Manual Module2 v43 - Cancer Biology and Therapeutics Program · 2019-11-08 · Oncomine 4.3 Training - Module 2: Target Discovery and Validation 3 No part of this document may be

Module 2:

Target Discovery and Validation

2 Oncomine 4.3 Training - Module 2: Target Discovery and Validation

No part of this document may be reproduced without written consent from Compendia Bioscience.

Table of Contents

1.1 Getting Started ............................................................................. 6

1.2 Nomination, validation, and associations of AGTR1 as an outlier in a subset of breast cancers ....................................................... 7

1.2.1 AGTR1 discovery and validation .................................... 7

1.2.2 Breast cancer biomarkers associated with AGTR1 outlier over-expression ............................................................ 14

1.2.3 Finding a Cell Line ........................................................ 18

1.2.4 Summary of nomination, validation, and molecular associations of AGTR1 as an outlier in breast cancer .. 20

1.3 FOXM1 validation and drug target development ........................ 22

1.3.1 Validation of FOXM1 over-expression in breast cancer 22

1.3.2 FOXM1’s role in the cell cycle and other important biological associations .................................................. 25

1.3.3 FOXM1 sensitivity to targeted drug therapies .............. 32

1.3.4 Summary of 1.3: FOXM1 validation and drug discovery ..................................................................................... 36

1.4 Summary .................................................................................... 37

1.4.1 Oncomine Support ....................................................... 37

Oncomine 4.3 Training - Module 2: Target Discovery and Validation 3


Target Identification and Validation with Oncomine ™

Prerequisites: Module 1: Introduction to Oncomine

Training Setup Required:

• Internet access

• Internet Explorer 6.0 (at minimum, Service Pack 3 is strongly recommended), or higher

• Firefox 2 is supported for PC and Mac OS X users

• Java script must be enabled

• Oncomine Login

Register as an Oncomine User

Register as new user:

• Navigate to: www.oncomine.com

• Select the “Not a user? Register now!” link.

• Complete registration using valid business email.

• Once registration is accepted a password will be sent to the email entered.

• Select the “Forgot password?” link to reset at any time.

• Return to www.oncomine.com and enter USER ID (Email) and PASSWORD to login to Oncomine.



Welcome to Oncomine 4.3 Training!

This training module is designed to illustrate the ability of Oncomine to nominate and validate outliers in Cancer. It also highlights the cell line and drug sensitivity data that are available.

By the end of this module, you should be familiar with:

• Using filters and performing meta-analysis

• The Gene Summary View

• Discovering and validating an outlier

• The Group By function

• Where to find cell line data

• The types of drug sensitivity and perturbation analyses available in Oncomine



Training Manual Conventions

1. Style Conventions

Bold Keyboard-entered text (MYC)

Bold and Italic Command series (All > Tumor vs. Normal > Prostate)

Title Capitalization Dialog box names (Search); menu names (Sort By)

2. Oncomine Notes

Oncomine Notes appear in boxes throughout this manual to provide general information designed to make Oncomine easier to use. There are two types of Oncomine Notes in this manual: Tip and Tech Note. These notes are enclosed in boxes to make them easy to distinguish and locate for future reference:

TIP: Tips provide added information about how Oncomine works, or offer suggestions about how to speed up an operation.

TECH NOTE: Technical notes provide additional information about various functions in Oncomine.

3. Oncomine Images

This manual provides many images from the Oncomine program to guide you through the training process. Look for the areas highlighted in red to help connect what is being discussed in the manual with what you should see on your computer screen:



1.1 Getting Started

Registration

Users need to be registered for Oncomine 4.3 (see page 3).

Once logged in, the screen shows the main menu for Oncomine, with drop-down menus at the top, latest content statistics, and search boxes. This is the starting point for any analysis in Oncomine.

Begin analysis in Oncomine by

• Adding Filters and Performing Meta-analysis for Target Discovery (1.1)

• Entering a Gene Name and Using the Gene Summary View (1.1 and 1.2)

• Selecting a Primary Concept from the Tree or Upload a Custom Concept (1.3)



1.2 Nomination, validation, and associations of AGTR1 as an outlier in a subset of breast cancers

1.2.1 AGTR1 discovery and validation

In this section, we will discover a gene having an outlier profile indicative of potential oncogenes: high expression in a subset of breast cancer samples. Filters, Analysis Comparison, Outlier Analysis and Gene Summary View are used to identify & validate highly ranked outliers.

Objective: Our lab’s interest is in breast cancer and we are seeking to identify genes having high expression in a fraction of clinical samples that may be related to disease. We also want to isolate those outlier genes that are targeted by approved drugs to identify novel indications. If these outlier genes are present, we would like to find associations between them and common biomarkers of breast cancer, such as the estrogen receptor. Can Oncomine help me discover & validate novel outliers in breast cancer?

Because we are interested in finding outliers in breast cancer, adding several filters immediately narrows our search field.

1. Add several filters to the Filter Box to begin.

a. Under Primary Filters > Cancer Type > select Breast Cancer.

b. Under Dataset Filters > Data Type > select mRNA. This removes any DNA copy number data and analyses from the results set.

c. Under Dataset Filters > Dataset Size > select 151+ samples. Larger sample sizes provide



a larger sample population from which to base outlier ranking. For example, the known outlier profile of ERBB2 over-expression in 25% of breast cancer patients may not be well represented in smaller datasets.

d. Under Primary Filters > Analysis Type select Outlier Analysis. Removing all other analyses allow one to easily pick outlier results for comparison across datasets.

After each filter is added, the Datasets tab and the Visualize panes dynamically update. When all of the above filters have been added, a list of datasets and respective analyses are displayed with the first analysis listed automatically selected for viewing in the Visualize pane.

While you can clearly see that selecting outliers is possible using check boxes next to analyses (red box), adding the Outlier Analysis filter returns only datasets in which outliers were computed and only the Outlier analyses.



2. Check the box next to Outlier 95th% for each of the first 3 datasets and click the Compare link above.

When comparing analyses across datasets in a “meta-analysis”, the Visualize pane will update with a heat map summarizing the Outlier score known as a Cancer Outlier Profile Analysis score (COPA). Note the genes, their median rank and the COPA score.

TECH NOTE: Outlier analysis is performed using the Cancer Outlier Profile Analysis (COPA), and is useful for identifying potential oncogenes that are differentially expressed in a subset of samples. This is important, because oncogene profiles may not be identified by typical two-class differential expression analysis due to the underlying heterogeneity of cancers at the molecular level.

Outliers are determined by the following procedure: 1) For each data set considering all samples, gene expression values are median-centered per gene, setting each gene’s median expression value to zero. 2) The median absolute deviation (MAD) is calculated per gene and scaled to 1 by dividing each gene expression value by its MAD. Of note, median and MAD are used for transformation as opposed to mean and standard deviation so that outlier expression values do not unduly influence the distribution estimates, and are thus preserved, post-normalization.3) For each gene in each dataset, COPA scores are computed as the 75th, 90th and 95th percentile of ascending transformed gene expression values. Thus, each gene in each dataset has 3 COPA scores, one at each percentile cutoff, representing the degree of over-expression in decreasing subsets of cases. 4) In each dataset, all genes are rank-ordered by the 3 COPA scores, generating 3 rank-ordered lists of genes per dataset.1.

1 Tomlins, SA, et al (2008). The role of SPINK1 in ETS-rearrangement negative prostate cancers. Cancer Cell 13(6) 519-528.



In the meta-analysis, there are a number of genes that were measured across all 3 datasets (gray boxes denote no measurement). Looking at these genes, ERBB2 is ranked in the top 60 by its median COPA score rank of 157 (click on page 3 of the heat map). Its high rank indicates that ERBB2 is most likely over-expressed in only a subset of breast cancer samples, a fact that is well known. A number of genomic neighbors are also highly ranked reflecting amplification at the DNA level (STARD3, GRB7).

These targets can be further refined by applying additional filter criteria on the Outlier comparison. Concepts can be applied as filters to isolate outlier genes that are highly ranked as outliers across multiple datasets AND have approved targeted drug therapies. Similarly, any concept can be applied to identify a particular aspect of biology and corresponding genes that are also outliers.

3. In the Search text box, type “Drugbank ” and then select the second auto-complete suggestion listed, “Drugbank Targets - FDA approved - Literature-defined Concepts,” to add as a filter.

4. Click the Compare link above the previously selected analysis to refresh the Outlier comparison map to display approved drug targets that are ranked by Outlier score across the multiple datasets.

A new ranking is displayed based on approved drug targets. Within these targets AGTR1 has the highest rank while ERBB2 is second. We also see that both genes were measured across all three analyses and had significant COPA scores (red boxes). Mouse over the cells for each analysis of AGTR1 to see these consistent results.

ERBB2 is a well known cancer gene involved in breast cancer and known to be highly amplified and expressed in a fraction of patients thus having an Oncomine “Outlier” profile. AGTR1 is a potentially new target in breast cancer and has an approved drug that may be beneficial in this new indication. (Losartan is the drug that targets AGTR1 to treat hypertension). Thus, we continue our analysis focusing on AGTR1.



NOTE: Before continuing with another analysis comparison, make sure to click on ‘Clear All’ in the Datasets tab to prevent unwanted outlier analysis selection from the previous comparison.

5. Click on the white ‘X’ in the Filter Box.

6. Type AGTR1 into the Search Box and hit enter.

7. After the page loads, mouse over the arrow next to Other Views, and select Gene Summary View.

TECH NOTE: If you are on the Overview page and you enter a gene name into the Search Box, you will automatically be taken to the Gene Summary View. Entering a gene name in any other view will require you to toggle to the Gene Summary View from the Other Views pull-down.



For AGTR1, we see 2 results of interest. The most eye-catching result is the red box under the Outlier heading that shows 36 Outlier analyses have ranked AGTR1 as a significant over-expressed outlier in breast cancer. The second result is also within breast cancer, with 10 datasets ranking AGTR1 over-expression in a particular subtype of breast cancer.

TECH NOTE: Mousing over the red or blue boxes will enable a pop-up box with information about the analyses. For example, mousing over the 36 analyses in the red box for AGTR1 will display the image below. It is important to note the number of significant datasets (36) out of the total datasets (39) in this category. While each gene has 6 outlier analyses—75th, 90th, and 95th for over-expression and 5th, 10th, 25th, for under-expression per dataset, only the most highly ranked over and under-expression are summarized in the mouseover.

8. Click on the red box with ‘36’ analyses in the Outlier Analysis column.

When the page loads, the list of significant analyses are ranked and ordered by AGTR1 over-expression outlier rank.



In order to validate AGTR1’s role as an outlier in multiple-independent analyses; take a moment to click on Outlier 90th% or Outlier 95th% for Yu, Chin, Minn 2, and Hess Breast analyses. Note the distribution of high AGTR1 over-expression in a small subset of the total population.

Conclusion

In this section of the module, we focused on

identifying potential cancer targets that have and Outlier profile in breast cancer datasets, and then validating a

specific target of interest across all of the datasets in Oncomine.



To narrow the results to our research interests, we began by adding several filters to the Filter Box to return results by cancer type, dataset type, sample size, and analysis type.

We compared 3 initial datasets to identify genes that were found to be significantly over-expressed in a fraction of samples across multiple datasets. We found a number of genes known to be over-expressed in a subset of breast cancers along with ERBB2. We further narrowed our search by filtering on genes that are approved drug targets, focusing on AGTR1as the most significant gene having an Outlier profile across several datasets and as the target of Losartan an approved drug used to treat hypertension. Based on median rank, COPA score, and this drug interaction data, we selected AGTR1 for further investigation.

Using the Gene Summary View, we quickly found 36 breast cancer Outlier analyses where AGTR1 demonstrated an outlier gene profile.

By looking at the bar graphs for individual analyses, at the 90th or 95th%, we can conclude that AGTR1 is a highly ranked over-expressed outlier in additional independent breast cancer analyses.

1.2.2 Breast cancer biomarkers associated with AGTR 1 outlier over-expression

Now that we have discovered and validated AGTR1 is highly expressed in a subset of breast cancers, we would like to know if it has any associations with particular known subtypes of breast cancer, i.e. estrogen and/or progesterone receptor, or ERBB2.

Using the same datasets in the previous section, we can use the ‘Group By’ drop-down menu in the Visualize pane to classify the samples within a dataset according to different parameters, such as, estrogen receptor status, survival or recurrence status, tumor size, primary site, etc.

1. Select Outlier 95th% under the Yu Breast 3 dataset.

2. Click on the arrow next to ‘Group By’ at the top of the Visualize pane, and from the drop-down menu, select ‘Estrogen Receptor Status’

After the Visualize pane updates, you will notice that in the 95th%, AGTR1 outlier samples are only estrogen receptor positive, but none are classified as estrogen receptor negative.



Take a moment to ‘Group By: Estrogen Receptor Status’ for the remaining datasets (when possible). Interestingly, AGTR1 outlier over-expression is observed only in a subset of estrogen receptor positive samples, but not all estrogen receptor positive samples. More importantly, AGTR1 outlier over-expression is not found in estrogen receptor negative samples.

Let’s see if there is an association between progesterone receptor status and AGTR1 outlier over-expression.

3. Select outlier 95th% under the Yu Breast 3 dataset.

4. Select ‘Progesterone Receptor Status’ from the ‘Group By’ drop-down menu.

AGTR1 is an outlier in both progesterone negative and positive breast cancer samples therefore we cannot make a statement regarding association between progesterone receptor and AGTR1 outlier over-expression.

5. To confirm the lack of association between progesterone receptor (PR) status and AGTR1 outlier over-expression, select another dataset to group by PR.

Now that we’ve looked at hormonal regulation, let’s examine potential relationships between ERBB2, a well-known breast cancer biomarker, and AGTR1 outlier status. ERBB2 is associated with aggressive breast

cancers and finding a positive or negative correlation with this growth factor receptor can help us further characterize the role of AGTR1 in breast cancer.

We could look for associations between AGTR1 and ERBB2 as we did for estrogen and progesterone receptors—selecting 95th percentiles and ‘Group[ing]



By’ a subtype in the bar graph if ERBB2 status is known for a sample. Here we will use expression data for ERBB2 to find associations using heat maps.

6. In the text box, type ERBB2 and hit enter.

7. In the ‘Order On’ drop-down menu, select AGTR1.

8. At the top of the list is the Yu Breast 3 dataset. Select Outlier 95th% and view the heat map in the Visualize pane.

Look at the heat map carefully. You will see that several samples have AGTR1 outlier over-expression and several samples have ERBB2 outlier over-expression (bright red color). From this initial analysis it appears that there is no positive correlation between AGTR1 and ERBB2 outlier over-expression. That is, no sample with AGTR1 outlier over-expression concurrently has ERBB2 outlier over-expression. Interestingly, AGTR1 and ERBB2 appear to be mutually exclusive as no sample with AGTR1 outlier status also has ERBB2 as an outlier, and vice versa. This exclusivity with ERBB2 has been confirmed by other publications.2

TIP: When viewing the heat maps, you can also ‘Group By: Estrogen Receptor Status’ to validate our previous findings.

2 Rhodes, et al. (2009). AGTR1 overexpression defines a subset of Breast Cancer and confers sensitivity to losartan, an ANGTR1 antagonist. PNAS. 106(25) 10284-10289.



Let’s examine other heat maps to see if our mutually exclusive theory is consistent across multiple independent datasets.

9. Select the outlier 90th% under the Chin Breast dataset.

10. Select the 95th% in the Minn 2 Breast dataset.

Conclusion

Following discovery and validation of AGTR1 outlier status in the previous section, we explored these results further and found molecular associations between AGTR1 and different subtypes of breast cancer.

The ‘Group By’ menu in the Visualize pane allowed us to group by different parameters, such as estrogen receptor status, and see if there is a correlation with AGTR1 outlier status.

Looking at bar graphs and grouping the samples by estrogen or progesterone receptor, we were able to determine that AGTR1 outliers only occur in a subset of estrogen receptor positive breast cancers. Additionally, we were unable to find an association between progesterone receptor and AGTR1 outlier status.

After entering ERBB2 into the Search Box, we used heat maps in the Visualize pane to find a relationship between the 2 biomarkers. We determined that AGTR1 outlier status and ERBB2 over-expression were mutually exclusive.



1.2.3 Finding a Cell Line

Now that we have found and validated a gene of interest in breast cancer, we would like to find cell lines with endogenous AGTR1 outlier over-expression or normal to low expression for in vitro and in vivo datasets.

1. Remove ERBB2, Outlier Analysis, and Breast Cancer from the Filter Box using the red “x” next to each.

2. Under Primary Filters > Dataset Type, select Cell Line Panel Datasets.

The Scherf CellLine dataset will automatically be selected for viewing in the Visualize pane.

3. Click on the arrow next to Scherf CellLine, and select ‘Cancer Type: Breast Cancer’.

The Visualize pane updates to highlight, in dark blue, the breast cancer cell lines and classify the cell lines based on General Cancer Type.

Mousing over the 4 breast cancer cell lines will tell you the expression value of AGTR1 for that particular cell line, as well as the name of the cell line. In the Scherf CellLine dataset, you can see the cell line with the highest expression of AGTR1 is Hs 578T. The cell line with the lowest expression of AGTR1 is MDA-MB-231.



4. Click on the arrow next to Scherf CellLine to hide these analyses.

5. Click on the arrow next to Shankavaram CellLine, and select ‘Cancer Type: Breast Cancer’.

The cell line with the highest expression of AGTR1 is Hs 578T, and in this analysis, Hs 587T has the highest over-expression value. Once again, the MDA-MB-231 cell line has the lowest expression of AGTR1.

Take a few minutes to look at the other analyses in the Datasets tab. You will begin to see an emerging gene expression pattern in the breast cancer cell lines. Hs 587T has the highest AGTR1 expression while MDA-MB-231 has the lowest expression.

Conclusion

In order to identify models for follow up experiments to test Losartan acting on AGTR1 in breast cancer, we analyzed AGTR1 in Oncomine Cell Line Panel Datasets and were able identify breast cancer cell lines having both high and low AGTR1 expression consistently across multiple independent datasets.

Hs 578T consistently measured endogenous over-expression. MDA-MB-231 had low AGTR1 expression.



1.2.4 Summary of nomination, validation, and molecu lar associations of AGTR1 as an outlier in breast cancer

In this section we nominated an outlier, AGTR1, in a subset of breast cancers and validated its status as a high-ranking over-expressed outlier in several independent analyses. We continued with our investigation of AGTR1 by finding associations with known biomarkers. We finished by finding appropriate cell line models for in vitro and in vivo datasets in the laboratory.

• Using several filters for Outlier analyses at the beginning of our search, we performed a Comparison across 3 independent datasets to identify potential Oultiers.

• A number of known oncogenes initially caught our attention. Filtering upon approved drug targets we identified a new gene, AGTR1, ranking second only to ERBB2 in our analysis.

• AGTR1 was of particular interest because it is a known target of Losartan, a drug used to treat hypertension. Finding interesting biology related to AGTR1 in the context of breast cancer could open the door for breast cancer treatment with an already approved drug.

• Using the Gene Summary View, we validated AGTR1 as an over-expressed outlier in many additional independent Breast Cancer analyses.

• Switching back into the Datasets tab, we looked at each individual dataset to assess AGTR1 outlier status at the 90th% and the 95th%.

• Next we explored relationships between AGTR1 and estrogen and progesterone receptor status. These analyses were also used to determine a relationship between the outlier and ERBB2.

• The ‘Group By’ menu in the Visualize pane allowed us to group by estrogen receptor or progesterone receptor to identify correlations with outlier profile.

• After grouping by different parameters we determined that only estrogen receptor positive samples harbor AGTR1 outliers, but not all estrogen receptor positive samples do so. AGTR1 outliers were never found in estrogen receptor negative breast cancer samples.

• We found no association between AGTR1 and progesterone receptor status, as outliers were found in both positive and negative samples.

• Typing ERBB2 into the Search Box allowed us to compare the expression of AGTR1 and ERBB2 in heat maps across many samples in independent analyses.

• We determined that the known biomarker, ERBB2, and our over-expressed outlier, AGTR1, are mutually exclusive.



• Lastly we used Cell Line Panel Dataset filter in Oncomine to identify Hs578T as a potential model for studying AGTR1 in breast cancer.



1.3 FOXM1 validation and drug target development

1.3.1 Validation of FOXM1 over-expression in breast cancer

My laboratory has been investigating over-expression of FOXM1 in breast cancer. We have confirmed its over-expression in several experiments but we are interested in finding its over-expression in specific pathological subtype and do not have access to these samples. If there is convincing biology associated with FOXM1, we would like to nominate drugs for future investigations in the lab.

1. Before beginning our investigation into FOXM1, click on the white ‘X’ in the Filter Box to clear all prior filters.

2. Type FOXM1 into the Search Box and hit enter.

3. In the Visualize pane, click on the arrow next to Other Views and select Gene Summary View.

When the Gene Summary View loads, change your Fold Change threshold to “All.” This brings up types of analyses with no fold change, such as grade



analyses. You can immediately see 14 analyses with over-expression of FOXM1 in a pathological subtype of breast cancer.

4. Click the red box with ‘14’ analyses.

When the Datasets and Visualize pane updates, the resulting list in the Datasets tab shows several significant analyses of FOXM1 over-expression in high-grade breast cancers. Three analyses show FOXM1 over-expression in high-grade ductal breast carcinoma (Desmedt Breast, Ma Breast 3, and Lu Breast, all underlined in red), with P-values of 5.05E-16, -1.27E-7, and 3.33E-13, respectively. The Desmedt Breast analysis and the Schmidt Breast analysis also show a positive association between FOXM1 and invasive breast carcinoma (P-value of 1.56E-17).

Conclusion

Summarization of FOXM1 in the Gene Summary View immediately drew our attention to 14 analyses with FOXM1 over-expression in a pathological subtype of breast cancer.



In order to drill down to the details of these 14 analyses we select the red box labeled ‘14’ in the Gene Summary View.

All of the top 14 analyses showed over-expression of FOXM1 in high-grade breast carcinomas, as compared to lower grade carcinomas.

These datasets validate the lab’s finding that FOXM1 is over-expressed in breast cancer, and it showed us that FOXM1 is over-expressed in a specific subset of breast cancers having high-grade pathology.

There were analyses with high FOXM1 expression in subsets of high-grade ductal breast cancers and high-grade invasive breast cancers.



1.3.2 FOXM1’s role in the cell cycle and other impo rtant biological associations

Because the Desmedt Breast dataset has several significant and interesting associations with FOXM1—high grade, invasive and the highest over-expression gene rank for FOXM1—we can choose to select the top 1% of genes over-expressed to use as a Primary Concept to search Oncomine for for biological and drug associations.

1. With the ‘Invasive Ductal Breast Carcinoma-High Grade’ analysis selected under the Desmedt Dataset, remove the FOXM1 filter.

NOTE: When the page has reloaded, the ‘Invasive Ductal Breast Carcinoma-High Grade’ analysis will remain selected.

2. In the Visualize pane, click on the arrow next to ‘+ primary concept’ and select Top 1% Over-expressed.



3. Before making the analysis a Primary Concept, you will be prompted with the choice of removing or maintaining the filters in the Filter Box. Please select, ‘Yes, remove all filters.’

The Concept Summary map present a view of all significant associations of the Primary Concept (Top 1% over-expressed in high grade, invasive breast cancer) set of genes with all other Concepts (gene sets) in Oncomine.

This visualization provides immediate cues that there is significant overlap of genes in our high grade invasive gene set with Clinical Outcomes (51), Molecular Subtypes Biomarkers (49) and Pathology Subtype Grade (28) all within Breast Cancer datasets.

4. Click on the cell displaying “49” under-expression in Subtype Biomarkers.

Here we see details of the association of the genes over-expressed in the high grade invasive breast cancer with those under-expressed in ER+ vs. ER- breast cancers. Associated concepts lists the gene sets having most significant overlap with our Primary Concept and when selected the details of this overlap, including the genes that are in common, are listed to the right.

It is important to note that the results are all under-expressed genes (blue down arrows), are ER+ or PR+, and are from multiple independent datasets (Author Tissue notation). This gives us confidence in the association with ER+ & PR+ breast cancers.



TECH NOTE: If you return to the ‘Invasive Ductal Breast Carcinoma- High Grade’ analysis in the Datasets tab, and make the Top 1% under-expressed the Primary Concept, the most significant Associated Concepts are genes over-expressed in estrogen receptor positive breast carcinomas. This is a nice way to validate the findings from above.

5. User red “x” to clear items from the filter box EXCEPT for the Concept filter.

6. Under Concepts > Concept Type > Biology Concepts > Biological Annotations, select GO Biological Process.



All of the Associated Concepts are related to cell growth and phases of the cell cycle. The most significant Associated Concept is mitosis with a P-value of 2.52E-25 and an odds ratio of 46.8.

Cytokinesis and DNA replication are phases of the cell cycle. Often tumors, especially those that are invading neighboring tissue or metastasizing, can take control of the cell cycle so the tumor produces immature and irregular cells thus adding to the tumor size.

As FOXM1 is not in our Filter Box, it is not required to be in the Associated Concepts, nor in the gene overlap. These Associated Concepts are similar to processes seen in invasive ductal breast carcinomas, and not necessarily FOXM1 controlled. However, previous datasets3 have confirmed the role of FOXM1 in regulating the cell cycle, and while it may not be in the gene overlap, it could be a very important upstream regulator of these cell cycle processes.

7. Remove the GO Biological Process filter and in the same branch of the Filter Tree, add GO Molecular Function.

8. Set the P-value threshold to 0.01.

3 Wonsey DR, et al (2005). Loss of Forkhead Transcription Factor FoxM1 Causes Centrosome Amplification and Mitotic Catastrophe. Cancer Research 65(12) 5181-5189



Once again, our list of Associated Concepts is small, but all of the Associated Concepts make biological sense. Protein serine/threonine kinase activity needs ATP binding in order to remove the phosphate group, so the presence of both of these significant Associated Concepts, ATP binding and kinase activity, act as an internal validation. These aforementioned Associated Concepts are significant at P-values of 3.29E-10 and 7.52E-4, respectively. Microtubule activity is important in cell restructuring during mitosis and cytokinesis.

9. Remove GO Molecular Function from the filter box and under Biology Concepts, select Pathway Concepts > HPRD Interaction Sets.



Take a moment to click on a few of the top Associated Concepts and view the details and gene list in the Visualize pane. While many of the HPRD Interaction Sets might not be familiar, take a moment to view the interaction gene list for some of the top Associated Concepts. You will notice a trend of “CDC…” interaction sets. These are Cell Division Cycle (CDC) genes, important in the regulation and maintenance of cell cycle. This is important as it confirms our previous findings that FOXM1 and other over-expressed genes in invasive ductal breast cancers play a role in controlling cell proliferation and tumor growth.

Conclusion

In order to find important biological associations with FOXM1 in high-grade breast cancers, we need to find an analysis that is representative of FOXM1 action in these samples.

Using the most significant analysis from the initial FOXM1 search, we made a Primary Concept from the ‘Invasive Ductal Breast Carcinoma-High Grade’ Desmedt Breast dataset.

There were many interesting Associated Concepts, including under-expression in ER+ breast cancer and other datasets with genes associated with Clinical Outcome, Stage, and Grade as expected.

We began to identify biological associations by adding the GO Biological Processes filter from the Concept Type branch in the Filter Tree.

The most significant Associated Concept was ‘mitosis’ ‘Cytokinesis’, ‘regulation of the cell cycle’, and ‘DNA replication’. These Associated Concepts reflect the processes involved in cell proliferation and subsequent growth of the tumor. FOXM1 is known to play a role in regulating these processes, so we can



conclude that once FOXM1 is over-expressed these tightly regulated processes are spun out of control, thus producing cancerous cells.

Adding the GO Molecular Function filter confirmed our previous results. The Associated Concepts included ATP binding and kinase activity. Its role in these 2 important processes may reveal a role for FOXM1 in receptor signaling and the possibility of FOXM1 being a drug target.

We also added the HPRD Interaction Sets filter, which, once again, confirmed our previous findings—FOXM1 is positively associated with regulation of the cell cycle.



1.3.3 FOXM1 sensitivity to targeted drug therapies

Now that we have found FOXM1 over-expression is important for cell growth and regulation, particularly involving serine/threonine kinase activity, we would like to find drug sensitivities and potential drug treatments.

1. Clear the HPRD Interaction Sets filter.

2. Under Primary Filters > Analysis Type > Differential Analysis > Pathway and Drug Analysis > Drug Sensitivity Analysis, select Targeted Therapy Sensitivity.

NOTE: We want to look at targeted therapies for an important and specific reason. In previous sections we have shown that FOXM1 is positively associated with serine/threonine kinase activity and there are already several drugs that target receptor kinase activity.

TECH NOTE: Oncomine ‘Analysis Type’ filters allow for the selection of Pathway and Drug Analyses.

Within the ‘Pathway and Drug Analysis’ branch, ‘Drug Sensitivity’ defines cell line gene expression that may predict sensitivity to a compound, i.e. expression profiling of lines previously determined to be sensitive to a drug. This profiling data is generated from untreated cells. Thus, the value of this data is applicable to untreated populations with the potential to predict sensitivity/response in patients.

‘Drug Perturbations’ define cell line gene expression after transfection or drug treatment. Thus, the value of this data is in understanding the biology of action & response to the drug.



Two of the three Associated Concepts are Dasatinib or Gefitinib sensitivity signatures—both of these drugs are known tyrosine kinase inhibitors. The most significant Associated Concept is the ‘Dasatinib Sensitive-Breast Cancer Cell Line- Top 10% over-expressed’ analysis from the Huang dataset.

If Gefitinib and Dasatinib are both tyrosine kinase inhibitors, why are they showing opposite effects in relation to my Primary Concept?

There are two possible reasons: first, the Dasatinib sensitivity analysis was carried out in a breast cancer cell line, which is the indication in which we are investigating FOXM1, but Gefitinib was studied in a lung cancer cell line. It has been shown that if Gefitinib is used in a cell line derived from a different cancer type, or from a resistant breast cancer cell line, the association with

decreased FOXM1 expression is not seen4.

Secondly, Dasatinib and Gefitinib act on the tyrosine kinase receptor in 2 different places—1 intracellular and 1 extracellular. It is possible that inhibiting the receptor at a different location could trigger a different signaling cascade.

Sirolimus sensitivity is shown for under-expressed genes reflecting its different mode of action as an anti-proliferative compared to Dasatinib.

3. Remove the Targeted Therapy Sensitivity filter.

4. Begin to type FOXM1 into the search box. Before hitting enter or selecting ‘FOXM1 (Gene)’ from the drop-down menu, look at the suggestions in the auto-complete box.

4 McGovern, UB, et al. (2009). Gefitinib (Iressa) represses FOXM1 expression via FOXO3a in breast cancer. Molecular Cancer Therapeutics 8(3). 582-591.



5. Select ‘FOXM1 siRNA knockdown Analysis (Analysis Type)’.

TECH NOTE: In this example, choosing the ‘FOXM1 siRNA Knockdown’ in the Pathway Perturbation or Analysis Type filters will lead us to the same dataset in the Dataset tab. However, in some datasets, there are not enough control samples to be able to perform an analysis (minimum of 3). Thus, this dataset would be seen using the Pathway Perturbation filter, but not with the Analysis Type filter.

NOTE: Simply pausing before hitting enter is a good way to see what other kinds of analyses Oncomine has in regards to our gene of interest, FOXM1.

It is very interesting that the knockdown of FOXM1 in a breast cancer cell line correlates with under-expression of genes in our Primary Concept (Wonsey CellLine). This once again validates our findings that FOXM1 is an important regulator of many parts of the cell cycle pathway and is important to controlling growth. This leads us to conclude that FOXM1 acts as a high-level controller of the genes that carry out these cellular activities, which are over-expressed in high-grade breast cancer.

Conclusion

Using the Targeted Therapy Sensitivity filter under the Drug Sensitivity branch of our Drug and Pathways Perturbation Analysis filters, we specifically looked at Targeted Therapy signatures in Oncomine with the hopes of finding drugs that would act on FOXM1.

Two out of the three most significant Associated Concepts were Dasatinib or Gefitinib sensitive signatures. Both of these drugs are known tyrosine kinase



inhibitors. The most significant Associated Concept was the ‘Dasatinib Sensitive-Breast Cancer Cell Line-Top 10% over-expressed’ with a P-value of 3.17E-11.

Earlier we found FOXM1 to be involved with kinase activity and with the Dasatinib signature being the most significant Associated Concept, we have reason to investigate FOXM1 as being a potential target of these already approved FDA drugs.

Although Gefitinib is also a tyrosine kinase inhibitor, it had a negative correlation with FOXM1 over-expression. This could be due to its use in a lung cancer cell line, as different cancers will have different regulators and pathways that drive tumor growth. The discrepancy could be due to the different areas of inhibition on the receptor.

We also looked at a siRNA knockdown of FOXM1 to see what processes and genes would be affected. A majority of the genes in our Primary Concept that were over-expressed in high-grade breast cancer were down-regulated after FOXM1 was knocked out. This finding once again validates previous analyses showing that FOXM1 is a key regulator of genes involved in cell cycle regulation.



1.3.4 Summary of 1.3: FOXM1 validation and drug dis covery

In this section, we wanted to validate the over-expression of FOXM1 and to discover FOXM1 over-expression in a subset of breast cancer samples. We were also interested in finding biologically important associations that would identify how the tumor cells are surviving. If possible we wanted to find drugs that could potentially interfere with FOXM1 activity.

• By using the Gene Summary View, we quickly identified FOXM1 over-expression in a subset of breast cancers

• After selecting the appropriate box in the Gene Summary View, we found FOXM1 expression to be found in a subset of high-grade breast carcinomas, and possibly a subset of ductal breast carcinomas.

• Viewing bar graphs of individual analyses, we validated FOXM1’s over-expression in multiple independent analyses.

• We chose the most significant analysis, ‘Invasive Ductal Breast Carcinoma-High Grade’ from the Desmedt Breast dataset, and used this as a Primary Concept to find associations with other Concepts (gene lists).

• From our Concept Summary Map we noted many Associated Concepts within Clinical Outcome, Molecular Subtype Biomarkers and Stage.

• Adding a series of filters from the Filter Tree, including GO Biological Processes, GO Molecular Function, and HPRD Interaction Sets, we found many important biological associations between FOXM1 over-expression and cell cycle properties.

• We found FOXM1 to be an important regulator of many cell cycle processes like cytokinesis and DNA replication. ATP binding and kinase activities were also found to be associated with FOXM1. Over-expression of FOXM1 can lead to de-regulation of these normal processes, and contribute to tumor growth.

• Using the Targeted Therapy Analysis filter, we identified FOXM1 as a potentially sensitive target of known tyrosine kinase inhibitors, Dasatinib and Gefitinib.

• siRNA knockdown of FOXM1 validated our previous findings that FOXM1 is an important regulator of many cell cycle activities that, when dysregulated, contribute to tumor cell proliferation.



1.4 Summary

In this module we focused on target identification and validation. In the first section our focus centered on AGTR1 outlier discovery, validation, finding important associations with known biomarkers, and using the cell line panel datasets to find cell lines for future datasets in the laboratory.

In the second half of the module, we validated a known over-expressed gene and found important biological associations with a breast cancer signature. We were able to use our validation and biological data to suggest FOXM1 as a target of approved drug therapies.

The ability to perform a meta-analysis enabled us to identify AGTR1 as an outlier in breast cancer. Further analysis showed AGTR1 was an outlier in a subset of estrogen receptor positive samples and was mutually exclusive with a known biomarker, ERBB2.

Mining Oncomine’s cell line data, we found 2 cell lines with AGTR1 over-expression that will be useful for carrying out in vitro and in vivo datasets in the laboratory.

AGTR1 is a known target of Losartan, a drug used to treat patients with hypertension. This opens the door for laboratory testing and then clinical trials of Losartan in breast cancer samples with AGTR1 outlier over-expression.

FOXM1 was known to be over-expressed in breast cancer, but further investigation in Oncomine led us to see that FOXM1 was consistently over-expressed in high-grade breast carcinomas. It was often found in invasive or ductal breast carcinomas as well.

Using a representative analysis, we built a Primary Concept with which to identify other Concepts (gene lists) and found many important biological associations. FOXM1 appears to act as a key regulator of many cellular processes, such as cell cycle, DNA replication, mitosis, cytokinesis, and protein kinases.

The drug sensitivity filter allowed us to see the possibility of Gefinitib acting as a kinase inhibitor, thus decreasing expression of FOXM1 targets.

We appreciate your comments and questions at [email protected].

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