Reposted from the Columbia University Medical Center Newsroom. Find the original article here .

Cancer bottlenecks
In an N-of-1 study, researchers at Columbia University use techniques from systems biology to analyze genomic information from an individual patient’s tumor. The goal is to identify key genes, called master regulators  (green circles), which, while not mutated, are nonetheless necessary for the survival of cancer cells. 

Columbia University Medical Center (CUMC) researchers are developing a new approach to cancer clinical trials, in which therapies are designed and tested one patient at a time. The patient’s tumor is “reverse engineered” to determine its unique genetic characteristics and to identify existing U.S. Food and Drug Administration (FDA)-approved drugs that may target them.

Rather than focusing on the usual mutated genes, only a very small number of which can be used to guide successful therapeutic strategies, the method analyzes the regulatory logic of the cell to identify genes and gene pairs that are critical for the survival of the tumor but are not critical for normal cells. FDA-approved drugs that inhibit these genes are then tested in a mouse model of the patient’s tumor and, if successful, considered as potential therapeutic agents for the patient — a journey from bedside to bench and back again that takes about six to nine months.

“We are taking a rather different approach to tailor therapy to the individual cancer patient,” said principal investigator Andrea Califano, PhD, Clyde and Helen Wu Professor of Chemical Systems Biology and chair of CUMC’s new Department of Systems Biology. “If we have learned one thing about this disease, it’s that it has tremendous heterogeneity both across patients and within individual patients. When we expect different patients with the same tumor subtype or different cells within the same tumor to respond the same way to a treatment, we make a huge simplification. Yet this is how clinical studies are currently conducted. To address this problem, we are trying to understand how tumors are regulated one at a time. Eventually, we hope to be able to treat patients not on an individual basis, but based on common vulnerabilities of the cancer cellular machinery, of which genetic mutations are only indirect evidence. Genetic alterations are clearly responsible for tumorigenesis but control points in molecular networks may be better therapeutic targets.”

Topology of cancer

The Columbia University Center for Topology of Cancer Evolution and Heterogeneity will combine mathematical approaches from topological data analysis with new single-cell experimental technologies to study cellular diversity in solid tumors. Image courtesy of Raul Rabadan.

The National Cancer Institute’s Physical Sciences in Oncology program has announced the creation of a new center for research and education based at Columbia University. The Center for Topology of Cancer Evolution and Heterogeneity will develop and utilize innovative mathematical and experimental techniques to explore how genetic diversity emerges in the cells that make up solid tumors. In this way it will address a key challenge facing cancer research in the age of precision medicine — how to identify the clonal variants within a tumor that are responsible for its growth, spread, and resistance to therapy. Ultimately, the strategies the Center develops could be used to identify more effective biomarkers of disease and new therapeutic strategies.

Comorbidity between Mendelian disease and cancer
Researchers in the Rabadan Lab have found that comorbidity between Mendelian diseases and cancer may result from shared genetic factors.

Genetic diseases can arise in a variety of ways. Mendelian disorders, for example, occur when specific mutations in single genes — called germline mutations — are inherited from either of one’s two parents. Well-known examples of Mendelian diseases include cystic fibrosis, sickle cell disease, and Duchenne muscular dystrophy. Other genetic diseases, including cancer, result from somatic mutations, which occur in individual cells during a person’s lifetime. Because the genetic origins of Mendelian diseases and cancer are so different, they are typically understood to be distinct phenomena. However, scientists in the Columbia University Department of Systems Biology have found evidence that there might be interesting genetic connections between them. 

In a paper just published in Nature Communications, postdoctoral research scientist Rachel Melamed and colleagues in the laboratory of Associate Professor Raul Rabadan report on a new method that uses knowledge about Mendelian diseases to suggest mutations involved in cancer. The study takes advantage of an enormous collection of electronic health records representing over 110 million patients, a substantial percentage of US residents. The authors show that clinical co-occurrence of Mendelian diseases and cancer, known as comorbidity, can be tied to genetic changes that play roles in both diseases. The paper also identifies several specific relationships between Mendelian diseases and the cancers melanoma and glioblastoma.

ALK-negative ALCL mutation map
A map of mutations observed in ALK-negative anaplastic large cell lymphoma. (Credit: Dr. Rabadan)

The following article is reposted with permission from the Columbia University Medical Center Newsroom. Find the original here.

The first-ever systematic study of the genomes of patients with ALK-negative anaplastic large cell lymphoma (ALCL), a particularly aggressive form of non-Hodgkin’s lymphoma (NHL), shows that many cases of the disease are driven by alterations in the JAK/STAT3 cell signaling pathway. The study also demonstrates, in mice implanted with human-derived ALCL tumors, that the disease can be inhibited by compounds that target this pathway, raising hopes that more effective treatments might soon be developed. The study, led by researchers at Columbia University Medical Center (CUMC) and Weill Cornell Medical College, was published today in the online edition of Cancer Cell.

Andrea Califano and Aris Floratos
Andrea Califano and Aris Floratos will lead an effort to reclassify tumors catalogued in TCGA according to their master regulators.

Andrea Califano and Aris Floratos, faculty members in the Columbia University Department of Systems Biology, have received a two-year, $624,236 subcontract to develop a new classification system of cancer subtypes. The agreement was awarded through a subcontract from Leidos Biomedical Research, Inc., which operates the Frederick National Laboratory for Cancer Research for the federal government.  

By performing an integrative analysis of genomic data from the Cancer Genome Atlas (TCGA) and proteomic data from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC), the researchers plan to recategorize tumors collected in TCGA based on the master regulator genes that determine their state. This is in contrast to other approaches based on expression of genes that reflect tissue lineage and proliferative processes. In addition, the team will link the genetics of each tumor sample to the specific master regulators that determine its state using a recently published novel algorithm (DIGGIT). Ultimately, the project aims to provide a more useful catalog of pan-cancer subtypes that could help to identify biomarkers and therapeutic targets for specific kinds of tumors, and ultimately provide a resource to guide the next generation of precision medicine.

“We have to reevaluate the way in which we organize tumors within subtypes, using both gene expression data and mutational data,” says Dr. Califano. “Right now the common approach is to classify tumor types based on rather generic genes that are differentially expressed between subtypes. But most of these genes play no role in actually driving the disease. We want to shift the emphasis and classify tumors based on the genes that truly regulate tumor state and survival.”

Sequence of genomic alterations in CLLA graph representing the sequence of genomic alterations in chronic lymphocytic leukemia (CLL). Each node represents a mutation, with arrows indicating temporal relationships between them. The size of the nodes indicates the number of patients in the study who exhibited the alteration, while the thickness of the lines shows how often the temporal relationships between nodes were seen. The method the researchers use enabled them to identify multiple, distinct evolutionary patterns in CLL.

As biologists have gained a better understanding of cancer, it has become clear that tumors are often driven not by a single mutation, but by a series of genetic changes that correspond to particular stages of cancer progression. In this sense, a tumor is constantly evolving, with different groups of cells that harbor distinctive mutations multiplying at different rates, depending on their fitness for particular disease states. As the search for more effective cancer diagnostics and therapies continues, one key question is how to disentangle the order in which mutations occur in order to understand how tumors change over time. Being able to predict how a tumor will behave based on signs seen early in the course of disease could enable the development of new diagnostics that could better inform treatment planning.

In a paper just published in the journal eLife, a team of investigators led by Department of Systems Biology Associate Professor Raul Rabadan reports on a new computational strategy for addressing this challenge. Their framework, called tumor evolutionary directed graphs (TEDG), considers next-generation sequencing data from tumor samples from a large number of patients. Using TEDG to analyze cancer cells in patients with chronic lymphocytic leukemia (CLL), they were able to develop a model of how the disease’s mutational landscape changes from its initial onset to its late stages. Their findings suggest that CLL may not be just the result of a single evolutionary path, but can evolve in alternative ways.

Differential decay rates in MDA-LM2 vs. MDA cells

The presence of the structural RNA stability element (sSRE) family of mRNA elements distinguishes transcript stability in metastatic MDA-LM2 breast cancer cell lines from that seen in its parental MDA cell line. Each bin contains differential decay rate measurements for roughly 350 transcripts. From left (more stable in MDA) to right (more stable in MDA-LM2), sRSE-carrying transcripts were enriched among those destabilized in MDA-LM2 cells. The TEISER algorithm collectively depicts sSREs as a generic stem-loop with blue and red circles marking nucleotides with low and high GC content, respectively. Also included are mutual information (MI) values and their associated z-scores. 

Gene expression analysis has become a widely used method for identifying interactions between genes within regulatory networks. If fluctuations in the expression levels of two genes consistently shift in parallel over time, the logic goes, it is reasonable to hypothesize that they are regulated by the same factors. However, such analyses have typically focused on steady-state gene expression, and have not accounted for modifications that messenger RNAs (mRNAs) can undergo during the time between their transcription from DNA and their translation into proteins. Researchers now understand that certain stem loop structures in mRNAs make it possible for proteins to bind to them, often causing RNA degradation and subsequently modulating protein synthesis. From the perspective of systems biology, this can have implications for the activity of entire regulatory networks, and recent studies have even suggested that aberrations in mRNA stability can play a role in disease initiation and progression.

In a new paper published in the journal Nature, Department of Systems Biology Professor Saeed Tavazoie and collaborators at the Rockefeller University describe a new computational and experimental approach for identifying post-transcriptional modifications and investigating their effects in biological systems. In a study of metastatic breast cancer, they determined that when the protein TARBP2 binds to a specific structural element in mRNA transcripts, it increases the likelihood that cancer cells will become invasive and spread. Interestingly, they also found that TARBP2 causes metastasis by binding transcripts of two genes — amyloid precursor protein (APP) and zinc finger protein 395 (ZNF395) — that have previously been implicated in Alzheimer’s disease and Huntington’s disease, respectively. Although the nature of this intersection between the regulatory networks underlying cancer and neurodegenerative diseases is unclear, the finding raises a tantalizing question about whether these very different disorders might be linked at some basic biological level.

Personalized Medicine

Illustration by Davide Bonazzi, courtesy of Columbia Medicine.

The cover article of the Spring 2014 issue of Columbia Medicine reports on a new, Columbia University-wide effort to harness the potential of new scientific approaches and technological developments to advance the personalized treatment of cancer and other diseases. Announced in February by Columbia President Lee Bollinger, an interdisciplinary task force has been formed to coordinate the scientific, policy, and clinical components necessary to achieve this goal. The Department of Systems Biology, including its Center for Computational Biology and Bioinformatics and JP Sulzberger Columbia Genome Center, has been identified as a key partner in this interdisciplinary effort. As the article reports:

Rapidly evolving technologies that make DNA sequencing dramatically faster and less expensive along with new technologies to monitor virtually all aspects of cell physiology have led to the generation of unprecedented amounts of information (big data) that is starting to yield to new computational approaches and high speed computers, all of which promise to make diagnosis and treatment as patient-specific and precise as possible.

To harness the potential created by these scientific advances for the benefit of patients, [College of Physicians & Surgeons] Dean Lee Goldman, MD, has made personalized medicine a key goal of the medical school’s strategic plan. “At Columbia, we have enthusiastic consensus in support of personalized medicine—the personalized application of scientific advances to modern diagnosis and treatment, easily accessible and attentive care for the people who entrust us with their health, and personalized education for each of our individual students,” says Dr. Goldman…

At the center of Columbia’s personalized medicine effort is the new Department of Systems Biology, which brings together researchers specializing in molecular biology, genetics, computational biology and bioinformatics, structural biology, mathematics, chemistry and chemical biology, physics, computer science, and other fields. According to founding director and chair Andrea Califano, PhD, the Clyde and Helen Wu Professor of Chemical Systems Biology, the new department seeks to provide an in-depth, systemwide characterization of all molecular interactions. It is this systems-level approach to disease biology that can systematically identify disease drivers and druggable targets for the 90 percent of cancer patients who lack a clearly actionable genetic mutation. This has become possible only in recent years through major advances in science and technology that require a fully interdisciplinary approach.

The article describes how research by Department of Systems Biology faculty members including Dr. Califano, Nicholas Tatonetti, Brent Stockwell, and Tuuli Lappalainen, along with that of investigators in departments across the university, is contributing to this ambitious initiative. Read the complete article here.

Comparing human and mouse prostate cancer networks

Computational synergy analysis depicting FOXM1 and CENPF regulons from the human (left) and mouse (right) interactomes showing shared and nonshared targets. Red corresponds to overexpressed targets and blue to underexpressed targets.

Two genes work together to drive the most lethal forms of prostate cancer, according to new research by investigators in the Columbia University Department of Systems Biology.  These findings could lead to a diagnostic test for identifying those tumors likely to become aggressive and to the development of novel combination therapy for the disease.

The two genes—FOXM1 and CENPF—had been previously implicated in cancer, but none of the prior studies suggested that they might work synergistically to cause the most aggressive form of prostate cancer. The study was published today in the online issue of Cancer Cell.

“Individually, neither gene is significant in terms of its contribution to prostate cancer,” said co-senior author Andrea Califano, the Clyde and Helen Wu Professor of Chemical Biology in Biomedical Informatics and Chair of the Department of Systems Biology. “But when both genes are turned on, they work together synergistically to activate pathways associated with the most aggressive form of the disease.”

Co-principal investigator Andrea Califano discusses the new study.

“Ultimately, we expect this finding to allow doctors to identify patients with the most aggressive prostate cancer so that they can get the most effective treatments,” said co-senior author Cory Abate-Shen, the Michael and Stella Chernow Professor of Urologic Sciences and also a member of the Department of Systems Biology. “Having biomarkers that predict which patients will respond to specific drugs will hopefully provide a more personalized way to treat cancer.”

Reversing glucocorticoid resistance

A representative example of tumor load analysis using bioluminescence imaging in mice following xenograft with T-ALL. Treatment with either MK2206 or dexamethasone showed limited efficacy, while combination treatment saw near complete elimination of tumor cells.

In a paper published in Cancer Cell, a team of researchers led by Adolfo Ferrando and Andrea Califano at Columbia University has identified the protein kinase AKT as a target for reversing resistance to glucocorticoid therapy in patients with acute lymphoblastic leukemia (ALL).  

Researchers in the Columbia University Department of Systems Biology and Herbert Irving Comprehensive Cancer Center have determined that measuring the expression levels of three genes associated with aging can be used to predict the aggressiveness of seemingly low-risk prostate cancer. Use of this three-gene biomarker, in conjunction with existing cancer-staging tests, could help physicians better determine which men with early prostate cancer can be safely followed with “active surveillance” and spared the risks of prostate removal or other invasive treatment. The findings were published today in the online edition of Science Translational Medicine.

More than 200,000 new cases of prostate cancer are diagnosed each year in the U.S. “Most of these cancers are slow growing and will remain so, and thus they do not require treatment,” said study leader Cory Abate-Shen, Michael and Stella Chernow Professor of Urological Oncology at Columbia University Medical Center (CUMC). “The problem is that, with existing tests, we cannot identify the small percentage of slow-growing tumors that will eventually become aggressive and spread beyond the prostate. The three-gene biomarker could take much of the guesswork out of the diagnostic process and ensure that patients are neither overtreated nor undertreated.”

Rabadan, Nature Genetics

An analysis of all gene mutations in nearly 140 brain tumors has uncovered most of the genes responsible for driving glioblastoma. The analysis found 18 new driver genes (labeled red), never before implicated in glioblastoma and correctly identified the 15 previously known driver genes (labeled blue). The graphs show mutated genes that are commonly found in varying numbers in glioblastoma (left), that frequently contain insertions (middle), and that frequently contain deletions (right). Genes represented by blue dots in the graphs were statistically most likely to be driver genes.

A team of Columbia University Medical Center researchers has identified 18 new genes responsible for driving glioblastoma multiforme, the most common—and most aggressive—form of brain cancer in adults. The study was published August 5, 2013, in the journal Nature Genetics.

The Columbia team used a combination of high-throughput DNA sequencing and a new method of statistical analysis developed by co-author Raul Rabadan, an assistant professor in the Department of Systems Biology, to generate a short list of candidate gene mutations that were highly likely to drive cancer, as opposed to mutations that have no effect.

Considering these results along with a previous study this group conducted, Rabadan and collaborators Antonio Iavarone and Anna Lasorella point out that approximately 15% of glioblastomas could now be targeted with drugs that have already been approved by the FDA. As Lasorella remarks in an article for the CUMC Newsroom, “There is no reason why these patients couldn’t receive these drugs now in clinical trials.”

The cell-of-origin model in cancer biology suggests that some tumors are more aggressive than others because of differences in the cell lineages from which they arise. In the prostate gland, there are three types of epithelial stem cell — luminal cells, basal cells, and rare neuroendocrine cells. There has been some discrepancy in the scientific literature, however, about whether luminal cells, basal cells, or both can act as a cell of tumor origin.

In a paper published online in the journal Nature Cell Biology, researchers in the laboratories of Columbia University Department of Systems Biology members Michael Shen, Andrea Califano, and Cory Abate-Shen undertook a comprehensive analysis of prostate basal cell properties in mouse models. They used a technique called genetic linkage marking to study an identical cell population in multiple assays of stem cell function.

The studies showed that discrepancies in the published literature arise because basal stem cell properties can change when studied outside their endogenous tissue microenvironment; that is, in ex vivo cell culture and tissue grafting assays. To avoid this problem, they suggest, genetic lineage tracing in vivo should be considered the gold standard for identifying physiologically relevant stem cells.


viSNE reveals the progression of cancer in a sample of cells taken from a patient with acute myeloid leukemia. Cells are colored according to intensity of expression of the indicated cell markers, enabling the comparison of expression patterns before and after relapse. For example, Fit3 is expressed primarily in the diagnosis sample, while CD34 emerges in the relapse sample.

Researchers in the Columbia Initiative in Systems Biology have developed a computational method that enables scientists to visualize and interpret high-dimensional data produced by single-cell measurement technologies such as mass cytometry. The method, called viSNE (visual interactive Stochastic Neighbor Embedding), has just been published in the online edition of Nature Biotechnology. It has particular relevance to cancer research and therapeutics. As Columbia University Medical Center reports:

Researchers now understand that cancer within an individual can harbor subpopulations of cells with different molecular characteristics. Groups of cells may behave differently from one another, including in how they respond to treatment. The ability to study single cells, as well as to identify and characterize subpopulations of cancerous cells within an individual, could lead to more precise methods of diagnosis and treatment.

“Our method not only will allow scientists to explore the heterogeneity of cancer cells and to characterize drug-resistant cancer cells, but also will allow physicians to track tumor progression, identify drug-resistant cancer cells, and detect minute quantities of cancer cells that increase the risk of relapse,” said co-senior author Dana Pe’er, associate professor of biological sciences and systems biology at Columbia.


Tumor-induced mRNA expression changes for individual biochemical reactions in central metabolism. 

A large study analyzing gene expression data from 22 cancer types has identified a broad spectrum of metabolic expression changes associated with cancer. The analysis, led by Dennis Vitkup, first author Jie Hu, a postdoctoral research scientist in the Vitkup lab, with a multi-institutional group of collaborators, also identified hundreds of potential drug targets that could cut off a tumor’s fuel supply or interfere with its ability to synthesize essential elements necessary for tumor growth. The study has just been published in the online edition of Nature Biotechnology .

As Columbia University Medical Center reports:

The results should ramp up research into drugs that interfere with cancer metabolism, a field that dominated cancer research in the early 20th century and has recently undergone a renaissance.

Attractor Metagenes - DREAM7

Team Attractor Metagenes receives its award at the DREAM7 Conference. Gustavo Stolovitzky (IBM Research), Adam Margolis (Sage Bionetworks), Dimitris Anastassiou, Tai-Hsien Ou Yang, Wei-Yi Cheng, Stephen Friend (Sage Bionetworks), Erhan Bilal (IBM Research)

The team of Professor Dimitris Anastassiou and graduate students Wei-Yi Cheng and Tai-Hsien Ou Yang has been recognized as the best performer in the Sage Bionetworks – DREAM Breast Cancer Prognosis Challenge. This challenge, one of four organized as part of the seventh Dialogue for Reverse Engineering Assessments and Methods (DREAM7), was designed to assess the ability of participants’ computational models to predict breast cancer survival using patient clinical information and molecular profiling data. As a reward for this accomplishment, the journal Science Translational Medicine has just published a paper from the Anastassiou lab describing their model. It is also the journal’s cover theme for this issue, which includes a second article describing the Challenge.

The Columbia University researchers based their DREAM entry on previous work to identify what they call “attractor metagenes,” sets of strongly co-expressed genes that they have found to be present with very little variation in many cancer types. Moreover, these metagenes appear to be associated with specific attributes of cancer including chromosomal instability, epithelial-mesenchymal transition, and a lymphocyte-specific immune response. As Wei-Yi Cheng comments in Sage Synapse, “We like to think of these three main attractor metagenes as representing three key ‘bioinformatic hallmarks of cancer,’ reflecting the ability of cancer cells to divide uncontrollably and invade surrounding tissues, and the ability of the organism to recruit a particular type of immune response to fight the disease.”

Transforming activity of FGFR-TACC fusion proteins

Representative microphotographs of hematoxylin and eosin staining of advanced FGFR3-TACC3-shp53–generated tumors show histological features of high-grade glioma.

A new paper published by Columbia University Medical Center researchers in the journal Science has determined that some cases of glioblastoma, the most aggressive form of primary brain cancer, result from the fusion of the genes FGFR and TACC. Raul Rabadan, a co-senior author on the study, led efforts to identify these genes by using quantitative methods to analyze the glioblastoma genome from nine patients, and then compare these results with more than 300 genomes from the Cancer Genome Atlas project.

The collaboration with cancer genomics expert Antonio Iavarone and co-senior author Anna Lasorella found that the protein produced by the FGFR-TACC fusion disrupts the mitotic spindle (the cellular structure that guides mitosis) and causes aneuploidy, an uneven distribution of chromosomes that causes tumorigenesis. The researchers also found that drugs that target this aberration can dramatically slow the growth of tumors in mice, suggesting a potential therapeutic target.

An extensive microRNA-mediated network of RNA-RNA interactions

Genome-wide inference of sponge modulators identified a miR-program mediated post-transcriptional regulatory (mPR) network including ~248,000 interactions.

For decades, scientists have thought that the primary role of messenger RNA (mRNA) is to shuttle information from the DNA to the ribosomes, the sites of protein synthesis. However, new studies now suggest that the mRNA of one gene can control, and be controlled by, the mRNA of other genes via a large pool of microRNA molecules, with dozens to hundreds of genes working together in complex self-regulating sub-networks.

In work published in the journal Cell, Andrea Califano, José Silva, and colleagues analyzed gene expression data in glioblastoma in combination with matched microRNA profiles to uncover a posttranscriptional regulation layer of surprising magnitude, comprising more than 248,000 microRNA (miR)-mediated interactions. These include ∼7,000 genes whose transcripts act as miR “sponges.” When two genes share a set of microRNA regulators, changes in expression of one gene affects the other. If, for instance, one of those genes is highly expressed, the increase in its mRNA molecules will “sponge up” more of the available microRNAs. As a result, fewer microRNA molecules will be available to bind and repress the other gene’s mRNAs, leading to a corresponding increase in expression.

Although such an effect had been previously elucidated, the range and relevance of this kind of interaction had not been characterized.

Systematic characterization of cancer genomes has revealed a staggering number of diverse alterations that differ among individuals, so that their functional importance and physiological impact remains poorly defined. In order to identify which genetic alterations are functional, the lab of Dr. Dana Pe’er has developed a novel Bayesian probabilistic algorithm, CONEXIC, to integrate copy number and gene expression data in order to identify tumor-specific “driver” aberrations, as well as the cellular processes they affect.

In work published in the journal Cell, the new method was applied on data from melanoma patients, identifying a list of 64 putative ‘drivers’ and the core processes affected by them. This list includes many known driver genes (e.g., MITF), which CONEXIC correctly identified and paired with their known targets. This list also includes novel ‘driver’ candidates including Rab27a and TBC1D16, both involved in protein trafficking. ShRNA-mediated silencing of these genes in short-term tumor-derived cultures determined that they are tumor dependencies and validated their computationally predicted role in melanoma (including target identification), suggesting that protein trafficking may play an important role in this malignancy.

Transcriptional Network for Mesenchymal Transformation of Brain Tumors

The mesenchymal signature of high-grade gliomas is controlled by six transcription factors. TFs involved in activation of MGES targets are shown in pink, those involved in repression are in purple.

High-grade gliomas, such as glioblastoma, are incurable partly because the tumor cells are widely disseminated throughout the brain. This capacity for invasive growth has been associated with the expression of genes more commonly transcribed in mesenchymal cells. In work published in the journal Nature, Antonio Iavarone, Andrea Califano, and colleagues have identified a small transcription factor network that is responsible for the mesenchymal behavior of glioma cells.

The authors applied a specific algorithm designed to infer causal transcription factor-target interactions to gene expression profiles from 176 samples of high-grade gliomas. They analyzed the resulting interactome with a new algorithm that enabled them to evaluate the transcription factor network in terms of a previously identified mesenchymal gene expression signature from high-grade gliomas. This identified the 53 transcription factors that are associated with regulating mesenchymal gene expression. Further analyses identified signal transducer and activator of transcription 3 (STAT3) and CAATT/enhancer binding protein-β (CEBPβ) as potential master regulators that control the expression of a substantial proportion of these mesenchymal genes. The authors conclude that systems biology approaches can be used to identify master transcription factors that are involved in malignant transformation, and such approaches could be applied to help dissect the complexity of other tumour phenotypes.