Regulators of mesenchymal GBM subtype

An example of tumor oncotecture. Transcription factors involved in the activation of mesenchymal glioblastoma subtype are shown in purple. Together, they comprise a tightly knit tumor checkpoint, controlling 74% of the genes in the mesenchymal signature of high-grade glioma. CEBP (both β and δ subunits) and STAT3 regulate the other three transcription factors in the tumour checkpoint, synergistically regulating the state of mesenchymal GBM cells. (Image: Nature Reviews Cancer)

In a detailed Perspective article published in Nature Reviews Cancer, Department of Systems Biology chair Andrea Califano and research scientist Mariano Alvarez (DarwinHealth) summarize more than a decade of work to propose the existence of a universal, tumor independent “oncotecture” that consistently defines cancer at the molecular level. Their findings, they argue, indicate that identifying and targeting highly conserved, essential proteins called master regulators — instead of the widely diverse genetic and epigenetic alterations that initiate cancer and have been the focus of much cancer research — could offer an effective way to classify and treat disease.

As coverage of the paper in The Economist reports:

ONE of the most important medical insights of recent decades is that cancers are triggered by genetic mutations. Cashing that insight in clinically, to improve treatments, has, however, been hard. A recent study of 2,600 patients at the M.D. Anderson Cancer Centre in Houston, Texas, showed that genetic analysis permitted only 6.4% of those suffering to be paired with a drug aimed specifically at the mutation deemed responsible. The reason is that there are only a few common cancer-triggering mutations, and drugs to deal with them. Other triggering mutations are numerous, but rare—so rare that no treatment is known nor, given the economics of drug discovery, is one likely to be sought. 

Facts such as these have led many cancer biologists to question how useful the gene-led approach to understanding and treating cancer actually is. And some have gone further than mere questioning. One such is Andrea Califano of Columbia University, in New York. He observes that, regardless of the triggering mutation, the pattern of gene expression—and associated protein activity—that sustains a tumour is, for a given type of cancer, almost identical from patient to patient. That insight provides the starting-point for a different approach to looking for targets for drug development. In principle, it should be simpler to interfere with the small number of proteins that direct a cancer cell’s behaviour than with the myriad ways in which that cancer can be triggered in the first place. (Read full article.)

PrePPI inputs
PrePPI predicts the likelihood that two proteins A and B are capable of interacting based on their similarities to other proteins that are known to interact. This requires integrating structural data (green) as well as other kinds of information (blue), such as evidence of protein co-activity in other species as well as involvement in similar cellular functions. PrePPI now offers a searchable database of unprecedented scope, constituting a virtual interactome of all proteins in human cells. (Image courtesy of eLife.) 

The molecular machinery within every living cell includes enormous numbers of components functioning at many different levels. Features like genome sequence, gene expression, proteomic profiles, and chromatin state are all critical in this complex system, but studying a single level is often not enough to explain why cells behave the way they do. For this reason, systems biology strives to integrate different types of data, developing holistic models that more comprehensively describe networks of interactions that give rise to biological traits. 

Although the concept of an interaction network can seem abstract, at its foundation each interaction is a physical event that takes place when two proteins encounter one another, bind, and cause a change that affects a cell’s activity. In order for this to take place, however, they need to have compatible shapes and physical properties. Being able to predict the entire universe of possible pairwise protein-protein interactions could therefore be immensely valuable to systems biology, as it could both offer a framework for interpreting the feasibility of interactions proposed by other methods and potentially reveal unique features of networks that other approaches might miss. 

In a 2012 paper in Nature, scientists in the laboratory of Barry Honig first presented a landmark algorithm and database they call PrePPI (Predicting Protein-Protein Interactions). At the time, PrePPI used a novel computational strategy that deploys concepts from structural biology to predict approximately 300,000 protein-protein interactions, a dramatic increase in the number of available interactions when compared with experimentally generated resources.

Since then, the Honig Lab has been working hard to improve PrePPI’s scope and usefulness. In a paper recently published in eLife they now report on some impressive developments. With enhancements to their algorithm and the incorporation several new types of data into its analysis, the PrePPI database now contains more than 1.35 million predictions of protein-protein interactions, covering about 85% of the entire human proteome. This makes it the largest resource of its kind. In parallel with these improvements, the investigators have also begun to apply PrePPI in new ways, using the information it contains to provide new kinds of insights into the organization and function of protein interaction networks.

Peter Sims & Jinzhou Yuan

Assistant Professor Peter Sims and postdoctoral research scientist Jinzhou Yuan displaying their platform for automated single cell RNA-Seq. Photo: Lynn Saville.

RNA sequencing (RNA-Seq) has become a workhorse technology for research in systems biology. Unlike genome sequencing, which reveals a sample’s DNA blueprint, RNA-Seq catalogs the constantly changing transcriptome; that is, it itemizes and quantifies the complete set of messenger RNA transcripts that are present in cells at a specific time and under specific conditions. In this way, RNA-Seq makes it possible to investigate how the information encoded in the genome is functionally transformed into observable traits, and provides valuable data for defining and comparing different biological states.

Conventional RNA-Seq generates an average summary of mRNA abundance across all of the cells in a sample. Recent research, however, has created a demand for higher resolution technologies capable of generating mRNA profiles at the level of single cells. In cancer biology, for example, there is an increasingly acute awareness that gene expression in the cells that make up malignant tumors is highly heterogeneous. This suggests that in order to understand how the cells work together to drive a tumor’s cancerous behavior, scientists need better methods for characterizing the entire ecology of cells of which it is made. Being able to quantify differences in gene expression cell by cell could be one valuable way to explore such complex environments and understand how they sustain malignancy.

Although several single cell RNA-seq technologies have been unveiled in the past two years, they are expensive to operate and are not optimized to produce data on the scale that is required for systems biology research, particularly in tissue specimens with limited numbers of cells. In a new paper just published in the journal Scientific Reports, however, researchers in the laboratory of Department of Systems Biology Assistant Professor Peter Sims describe a novel approach that offers several important advantages over other existing methods.

The new, automated platform builds on previous innovations in the Sims Lab to offer a cheap, efficient, and reliable way to simultaneously measure gene expression in thousands of individual cells from a single tissue sample. Using custom designed microwell plates, microfluidics, temperature control systems, and software, the technology captures, tags, and generates a readout of the complete transcriptome in each cell, providing robust data that can then be analyzed to distinguish functional diversity among the cells in the sample. Already, the technology is playing a key role in several research projects being conducted in the Department of Systems Biology and promises to become even more powerful as the field of single cell genomics continues to evolve.

September 28, 2016

Can Math Crack Cancer's Code?

An essay coauthored by Andrea Califano (Chair, Department of Systems Biology) and Gideon Bosker and published in the Wall Street Journal asks whether quantitative modeling could reveal the keys for turning cancer off. They write:

  • Disappointed with the slow pace of discovery and inclined to look for elegant, universal explanations for nature’s conundrums, many cancer researchers have increasingly been asking: Is there some sort of “Da Vinci Code” for cancer? And can we crack it using mathematics?

    Quantitative modeling has been extremely successful in disciplines as diverse as astronomy, physics, economics and computer science. Can “cancer quants”—scientists applying quantitative analyses to the landscape of cancer biology—find the answers we seek? And, if so, what would the new paradigm look like? 

The essay goes on to describe how computational methods developed in the Califano Lab are being tested in personalized N of 1 clinical trials to identify essential checkpoints in the molecular regulatory networks that sustain individual patients' tumors — as well as drugs capable of targeting them.

Click here to read the essay. (subscription may be required)

Yufeng Shen
Yufeng Shen's lab is interested in developing better computational methods for identifying rare genetic variants that increase disease risk.

On the surface, birth defects and cancer might not seem to have much in common. For some time, however, scientists have observed increased cancer risk among patients with certain developmental syndromes. One well-known example is seen in children with Noonan syndrome, who have an eightfold increased risk of developing leukemia. Recently, researchers studying the genetics of autism also observed mutations in PTEN, an important tumor suppressor gene. Although such findings have been largely isolated and anecdotal, they raise the tantalizing question of whether cancer and developmental disorders might be fundamentally linked.

According to a paper recently published in the journal Human Mutation, many of these similarities might not be just coincidental, but the result of shared genetic mutations. The study, led by Yufeng Shen, an Assistant Professor in the Columbia University Departments of Systems Biology and Biomedical Informatics, together with Wendy Chung, Kennedy Family Associate Professor of Pediatrics at Columbia University Medical Center, found that cancer-driving genes also make up more than a third of the risk genes for developmental disorders. Moreover, many of these genes appear to function through similar modes of action. The scientists suggest that this could make tumors “natural laboratories” for pinpointing and predicting the damaging effects of rare genetic alterations that cause developmental disorders.

“In comparison with cancer, there are relatively few patients with developmental disorders,” Shen explains, “For geneticists, this makes it hard to identify the risk genes solely based on statistical evidence of mutations from these patients. This study indicates that we should be able to use what we learn from cancer genetics — where much more data are available — to help in the interpretation of genetic data in developmental disorders.”

Master regulators of tumor homeostasis

In this rendering, master regulators of tumor homeostasis (white) integrate upstream genetic and epigenetic events (yellow) and regulate downstream genes (purple) responsible for implementing cancer programs such as proliferation and migration. CaST aims to develop systematic methods for identifying drugs capable of disrupting master regulator activity.

The Columbia University Department of Systems Biology has been named one of four inaugural centers in the National Cancer Institute’s (NCI) new Cancer Systems Biology Consortium. This five-year grant will support the creation of the Center for Cancer Systems Therapeutics (CaST), a collaborative research center that will investigate the general principles and functional mechanisms that enable malignant tumors to grow, evade treatment, induce disease progression, and develop drug resistance. Using this knowledge, the Center aims to identify new cancer treatments that target master regulators of tumor homeostasis.

CaST will build on previous accomplishments in the Department of Systems Biology and its Center for Multiscale Analysis of Genomic and Cellular Networks (MAGNet), which developed several key systems biology methods for characterizing the complex molecular machinery underlying cancer. At the same time, however, the new center constitutes a step forward, as it aims to move beyond a static understanding of cancer biology toward a time-dependent framework that can account for the dynamic, ever-changing nature of the disease. This more nuanced understanding could eventually enable scientists to better predict how individual tumors will change over time and in response to treatment.

Factors affecting protein activity
Following gene transcription and translation, a protein can undergo a variety of modifications that affect its activity. By analyzing downstream gene expression patterns in single tumors, VIPER can account for these changes to identify proteins that are critical to cancer cell survival.

In a paper just published in Nature Genetics, the laboratory of Andrea Califano introduces what it describes as the first method capable of analyzing a single tumor biopsy to systematically identify proteins that drive cancerous activity in individual patients. Based on knowledge gained by modeling networks of molecular interactions in the cell, their computational algorithm, called VIPER (Virtual Inference of Protein activity by Enriched Regulon analysis), offers a unique new strategy for understanding how cancer cells survive and for identifying personalized cancer therapeutics.

Developed by Mariano Alvarez as a research scientist in the Califano laboratory, VIPER has become one of the cornerstones of Columbia University’s precision medicine initiative. Its effectiveness in cancer diagnosis and treatment planning is currently being tested in a series of N-of-1 clinical trials, which analyze the unique molecular characteristics of individual patients’ tumors to identify drugs and drug combinations that will be most effective for them. If successful, it could soon become an important component of cancer care at Columbia University Medical Center.

According to Dr. Califano, “VIPER makes it possible to find actionable proteins in 100% of cancer patients, independent of their genetic mutations. It also enables us to track tumors as they progress or relapse to determine the most appropriate therapeutic approach at different points in the evolution of disease. So far, this method is looking extremely promising, and we are excited about its potential benefits in finding novel therapeutic strategies to treat cancer patients.”

Clonal evolution in GBM tumors
The researchers' model of tumor evolution indicates that different clonal lineages branch from a common ancestral cell and then diversify, independently causing aggressive tumor behavior at different stages of disease.

Glioblastoma multiforme (GBM) is the most common and most aggressive type of primary brain tumor in adults. Existing treatments against the disease are very limited in their effectiveness, meaning that in most patients tumors recur within a year. Once GBM returns, no beneficial therapeutics currently exist and prognosis is generally very poor.

To better understand how GBM evades treatment, an international team led by Antonio Iavarone and Raul Rabadan at the Columbia University Center for Topology of Cancer Evolution and Heterogeneity has been studying how the cellular composition of GBM tumors changes over the course of therapy. In a paper just published online by Nature Genetics, they provide the first sketch of the main routes of GBM tumor evolution during treatment, showing that different cellular clones within a tumor become dominant within specific tumor states. The study uncovers important general principles of tumor evolution, novel genetic markers of disease progression, and new potential therapeutic targets.

Andrea CalifanoAndrea Califano, the Clyde and Helen Wu Professor of Chemical Systems Biology and Chair of the Columbia University Department of Systems Biology, has been named a recipient of a National Cancer Institute Outstanding Investigator Award. The seven-year grant will support the development of systematic approaches for identifying the molecular factors that lead to cancer progression and to the emergence of drug resistance at the single-cell level. 

Breast cancer cells

A histological slide of cancerous breast tissue. The pink "riverways" are normal connective tissue while areas stained blue are cancer cells. (Source: National Cancer Institute)

Investigators at Columbia University Medical Center and the Icahn School of Medicine at Mount Sinai have discovered a molecular signaling mechanism that drives a specific type of highly aggressive breast cancer. As reported in a paper in Genes & Development, a team led by Jose Silva and Andrea Califano determined that the gene STAT3 is a master regulator of breast tumors lacking hormone receptors but testing positive for human epidermal growth receptor 2 (HR-/HER2+). The researchers also characterized a pathway including IL-6, JAK2, STAT3, and S100A8/9 — genes already known to play important roles within the immune response — as being essential for the survival of HR-/HER2+ cancer cells. Additional tests showed that disrupting this pathway severely limits the ability of these cells to survive.

These findings are particularly exciting because the pathway the researchers identified contains multiple targets for which known FDA-approved drugs exist. The paper reports that when these drugs were tested in disease models, the cancer cells showed a dramatic response, suggesting promising strategies for the treatment of the HR-/HER2+ cancer subtype. A clinical trial is now underway to investigate the effects of these approaches in humans.


PhenoGraph, a new algorithm developed in Dana Pe'er's laboratory, proved capable of accurately identifying AML stem cells, reducing high-dimensional single cell mass cytometry data to an interpretable two-dimensional graph. Image courtesy of Dana Pe'er.

A key problem that has emerged from recent cancer research has been how to deal with the enormous heterogeneity found among the millions of cells that make up an individual tumor. Scientists now know that not all tumor cells are the same, even within an individual, and that these cells diversify into subpopulations, each of which has unique properties, or phenotypes. Of particular interest are cancer stem cells, which are typically resistant to existing cancer therapies and lead to relapse and recurrence of cancer following treatment. Finding better ways to distinguish and characterize cancer stem cells from other subpopulations of cancer cells has therefore become an important goal, for once these cells are identified, their vulnerabilities could be studied with the aim of developing better, long lasting cancer therapies.

In a paper just published online in Cell, investigators in the laboratories of Columbia University’s Dana Pe’er and Stanford University’s Garry Nolan describe a new method that takes an important step toward addressing this challenge. As Dr. Pe’er explains, “Biology has come to a point where we suddenly realize there are many more cell types than we ever imagined possible. In this paper, we have created an algorithm that can very robustly identify such subpopulations in a completely automatic and unsupervised way, based purely on high-dimensional single-cell data. This new method makes it possible to discover many new cell subpopulations that we have never seen before.”

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.