Issue 14 : February, 2016

CTD² Program Highlight
CTD² Dashboard: A Platform to Explore Evidence-based Observations

Subhashini Jagu, Ph.D., Jessica Mazerik, Ph.D., Nadia Jaber, Ph.D., Paul Clemons, Ph.D., and Kenneth Smith, Ph.D.

Searching for concise conclusions on a particular subject within a long list of publications could be difficult and time-consuming, requiring a researcher to read through each publication, identify the sections of interest and analyze the associated figures. In addition, the exponential growth of the genomics field has led to the generation of massive amounts of primary data, often stored in large databases which can be difficult to navigate without bioinformatics expertise. It is also challenging for investigators to determine the significance and strength of a particular result from primary genomics data alone. Therefore, there is a major need for a searchable web interface assembling preliminary results, connecting them with subsequent evidence that reinforces or builds upon the original finding, and conveying the extent to which the results have been functionally validated.

Introduction to the CTD2 Dashboard

The Cancer Target Discovery and Development (CTD2) Network Centers at the Broad Institute, Cold Spring Harbor Laboratories/Memorial Sloan Kettering Cancer Center, and Columbia University have developed the “CTD2 Dashboard”, which compiles the CTD2 Network-generated conclusions, or “observations”, associated with particular subjects (the focus of experiments; they are categorized into classes, such as gene, cell line, animal model, or perturbagen). It provides related evidence (bulk datasets, data-related figures, links to source data, and references) for each observation. Subjects are assigned “roles”, or functions, based on the evidence. CTD2 Center-designated roles are standardized and include biomarkers, disease, master regulators, interactors, oncogenes, perturbagens, candidate drugs, and targets. These roles allow for easier browsing and searching, and help CTD2 Dashboard users draw conclusions about the significance of findings. Importantly, the CTD2 Dashboard was designed to allow easy navigation and use by computational experts, those with little bioinformatics experience, and others in between. Thus, the CTD2 Dashboard is designed for users to quickly and easily find, analyze, and build upon Network-generated experimental results.  

The Network has created a classification system to rank observations in the CTD2 Dashboard into “Evidence Tiers” based on the strength of the supporting data. Observations are placed into the appropriate tier, from 1 (lowest) to 3 (highest), based on the extent of characterization associated with the observation. As the Tier level increases, evidence correlation is strengthened and conclusions are more clinically relevant. Briefly, Tier 1 is assigned to preliminary positive observations, Tier 2 is associated with confirmation of primary results in vitro, and Tier 3 labels results that have been validated in a cancer-relevant model in vivo. A major advantage of the Dashboard is that it compiles all of the Network-generated evidence associated with a particular subject in one place, thereby displaying the progression of evidence from Tier 1 to 3. The research community can therefore use the Tier ranking system to determine which observations in the CTD2 Dashboard are robust enough to serve as a basis for further validating results in vivo and developing novel therapeutic targets and biomarkers for cancer.

Dashboard Functions

The CTD2 Dashboard provides users a variety of ways to browse and search observations and make their own discoveries.

To provide an example of CTD2 Dashboard functions, we highlight the many Network-generated observations for the MYC gene. Although MYC is a well-characterized gene, with known roles in cancer, it still remains elusive as a therapeutic target. Future research that builds on existing CTD2 Dashboard observations on MYC could lead to clinically-relevant applications.

Users can browse observations by clicking on a category of subjects on the CTD2 Dashboard homepage.

The Dashboard homepage links to pre-configured browse pages


Figure 1: The Dashboard homepage links to pre-configured browse pages. Separating the observations into categories facilitates easier exploration.

The CTD2 Dashboard provides three browse categories: Biomarkers, Targets, Genes & Proteins; Compounds & Perturbagens; and Disease Context (Figure 1). Clicking on a category will bring the user to a browse page, which displays a table of subjects under that particular category (Figure 2).  For each subject, the number of observations in each Tier is listed. A subject is only designated with a role if it has more than one associated observation to support that role. Depending on the evidence available, subjects may have more than one role; users can change the display settings to all roles, or choose particular roles by clicking on the “select roles” button next to the heading on each browse page.

The “Biomarkers, Targets, Genes & Proteins” browse page provides a tabular presentation of class, subject, and observations

Figure 2: The “Biomarkers, Targets, Genes & Proteins” browse page provides a tabular presentation of class, subject (listed in the “Name” column, for example, MYC), and observations. Clicking on “select roles” will allow the user to change the roles displayed in the browse page table. The browse page is shown here with all possible roles selected (target, biomarker, background, candidate master regulator, interactor, master regulator and oncogene). The MYC gene is designated with the role of “oncogene” based on observations in Tiers 1, 2, and 3.

Users can view all Network-generated observations associated with a particular subject.

Selecting a subject from a browse page table produces a list of associated observations

Figure 3: Selecting a subject from a browse page table (example shown: MYC) produces a list of associated observations, each with the associated Tier rank and the Network Center that produced the finding. At the top of the page, basic information about the subject is displayed. The subject pages are interactive; users can follow links to the associated entries in Entrez, UniProt and cBioPortal.

From the browse page table, users can click on a specific subject, such as MYC, to see all of the associated observations (Figure 3). Alternatively, users can click on the observation number under a specific Tier, to view only observations in that Tier. Users can then click on the “details” link to navigate to an individual observation page and find the supporting evidence, complete with links to references, data files, figures and/or associated summary stories (Figure 4). Each observation page also contains a table displaying the additional subjects associated with the observation, along with their roles and a link to that subject’s page. This allows users to easily explore all aspects of a particular observation in a single location, rather than perform multiple searches.

Each individual observation page displays the observation, Tier level, and associated Center

Figure 4: Each individual observation page displays the observation, the Tier level, and the associated Center. Below that, a table lists all subjects associated with the observation (such as AKT1 and MCF10A, shown here) and their role in the observation. Each subject is linked to its respective page in the CTD2 Dashboard. The Evidence table lists all of the Center-generated evidence supporting the observation. In the Details column, users can find figures, links to references, data files, and/or associated summary stories.

Users can retrieve evidence and observations pertinent to their queries by searching across subjects using standardized terms and vocabulary.

For example, users can search for genes using synonyms or Entrez Gene ID, proteins with UniProt ID, and compounds with PubChem or Chemical Abstract Service (CAS) numbers. This non-restrictive search feature allows users to customize searches to their individual preferences. The search feature can be found on the homepage. Searching for a subject will retrieve all observations available in the CTD2 Dashboard, regardless of role or Tier.

Users are able to compile a list of genes for further analysis by adding them to the Dashboard’s “Gene Cart” while browsing or searching.

After genes are added to the cart, users can query databases of molecular interactions (DNA-DNA, DNA-protein, and protein-protein) within a variety of tissue- and disease-specific interactomes. By selecting a type and version for interactome viewing, users can create molecular interaction network maps based on the Cellular Networks Knowledge Base (Figure 5). This feature allows users to build interactomes directly from a platform that also has general and experimental details for each gene of interest.

Genes of interest can be added to the Gene Cart by clicking the + next to the gene

Figure 5: Genes of interest, such as MYC, can be added to the Gene Cart by clicking the + next to the gene. Then, users can click “gene cart” in the menu bar to go to the cart and select a gene of interest to create an interactome. Once a user enters gene cart and clicks “Find gene interactions in Networks”, the CTD2 Dashboard will walk users through steps necessary to produce an interactome.

Dashboard visitors can read stories associated with publications.

Stories are summaries of research findings, written for a broad scientific audience (Figure 6). Stories are written such that users from all research fields can easily grasp the experimental design, analyses and conclusions. When CTD2 Dashboard subjects are mentioned in a story, they are linked to their respective observations pages in the CTD2 Dashboard.

Dashboard stories highlight research from the CTD^2 network

Figure 6: Dashboard stories highlight research from the CTD2 network. Links to the latest stories are displayed on the homepage. Clicking “More stories” will bring the user to a list of all available stories, sorted by date of addition to the CTD2 Dashboard. Users can choose to see observations associated with the story, or view the full story.

A major advantage of the CTD2 Network Dashboard is that it allows users to visualize the flow of research from hypothesized correlations to validated molecular relationships, and to view each finding as an easily digestible summary. The CTD2 Dashboard acts as a “one stop shop,” allowing users to access multiple types of general and experimental information and various external analytical tools from one website. This streamlined process of exploration will hopefully lead to more hypotheses, discoveries and clinical interventions.

As the Dashboard continues to be updated with new Network-generated data, its importance as a resource for the research community will be enhanced. For more information on the CTD2 Dashboard organization including term definitions, please read Navigating and Understanding Dashboard Content. All interested researchers are encouraged to visit the Dashboard and provide feedback.  

We would like to acknowledge the CTD2 Dashboard development team: Arman Aksoy, Andrea Califano, Paul Clemons, Vlado Dancik, Tanja Davidsen, Aris Floratis, Daniela Gerhard, Benjamin Gross, Leandro Hermida, Nadia Jaber, Subhashini Jagu, Zhou Ji, Ava Li, Jinyu Li, Jessica Mazerik, Don Monroe, Chris Sander, Stuart Schreiber, Kenneth Smith, and Cliff Wong.


Guest Editorial
CRISPRi and CRISPRa: New Functional Genomics Tools Provide Complementary Insights into Cancer Biology and Therapeutic Strategies

Martin Kampmann, Ph.D.
A photo of the author Martin Kampmann

A central goal of research for targeted cancer therapy, or precision oncology, is to reveal the intrinsic vulnerabilities of cancer cells and exploit them as therapeutic targets. Examples of cancer cell vulnerabilities include driver oncogenes that are essential for the initiation and progression of cancer, or non-oncogene addictions resulting from the cancerous state of the cell. To identify vulnerabilities, scientists perform genetic “loss-of-function” and “gain-of-function” studies to better understand the roles of specific genes in cancer cells. In this article, we describe the genetic engineering system clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) and how it is used to perform loss-of-function and gain-of-function experiments in a high-throughput manner1. We then explore how these studies can be applied to identify vulnerabilities, find new drug targets, and better understand how cancer cells develop drug resistance.

Adaptations of the CRISPR/Cas9 systems

The CRISPR/Cas9 system was first discovered in bacteria, which use it as an immune mechanism to degrade viral and plasmid DNA2. Scientists have since modified this system to edit and study genomic DNA in mammalian cells. Engineered small guide RNAs (sgRNA) target Cas9 to specific DNA segments, where it introduces double-stranded cuts in the DNA. This DNA damage activates a normal but error-prone cellular process to repair double strand breaks. Thus, CRISPR/Cas9 cutting frequently introduces DNA mutations and results in complete loss-of-function of the targeted gene (Figure 1A). Scientists have exploited this approach for mammalian loss-of-function experiments2.

Several research groups have enhanced the CRISPR/Cas9 system by incorporating a catalytically dead Cas9 (dCas9) fused to other functional proteins2. dCas9-fusion proteins are recruited to DNA sequences via sgRNA, but the catalytically inactive dCas9 cannot cut the DNA. Instead, the proteins fused to dCas9 manipulate transcription of the targeted genes. RNA polymerase mediates transcription and several factors affect the ability of RNA polymerase to bind DNA. For example, transcription repressors inhibit RNA polymerase binding while transcription activators promote binding. The CRISPR system with dCas9 was modified to alter transcription by taking advantage of these factors.

When dCas9 is fused to Kruppel associated box (KRAB), a transcriptional repressor domain, transcription is repressed and the system is referred to as CRISPR interference (CRISPRi)2 (Figure 1B). Compared with CRISPR cutting approaches, CRISPRi is inducible, reversible, and non-toxic; it also enables knockdown of non-coding RNAs1. When dCas9 is fused to the SunTag, a sequence containing multiple copies of the activator recruitment domain of general control protein (GCN4), the system activates transcription and is referred to as CRISPR activation (CRISPRa) (Figure 1C). The SunTag3 recruits multiple copies of various proteins, such as a tandem array of the transcriptional activator virus protein 16 (VP16) to activate transcription in a robust manner. Together, CRISPRi and CRISPRa enable control of gene transcription by several orders of magnitude and have been shown to exhibit few off-target effects, meaning the technique primarily affects only the intended genes1.

Cartoon explaining CRISPR techniques

Figure 1. Variations of CRISPR/Cas9 used for genetic screens: (A) CRISPR cutting: Catalytically active Cas9 is directed to coding sequences to introduce DNA breaks, which are subject to error-prone cellular repair. (B) CRISPRi: Cas9 lacking the cutting function (dCas9) fused to a transcriptional repressor (KRAB) domain is recruited to the transcriptional start site (TSS) of genes to repress their transcription. (C) CRISPRa: dCas9 fused to a SunTag, an epitope tag containing 10 copies of GCN4, which recruits VP64 transcriptional activators and superfold green fluorescent protein molecules (to improve protein solubility) to the TSS to activate transcription.

Systematic mapping of genetic interactions

To reveal cancer vulnerabilities, CRISPRi and CRISPRa can be used in genetic screens to alter the expression of single genes, or to alter two genes to detect genetic interactions and thereby reveal pathway relationships. Synthetic lethality is a situation in which cells may viably carry mutations in two different genes independently, but if a single cell has mutations in both genes, the combination is lethal. Synthetic lethality can be exploited in cancer therapy; this is especially useful when a gene cannot be targeted pharmacologically, but targeting its synthetic lethal gene partner induces cell death.

Synthetic lethal gene pairs are rare and difficult to predict. Previously, we used another genetic manipulation technique, short hairpin RNAs, to systematically map genetic interactions for large sets of genes in mammalian cells4,5. The inherent off-target effects of shRNAs required that we target each gene using three independent shRNAs in a double-shRNA library. CRISPRi dramatically reduces off-target effects, which reduces the number of sgRNAs required per gene to one or two, enabling us to screen up to 9 times as many gene combinations in an experiment of a similar scale. For example, we could perform a CRISPRi screen of ~250,000 gene combinations using the same number of cells as we use in an shRNA screen of ~26,000 gene combinations. Experiments using CRISPR technology will allow researchers to test more gene combinations with more complete control and more readily identify synthetic lethal gene pairs.

Importantly, CRISPRa will now make it possible to include gain-of-function variant genes in genetic interaction maps. These are highly relevant from a clinical point of view because we currently do not have targeted therapies available for many gain-of-function oncogenes, such as Kirsten rat sarcoma (KRAS). The discovery of synthetic lethal relationships from such loss-of-function and gain-of-function screens could guide the development of new therapeutic strategies.

Understanding genetic control of drug sensitivity

While using targeted drugs to treat cancers has enormous potential, two clinically relevant challenges include predicting which patients will respond to a targeted drug, and overcoming the frequent problem of drug resistance. Functional genomic approaches can pinpoint genes that control the sensitivity of cancer cells to drugs. Such genes are potential biomarkers for matching cancer patients with targeted drugs and providing insights into possible mechanisms of drug resistance.

The CRISPRi “knock-down” system has a special advantage over “knock-out” screens because sgRNAs can be engineered with point mutants to vary the targeting ability of the sgRNAs and create different levels of targeted knockdown. Gene expression can be modulated to study how cells behave when an essential gene is expressed at low levels. In a previous shRNA-based screen for genes controlling sensitivity to the proteasome inhibitor carfilzomib in multiple myeloma cells, we found that partial knockdown of essential components of the 19S proteasomal regulator paradoxically confers carfilzomib resistance6. In agreement with this experimental finding, multiple myeloma patients with lower levels of 19S subunits did not respond as well to carfilzomib-based therapy6. This suggests that components of the 19S proteasome could act as potential biomarkers for carfilzomib resistance. Our study also predicted suitable and unsuitable targets for combination therapy with carfilzomib6.

Importantly, drug resistance in cancer cells often involves gain-of-function events, such as gene amplification or point mutations. Such alterations can cause over-activation of proteins which can help resist the killing mechanisms of drugs. CRISPRa gain-of-function screens are ideally suited to reveal such mechanisms of drug resistance.

CRISPRi/a is a less expensive and more efficient method of gene repression and activation that can be used in many applications to answer some of the most important questions in cancer genomics research, with the goal of improving clinical diagnostic and treatment approaches. By helping to identify cancer vulnerabilities and reveal mechanisms of drug resistance, CRISPRi/a are valuable tools that can ultimately be used to discover and better understand targeted therapies for precision medicine.


  1. Gilbert LA, Horlbeck MA, Adamson B, Villalta JE, Chen Y, Whitehead EH, Guimaraes C, Panning B, Ploegh HL, Bassik MC, Qi LS, Kampmann M, Weissman JS (2014). Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation. Cell 159(3):647-61 (PMID: 25307932)
  2. Boettcher M, McManus MT (2015). Choosing the Right Tool for the Job: RNAi, TALEN, or CRISPR. Mol Cell 58(4):575-85 (PMID: 26000843)
  3. Tanenbaum ME, Gilbert LA, Qi LS, Weissman JS, Vale RD (2014). A protein-tagging system for signal amplification in gene expression and fluorescence imaging. Cell 159(3):635-46 (PMID: 25307933)
  4. Kampmann M, Bassik MC, Weissman JS (2013). Integrated platform for genome-wide screening and construction of high-density genetic interaction maps in mammalian cells. Proc Natl Acad Sci U S A 110(25):E2317-26.(PMID: 23739767)
  5. Bassik MC, Kampmann M, Lebbink RJ, Wang S, Hein MY, Poser I, Weibezahn J, Horlbeck MA, Chen S, Mann M, Hyman AA, Leproust EM, McManus MT, Weissman JS (2013). A systematic mammalian genetic interaction map reveals pathways underlying ricin susceptibility. Cell 152(4):909-22. (PMID: 23394947)
  6. Acosta-Alvear D, Cho MY, Wild T, Buchholz TJ, Lerner AG, Simakova O, Hahn J, Korde N, Landgren O, Maric I, Choudhary C, Walter P, Weissman JS, Kampmann M (2015). Paradoxical resistance of multiple myeloma to proteasome inhibitors by decreased levels of 19S proteasomal subunits. eLife 4:e08153 (PMID: 26327694)


Precision Oncology
A Personal Story of Targeted Therapy

edited by Thomas Calder, Ph.D., Jessica Mazerik, Ph.D., and Nadia Jaber, Ph.D.

“We went to the hospital that day, and the doctor said come packed and ready for Harrison to receive a harsh bone marrow transplant, or to receive good news and go home. The doctor was preparing us for the targeted therapy to not work. So we came packed and prepared to either move into the bone marrow unit or go home. During the visit, the doctor himself went down to look at the slide of Harrison’s blood. When he came back up, he walked in and exclaimed, ‘It worked! We have a homerun!’ The whole place just erupted in cheers – all the nurses and staff. It was an amazing day.”

This is Steve McKinion describing the day he learned his son Harrison’s cancer was in remission. After Harrison failed to respond to standard-risk chemotherapy, his doctors decided to try an experimental precision oncology strategy discovered by TARGET investigators and others. The treatment saved his life.

Harrison suffered from a white blood cell cancer known as Philadelphia chromosome-like acute lymphoblastic leukemia (Ph-like ALL). Harrison was pictured with his oncologist in Issue 13 of the OCG eNewsletter as an example of how targeted therapies discovered by TARGET investigators, including Drs. Charles Mullighan and Steve Hunger, are helping to save lives. OCG spoke with Steve McKinion to get a parent’s perspective of how precision oncology as a treatment strategy ultimately affected their family by bringing his son’s cancer into remission.

A photo of Harrison with his family

Harrison (far right) enjoys family time with (left to right) his brother, mother, father and sister.

How old was Harrison when you discovered he had cancer and what were his initial symptoms?

Harrison had just turned 10 years old. He was sick on his birthday and had not been feeling well for several weeks prior. He was having leg pain, his lips were very pale, he was clearly anemic, he had night sweats, and he was lethargic – all symptoms that were strange for such an incredibly active little kid. Of course, we had no idea these were signs of leukemia. We all believed Harrison simply had a virus that would be gone in a couple weeks. We finally went to the doctor when one of his teachers voiced concern that Harrison was so pale and lethargic at school. The pediatricians and nurses informed us Harrison’s white blood cell counts were very high and red blood cell counts were very low. They told us “you need to go to the hospital; they will be expecting you.” Once there, he was quickly diagnosed with acute lymphoblastic leukemia (ALL).

What was his initial treatment and how did he end up on targeted therapy?

Harrison was diagnosed with ALL just 5 days after turning 10 years old, so his doctor was uncertain if he should be treated with chemotherapy for standard-risk or high-risk patients*. Initially the doctor administered the standard-risk induction therapy (in Harrison’s planned 3-year ALL treatment regimen, the first 28-day phase aimed to “induce” remission of the ALL).  Upon completion of the standard-risk induction treatment four weeks later, a bone marrow aspirate showed that Harrison’s leukemia remained virtually unaffected. Luckily, around that time Harrison’s doctors discovered that his cancer cells contained a genetic fusion that was likely responsible for the disease. His doctors researched this particular fusion and found a study published by Dr. Charles Mullighan (TARGET ALL investigator) at St. Judes Children’s Research Hospital. Dr. Mullighan, along with the TARGET ALL project team and other investigators, described how they used tyrosine kinase inhibitors to treat and cure mice afflicted with ALL caused by the same genetic fusion1. Our doctors decided to try this targeted approach by treating Harrison with the tyrosine kinase inhibitor (TKI), imatinib, along with an extended chemotherapy regimen used in high-risk patients. About 3 days after starting targeted TKI therapy and the more aggressive chemotherapy treatment, Harrison was a completely different kid; the effect was almost immediate. Two weeks later, his bone marrow aspirate showed that Harrison was in remission – no signs of the cancer!

*Editor’s note: Age is a key determinant of risk for this type of cancer; 1-9 years is considered standard-risk, and 10 years and older is considered higher risk.

Can you speak about the side effects of both treatments?

Harrison started on induction chemotherapy for standard-risk patients. The side effects included: hair loss, mouth sores, water weight gain, and low blood counts that required multiple blood transfusions – all typical side effects. One drug commonly used to treat ALL caused a coagulation problem for Harrison, and he had a stroke. It was awful. That was one of the rare side effects that Harrison experienced before he failed to respond and was switched to imatinib and a more agressive chemotherapy regimen. The side effects from the chemotherapy continued, and the doctor had to scale back the chemotherapy to minimize the combined effects with the targeted treatment. At one point, Harrison had the chemotherapy removed completely for weeks until his counts recovered.

For the targeted therapy specifically, the doctors were concerned that imatinib would impair his liver function (a known rare side effect of the drug), but Harrison had very few problems with his liver function. The only side effect that we observed from the tyrosine kinase inhibitor was lower blood cell counts. Once he went into remission and began the final phase of treatment (maintenance phase)*, his blood levels all balanced out.

*Editor’s note: Conventional treatment for childhood ALL can last for years and is generally given in three phases: remission induction, consolidation/intensification, and maintenance. For Harrison, the maintenance phase included targeted therapy and chemotherapy. To learn more about pediatric ALL and current treatment strategies, visit the NCI pediatric ALL website.

What is the current status of your son’s cancer?

Harrison finished his treatment protocol on April 1, 2015; he remains in remission and is already bouncing back. The doctor is keeping Harrison on targeted TKI therapy to prevent the cancer from coming back. He will remain on targeted therapy until research shows that stopping the medication is not likely to induce a relapse. At the first check-up after completing treatment, the doctors and nurses performed a bone marrow aspirate and spinal tap, and checked peripheral blood to look for leukemia cells. All of the results from these diagnostic techniques, along with Fluorescence In Situ Hybridization (FISH) to test for the presence of the specific gene fusion targeted by the imatinib came back negative for disease. There were no signs of cancer! Currently, his blood counts are climbing to within normal range and he’s doing quite well. Harrison is active in school and sports again.

Schematic timeline of Harrison's cancer treatment

A schematic timeline of Harrison's treatment

How has precision oncology impacted your family?

The easy answer is that it saved my son’s life. Drilling down from there, it preserved his quality of life during treatment because he was able to avoid harsher therapies, such as a bone marrow transplant, that may or may not have been effective. The standard-risk induction treatment is bad enough, but treatment for refractory ALL (ALL that fails to respond to the 28-day induction phase of chemotherapy) is horrific. Harrison has been undergoing treatment for his ALL for 3.5 years, but due to targeted therapy, he did not have to endure the more severe and toxic treatments such as those for patients with refractory ALL. He was able to continue playing baseball, going to school, and transition from pre-adolescence into being a teenager in a somewhat normal fashion, even though he was being treated for cancer. While Harrison might have some long-term effects from the chemotherapy, and while he will be on targeted therapy idenifitely, he will be protected from the more serious long-term effects of the harsher treatments. This has meant the world to us.

In the grand scheme of things, rolling the dice to try a new targeted therapy on Harrison has opened the door for other kids. Harrison’s case led oncologists to treat other kids with this particular fusion according to his protocol, and it essentially saved their lives as well. We have actually met one of these patients and heard of others. Our hope is that targeted therapy will help to save more lives, and allow fewer and fewer kids to undergo harsh cancer treatments.



  1. Roberts, K.G. et al. (2012) Genetic alterations activating kinase and cytokine receptor signaling in high-risk acute lymphoblastic leukemia. Cancer Cell 22, 153-66 (PMID: 22897847)

TARGET Program Highlight
Discovery of Novel Mutations in a Subtype of Wilms Tumor Reveals Underlying Biology

Nadia Jaber, Ph.D. and Elizabeth Perlman, Ph.D.
Picture of scientist looking through a microscope

Wilms tumor is the most common childhood kidney cancer, however, relatively little is known about its genetic causes. Therapeutically Applicable Research to Generate Effective Treatments (TARGET) investigators recently identified multiple in-frame insertion/deletion mutations in the myeloid/lymphoid of mixed lineage leukemia; translocated to 1 (MLLT1) gene in a subtype of Wilms tumors1. Remarkably, similar MLLT1 insertion/deletion variants were not identified in any other pediatric cancer studied by TARGET, nor have they been reported in dbSNP or COSMIC databases, or in the 1000 Genomes Pilot Projects. Wilms tumors with MLLT1 mutations present at an earlier age and exhibit specific associated precursor lesions. These findings suggest that MLLT1 mutations may be critical to the development of some Wilms tumors. Understanding the molecular changes underlying childhood cancers will advance the discovery of new diagnostic, prognostic and treatment options.

There are around 500 new cases of Wilms tumor (WT) diagnosed every year in the United States2. WT is an embryonal cancer, which means that it resembles the developing tissues in an early stage embryo and is thought to derive from early embryonic stem cells (Figure 1). WT are associated with two types of precursor lesions called perilobar and intralobar nephrogenic rests. WT is distinguished into subtypes by histology; most Wilms tumors display a more favorable histology (FHWT) and have a better prognosis, while unfavorable histology or anaplastic WT is a rare subtype that is often accompanied by poor patient outcomes. Some cases of WT are caused by mutations in the Wilms tumor 1 (WT1) gene; these cases tend to present as early as infancy, span both favorable and anaplastic histologies and are associated with multiple intralobar nephrogenic rests. Activation of the Wnt pathway, such as by mutations in Wilms Tumor on the X (WTX) and β-catenin 1 (CTNNB1), is required for the development of many WT1-mutant tumors.     

Image of normal and cancerous kidney tissue

Figure 1: Hemotoxylin and Eosin (H&E) staining of normal kidney and Wilms Tumor tissue. One of the many reasons Wilms tumor (right) attracts investigators

studying both cancer and early development is its striking resemblance to the early fetal kidney (left).
Image courtesy of Elizabeth Perlman, Ph.D.

To investigate the genomic characteristics of Wilms tumor, Dr. Perlman and colleagues on the TARGET Kidney Tumors project team performed whole genome or whole exome sequencing on a discovery set of 77 relapsed FHWT patient samples, using case-matched blood and/or adjacent normal tissue as controls. Bioinformatic analysis identified 825 somatic variants including known alterations in WT1, WTX, and CTNNB1, as well as unexpected mutations in SIX homeobox 1/2 (SIX1/2), and miRNA processing genes, drosha ribonuclease type III (DROSHA) and DGCR8 microprocessor complex subunit (DGCR8)3.

The researchers additionally discovered variants in the MLLT1 gene in seven patient cases (11%), which were further verified by targeted-capture sequencing. MLLT1 is a component of the super-elongation complex, which controls RNA polymerase II-mediated mRNA elongation. MLLT1 interacts with several subunits of the complex via an N-terminal YEATS domain. Through these interactions, MLLT1 connects RNA polymerase II with transcription elongation factors, thereby regulating transcription. The identified MLLT1 mutations consist of both insertions and deletions, and notably are all in-frame. Variants in five of the seven cases have an identical nine-nucleotide insertion, one has a six-nucleotide deletion and the other a seven-nucleotide deletion plus a one-nucleotide insertion. Of note, two MLLT1-mutant tumors also had CTNNB1 mutations and all had evidence of Wnt activation through gene expression, similar to tumors with WT1 mutations. TARGET investigators have not found these MLLT1 mutations in other tumor types studied (including neuroblastoma, osteosarcoma, acute lymphocytic and myelogenous leukemia). In addition, no germline MLLT1 mutations were found in the FHWT dataset studied.

The discovery set consisted of only relapsed FHWT samples; therefore TARGET kidney tumor researchers sought to determine the frequency of incidence of MLLT1 mutations among a more broad population of FHWT patients. They screened a validation set of 475 more randomly-selected FHWT cases and identified MLLT1 insertion/deletion mutations in 19 tumors (4%). Similar to the discovery set, six of the MLLT1-mutant tumors were accompanied by mutations in Wnt pathway genes, CTNNB1 or WTX. Interestingly, the median age of diagnosis was significantly lower in MLLT1-mutant FHWT compared to MLLT1 wild-type, suggesting earlier onset of disease. In addition, intralobar nephrogenic rests were significantly increased in MLLT1-mutant FHWT, and in several cases there were multiple intralobar nephrogenic rests. Dr. Perlman believes this is one of “the most surprising findings,” given that multiple intralobar nephrogenic rests have “previously been associated with germline WT1 mutations”. Although the investigators did not identify any germline MLLT1 mutations, the presence of intralobar nephrogenic rests suggests that MLLT1 mutations may occur very early in kidney development.

The investigators then compared gene expression patterns between MLLT1-mutant and MLLT1 wild-type tumors in the discovery set. They identified significant differences in 96 genes, including increases in Homeobox A13 (HOXA13) and v-myc Avian Myelocytomatosis Viral Oncogene Homolog (MYC). Interestingly, they also noted increased expression of HOXA Distal Transcript Antisense RNA (HOTTIP), a lncRNA which mediates expression of HOXA13. To validate these findings, the researchers transfected HEK293 embryonic kidney cells with mutant or wild-type MLLT1. In cells transfected with mutant MLLT1 both HOXA13 and HOTTIP RNA levels were significantly increased compared to cells transfected with wild-type or vector control, confirming the results observed in the screen.

To investigate the molecular function of mutant MLLT1 in FHWT, TARGET researchers evaluated the mutant protein structure and functionality. Because MLLT1 is highly homologous with myeloid/lymphoid of mixed lineage leukemia; translocated to 3 (MLLT3 or AF9), another YEATS domain-containing protein, the team used the crystal structure of AF9 as a template to model MLLT1. Interestingly, all of the MLLT1 mutations identified in the screen are found in in the same loop of the YEATS domain, but are not predicted to significantly distort the structure. The YEATS domain is involved in recognition and/or establishment of modified histones, and AF9 YEATS domain binds with high affinity to acetylated H3K9 and H3K27. The researchers found that like AF9, wild-type MLLT1 YEATS domain binds to acetylated H3K9, although with lower affinity than AF9, and the identified MLLT1 mutations abolish or alter this interaction. It remains to be determined whether MLLT1 binds acetylated histones in vivo.

The TARGET Kidney Tumors project team sought to identify genetic alterations in Wilms tumor by analyzing whole genome or whole exome sequencing of FHWT samples. The researchers identified novel in-frame mutations in MLLT1 in two cohorts of FHWT patients but not other childhood cancers studied by TARGET. Given that MLLT1 is a subunit of the super-elongation complex, the researchers suspect that the identified novel mutations may alter mRNA transcription, which is known to affect development and can lead to tumorigenesis. HOXA13 and MYC are known to be regulated through transcriptional elongation by the super-elongation complex, but it remains to be determined how the MLLT1 mutations lead to increases in these genes. Determining if HOXA13 and MYC play a role in WT development will also be of interest for future investigations. Understanding the function of these novel MLLT1 mutations in the etiology of a subset of Wilms tumors will ultimately aid the identification of therapeutic opportunities.



  1. Perlman EJ, Gadd S, Arold ST, Radhakrishnan A, Gerhard DS, Jennings L, Huff V, Guidry Auvil JM, Davidsen TM, Dome JS, Meerzaman D, Hsu CH, Nguyen C, Anderson J, Ma Y, Mungall AJ, Moore RA, Marra MA, Mullighan CG, Ma J, Wheeler DA, Hampton OA, Gastier-Foster JM, Ross N, Smith MA (2015). MLLT1 YEATS domain mutations in clinically distinctive favorable histology Wilms tumors. Nature Commun. 4(6):10013 (PMID: 26635203)
  2. American Cancer Society (2015). What are the key statistics about Wilms tumor?
  3. Walz AL, Ooms A, Gadd S, Gerhard DS, Smith MA, Guidry Auvil JM, Meerzaman D, Chen QR, Hsu CH, Yan C, Nguyen C, Hu Y, Bowlby R, Brooks D, Ma Y, Mungall AJ, Moore RA, Schein J, Marra MA, Huff V, Dome JS, Chi YY, Mullighan CG, Ma J, Wheeler DA, Hampton OA, Jafari N, Ross N, Gastier-Foster JM, Perlman EJ (2015). Recurrent DGCR8, DROSHA, and SIX homeodomain mutations in favorable histology Wilms tumors. Cancer Cell. 27(2): 286-97. (PMID: 25670082)

OCG Perspective
Delving into Science Communication: a Scientist’s Journey Outside the Bench

Thomas Calder, Ph.D.
A photo of the author Thomas Calder

Over the past year, I have had the privilege of working as a Health Communication Fellow in the Office of Cancer Genomics (OCG) at the National Cancer Institute. I was initially drawn to this fellowship because I wanted to improve my ability to convey complex scientific material to various audiences and also jump-start my career on a new path outside the bench. Mid-way into graduate school, I decided that I did not want to follow the traditional career path towards a professorship. I found myself much more drawn to outreach, activism, and learning about the policies that affect scientists. In graduate school at UT Southwestern, I pursued these interests by starting a club called, “Science Policy, Education, and Communication” (SPEaC) to help improve the line of communication between scientists, the public, and politicians. Basically, I wanted to strengthen the scientific enterprise (even if it was in small ways) by working to encourage greater interactions between these groups. As graduation drew near, I applied for several communication and policy fellowships to continue pursuing these interests. After months of waiting to hear back from different fellowship selection committees, I was ecstatic to find out that I was selected to be a health communication fellow in OCG.

The first few weeks as a fellow were a challenging, but exciting experience. Not only did I have to adjust to working in front of a computer all day, but I also had to acquaint myself with the field of cancer genomics. I received my doctoral training in molecular microbiology; so to expand my knowledge base, I spent these initial weeks reading reviews and publications to learn more about cancer biology, and cancer genomics specifically. From this reading I was very intrigued at how some cancers behave so similarly to the human bacterial pathogens that I had studied in graduate school. For example, bacteria and tumor cells can both adapt to their environment and gain resistance to drugs. This is why antimicrobial resistance is such a growing issue, and one of the reasons why so many cancer patients relapse. From a humanitarian standpoint, I also found myself drawn to cancer biology, just as I did to microbiology. Both cancer and pathogenic organisms kill millions of people every year, and they do not discriminate; they “infect” adults and children alike. OCG programs will hopefully save many lives by making scientific breakthroughs in the field of cancer genetics and by working to translate discoveries into new diagnostic methods and cancer treatments.

Once I familiarized myself with cancer genomics and the type of work being performed at OCG, I became heavily involved with several important communication tasks in the office. My daily activities involved: writing news articles about publications coming out of OCG supported labs, coordinating and authoring articles for the OCG eNewsletter, and aiding OCG staff with website content. As a scientist, I was mainly accustomed to writing in a scientific style with complex terminology, so all of these various writing projects allowed me to improve my writing style to better convey information to various audiences.

In addition to this work, I also became actively involved in helping to lead communication initiatives for the OCG program, “Therapeutically Applicable Research to Generate Effective Treatments” (TARGET), which aims to support targeted cancer therapy research to improve diagnostic technologies and treatment therapies for several childhood cancers. For this program, I attended weekly meetings and worked closely with the program manager, Dr. Jaime Guidry-Auvil, to update website content and create new webpages for the TARGET program. There was one major discovery from this program that captured my attention during my entire fellowship. Several TARGET investigators, led by Dr. Charles Mullighan and Dr. Steven Hunger, discovered that many patients with Philadelphia chromosome-like acute lymphoblastic leukemia (Ph-like ALL), a white blood cell cancer, harbor different genetic fusions that activate a kinase in the cell. They found that these fusions could be directly targeted with tyrosine kinase inhibitor drugs. This treatment strategy is known as precision oncology, because it involves the use of drugs designed to target precise genomic alterations in cancer.

The clinical implications of this discovery are very exciting so we asked Drs. Mullighan and Hunger to write an article about their work in the previous eNews issue (see article here). I found their story fascinating for two reasons. First, it provided an example of how precision medicine can be used to transform the way diseases are treated – by tailoring treatments to specific genetic signatures or other characteristics. Second, their eNews article came with the face of a young boy whose life was saved by this newly discovered targeted treatment. By seeing a young boy smiling with his oncologist, the picture added a human touch to the story, and in a sense, it fully unified the importance of basic science research with real clinical results. I found the boy’s story so powerful, that I contacted his parents and requested to interview them for an eNews article. They were very gracious and eager to share his story, which can be seen in this eNews issue.

I am very grateful for my time as a health communication fellow and to the OCG staff for helping me to become a better science communicator. The skills that I learned in this office allowed me to be a competitive job applicant for other positions and helped me obtain my dream job as a health policy analyst at the National Institute of Allergy and Infectious Diseases (NIAID). In this new position, I work at the forefront of science policy issues and serve as a liaison for different groups, including the public, NIAID staff, and other members of the federal government. This liaison role requires effective communication skills to adapt writing styles to various audiences. I believe the training that I received from my fellowship is proving to be invaluable as I pursue this new endeavor.