods for identification of driver genes and their temporal order within a single patient (intra-tumor) as well as across a cohort of patients (inter-tumor). In this paper, we develop a probabilistic model for tumor progression, in which the driver genes are clustered into several ordered driver pathways. We develop an efficient inference algorithm that exhibits favorable scalability to the number of genes and samples compared to a previously introduced ILP-based method. Adopting a probabilistic approach also allows principled approaches to model selection and uncertainty quantification. Using a large set of experiments on synthetic datasets, we demonstrate our superior performance compared to the ILP-based method. We also analyze two biological datasets of colorectal and glioblastoma cancers. We emphasize that while the ILP-based method puts many seemingly passenger genes in the driver pathways, our algorithm keeps focused on truly driver genes and outputs more accurate models for cancer progression.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Spatial transcriptomics maps gene expression across tissues, posing the challenge of determining the spatial arrangement of different cell types. However, spatial transcriptomics spots contain ...multiple cells. Therefore, the observed signal comes from mixtures of cells of different types. Here, we propose an innovative probabilistic model, Celloscope, that utilizes established prior knowledge on marker genes for cell type deconvolution from spatial transcriptomics data. Celloscope outperforms other methods on simulated data, successfully indicates known brain structures and spatially distinguishes between inhibitory and excitatory neuron types based in mouse brain tissue, and dissects large heterogeneity of immune infiltrate composition in prostate gland tissue.
The insulin-like growth factor 1 receptor (IGF-1R) plays crucial roles in developmental and cancer biology. Most of its biological effects have been ascribed to its tyrosine kinase activity, which ...propagates signaling through the phosphatidylinositol 3-kinase and mitogen-activated protein kinase pathways. Here, we report that IGF-1 promotes the modification of IGF-1R by small ubiquitin-like modifier protein-1 (SUMO-1) and its translocation to the nucleus. Nuclear IGF-1R associated with enhancer-like elements and increased transcription in reporter assays. The SUMOylation sites of IGF-1R were identified as three evolutionarily conserved lysine residues-Lys(1025), Lys(1100), and Lys(1120)-in the beta subunit of the receptor. Mutation of these SUMO-1 sites abolished the ability of IGF-1R to translocate to the nucleus and activate transcription but did not alter its kinase-dependent signaling. Thus, we demonstrate a SUMOylation-mediated mechanism of IGF-1R signaling that has potential implications for gene regulation.
Adenosine-to-inosine (A-to-I) RNA editing targets double-stranded RNA stem-loop structures in the mammalian brain. It has previously been shown that miRNAs are substrates for A-to-I editing. For the ...first time, we show that for several definitions of edited miRNA, the level of editing increases with development, thereby indicating a regulatory role for editing during brain maturation. We use high-throughput RNA sequencing to determine editing levels in mature miRNA, from the mouse transcriptome, and compare these with the levels of editing in pri-miRNA. We show that increased editing during development gradually changes the proportions of the two miR-376a isoforms, which previously have been shown to have different targets. Several other miRNAs that also are edited in the seed sequence show an increased level of editing through development. By comparing editing of pri-miRNA with editing and expression of the corresponding mature miRNA, we also show an editing-induced developmental regulation of miRNA expression. Taken together, our results imply that RNA editing influences the miRNA repertoire during brain maturation.
We present GSR, a probabilistic model integrating gene duplication, sequence evolution, and a relaxed molecular clock for substitution rates, that enables genomewide analysis of gene families. The ...gene duplication and loss process is a major cause for incongruence between gene and species tree, and deterministic methods have been developed to explain such differences through tree reconciliations. Although probabilistic methods for phylogenetic inference have been around for decades, probabilistic reconciliation methods are far less established. Based on our model, we have implemented a Bayesian analysis tool, PrIME-GSR, for gene tree inference that takes a known species tree into account. Our implementation is sound and we demonstrate its utility for genomewide gene-family analysis by applying it to recently presented yeast data. We validate PrIME-GSR by comparing with previous analyses of these data that take advantage of gene order information. In a case study we apply our method to the ADH gene family and are able to draw biologically relevant conclusions concerning gene duplications creating key yeast phenotypes. On a higher level this shows the biological relevance of our method. The obtained results demonstrate the value of a relaxed molecular clock. Our good performance will extend to species where gene order conservation is insufficient.
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