GV was supported by DARPA award W911NF-16-0575.

1. Exploring factors containing a gene of interest
<ol>
	<li>Open a web browser and go to http://corex.isi.edu, the CorExplorer home page.</li>
	<li>On the right side under Quick Links, click on the + expand button next to Ovarian (TCGA-OV) to see a summary of the CorEx factor graph that was trained on the TCGA ovarian cancer data (shown in Figure 1). Optionally, click on others to compare.</li>
	<li>Once finished inspecting the factor graphs, click on Lung (TCGA-LUAD) to access the CorExplorer page for lung cancer RNA-seq.
	<ol>
		<li>Explore the CorEx factor graph for a gene of interest using the CorExplorer &#8216;Factor Graph&#8217; window.
		<ol>
			<li>Move the mouse cursor over the factor graph display window. Zoom into the factor graph using the mouse scroll wheel or trackpad to see details of the graph such as the most important genes in each factor and the connections between nodes at different layers. Alternatively, click and drag to move the view area or any node.</li>
			<li>To find a target gene (here we&#39;ll use BRCA1), click on the Gene dropdown menu at the top of the factor graph window. Type &#8216;BRCA1&#8217; to select it in the dropdown list and press Return to make the view zoom to factor 26, the factor with which BRCA1 is most strongly correlated.</li>
			<li>Reposition the mouse over the graph display and scroll to zoom out to see the Level 2 node, L2_8, and its associated factors that are neighbors to factor 26. Note that only genes with weight greater than the threshold indicated on the Min link weight slider are shown.</li>
			<li>To see all of the genes associated with the factor, click on the L1_26 node and select Load additional genes in the pop-up window. When the word &#8216;Done&#8217; appears, close the pop-up window.</li>
			<li>Now go back to the header section above the factor graph window and grab and drag the Min link weight modifier. Now, as the link weight slider is moved down to 0.05, other genes in factor L1_26, including BRCA2, will appear in weight order. Optionally, reposition nodes by grabbing and dragging to improve layout.</li>
		</ol>
		</li>
		<li>Determine how stratification of patients with respect to the factor affects survival by querying in the survival window.
		<ol>
			<li>In the survival window, uncheck Sort by p-val, then select factor 26 in the Single Factor dropdown menu in order to show survival curves for factor 26.</li>
			<li>Scroll down the survival graph to show the number of patients at risk along the x-axis.</li>
		</ol>
		</li>
		<li>Find associations with biological function by querying within the Annotation window.
		<ol>
			<li>In the annotation window, to sort the Factor dropdown menu by factor number rather than False Discovery Rate (FDR), uncheck FDR sort.</li>
			<li>Scroll and click to select factor 26 in the annotation window dropdown to show enrichment annotations for the factor.</li>
			<li>Scroll down the annotation list until DNA repair is visible and click on it to immediately see associated genes highlighted in yellow on the graph display. See the middle panel of Figure 2.</li>
			<li>Note that factors disappear or appear as different GO terms are selected, according to whether or not they are enriched for genes with the selected annotation, e.g. &#8216;intrinsic apoptotic signaling pathway in response to DNA damage.&#8217;</li>
		</ol>
		</li>
		<li>Explore the factors further by adding windows with different functionality.
		<ol>
			<li>From the top menu bar, add a protein-protein interaction network (PPI) window by selecting PPI from the Add Window dropdown, then click the Add button to add a PPI graph window to the display area. In the PPI graph window, choose factor &#8216;Layer1: 26&#8217; to show the protein-protein interactions. Note the density of connections.</li>
			<li>From top menu bar, instead of PPI, select Heatmap from the Add Window dropdown, then click the Add button to add a heatmap window to the display area. In the heatmap window, choose factor &#8216;Layer1: 26&#8217; to show the gene expression patterns.</li>
			<li>Grab and reposition the heatmap window so that the survival window is also visible. Along the top of the heatmap, observe how the orange/blue/grey colored bar corresponds to patient risk strata on survival graph. Results are shown in the bottom of Figure 2.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
2. Filtering and interpreting CorEx factors using gene weight, survival, and annotation data
<ol>
	<li>Filter for factors of interest using survival and cluster quality.
	<ol>
		<li>From the Dataset dropdown menu at the top, select TCGA_OVCA to go to the CorExplorer page for the TCGA ovarian cancer RNA-seq.</li>
		<li>Once the page has loaded, note from the survival window that the factor with the largest survival differential for different strata is 114.</li>
		<li>At the top of the factor graph window select &#8216;Layer1: 114&#8217; from the Factor dropdown.</li>
		<li>Grab the link weight slider with the mouse and move it up to 0.5. Note that the large number of genes in factor 114 (1609), with none having weight &#62;0.35, indicates a relatively weak clustering.</li>
		<li>Next, expand the list of factors in the survival window and select the next best factor in the survival window dropdown, factor 39, to show its associated survival curves.</li>
		<li>Select factor 39 in the annotation window by clicking on it. The significant GO and KEGG annotations are shown.</li>
	</ol>
	</li>
	<li>To gain a better understanding of the biological role of genes in factor 39, interpret the factors using neighborhood annotation information as follows.
	<ol>
		<li>At the top of the factor graph window, select factor &#8216;Layer1: 39&#8217; in the factor dropdown. Then, move the mouse over the factor graph window and zoom out to reveal the entire L2_14 cluster with 6 factors: 14, 32, 39, 42, 52, and 82 (shown in Figure 3).</li>
		<li>To understand the relative significance of the factors linked to the L2_14 node, start by viewing survival differentials for each of the L2_14 factors. Uncheck Sort by p-val in the survival window and then click on each of the factor numbers in succession. Doing this, note that only factors 14, 32, and 39 display a survival association.</li>
		<li>Now from the top menu bar, select PPI from the Add Window dropdown once again. Press Add to add a PPI graph window to the display area. In the PPI graph window, select factor &#8216;Layer1: 52&#8217; to show the protein-protein interactions that are significant. An example layout of windows at this point is shown in Figure 3.</li>
		<li>Click the View at StringDB link at the bottom of the PPI window to link out to the StringDB online database. Click Continue from the first screen, then select the Analysis tab below the network graph as before to get an online GO analysis for the PPI network genes. The top cellular component is &#8216;MHC class II protein complex.&#8217;</li>
		<li>Return to the CorExplorer tab and PPI window and select factor 32, this time from the factor dropdown. Click the link View at StringDB out to the StringDB analysis. The top cellular component is &#8216;MHC class I protein complex,&#8217; in contrast to class II for factor 52 in the previous step!</li>
		<li>Finally, go back to the PPI window and select &#8216;Layer1: 39&#8217; from the factor dropdown menu at the top. Click the link View at StringDB to link out to the StringDB analysis.</li>
		<li>Click Continue from the first screen, then select the Analysis tab below the network graph to get an online GO analysis for the PPI network genes. Observe that the top molecular function is &#8216;CXCR3 chemokine receptor binding.&#8217;</li>
	</ol>
	</li>
</ol>
3. Using survival and database annotations to look for promising therapeutic combinations
<ol>
	<li>Switch to the TCGA melanoma CorExplorer by selecting TCGA_SKCM from the Dataset dropdown menu.</li>
	<li>Note that the factor with the largest survival differential is factor 171. Examine the factor 171 annotations by scrolling and note that &#8216;immune response&#8217; and &#8216;cytokine-mediated signaling pathway&#8217; are near the top (as they were for the top ovarian factor).</li>
	<li>To find a complementary factor, examine the top survival-associated factors along with their top annotation terms. To do this, click on the Dataset overview link in the top menu bar to open a separate tab containing a table with dataset processing details as well as a summary of top factors according to p-value of the survival differential. Note that the first non-immune factor is 88.</li>
	<li>Return to the TCGA_SKCM browser tab.</li>
	<li>Select factor 88 in the survival, annotation, and graph windows. The top several GO terms are related to &#8216;rRNA processing&#8217; and &#8216;mitochondrion organization,&#8217; confirming it as distinct from the immune-related factors.</li>
	<li>In the survival window, on the paired factors dropdown, select &#8216;88_171&#8217; to see how survival is improved for patients in the middle stratum for the combined 171 and 88 expression factors. Annotation and survival comparisons are illustrated in Figure 4.</li>
</ol>
4. Finding commonalities and differences of gene expression variation across tumor types using the Search page
<ol>
	<li>Click on the CorExplorer heading to return to the front page.</li>
	<li>Click on Search on the top menu bar to go to a page allowing searching over all the datasets on the CorExplorer site.</li>
	<li>In the Gene search box, enter &#8216;FLT1&#8217; (VEGFR1) and hit Return or press Search. FLT1 is found with a relatively high weight in the following factors: OVCA - 76, LUAD - 162, SKCM - 195 and SKCM - 184, as well as COAD - 112 and COAD - 74.</li>
	<li>Alternatively, search for a related GO term across all the datasets. Try this in the &#8216;GO Search&#8217; box by typing &#8216;angiogenesis&#8217; and hitting Return or pressing Search. All FLT1 factors, with the exception of SKCM-195, are listed as statistically enriched for &#8216;angiogenesis&#8217; genes&#8211;factor 195 does, in fact, have the annotation, but below the default 10-8 threshold. Search results for this and the prior step are shown in Figure 5.</li>
	<li>As further examples, in the GO search box, first type &#8216;epidermal growth factor receptor.&#8217; Only LUAD is enriched for this term, a well-known stratification factor for lung cancer. Next, type &#8216;mesenchymal&#8217; in the search box. This term is enriched in gene expression groups for OVCA, where it is a well-studied stratification factor.</li>
</ol>

<ol>
	<li>Petryszak, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+RNASeq-er+API-a+gateway+to+systematically+updated+analysis+of+public+RNA-seq+data.">The RNASeq-er API-a gateway to systematically updated analysis of public RNA-seq data.</a> Bioinformatics. 33, 2218-2220 (2017).</li><li>Steeg, G. V., Galstyan, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Maximally+Informative+Hierarchical+Representations+of+High-Dimensional+Data.">Maximally Informative Hierarchical Representations of High-Dimensional Data.</a> Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics (AISTATS). , (2015).</li><li>Ver Steeg, G., Galstyan, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Discovering+structure+in+high-dimensional+data+through+correlation+explanation.">Discovering structure in high-dimensional data through correlation explanation.</a> Advances in Neural Information Processing Systems. , (2014).</li><li>Pepke, S., Ver Steeg, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comprehensive+discovery+of+subsample+gene+expression+components+by+information+explanation:+therapeutic+implications+in+cancer.">Comprehensive discovery of subsample gene expression components by information explanation: therapeutic implications in cancer.</a> BMC medical Genomics. 10, 12 (2017).</li><li>Byron, S. A., Van Keuren-Jensen, K. R., Engelthaler, D. M., Carpten, J. D., Craig, D. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Translating+RNA+sequencing+into+clinical+diagnostics:+opportunities+and+challenges.">Translating RNA sequencing into clinical diagnostics: opportunities and challenges.</a> Nature Reviews Genetics. 17, 257 (2016).</li><li>Cancer Genome Atlas Research Network. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comprehensive+molecular+profiling+of+lung+adenocarcinoma.">Comprehensive molecular profiling of lung adenocarcinoma.</a> Nature. 511, 543 (2014).</li><li>Cancer Genome Atlas Network. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comprehensive+molecular+characterization+of+human+colon+and+rectal+cancer.">Comprehensive molecular characterization of human colon and rectal cancer.</a> Nature. 487, 330 (2012).</li><li>Akbani, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genomic+classification+of+cutaneous+melanoma.">Genomic classification of cutaneous melanoma.</a> Cell. 161, 1681-1696 (2015).</li><li>Cancer Genome Atlas Research Network. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrated+genomic+analyses+of+ovarian+carcinoma.">Integrated genomic analyses of ovarian carcinoma.</a> Nature. 474, 609 (2011).</li><li>Grossman, R. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Toward+a+shared+vision+for+cancer+genomic+data.">Toward a shared vision for cancer genomic data.</a> New England Journal of Medicine. 375, 1109-1112 (2016).</li><li>Moynahan, M. E., Chiu, J. W., Koller, B. H., Jasin, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brca1+controls+homology-directed+DNA+repair.">Brca1 controls homology-directed DNA repair.</a> Molecular Cell. 4, 511-518 (1999).</li><li>Szklarczyk, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=STRING+v11:+protein&#8211;protein+association+networks+with+increased+coverage,+supporting+functional+discovery+in+genome-wide+experimental+datasets.">STRING v11: protein&#8211;protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.</a> Nucleic Acids Research. 47, 607-613 (2018).</li><li>Durgeau, A., Virk, Y., Corgnac, S., Mami-Chouaib, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Recent+advances+in+targeting+CD8+T-cell+immunity+for+more+effective+cancer+immunotherapy.">Recent advances in targeting CD8 T-cell immunity for more effective cancer immunotherapy.</a> Frontiers in Immunology. 9, 14 (2018).</li><li>Sato, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intraepithelial+CD8++tumor-infiltrating+lymphocytes+and+a+high+CD8+/regulatory+T+cell+ratio+are+associated+with+favorable+prognosis+in+ovarian+cancer.">Intraepithelial CD8+ tumor-infiltrating lymphocytes and a high CD8+/regulatory T cell ratio are associated with favorable prognosis in ovarian cancer.</a> Proceedings of the National Academy of Sciences of the United States of America. 102, 18538-18543 (2005).</li><li>De Moura, M. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mitochondrial+respiration-an+important+therapeutic+target+in+melanoma.">Mitochondrial respiration-an important therapeutic target in melanoma.</a> PLoS One. 7, 40690 (2012).</li><li>Folkman, J., Merler, E., Abernathy, C., Williams, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Isolation+of+a+tumor+factor+responsible+for+angiogenesis.">Isolation of a tumor factor responsible for angiogenesis.</a> Journal of Experimental Medicine. 133, 275-288 (1971).</li><li>Takahashi, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Vascular+endothelial+growth+factor+(VEGF),+VEGF+receptors+and+their+inhibitors+for+antiangiogenic+tumor+therapy.">Vascular endothelial growth factor (VEGF), VEGF receptors and their inhibitors for antiangiogenic tumor therapy.</a> Biological and Pharmaceutical Bulletin. 34, 1785-1788 (2011).</li><li>Subramanian, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gene+set+enrichment+analysis:+a+knowledge-based+approach+for+interpreting+genome-wide+expression+profiles.">Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.</a> Proceedings of the National Academy of Sciences of the United States of America. 102, 15545-15550 (2005).</li><li>Cerami, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+cBio+Cancer+Genomics+Portal:+An+Open+Platform+for+Exploring+Multidimensional+Cancer+Genomics+Data.">The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data.</a> Cancer Discovery. 2, 401-404 (2012).</li><li>Gao, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrative+Analysis+of+Complex+Cancer+Genomics+and+Clinical+Profiles+Using+the+cBioPortal.">Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal.</a> Science Signalling. 6, 1 (2013).</li><li>Reich, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GenePattern+2.0.">GenePattern 2.0.</a> Nature Genetics. 38, 500 (2006).</li><li>Wang, Y. E., Kutnetsov, L., Partensky, A., Farid, J., Quackenbush, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=WebMeV:+A+Cloud+Platform+for+Analyzing+and+Visualizing+Cancer+Genomic+Data.">WebMeV: A Cloud Platform for Analyzing and Visualizing Cancer Genomic Data.</a> Cancer Research. 77, 11-14 (2017).</li><li>. Morpheus Available from: <a target="_blank" href="https://software.broadinstitute.org/morpheus">https://software.broadinstitute.org/morpheus</a> (2019)</li><li>Weitschek, E., Lauro, S. D., Cappelli, E., Bertolazzi, P., Felici, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CamurWeb:+a+classification+software+and+a+large+knowledge+base+for+gene+expression+data+of+cancer.">CamurWeb: a classification software and a large knowledge base for gene expression data of cancer.</a> BMC Bioinformatics. 19, 354 (2018).</li><li>Chou, P. -. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=tACCo,+a+Database+Connecting+transcriptome+Alterations,+pathway+Alterations+and+Clinical+outcomes+in+Cancers.">tACCo, a Database Connecting transcriptome Alterations, pathway Alterations and Clinical outcomes in Cancers.</a> Scientific Reports. 9, 3877 (2019).</li></ol>

The authors declare that they have no competing financial interests.

We have presented the CorExplorer site, a publicly accessible web server for interactive exploration of maximally correlated gene expression factors learned from tumor RNA-seq by the CorEx algorithm. We have shown how the website may be used to stratify patients according to tumor gene expression, and how such stratification corresponds to biological function and survival.
Other webservers for RNA-seq analysis have been built. Differential and co-expression analysis for tumors can be examined and integrated with other data types in cbioPortal19,20. The servers GenePattern21, Mev22, and Morpheus23, incorporate established clustering techniques such as principal component analysis (PCA), kmeans, or self-organizing maps (SOMs). More innovative efforts include CamurWeb24, based upon an automated rule-generating classifier, and TACCO25, which implements random forest classifiers and lassos. The CorEx algorithm used here optimizes multivariate information in order to find a hierarchy of factors that explain patterns in data. The nonlinear and hierarchical factor learning appears to yield improved interpretability relative to the linear global factors found via PCA4. Additionally, the technique&#39;s fine-grained parsing of sample signals allows precise tumor comparisons vis-&#224;-vis more commonly used broad subtypes. This combination of overlapping and hierarchical factor analysis distinguishes the CorExplorer from most other approaches and necessitates new tools for visualization and summarization.
A critical part of the CorExplorer factor analysis is the capability to explore not just several, but over 100 factors with informative gene patterns that are placed within an overlapping hierarchy. The CorExplorer facilitates the mining of these myriad factors for biological and clinical associations and allows for exceptionally detailed characterization of individual tumors. The unsupervised learning of such a large number of factors means that not all will be relevant to disease biology. In such a case, it is essential to either use annotations or known genes to pull out factors of interest or search for factors associated with clinical data such as survival. Thus, the CorExplorer allows users to implement this very important filtering step. The presence of factor gene patterns in a tumor may even suggest an approach to personalized oncology treatment. Further, the multiplicity of factor scores for each tumor that allows for discovery of potentially useful therapeutic combinations.
It is sometimes the case that no significant GO annotations appear for factors highly correlated with survival. While this may occur due to noisy or under sampled data, there are other possible causes such as a cluster size that is too small to register significant enrichment scores or the group being a &#8216;basket&#8217; of single genes from diverse pathways without coherent biological association. Additionally, a category of annotation different from the KEGG and GO biological process, e.g. cellular compartment, may be appropriate. These can be accessed by linking out to StringDB as demonstrated in the protocol. The Gene Ontology enrichment analysis on the CorExplorer site currently does not account for the gene weighting in a factor, though this will likely be remedied in the near future. Note a gene list option is available under &#8216;Add Window&#8217; that allows for download of the complete factor gene list for further analysis with external tools.
For the purposes of the website, CorEx was run on each of the datasets five times and the run that resulted in the greatest overall Total Correlation was retained. Having a statistical representation of the results of multiple runs may be more informative and is a goal for future work. Additionally, the set of tumor types available on the server is rather small, but we expect this to expand over time according to user interest.
As outlined above, the CorExplorer visualizes CorEx RNA-seq factor relationships along with clinical and database information, thus enabling a variety of different modes of interrogation. We are hopeful that this tool will lead to further work to utilize the power of RNA-seq analysis for discovery and clinical application in oncology.

Since its introduction just over a decade ago, RNA-seq has become a ubiquitous tool for measuring gene expression1. This is because it enables rapid and cheap de novo profiling of the entire transcriptome of a sample. However, RNA-seq tumor data reflects an underlying biology that is intrinsically complex and often under-sampled, while the data itself is high-dimensional and noisy. This presents a significant challenge for extracting reliable signals. The CorEx algorithm leverages multivariate mutual information to find subtle patterns in such situations2,3 . This technique was previously adapted to analyze ovarian tumor RNA-seq samples from the The Cancer Genome Atlas (TCGA) and in this context it appeared to have significant advantages over more commonly used analysis methods4.
Though the use of RNA-seq is enormously widespread in research applications, including in oncology, those efforts have not led to broad utilization for the purposes of clinical interventions5. Part of the reason for this is a lack of user-friendly algorithms and software targeted to these specific problems. To help bridge this gap, we have designed the CorExplorer web portal to enable researchers from a variety of backgrounds to study gene expression factors of tumor RNA-seq samples as found by the CorEx machine learning algorithm. The CorExplorer portal supports interactive visualization and querying of factors from several different tumor types including lung, colon, melanoma, and ovarian6,7,8,9,10, with the intent of helping researchers to sift through the data correlations and identify candidate pathways to stratify patients for therapeutic purposes.
We expect the CorExplorer portal may be useful to several types of users. The portal was designed with the user in mind who wishes to understand the broad factors driving tumoral gene expression differences in public databases and possibly also place individual gene expression profiles in the context of tumors with similar characteristics. In addition to the representative protocols outlined here, CorExplorer investigations may serve as a starting point to suggest hypotheses for further testing, to compare and contrast CorEx findings on datasets outside of the CorExplorer, and to connect pathological expression signatures of one or a few genes in an individual tumor to larger groups that may be coordinately affected. Finally, it may serve as a user-friendly introduction to the application of machine learning to RNA-seq for those getting started in the field.

<table>
	<tbody>
		<tr>
			<td>Public server for CorExplorer website</td>
			<td>USC</td>
			<td>http://corex.isi.edu</td>
			<td>Intel Xeon E5-2690 4-core 2.6 GHz, 8GB RAM. Backend architecture is LAMP: Linux, Apache, MySQL, PHP.</td>
		</tr>
		<tr>
			<td>Web browser</td>
			<td>Google/Apple</td>
			<td>Chrome/Safari</td>
			<td>Verified web browsers.</td>
		</tr>
	</tbody>
</table>

analyzing tumor gene expression factors with the corexplorer web portal

Differential gene expression analysis is an important technique for understanding disease states. The machine learning algorithm CorEx has shown utility in analyzing differential expression of groups of genes in tumor RNA-seq in a way that may be helpful for advancing precision oncology. However, CorEx produces many factors that can be challenging to analyze and connect to existing understanding. To facilitate such connections, we have built a website, CorExplorer, that allows users to interactively explore the data and answer common questions related to its analysis. We trained CorEx on RNA-seq gene expression data for four tumor types: ovarian, lung, melanoma, and colorectal. We then incorporated corresponding survival, protein-protein interactions, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments, and heatmaps into the website for association with the factor graph visualization. Here we employ example protocols to illustrate use of the database for comprehending the significance of the learned tumor factors in the context of this external data.

Searching for the gene &#8216;BRCA1&#8217; in the lung cancer dataset reveals it to be most strongly associated with CorEx factor 26 (Figure 2). GO term enrichment for this factor is seen to be extremely high, with DNA repair exhibiting an FDR of only 1 x 10-19. The selection also draws attention to the second level cluster L2_8 that has six closely related factors as children. Selecting &#8216;DNA repair&#8217; in either the GO term annotations or the factor graph&#39;s GO enriched dropdown highlights associated genes in each of the factors, with the factor 26 having by far the most, as expected11. The protein-protein interaction network is strongly connected, further supporting the tightly linked functionality of the genes in factor 26. The associated survival graph suggests a possible association with patient survival, but this would have to be confirmed in a larger dataset.
Starting with survival can allow dissection of reasons for improved survival associated with particular gene expression groups. As an example, the top factor influencing survival for ovarian cancer is seen to be number 39, which is strongly enriched for genes associated with the immune system (Figure 3). Five other factors associated with the same level 2 node are also indicated to be immune-related, however the survival impact appears to be strongly variable among them, with 39 being the highest and 52 being the lowest. Adding a protein-protein interaction window for a factor shows the immediate interaction network and allows for link out to the StringDB12 website to query various enrichments for the PPI network genes. By doing this for each of the L2_14 factors in turn, one finds that StringDB enrichments for the PPI network genes suggest the following possible explanation for the associations with survival. Factor 32 contains genes that make up the major histocompatibility complex (MHC) class I protein complex, which is recognized by cytotoxic T lymphocytes. Factor 39 corresponds to cytokine signaling and CXCR3 receptor binding, related to CD8+ T lymphocytes. Both of these factors appear to confer a significant survival advantage for patients exhibiting relatively high expression of the corresponding genes. Cytotoxic CD8+ T lymphocytes are primarily responsible for anti-tumor immunity. Factor 52, on the other hand, is comprised of genes coding for proteins in the MHC class II complex which are recognized primarily by CD4+ T helper cells rather than directly by cytotoxic T lymphocytes. The remaining L2_14 factors reflect generalized immune system activation that doesn&#39;t differentiate the two types of lymphocyte populations. A survival association specific to cytotoxic T lymphocyte recognition of MCH class I cellular antigens is consistent with our understanding of antitumor immunity in general and from other cancers such as melanoma13,14.
The web portal supports the discovery of pairs of factors with complementary functions that may suggest effective tumor-specific combination therapies. The dataset overview can be scanned for factors that show a correlation with survival yet have distinct GO enrichments. For melanoma (TCGA_SKCM; Figure 4), it is seen that the top survival factor 171 is immune related, while factor 88 down the list shows enrichment for genes related to mitochondrion organization. Indeed, this has been suggested as a target in melanoma15. Adding survival windows to the CorExplorer page allows comparison of stratification using the factor pair to that of each factor individually, showing that favorable gene expression patterns from both groups exhibits a trend of survival better than that for either factor alone. The top stratum does not appear to be improved however, suggesting immunotherapy only may be the best option for some patients.
Commonalities and differences among tumors can be seen by searching across datasets for genes or GO terms (Figure 5). As an example, FLT1 (aka VEGFR1) is a well-studied pro-angiogenic marker16,17. When it is put into the search bar, all of the tumors have factors in which FLT1 plays a major role. Conversely, when the GO term &#8216;angiogenesis&#8217; is input on the search page, 5 out of 6 of the FLT1 groups appear with that enrichment. All FLT1 factors, with the exception of SKCM-195, are listed as statistically enriched for &#8216;angiogenesis&#8217; genes. The sixth factor does, in fact, have the annotation, but below the default 10-8 threshold. When the weighting within the factor list is utilized in an alternative enrichment calculator, e.g., Gene Set Enrichment Analysis (GSEA)18, the sixth factor is found to be significantly enriched for &#8216;angiogenesis&#8217; genes as well.
It is important to check the heatmaps to ensure the gene expression pattern is of adequate quality to support biological interpretations. Heatmaps that show strong clear variation may exhibit either coordinated expression of the factor genes ranging from low to high or more complex patterns with some genes having low expression correlated with others having high (Figure 6). A key marker of a high-quality grouping is the presence of several genes with a smooth variation in expression as a function of factor score. The factor heatmaps show samples ordered according to factor score, thus there should be a smooth gradient moving from left to right. However, this can fail to happen in at least two different ways. Most commonly, the correlations may be extremely noisy (Figure 5C), calling into question the robustness and utility of any inferences regarding survival and/or biological function. Also, patterns that happen only in a small minority of samples may not conform to the model of three expression states assumed by the CorEx algorithm, resulting in a misleading classification of the samples (right side of Figure 5D).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60431/60431fig01.jpg" /> 
Figure 1: CorExplorer front page.&#160;After clicking on + next to Ovarian Cancer under Quick Links, factor graph details are shown. The CorEx hierarchical model is made up of input variables (gene expression in this case) on the bottom layer and inferred latent factors in the higher layers. <a href="https://www.jove.com/files/ftp_upload/60431/60431fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/60431/60431fig02.jpg" /> 
Figure 2: Using a gene name to guide exploration.&#160;The figure shows a series of screenshots illustrating exploration of CorEx lung cancer factors strongly related to BRCA1. First, selecting &#8216;BRCA1&#8217; in the Gene dropdown box for the factor graph causes the graph view to zoom in on the factor for which BRCA1 has greatest weight. Zooming out a bit frames the layer two node L2_8 connecting that factor to other related ones. Survival and annotations can be compared: clicking on the GO term DNA repair highlights annotated genes. A PPI window is added to show the network interactions for genes in the factor. Using the Add Window button to add a heat map shows association of expression patterns with survival, suggesting increased expression of DNA repair genes may be associated with decreased survival. <a href="https://www.jove.com/files/ftp_upload/60431/60431fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/60431/60431fig03.jpg" /> 
Figure 3: Using clinical data (survival) to guide exploration.&#160;Exploring the top survival-associated factor (39) for ovarian cancer reveals interesting relationships among neighboring factors. After selecting factor 39 in the factor graph and zooming out a bit, the layer two factor linked to factor 39 is seen to have five other associated factors. An additional survival window allows direct comparison of the associated survival differentials. Factors 39 and 32 both show a positive survival correlation, in contrast to factor 52, which does not. The protein-protein interaction networks are all well-defined. Linking out to StringDB allows comparison of the GO annotations (not shown): Factor 39 is associated with a cytokine signaling network related to cytotoxic CD8+ T lymphocyte activation and factor 32 is dominated by MHC class I antigen presenting proteins that trigger recognition by such lymphocytes; the neighboring factors, however, are dominated by other immune system components such as CD4+ helper T cells and show no survival correlation. <a href="https://www.jove.com/files/ftp_upload/60431/60431fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/60431/60431fig04v3.jpg" /> 
Figure 4: Exploring top survival factors suggests potential therapeutic combinations.&#160;The &#8216;Datasets&#8217; link on the home page menu bar leads to a concise table of survival factors ordered by p-value, along with the top GO annotation (not shown). Using this information for melanoma, the combination of factor 171 for immune function with factor 88 for mitochondrion organization appears complementary. The figure shows annotation windows for each of the factors side-by-side to contrast them. Survival curves for patients stratified by the two factors individually or together indicate that the combination increases the survival differential compared to either factor alone. <a href="https://www.jove.com/files/ftp_upload/60431/60431fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/60431/60431fig05.jpg" /> 
Figure 5: The Search page facilitates pan-cancer analysis.&#160;Genes or GO biological process terms can be searched for across all datasets using the Search link from the home page. The figure shows search results for the gene FLT1 and the GO term &#8216;angiogenesis&#8217;. The results show the presence of FLT1 in factors annotated with the term &#8216;angiogenesis&#8217; across cancers. <a href="https://www.jove.com/files/ftp_upload/60431/60431fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/60431/60431fig06v2.jpg" /> 
Figure 6: Heatmaps can be used to qualitatively assess correlations among genes and samples according to factor score.&#160;High quality gene expression relationships are shown by smooth gradation when patients are ordered by factor score in the heatmaps. The leftmost heatmap for factor 18 is one example. The patterns may also encompass complex signatures of up and down expression as in the middle large heatmap for factor 11. Lower quality patterns sometimes show abrupt changes in expression for a subgroup of patients as in the factor 9 heatmap on the right or simple very noisy correlations as in the factor 161 heatmap at the lower right. <a href="https://www.jove.com/files/ftp_upload/60431/60431fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal at JoVE.com

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

We introduce the CorExplorer web portal, an resource for exploration of tumor RNA sequencing factors found by the machine learning algorithm CorEx (Correlation Explanation), and show how factors can be analyzed relative to survival, database annotations, protein-protein interactions, and one another to gain insight into tumor biology and therapeutic interventions.

Differential gene expression analysis is an important technique for understanding disease states. The machine learning algorithm CorEx has shown utility ...

analyzing-tumor-gene-expression-factors-with-corexplorer-web

Research

JoVE Journal

Cancer Research

7.4K Views.  Lyrid LLC. We introduce the CorExplorer web portal, an resource for exploration of tumor RNA sequencing factors found by the machine learning algorithm CorEx (Correlation Explanation), and show how factors can be analyzed relative to survival, database annotations, protein-protein interactions, and one another to gain insight into tumor biology and therapeutic interventions.

Video: Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

The Drosophila Imaginal Disc Tumor Model: Visualization and Quantification of Gene Expression and Tumor Invasiveness Using Genetic Mosaics

Drosophila melanogaster has emerged as a powerful experimental system for functional and mechanistic studies of tumor development and progression in the context of a whole organism. Sophisticated techniques to generate genetic mosaics facilitate induction of visually marked, genetically defined clones surrounded by normal tissue. The clones can be analyzed through diverse molecular, cellular and omics approaches. This study describes how to generate fluorescently labeled clonal tumors of varying malignancy in the eye/antennal imaginal discs (EAD) of Drosophila larvae using the Mosaic Analysis with a Repressible Cell Marker (MARCM) technique. It describes procedures how to recover the mosaic EAD and brain from the larvae and how to process them for simultaneous imaging of fluorescent transgenic reporters and antibody staining. To facilitate molecular characterization of the mosaic tissue, we describe a protocol for isolation of total RNA from the EAD. The dissection procedure is suitable to recover EAD and brains from any larval stage. The fixation and staining protocol for imaginal discs works with a number of transgenic reporters and antibodies that recognize Drosophila proteins. The protocol for RNA isolation can be applied to various larval organs, whole larvae, and adult flies. Total RNA can be used for profiling of gene expression changes using candidate or genome-wide approaches. Finally, we detail a method for quantifying invasiveness of the clonal tumors. Although this method has limited use, its underlying concept is broadly applicable to other quantitative studies where cognitive bias must be avoided.

The Drosophila Imaginal Disc Tumor Model: Visualization and Quantification of Gene Expression and Tumor Invasiveness Using Genetic Mosaics

This protocol demonstrates how to generate fluorescently marked, genetically defined clonal tumors in the Drosophila eye/antennal imaginal discs (EAD). It describes how to dissect the EAD and brain from the third instar larvae and how to process them to visualize and quantify gene expression changes and tumor invasiveness.

Drosophila melanogaster has emerged as a powerful experimental system for functional and mechanistic studies of tumor development and progression in the ...

Identification and Characterization of Metastatic Factors by Gene Transfer into the Novel RIP-Tag; RIP-tva Murine Model

Metastatic cancer accounts for 90% of deaths in patients with solid tumors. There is an urgent need to better understand the drivers of cancer metastasis and to identify novel therapeutic targets. To investigate molecular events that drive the progression from primary cancer to metastasis, we have developed a bitransgenic mouse model, RIP-Tag; RIP-tva. In this mouse model, the rat insulin promoter (RIP) drives the expression of the SV40 T antigen (Tag) and the receptor for subgroup A avian leukosis virus (tva) in pancreatic &#946; cells. The mice develop pancreatic neuroendocrine tumors with 100% penetrance through well-defined stages that are similar to human tumorigenesis, with stages including hyperplasia, angiogenesis, adenoma, and invasive carcinoma. Because RIP-Tag; RIP-tva mice do not develop metastatic disease, genetic alterations that promote metastasis can be identified easily. Somatic gene transfer into tva-expressing, proliferating pancreatic &#946; premalignant lesions is achieved through intracardiac injection of avian retroviruses harboring the desired genetic alteration. A titer of &#62;1 x 108 infectious units per ml is considered appropriate for in vivo infection. In addition, avian retroviruses can infect cell lines derived from tumors in RIP-Tag; RIP-tva mice with high efficiency. The cell lines can also be used to characterize the metastatic factors. Here we demonstrate how to utilize this mouse model and cell lines to assess the functions of candidate genes in tumor metastasis.

Identification and Characterization of Metastatic Factors by Gene Transfer into the Novel RIP-Tag; RIP-tva Murine Model

We present a protocol to demonstrate a novel somatic gene transfer system utilizing RIP-Tag; RIP-tva mouse model to study the function of genes in metastasis. The avian retroviruses are delivered intracardiacally to ensure gene transfer into pre-malignant, noninvasive lesions of pancreatic &#946; cells in adult mice.

Metastatic cancer accounts for 90% of deaths in patients with solid tumors. There is an urgent need to better understand the drivers of cancer metastasis ...

Conditional Knockdown of Gene Expression in Cancer Cell Lines to Study the Recruitment of Monocytes/Macrophages to the Tumor Microenvironment

siRNA and shRNA-mediated knock down (KD) methods of regulating gene expression are invaluable tools for understanding gene and protein function. However, in the case that the KD of the protein of interest has a lethal effect on cells or the anticipated effect of the KD is time-dependent, unconditional KD methods are not appropriate. Conditional systems are more suitable in these cases and have been the subject of much interest. These include Ecdysone-inducible overexpression systems, Cytochrome P-450 induction system1, and the tetracycline regulated gene expression systems. 
The tetracycline regulated gene expression system enables reversible control over protein expression by induction of shRNA expression in the presence of tetracycline. In this protocol, we present an experimental design using functional Tet-ON system in human cancer cell lines for conditional regulation of gene expression. We then demonstrate the use of this system in the study of tumor cell-monocyte interaction.

This protocol serves as a scheme for setting up a functional Tet-ON system in cancer cell lines and its subsequent use, in particular for studying the role of tumor cell-derived proteins in recruitment of monocytes/macrophages to the tumor microenvironment.

siRNA and shRNA-mediated knock down (KD) methods of regulating gene expression are invaluable tools for understanding gene and protein function. However, ...

Radionuclide-fluorescence Reporter Gene Imaging to Track Tumor Progression in Rodent Tumor Models

Metastasis is responsible for most cancer deaths. Despite extensive research, the mechanistic understanding of the complex processes governing metastasis remains incomplete. In vivo models are paramount for metastasis research, but require refinement. Tracking spontaneous metastasis by non-invasive in vivo imaging is now possible, but remains challenging as it requires long-time observation and high sensitivity. We describe a longitudinal combined radionuclide and fluorescence whole-body in vivo imaging approach for tracking tumor progression and spontaneous metastasis. This reporter gene methodology employs the sodium iodide symporter (NIS) fused to a fluorescent protein (FP). Cancer cells are engineered to stably express NIS-FP followed by selection based on fluorescence-activated cell sorting. Corresponding tumor models are established in mice. NIS-FP expressing cancer cells are tracked non-invasively in vivo at the whole-body level by positron emission tomography (PET) using the NIS radiotracer [18F]BF4-. PET is currently the most sensitive in vivo imaging technology available at this scale and enables reliable and absolute quantification. Current methods either rely on large cohorts of animals that are euthanized for metastasis assessment at varying time points, or rely on barely quantifiable 2D imaging. The advantages of the described method are: (i) highly sensitive non-invasive in vivo 3D PET imaging and quantification, (ii) automated PET tracer production, (iii) a significant reduction in required animal numbers due to repeat imaging options, (iv) the acquisition of paired data from subsequent imaging sessions providing better statistical data, and (v) the intrinsic option for ex vivo confirmation of cancer cells in tissues by fluorescence microscopy or cytometry. In this protocol, we describe all steps required for routine NIS-FP-afforded non-invasive in vivo cancer cell tracking using PET/CT and ex vivo confirmation of in vivo results. This protocol has applications beyond cancer research whenever in vivo localization, expansion and long-time monitoring of a cell population is of interest.

We describe a protocol for preclinical in vivo tracking of cancer metastasis. It is based on a radionuclide-fluorescence reporter combining the sodium iodide symporter, detected by non-invasive [18F]tetrafluoroborate-PET, and a fluorescent protein for streamlined ex vivo confirmation. The method is applicable for preclinical in vivo cell tracking beyond tumor biology.

Metastasis is responsible for most cancer deaths. Despite extensive research, the mechanistic understanding of the complex processes governing metastasis ...

Fabrication of Tongue Extracellular Matrix and Reconstitution of Tongue Squamous Cell Carcinoma In Vitro

In order to construct an effective and realistic model for tongue squamous cell carcinoma (TSCC) in vitro, the methods were created to produce decellularized tongue extracellular matrix (TEM) which provides functional scaffolds for TSCC construction. TEM provides an in vitro niche for cell growth, differentiation, and cell migration. The microstructures of native extracellular matrix (ECM) and biochemical compositions retained in the decellularized matrix provide tissue-specific niches for anchoring cells. The fabrication of TEM can be realized by deoxyribonuclease (DNase) digestion accompanied with a serious of organic or inorganic pretreatment. This protocol is easy to operate and ensures high efficiency for the decellularization. The TEM showed favorable cytocompatibility for TSCC cells under static or stirred culture conditions, which enables the construction of the TSCC model. A self-made bioreactor was also used for the persistent stirred condition for cell culture. Reconstructed TSCC using TEM showed the characteristics and properties resembling clinical TSCC histopathology, suggesting the potential in TSCC research.

Fabrication of Tongue Extracellular Matrix and Reconstitution of Tongue Squamous Cell Carcinoma In Vitro

A method is shown here for the preparation of the tongue extracellular matrix (TEM) with efficient decellularization. The TEM can be used as functional scaffolds for the reconstruction of a tongue squamous cell carcinoma (TSCC) model under static or stirred culture conditions.

In order to construct an effective and realistic model for tongue squamous cell carcinoma (TSCC) in vitro, the methods were created to produce ...

Digital Polymerase Chain Reaction Assay for the Genetic Variation in a Sporadic Familial Adenomatous Polyposis Patient Using the Chip-in-a-tube Format

The quantitative analysis of human genetic variation is crucial for understanding the molecular characteristics of serious medical conditions, such as tumors. Because digital polymerase chain reactions (PCR) enable the precise quantification of DNA copy number variants, they are becoming an essential tool for detecting rare genetic variations, such as drug-resistant mutations. It is expected that molecular diagnoses using digital PCR (dPCR) will be available in clinical practice in the near future; thus, how to efficiently conduct dPCR with human genetic material is a hot topic. Here, we introduce a method to detect Adenomatous polyposis coli (APC) somatic mosaicism using dPCR with the chip-in-a-tube format, which allows eight dPCR reactions to be simultaneously conducted. Care should be taken when filling and sealing the reaction mixture on the chips. This article demonstrates how to avoid the over- and underestimation of positive partitions. Furthermore, we present a simple procedure for collecting the dPCR product from the partitions on the chips, which can then be used to confirm the specific amplification. We hope that this methods report will help promote the dPCR with the chip-in-a-tube method in genetic research.

Digital polymerase chain reaction (PCR) is a useful tool for the high-sensitivity detection of single nucleotide variants and DNA copy number variants. Here, we demonstrate key considerations for measuring rare variants in the human genome using digital PCR with the chip-in-a-tube format.

The quantitative analysis of human genetic variation is crucial for understanding the molecular characteristics of serious medical conditions, such as ...

Analyzing Tumor and Tissue Distribution of Target Antigen Specific Therapeutic Antibody

Monoclonal antibodies are high affinity multifunctional drugs that work by variable independent mechanisms to eliminate cancer cells. Over the last few decades, the field of antibody-drug conjugates, bispecific antibodies, chimeric antigen receptors (CAR) and cancer immunotherapy has emerged as the most promising area of basic and therapeutic investigations. With numerous successful human trials targeting immune checkpoint receptors and CAR-T cells in leukemia and melanoma at a breakthrough pace, it is highly exciting times for oncologic therapeutics derived from variations of antibody engineering. Regrettably, a significantly large numbers of antibody and CAR based therapeutics have also proven disappointing in human trials of solid cancers because of the limited availability of immune effector cells in the tumor bed. Importantly, nonspecific distribution of therapeutic antibodies in tissues other than tumors also contribute to the lack of clinical efficacy, associated toxicity and clinical failure. As faithful translation of preclinical studies into human clinical trails are highly relied on mice tumor xenograft efficacy and safety studies, here we highlight a method to test the tumor and general tissue distribution of therapeutic antibodies. This is achieved by labeling the protein-A purified antibody with near Infrared fluorescent dye followed by live imaging of tumor bearing mice.

Here we present a protocol to study the in vivo localization of antibodies in mice tumor xenograft models.

Monoclonal antibodies are high affinity multifunctional drugs that work by variable independent mechanisms to eliminate cancer cells. Over the last few ...

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Many cancers are characterized by chromosomal translocations which result in the expression of oncogenic fusion transcription factors. Typically, these proteins contain an intrinsically disordered domain (IDD) fused with the DNA-binding domain (DBD) of another protein and orchestrate widespread transcriptional changes to promote malignancy. These fusions are often the sole recurring genomic aberration in the cancers they cause, making them attractive therapeutic targets. However, targeting oncogenic transcription factors requires a better understanding of the mechanistic role that low-complexity, IDDs play in their function. The N-terminal domain of EWSR1 is an IDD involved in a variety of oncogenic fusion transcription factors, including EWS/FLI, EWS/ATF, and EWS/WT1. Here, we use RNA-sequencing to investigate the structural features of the EWS domain important for transcriptional function of EWS/FLI in Ewing sarcoma. First shRNA-mediated depletion of the endogenous fusion from Ewing sarcoma cells paired with ectopic expression of a variety of EWS-mutant constructs is performed. Then RNA-sequencing is used to analyze the transcriptomes of cells expressing these constructs to characterize the functional deficits associated with mutations in the EWS domain. By integrating the transcriptomic analyses with previously published information about EWS/FLI DNA binding motifs, and genomic localization, as well as functional assays for transforming ability, we were able to identify structural features of EWS/FLI important for oncogenesis and define a novel set of EWS/FLI target genes critical for Ewing sarcoma. This paper demonstrates the use of RNA-sequencing as a method to map the structure-function relationship of the intrinsically disordered domain of oncogenic transcription factors.

Intrinsically disordered domains are important for oncogenic fusion transcription factor function. To therapeutically target these proteins, a more detailed understanding of the regulatory mechanisms employed by these domains is required. Here, we use transcriptomics to map important structural features of the intrinsically disordered EWS domain in Ewing sarcoma.

Many cancers are characterized by chromosomal translocations which result in the expression of oncogenic fusion transcription factors. Typically, these ...

Generation of Zebrafish Larval Xenografts and Tumor Behavior Analysis

Zebrafish larval xenografts are being widely used for cancer research to perform in vivo and real-time studies of human cancer. The possibility of rapidly visualizing the response to anti-cancer therapies (chemo, radiotherapy, and biologicals), angiogenesis and metastasis with single cell resolution, places the zebrafish xenograft model as a top choice to develop preclinical studies.

The zebrafish larval xenograft assay presents several experimental advantages compared to other models, but probably the most striking is the reduction of size scale and consequently time. This reduction of scale allows single cell imaging, the use of a relatively low number of human cells (compatible with biopsies), medium-high-throughput drug screenings, but most importantly enables a significant reduction of the time of the assay. All these advantages make the zebrafish xenograft assay extremely attractive for future personalized medicine applications.

Many zebrafish xenograft protocols have been developed with a wide diversity of human tumors; however, a general and standardized protocol to efficiently generate zebrafish larval xenografts is still lacking. Here we provide a step-by-step protocol, with tips to generate xenografts and guidelines for tumor behavior analysis, whole-mount immunofluorescence, and confocal imaging quantification.

Here, we provide a step-by-step protocol, with tips to generate xenografts and guidelines for tumor behavior analysis, whole-mount immunofluorescence, and confocal imaging quantification.

Zebrafish larval xenografts are being widely used for cancer research to perform in vivo and real-time studies of human cancer. The possibility of rapidly ...

Portal Vein Injection of Colorectal Cancer Organoids to Study the Liver Metastasis Stroma

Hepatic metastasis of colorectal cancer (CRC) is a leading cause of cancer-related death. Cancer-associated fibroblasts (CAFs), a major component of the tumor microenvironment, play a crucial role in metastatic CRC progression and predict poor patient prognosis. However, there is a lack of satisfactory mouse models to study the crosstalk between metastatic cancer cells and CAFs. Here, we present a method to investigate how liver metastasis progression is regulated by the metastatic niche and possibly could be restrained by stroma-directed therapy. Portal vein injection of CRC organoids generated a desmoplastic reaction, which faithfully recapitulated the fibroblast-rich histology of human CRC liver metastases. This model was tissue-specific with a higher tumor burden in the liver when compared to an intra-splenic injection model, simplifying mouse survival analyses. By injecting luciferase-expressing tumor organoids, tumor growth kinetics could be monitored by in vivo imaging. Moreover, this preclinical model provides a useful platform to assess the efficacy of therapeutics targeting the tumor mesenchyme. We describe methods to examine whether adeno-associated virus-mediated delivery of a tumor-inhibiting stromal gene to hepatocytes could remodel the tumor microenvironment and improve mouse survival. This approach enables the development and assessment of novel therapeutic strategies to inhibit hepatic metastasis of CRC.

Portal vein injection of colorectal cancer (CRC) organoids generates stroma-rich liver metastasis. This mouse model of CRC hepatic metastasis represents a useful tool to study tumor-stroma interactions and develop novel stroma-directed therapeutics such as adeno-associated virus-mediated gene therapies.

Hepatic metastasis of colorectal cancer (CRC) is a leading cause of cancer-related death. Cancer-associated fibroblasts (CAFs), a major component of the ...