R2 Training Courses. Release The R2 support team

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1 R2 Training Courses Release The R2 support team Nov 08, 2018

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3 Students Course 1 Student Course: Investigating Intra-tumor Heterogeneity Introduction Tumors and origins: a first impression of your data Urgency of research: patient material Which genes make a difference? Creating signatures Identifying groups: using signatures to classify other datasets Using scores for further characterization Finding causes: homing in on transcription factors Proving causes: manipulating cell lines Creating hypotheses: relating to chromatin modification data Suggesting therapy i

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5 This contains a collection of training courses for R2; a biologist friendly, web based genomics analysis and visualization application developed by Jan Koster at the department of Oncogenomics in the Academic Medical Center (AMC) Amsterdam, the Netherlands. For citations, please include the following webcite: R2: Genomics Analysis and Visualization Platform ( Copyright (c) Jan Koster Table of Contents Students Course 1

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7 CHAPTER 1 Student Course: Investigating Intra-tumor Heterogeneity Analyse tumor heterogeneity in Neuroblastoma using the R2 data analysis platform This resource is located online at Introduction Cancer is a very complex disease. Much more complicated than originally anticipated when the first mutations were found to be causal for specific cancers. During the lectures you ve been shown how this works in colorectal cancer, where a well defined path of subsequently gained mutations leads to more aggressive tumorigenic cell types (the Vogelstein model). Figure 1: Mutation paths during cancer progression. Although there has been extensive research into similar mutation mechanisms in Neuroblastoma (also in the AMC Oncogenomics group), such a mechanism has not been found for this type of cancer. In this practical work session we will try to bring you to the cutting edge of research into this often deadly childhood tumor. From the lectures you should have learned already that this tumor consists of different cancer cell types. There is reason to believe that this heterogeneity causes the high percentage of relapses in the aggressive subtype of Neuroblastoma. Children developing a relapse almost always die. Fortunately new technologies have become available to molecular biology. These enable us to study not only mutations and RNA expression of genes but also 3

8 study the epigenetic modifications of the DNA-associated histones. And in addition genes can now be manipulated in cell lines and living tissues. Using advanced data analysis, statistics and clustering methods, the field of bioinformatics tries to derive new insights from these experimental data and help molecular biologists to generate hypotheses that can be tested experimentally. Today you will use the web-based genomics analysis and visualization platform R2. R2 provides you with a set of bioinformatics tools to investigate recent patient and experimental data from Neuroblastoma tumors and cell lines. Neuroblastoma is a pediatric tumor of the peripheral adrenergic lineage, which is neural crest derived. During embryogenesis, cells delaminate from the neural crest, migrate ventrally and differentiate into adrenalineor noradrenaline-producing cells. Neuroblastomas typically express enzymes for the adrenaline-synthesis route. High-stage neuroblastomas usually go into complete remission upon therapy but often relapse as therapy-resistant disease. Using recent molecular biology data gathering techniques and advanced bioinformatic data analysis algorithms we set out to investigate this nasty characteristic of Neuroblastoma tumors. From four patients we obtained tumor biopsies that were taken in culture. Each biopsy gave rise to two phenotypically divergent cell lines. 1.2 Tumors and origins: a first impression of your data For a start we ll investigate established childhood tumor cell lines, including neuroblastoma. Established cell lines can be grown and passaged in culture indefinetely. A typical example is the classic HeLa cell line, taken from a cervical adenocarcinoma of Henrieta Lacks in 1951 that has been in culture since. How do profiles of neuroblastoma cell lines relate to cell lines of other tumors? Additional data about classical cell lines from other childhood tumors is available in the resources of the scientific community. For each publication scientists are required to make their data available in public repositories. We can use these in a larger public dataset of other tumor cell lines and see how they relate. Data used: 86 cell lines derived from 6 different childhood tumors (Cellline Childhood cancer - ITCC MAS5.0 - u133p2) Techniques used: mrna Microarray expression Analysis used individual gene selection t-sne: t-distributed stochastic neighbor embedding statistics References Chapter 2.2 & 2.3 Weinberg Expression of key genes Go to R2 by clicking on the button below: You re now on the R2 main page. This web based molecular biology data analysis platform contains a wealth of data and methods to analyze these. Step by step researchers are guided through a web of data analysis possibilities. The portal of R2 shows this principle; step through each of the fields to develop your analysis of choice. In this case we re first going to see if and how the mrna expression of several genes changes through a single dataset. The proper dataset described above has been selected already. Can you think of a gene that might mark differences between these tumor models? In field 4 type the name of the gene and click Next 4 Chapter 1. Student Course: Investigating Intra-tumor Heterogeneity

9 Leave all settings at default and click Next A graph shows the expression of this gene s mrna in the whole set of childhood tumor cell lines. Samples are along the x-axis, mrna expression values of the gene in a sample are on the y-axis. Below the graph is the available annotation for the samples shown in colored tracks. Hover with your mouse over data points to show additional information. The expression values on the y-axis are logarithmic; set the Transform option to none, and select Track and Gene sort for the Extra Graph Option. Sample annotation is stored in R2 in so called tracks, for use track choose the itcc_model track that contains the information which sample belongs to which tumor type and click Adjust Settings to obtain a more explicit picture. Now try the gene MYCN (Click the Go to Main link in the left upper corner) Can you say something about the role this gene can play in cancer? Using the data annotation track below the graph, what can you say about neuroblastoma? Clustering with tsne maps We ve seen that the expression of genes differs among the samples and some types of tumors seem to specifically express certain genes. To further explore the type of data we re dealing with, an unbiased unsupervised type of clustering analysis is a good idea. One recently developed algorithm is the tsne map. Click the button below to show the tsne map in R2 Colors are not set by default, under ColorMode select Color by Track and use the itcc_model track, click Next to show the changes Can you relate the tumors to a type of tissue? (Note: ALL stands for Acute Lymphocytic Leukemia) What do you note about the clustering of neuroblastoma cell lines with respect to the lineage of origin? If you had to choose two cell lines for further investigation of lineage identity in neuroblastoma, which would you choose? 1.3 Urgency of research: patient material In the former step we derived that neuroblastoma cell lines seem to group with cell lines of different developmental lineages. We have recently established new cell line pairs from neuroblastoma patients. In some cases multiple cell lines were obtained from the same biopsy. These cell lines share genetic defects and are therefore called isogenic cell line pairs. A microscopy image of each pair is provided below Urgency of research: patient material 5

10 Figure 2: Bright field image of isogenic cell line pairs. What do you note about the morphology of the cell lines? We profiled the mrna expression of genes using Affymetrix mrna chips in three of these pairs and of a previously established neuroblastoma cell line that after culturing gave rise to two very divergent phenotypes. The resulting gene expression patterns can be used to perform a hierarchical clustering. An example of such clustering resulting in an ordered heatmap is provided below 6 Chapter 1. Student Course: Investigating Intra-tumor Heterogeneity

11 Figure 3: Heatmap: unsupervised clustering of samples using the distribution of the expression data combined with the clustering of genes based on their expression through the samples. Data used: Cell lines recently derived from three tumors from one patient. Two biopsies per tumor were taken. This dataset is combined with two classical Neuroblastoma cell lines that clustered far apart: SHEP and SY5Y (Mixed Neuroblastoma (MES-ADRN) - Versteeg MAS5.0 - u133p2) Techniques used: mrna Microarray expression Analysis used Toplister: unsupervised gene selection Unsupervised hierarchical clustering Heatmap visualization For this analysis we ll directly go to one of the analysis tools of R2: Toplister. The Toplister can assess which genes behave different throughout a dataset. It does so by selecting the genes whose expression values have the largest standard deviation within a given set of samples. This gives an unbiased view of the differences in gene expression. Go to R2 by clicking the button below Click Next; a list of genes appears Do you recognize any genes that explain the difference in phenotype? Use the mousewheel to scroll to the bottom of the page (or click on the shoe-print at the top of the page). Here you can choose to perform an additional analysis. The heatmap vizualization produces a hierarchically clustered view of the expression values of the top 100 genes. What number of groups do you expect? 1.3. Urgency of research: patient material 7

12 Click on Heatmap(z-score) The cell line pairs from the patient were also investigated for the tumor stem cell marker gene CD133 and for their migration capability. See the results in the figure below: Figure 4: CD133 Facs analysis and transwell migration assay of isogenic pairs Given these observations, what origin can you assign to each group? 1.4 Which genes make a difference? Creating signatures We have identified two different types of cells that occur within the same patient. Neuroblastoma apparently has a heterogenous nature. What genes determine the difference between the two types? We ll use RNA expression data again but now we will use a predefined, supervised classification in groups to search for genes that characterize this classification best, or in other words, that are differentially expressed between these two groups. Data used: Mixed Neuroblastoma (MES-ADRN) - Versteeg MAS5.0 - u133p2 (same as above) Gene Ontology Broad curated hallmark datasets Techniques used: mrna arrays Analysis used Differential Expression: supervised gene selection Gene Ontology Analysis: Overrepresentation calculation Go to the main page of R2 by clicking the button below In Field 3 choose Find Differential expression between groups and click Next 8 Chapter 1. Student Course: Investigating Intra-tumor Heterogeneity

13 This dataset has been annotated with type information. Each sample was assigned to either the MESenchymal or the ADReNergic type, in R2 this is called a track. Choose the proper track in the Select a track dropdown. Since we have only 8 samples make sure that the multiple testing correction is set to No correction. Click Next twice A list of differentially expressed genes appears with correlation p-value < 0.01 in this dataset is shown. Click on the hyperlinked name of your favorite gene to see its expression in the sample set; try an oppositely correlating gene as well Go back to the window with the differentially expressed genes. This is still open in one of your browser tabs. Click on the Heatmap(zscore) button in the right menu panel; a heatmap shows the expression of the differentially expressed genes for each sample. How is this figure different from the former? For future use, this list of genes has been stored for you in R2 as signatures (aka genesets or categories). The list has been split into two categories: one set of genes that is highly expressed in the MES type of samples (r2_mesadrn_mes) and one set of genes highly expressed in the ADRN type of samples (r2_mesadrn_adrn). R2 provides additional analysis for sets of genes that can be accessed from the right panel of menu buttons. As a first analysis step we can check a data resource called the Gene Ontology that provides a tree of systematically ordered biological terms that is used to formally describe the biological role of each gene. The Gene Ontology Analysis tool in R2 calculates for each of the thousands of groups of genes that are annotated with a specific biological term whether your set of choice is over-represented in them. On the page with the differentially expressed genes, select the Gene Ontology Analysis button in the menu on the right What can you say about the function of the differentially expressed genes? Now scroll down to the end of the page (or click the filter button in the left upper corner of the page) and adapt the settings such that only the Biological Process branch of the Gene Ontology is selected, and select only the genes that are higher expressed in the MES type of cells cells? What can you say about the function of the group of genes that are upregulated in the MES type of In R2 there are much more sets of genes that have been found to be implemented in specific processes. The Broad Institute has compiled quite some of these sets of genes that characterize hallmark biological processes. Go back to the window with the differentially expressed genes. Select the Gene set analysis option from the right menu Select the geneset_broad_2015_hallmark geneset and click Next 1.4. Which genes make a difference? Creating signatures 9

14 Which hallmark category of genes pops up as most important? Can you explain this? 1.5 Identifying groups: using signatures to classify other datasets We now have a signature that distinguishes between the two types of cells. We also obtained some hints about functional characteristics of these cells. How does this signature behave in other datasets? Does the same set of genes tell us something about other sets of tumors or cell lines? This is the next step in our analysis.we ve assembled a more complex dataset by gathering the dataset of the 4 pairs of cell lines, additional neuroblastoma cell lines from the first dataset and publicly available data of non-malignant human neural crest tissue. The neural crest undergoes a mesenchymal transition and gives rise to cell types from the adrenergic lineage. Data used: A combination of the 8 cell lines above, additional neuroblastoma cell lines and cells from the neural crest lineage (Mixed Neuroblastoma (MES-ADRN-CREST) - Versteeg/Etchevers MAS5.0 - u133p2) Techniques used: mrna expression data Analysis used Heatmap analysis Go to the main portal of R2 by clicking the button below; the dataset described above is automatically selected In field 3 select View Geneset Click Next and select geneset_r2provided_genelists Click Next, leave selection as is and click Next Select both signatures that were derived before by CTRL click on the MES (r2_mesadrn_mes) and ADRN (r2_mesadrn_adrn) signatures and click Next Which cell types group together? How does this relate to the above? When observing such clear-cut patterns it is good scientific practice to test this in additional datasets. The database of R2 contains an additional dataset consisting of neuroblastoma cell lines that were profiled by a French research team. Click on the button below to go there and perform the same analysis. Do you observe similar patterns? 10 Chapter 1. Student Course: Investigating Intra-tumor Heterogeneity

15 1.6 Using scores for further characterization The expression patterns of these specific signatures can be used to compare cell types. We can do this by summarizing the expression data of all genes in the signature in each cell type in one value; a signature score. The figure below shows the signature score of the MES part of the signature in a specific sample. Figure 5: The signature score as calculated for a specific ample in the MES signature. R2 has calculated these scores for all samples in both signatures. We re going to find out how they relate. Data used: Mixed Neuroblastoma (MES-ADRN-CREST) - Versteeg/Etchevers MAS5.0 - u133p2 Techniques used: mrna expression data Analysis used Signature scores Go back to the main portal of R2 by clicking the button below. In field 3 choose Relate 2 tracks and click Next First we ll explore the scores in each signature separately; on the X-axis (Select X track) we ll use the unique sample id (lab_id) and on the Y-axis the signature score track that R2 has generated for the MES signature (u-34_mesadrn_mes(#)). Click Next. A graph is generated for each sample the signature score for the mesadrn_mes signature is shown, select Color by Track for ColorMode and try different tracks. Click Adjust Settings to view the result. Now select for the Y-axis the ADRN part of the signature, click Adjust Settings to view the result Using scores for further characterization 11

16 Now we re going to compare the signature scores; select the MES signature for the X track If you have time you can also try the Color by Gene ColorMode, choose a gene of interest (Note: the dropdown selection is linked to the database, wait for the proper selections to popup... ) What conclusion would you draw from these figures? 1.7 Finding causes: homing in on transcription factors Apparently there are two types of cells in Neuroblastoma tumors. Neuroblastoma seems to be a heterogenous tumor. Transcription factors are known to determine gene expression programs in cells. These gene expression programs determine the development of the cell. Can we find out which TF s might influence the difference between both of these cell lines? Data used: Mixed Neuroblastoma (MES-ADRN-CREST) - Versteeg/Etchevers MAS5.0 - u133p2 Transcription factor annotation from Gene Ontology NCBI (National Center for Biotechnology Information - USA) Gene information database Techniques used: mrna expression data Analysis used Differential expression: supervised gene selection Go back to the main portal of R2 by clicking the button below. Again we re going to find out which genes make a difference, but now in a specific subset that has been annotated to have Transcription Factor activity. This is gathered from databases that collect that information from peer reviewed publications. In field 3 select Find Differential expression between groups Click Next Make sure to select the proper track Select a track. We re now also going to filter for a specific GeneCategory; select the Transcription factors (TF(945)). Click Next. In the next screen we re asked to further filter for a specific type of samples to compare, we re focusing on the difference between ADRN and MES; select these. Click Next. A list of genes appears. Investigate the top 4 by clicking on the hyperlinked gene symbols. This brings you to the expression view of the gene. From here you can also access the NCBI gene database containing additional information on the function of the gene and related scientific publications. Do this by clicking on the hyperlinked GeneID number in the top table. Armed with this information, which gene would you choose for further research? Why? 12 Chapter 1. Student Course: Investigating Intra-tumor Heterogeneity

17 1.8 Proving causes: manipulating cell lines From experiments it is known that cells can change their nature, some cells exhibit a certain plasticity. Can you explain why this is of relevance to cancer? From experiments in our lab it became evident that the two cell types found in Neuroblastoma were able to switch. After a given period of time cells in dishes changed their nature as was proven by the expression of certain marker proteins on their surface.now that we have a candidate Transcription Factor we can try to investigate its relevance in this plasticity by manipulating the gene in cell lines we grow in the lab. Can you think of ways to manipulate genes in cell lines? The TF was inducibly expressed in the SKNBE cell line and this was monitored through time for its gene expression using Affymetrix mrna arrays. The resulting data was added to the dataset we used above for comparison. To which of the cell types does SKNBE belong? Data used: A combination of the 4 cell line pairs, additional classical Neuroblastoma cell lines, cells from the neural crest lineage and lines that had the TF inducible expressed for increasing periods (Mixed Neuroblastoma (MES-ADRN-Crest-Exp) - Versteeg MAS5.0 - u133p2) Techniques used: Inducible gene expression mrna expression data Analysis used Signature score comparison Go to the R2 main page by clicking the button below, the correct dataset will be selected. Select in field 3 the Relate 2 tracks option. R2 has calculated signature scores for all samples in both signatures; in this dataset these tracks are called adrn_score and mes_score. Relate the two tracks, adapt the ColorMode to Color by Track and try the mes_adrn_time track. This track contains information on the time that the PRRX1 gene expression was induced in the SKNBE cell line. What is your conclusion from this experiment? 1.8. Proving causes: manipulating cell lines 13

18 1.9 Creating hypotheses: relating to chromatin modification data Apparently this TF is capable of shifting cells from one state to the other. How can we further determine causal relations and ideally targetable processes in these cancer cells? How is a switch dynamically possible? A growing body of evidence implicates enhancers as key elements defining cell identity but the relationship of these enhancers to intratumoral heterogeneity is unknown. We performed ChIP seq analysis of the H3K27ac histone modifications for the isogenic cell line pairs. Data used: Four MES and five ADRN neuroblastoma cell lines, including three isogenic cell line pairs. Techniques used: ChIP seq analysis References Chapter 1.8 Weinberg Analysis Genome Browser: analyzing histone modifications marking active enhancers Differential Expression Can you explain what the goal of this experiment was? First we ll check one of the HAND genes, known to play a role in the development of the sympatho-adrenal lineage from the neural crest. What do you expect for the H3K27ac signals? Click on the button below to show the ChIP-Seq data for HAND1 in the four mesenchymal and five adrenergic neuroblastoma cell lines. For your convenience the signals are colored according to the type (MES or ADRN) of cell line. Regions encoding genes are drawn at the bottom of the graph. When in red they re encoded in the reverse direction, coding exons are darker. Can you explain this graph? What do you expect for the expression of this gene? The chromatin state is especially important for transcription factors; we ll re-visit the list of transcription factors that are differentially expressed between the MES and ADRN cell lines. Why are Transcription Factors of interest in this setting? 14 Chapter 1. Student Course: Investigating Intra-tumor Heterogeneity

19 Perform the differential expression analysis again by clicking on the button below Use both expression analysis and the enhancer data in the genomebrowser to decide which transcription factors would be worthwhile to further investigate. In the genomebrowser you can type the name of the gene in the left upper corner textfield. To further explore the larger region around the gene you can use the zoom buttons at the top of the page Which Transcription Factor would you consider for further study? 1.10 Suggesting therapy With the current new knowledge you derived above, can you think of a strategy to use the fact that neuroblastoma is a heterogenous tumor consisting of a mesenchymal, motile cell type and a adrenergic, differentiated cell type for therapeutic options? Follow the links above to use the differential expression analysis and the genomebrowser information Hints: There is a category drugtargets in R2 to select druggable proteins; you can select these in the same dropdown where the TF selection was done. Another very interesting one is the kinase category, this contains known kinases that have active roles in pathways. Knowledge about pathways can be exploited as well The NCBI database can provide additional information from literature about the genes of interest. Be creative, you might find something interesting! Which strategy do you suggest? Suggesting therapy 15