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Why Some Brain Tumors Respond to Immunotherapy

Columbia researchers have learned why some glioblastomas—the most common type of brain cancer—respond to immunotherapy. The findings, reported by the CUIMC Newsroom, could help identify patients who are most likely to benefit from treatment with immunotherapy drugs and lead to the development of more broadly effective treatments.

The study, led by Raul Rabadan, PhD, professor of systems biology and biomedical informatics at Columbia University Vagelos College of Physicians and Surgeons and the Herbert Irving Comprehensive Cancer Center, was published online in the journal Nature Medicine. 

Fewer than 1 in 10 patients with glioblastoma­ respond to immunotherapy, which has shown remarkable success in the past few years in treating a variety of aggressive cancers. But there has been no way to know in advance which glioblastoma patients will respond. Like many other cancers, glioblastomas are able to prevent the immune system from attacking cancer cells. Cancers sometimes put the brakes on the immune system by acting on a protein called PD-1. Immunotherapy drugs called PD-1 inhibitors are designed to release those brakes, unleashing the immune system. Given the success of PD-1 inhibitors in other cancers, doctors were hopeful that the immunotherapy drugs would help patients with glioblastoma. 

To understand why only a few of these tumors respond to the immunotherapy drugs, Dr. Rabadan’s team took a comprehensive look at the tumor microenvironment—which includes the tumor itself and all of the cells that support it—in 66 glioblastoma patients before and after treatment with PD-1 inhibitors (nivolumab or pembrolizumab). Of these, 17 had a response to the drugs of six months or longer.

Nonresponsive tumors had more mutations in a gene called PTEN, which led to higher levels of macrophages, immune cells that usually help the body fight bacteria and other invaders. But in glioblastoma, the macrophages release a number of growth factors that promote the survival and spread of cancer cells.

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Prostate Cancer Grant Spotlights Columbia’s Aim to Deliver Precision Cancer Medicine

 

PCF Challenge Award PIs
Principal investigators on the PCF Challenge Award grant, from left to right: Andrea Califano, Michael Shen and Charles Drake.

Columbia University Irving Medical Center experts in prostate cancer will lead a new team research project that tests a novel approach for personalized cancer treatment. 

The two-year project, funded by a $1 million Challenge Award from the Prostate Cancer Foundation (PCF) , combines cutting-edge techniques that include computational methods for targeted drug therapy, single-cell RNA sequencing and novel cancer immunotherapy. The combined approaches are behind a proof-of-concept clinical trial for patients with lethal metastatic prostate cancer.  

PCF Challenge Awards fund projects that bring together experts from a number of related fields to form a team focused on the creation of innovative, effective therapies for advanced prostate cancer. As part of Columbia’s grant, the new clinical trial will take place at the James J. Peters VA Medical Center (also known as the Bronx VA), a partner of Columbia University Irving Medical Center (CUIMC) and New York-Presbyterian .

PCF is recognized as the leading philanthropic organization for prostate cancer research. For the team at Columbia’s Herbert Irving Comprehensive Cancer Center (HICCC ), receiving a Challenge Award from the foundation was more than just an exciting achievement. It underscores CUIMC’s continued commitment to strengthen and expand its expertise in prostate cancer research and care through investments in faculty recruitment, enhanced emphases on bolstering basic science research and clinical trials centered on the disease and direct engagement with PCF. 

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Novel Method Identifies New Risk Genes for Developmental Disorders

Yufeng Shen Episcore

The epigenomic profile of RBFOX2, a haploinsufficient gene recently identified as a risk gene of congenital heart disease. Each small box represents 100 bp region around transcription start sites (TSSs) and the shade of the color reflect the strength of the histone mark signal in tissues under normal conditions. RBFOX2 has large expansion of active histone marks (H3K4me3 and H3K9ac), especially in heart and epithelial tissues (purple and gray rows), and tissue-specific suppression mark (H3K27me3) in blood samples.(Credit: Shen lab)

The genetics of developmental disorders, such as congenital heart disease and autism, are highly complex. There are roughly 500 to 1,000 risk genes that can lead to each of these diseases, and to date, only about a few dozen have been identified. Scientists have ramped up efforts to develop computational approaches to address challenges in accurately identifying genetic risk factors in ongoing genetic studies, and the availability of such tools would greatly assist researchers in gaining a deeper understanding of the root causes of these diseases. 

Focusing on haploinsufficiency, a key biological mechanism of genetic risk in developmental disorders, Yufeng Shen , PhD, and his lab have developed a novel computational method that enables researchers to find new risk genes in these diseases. Their key idea is that the expression of haploinsufficient genes must be precisely regulated during normal development, and such regulation can be manifested in distinct patterns of genomic regulatory elements. Using data from the NIH Roadmap Epigenomics Project, they showed there is a strong correlation of certain histone marks and known haploinsufficient genes. Then based on supervised machine learning algorithms, they developed a new method, which they call Episcore , to predict haploinsufficiency from epigenomic data representing a broad range of tissue and cell types. Finally, they demonstrate the utility of Episcore in identification of novel risk variants in studies of congenital heart disease and intellectual disability.  

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Stress Wracks Worm Nerves, Leaving Lasting Memories

Scientists stunted the puberty of male worms by starving them before they underwent sexual maturation. In the study, published in Nature and led by Oliver Hobert,PhD, researchers suggested that stress from starvation even days before sexual maturation prevented normal changes in the wiring patterns of key neuronal circuits, which caused adult male worms to act immature.

“We found that environmental stress can permanently and profoundly impact the connectivity of a developing nervous system,” said Dr. Hobert, professor of biological sciences at Columbia University and a faculty member of the Department of Systems Biology.

The researchers’ results also suggested that these responses to stress were, in part, controlled by serotonin, a neurotransmitter associated with depression in humans.

Initially, Emily Bayer, a graduate student in the Hobert Lab and co-author of the work, stressed out immature worms when she accidentally left them unattended for a few weeks. This caused the worms to pause their normal growth and enter what scientists call a “dauer state.”

Eventually, Bayer returned the worms to their normal environment and let them grow into adults. After examining the nervous systems of stressed worms, she noticed something unusual. Normally, some of the neuronal connections in the males’ tails are eliminated, or pruned, during sexual maturation. Instead, she found that immature connections in the stressed worms remained. Follow-up experiments suggested that this was strictly caused by starvation and no other forms of stress – such as heat – could have caused the dauer state.

“I was totally surprised. In fact, I never thought stressing the worms out would matter,” said Bayer. 

She also found that starvation before sexual maturation caused male adult worms to act immaturely during behaviors known to be controlled by these circuits. Unlike normal adult males, the stressed worms were highly sensitive to a noxious chemical called SDS. Stressed worms swam away from SDS while normal males barely responded. The stressed worms also had problems mating. Specifically, they spent much less time in contact with hermaphrodite worms than normal males.

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New Study Explains Why Genetic Mutations Cause Disease in Some People but Not in Others

composite image of the scientists and research figure
Tuuli Lappalainen (top photo) and Stephane Castel co-led the new study. The hypothesis of the study is illustrated here with an example in which an individual is heterozygous for both a regulatory variant and a pathogenic coding variant. The two possible haplotype configurations would result in either decreased penetrance of the coding variant, if it was on the lower-expressed haplotype, or increased penetrance of the coding variant, if it was on the higher-expressed haplotype. (Composite image courtesy of NYGC)

Researchers at the New York Genome Center (NYGC) and Columbia University's Department of Systems Biology have uncovered a molecular mechanism behind one of biology’s long-standing mysteries: why individuals carrying identical gene mutations for a disease end up having varying severity or symptoms of the disease. In this widely acknowledged but not well understood phenomenon, called variable penetrance, the severity of the effect of disease-causing variants differs among individuals who carry them. 

Reporting in the Aug. 20 issue of Nature Genetics, the researchers provide evidence for modified penetrance, in which genetic variants that regulate gene activity modify the disease risk caused by protein-coding gene variants. The study links modified penetrance to specific diseases at the genome-wide level, which has exciting implications for future prediction of the severity of serious diseases such as cancer and autism spectrum disorder.

NYGC Core Faculty Member and Systems Biology Assistant Professor Dr. Tuuli Lappalainen, PhD, led the study alongside post-doctoral research fellow Dr. Stephane Castel.

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New Insights on How the Reprogramming Factor LIN28 Regulates its Targets

LIN28 Selectively Modulates Subclass Let-7 miRNAs
The proposed model of selective Let-7 microRNA suppression modulated by the bipartite LIN28 binding.(Image courtesy of Zhang Lab)

A new study led by Chaolin Zhang, PhD , assistant professor of systems biology , published today as the cover story of  Molecular Cell , sheds light on a critical RNA-binding protein that is widely researched for its role in stem cell biology and its ties to cancer progression in multiple tissues.

The LIN28 RNA-binding protein, initially found in worms about 15 years ago, is specifically expressed in stem cells.  It became well known because the protein is one of the four factors that were used to “reprogram” skin cells to induced pluripotent stem cells, or iPSCs, a breakthrough that was awarded the Nobel Prize in 2012. More recently, it was determined that the LIN28 RNA-binding protein can also be reactivated in cancer to drive tumor growth and progression. Due to its critical importance in developmental and cancer biology, scientists want to understand the role LIN28 plays at the molecular level. This new study provides some understanding of how the LIN28 protein suppresses a specific family of microRNAs, called Let-7, which are selectively lost in cancer.

“Let-7 microRNAs are the major downstream targets controlled by LIN28 identified so far. While LIN28 is mostly found in stem cells, Let-7 is only detected in differentiated cells because of stem cell-specific suppression by LIN28. However, the interplay between the two is still not well understood,” says Dr. Zhang, who is also a member of Columbia University’s Center for Motor Neuron Biology and Disease . “This study contributes to our understanding of how LIN28 suppresses Let-7, as well as provides a refined model for this important, rather complex molecular pathway.”

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Matching Cancer Patients to Personalized Therapies

Califano-Cancer Bottleneck
The N of 1 trial leverages systems biology techniques to analyze genomic information from a patient’s tumor. The goal is to identify key genes, called master regulators (green circles), which, while not mutated, are necessary for cancer cell survival. Master regulators are aberrantly activated by patient-specific mutations (X symbols) in driver genes (yellow circles), which are mutated in large cancer cohorts. Passenger mutations (blue circles) that are not upstream of master regulators have no effect on the tumor. (Image: Courtesy of the Califano Lab)

A novel N of 1 clinical trial led by the Califano Lab at Columbia University Irving Medical Center is focusing on rare or untreatable malignancies that have progressed on multiple lines of therapy, with the goal of identifying and providing more effective, customized therapies for patients. The approach is grounded in a computational platform developed over the last 14 years by the Califano Lab to allow accurate identification of a novel class of proteins that represent critical tumor vulnerabilities and of the drug or drug combination that can most effectively disarm these proteins, thus killing the tumor. Platform predictions are then validated in direct tumor transplants in mice, also known as Patient-Derived Xenografts (PDX). 

“We call these proteins master regulators and have developed innovative methodologies that allow their discovery on an individual patient basis,” said Dr. Andrea Califano , Clyde and Helen Wu Professor of Chemical and Systems Biology and chair of the Department of Systems Biology at Columbia. 

N-of-1 brochure to learn more.
N-of-1 brochure to learn more.

 

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Targeting the Engine Room of the Cancer Cell

Califano OncoTreat
Schematic diagram for the OncoTreat clinical pipeline. The pipeline consists of a series of pre-computed components, including a reference set of more than 13,000 tumor expression profiles representing 35 different tumor types, a collection of 28 tissue context-specific interactomes and a database of context-specific mechanism of action (MoA) for >400 FDA-approved drugs and investigational compounds in oncology. The transcriptome of the perturbed cell lines is profiled at low cost by PLATE-Seq. The process begins with the expression profile of a single patient sample, which is compared against the tumor databank to generate a tumor gene expression signature. This signature is interpreted by VIPER using a context-matched interactome to identify the set of most dysregulated proteins, which constitute the regulators of the tumor cell state – tumor checkpoint. These proteins are then aligned against the drugs’ and compounds’ MoA database, to prioritize compounds able to invert the activity pattern of the tumor checkpoint. (Image courtesy of Califano Lab)

Researchers at Columbia University Irving Medical Center (CUIMC) have developed a highly innovative computational framework that can support personalized cancer treatment by matching individual tumors with the drugs or drug combinations that are most likely to kill them. 

The study, online today in Nature Genetics , by Dr. Andrea Califano of Columbia University Irving Medical Center and Dr. Irvin Modlin of Yale University and Wren Laboratories LLC, co-senior author on the study, with collaborators from 17 research centers worldwide, details a proof of concept for a novel analytical platform applicable to any cancer type and validates its predictions on gastroenteropancreatic neuroendocrine tumors (GEP-NETs). The latter represent a rare class of tumors of the digestive system that, when metastatic, are associated with poor survival. 

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Two Faculty Win Vagelos Precision Medicine Pilot Award

Harmen and Tuuli
Harmen Bussemaker (left) and Tuuli Lappalainen

Harmen Bussemaker, PhD, and Tuuli Lappalainen, PhD, have received an inaugural Roy and Diana Vagelos Precision Medicine Pilot Award for a collaboration that will bridge quantitative genetics and mechanistic biology to obtain a mechanistic understanding of regulatory effects of genetic variants in humans.

Drs. Bussemaker and Lappalainen, both faculty in Columbia’s Department of Systems Biology, represent one of three winning proposals out of a pool of 56 applications. Their project titled, “Elucidating the tissue-specific molecular mechanisms underlying disease associations through integrative analysis of genetic variation and molecular network data”, will help to advance Columbia University’s efforts in precision medicine basic science research. 

As reported by Columbia Precision Medicine, the investigators’ research objectives include: to dissect the molecular mechanisms underlying tissue-specificity of genetic regulatory variants and to map network-level regulatory variants that cause protein-level transcription factor (TF) activity to vary between individuals. The investigators will infer TF activity based on DNA binding specificity models of human TFs, and use it as a tissue-specific parameter of the cellular environment. They will also map trans-acting genetic variants that affect TF activity (coined ‘aQTLs’ by one of the investigators) in each tissue. The investigators hope to elucidate which transcription factors are driving the functional impact and tissue specificity of any particular eQTL, genomic loci that contribute to variation in gene expression levels. 

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Organoids Created from Patients’ Bladder Cancers Could Guide Treatment

Organoids bladder cancer

Organoids created from the bladder cancers of patients mimic the characteristics of each patient’s tumor and may be used in the future to identify the best treatment for each patient. Images: Michael Shen

Columbia University Irving Medical Center (CUIMC) and NewYork-Presbyterian researchers have created patient-specific bladder cancer organoids that mimic many of the characteristics of actual tumors. As reported by CUIMC, the use of organoids, tiny 3-D spheres derived from a patient’s own tumor, may be useful in the future to guide treatment of patients.

The study was published April 5 in the online edition of Cell.

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Columbia, Broad Institute to Identify More Effective Therapies for Gastric and Esophageal Cancers

Broad, Columbia collaborators
Three of the investigators in new Columbia, Broad Institute research collaboration aimed at gastric and esophageal cancer; L to R: Dr. Andrea Califano, Dr. Cory Johannessen, and Dr. Adam Bass (Johannessen image: Martin Adolfsson; Bass image: Sam Ogden/Dana-Farber Cancer Institute)

A research collaboration underway between Columbia’s Department of Systems Biology, the Broad Institute of MIT and Harvard, and Columbia University Medical Center (CUMC) is working to accelerate the discovery of new cancer drug combinations targeted at gastric and esophageal cancer. These tumors have not yet attracted prominent research focus and attention, and yet the general outcome for patients with these diseases is poor. According to the American Cancer Society, survival rates are only 20% at five years after diagnosis.

The newly formed research alliance between research teams at Columbia and at the Broad Institute came about thanks to a four-year gift by the Price Family Foundation, known for its philanthropic support of education, health, and biomedical research.

The Columbia-Broad team includes Dr. Andrea Califano , cofounder and chair of the Department of Systems Biology; Dr. Adam Bass , associate member of the Broad Institute; Dr. Cory Johannessen, senior research scientist at the Broad Institute; Dr. Josh Sonnett , the director of The Price Family Center for Comprehensive Chest Care, Lung and Esophageal Center at Columbia; and Dr. Naiyer A. Rizvi , the Price Chair in Clinical Translational Research at Columbia.

In 2016, the Price Family Foundation suggested that a team of scientists at the Broad Institute meet with researchers from CUMC. At the time, the Foundation was eager to leverage the project at the Broad—where researchers had uncovered an interesting finding for gastric and esophageal cancer—with innovative cancer systems biology work it was supporting at CUMC, focusing on the same diseases.

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It’s All In the Math: New Tool Provides Roadmap for Cell Development

Researchers at Columbia University Medical Center have created a new tool to describe the many possible ways in which a cell may develop. Rooted in the mathematical field of topology, the tool provides a roadmap that offers detailed insight into how stem cells give rise to specialized cells. 

The study was published May 1st in Nature Biotechnology. 

Every organism begins with one cell. As that cell divides, its copies branch off to become specialized cells—such as heart, bone, or brain cells—in a process known as differentiation. To understand the internal and external cues that move cells along this path, scientists can sequence their RNA—the molecular messenger that translates DNA into proteins and other products. 

Sequencing RNA from a batch of cells is not ideal, however, because the cells are usually in different states of development. To address this problem, scientists have developed single-cell RNA sequencing. “It’s like a new microscope, giving us the ability to study many biological phenomena at once,” said Raul Rabadan, PhD, associate professor of systems biology and biomedical informatics at Columbia and co-author of the paper. “However, researchers are still left with the problem of understanding the relationships between different cell states, which drive the process of development.” 

To study cellular development, scientists use mathematical tools to analyze massive amounts of sequencing data. But these tools rely on underlying assumptions that narrow the possible results. “Due to the complexity involved in cellular development, models that make assumptions actually limit your ability to make new discoveries,” said Abbas Rizvi, PhD, a postdoctoral research scientist in Columbia’s Department of Biochemistry & Molecular Biophysics and the lead author of the paper. 

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Sex May Not Have Evolved Without Changes in Immune System

Sexual reproduction may have never become possible if organisms hadn’t evolved a way to restrain the immune system during fertilization, according to a new study from the lab of Sagi Shapira, PhD, assistant professor of systems biology.

The study, published today in Immunity, took an in-depth look at how vertebrate eggs are fertilized.

To fight invading pathogens, all organisms (including vertebrate cells) are programmed to detect and attack any DNA and foreign RNA found outside of the nucleus in the cell’s cytoplasm. It’s usually a safe bet that any DNA found in the cytoplasm is from a foreign microbe, because the cell’s own DNA is safely sequestered in the nucleus. But during fertilization, DNA and RNA from sperm may be briefly exposed to the cytoplasm of an egg—and to the danger of being recognized and attacked.

For fertilization to succeed, Dr. Shapira reasoned that something must prevent the immune system from attacking DNA during fertilization and searched for candidates in the genome.

The search revealed a gene called NLRP14, which encodes a protein that Dr. Shapira’s laboratory demonstrated to play a role in the innate immune system. Without NLRP14, the immune system induces a strong inflammatory response to DNA and RNA found in the cytoplasm, and the fertilization process comes to a halt.

The finding could lead to new ways to treat infertility or develop novel contraceptives.
NLRP14 and related genes are found in many other organisms, Dr. Shapira says, “and safeguarding the genetic material is hardwired into every organism. So, evolving machinery to inhibit that process in gametes may have been a prerequisite for the evolution of sexual reproduction.”

The finding could lead to new ways to treat infertility (about 2 percent of people carry a NLRP14 mutation) or, conversely, to develop novel contraceptives.

In addition, since NLRP14 suppresses a critical arm of the immune system, it may serve as a viable therapeutic target for tuning immune responses in various disease states (i.e., to dampen in the case of autoimmune diseases like IBD, asthma, and lupus, and enhance in the case of cancer).

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Cancer Oncotecture: A Universal Model of Tumor Survival

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.)

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Database of Protein-Protein Interactions Opens New Possibilities for Systems Biology

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.

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DAMAGES: A Method for Predicting Rare Disease Risk Genes

Cell Types in Autism

By inventing a new computational pipeline called DAMAGES, Chaolin Zhang and Yufeng Shen showed that brain cell types on the left of the plot are more prone to have rare autism risk mutations than cell types at the right. Narrowing the focus to these types of cells also helped to identify a molecular signature of the disorder that involves haploinsufficiency. Figure: Human Mutation.

Autism, a spectrum of neurodevelopmental disorders typically identified during early childhood, is widely thought to be the result of genetic alterations that change how the growing brain is wired. Nevertheless, despite a substantial effort in the field of autism genetics, the specific alterations that place one child at greater risk than another remain elusive. Although the list of alterations associated with autism is growing, it has been difficult to conclusively distinguish those that truly increase disease risk from those that are merely coincident with it. One troubling reason for this is that research so far seems to indicate that specific genetic abnormalities associated with autism risk are extremely rare, with many being found only in single patients. This has made it hard to reproduce findings conclusively.

In a paper recently published in the journal Human Mutation, Department of Systems Biology faculty members Chaolin Zhang and Yufeng Shen describe a method and some new findings that could help to more precisely identify rare autism-driving alterations. A new analytical pipeline they call DAMAGES (Disease Associated Mutation Analysis using Gene Expression Signatures) uses a unique approach to identifying autism risk genes, looking at differences in gene expression among different cell types in the brain in order to focus more specifically on mechanisms that are likely to be relevant for autism. Using this approach, they identified a pronounced molecular signature that is shared by disease risk genes due to haploinsufficiency, a type of genetic alteration that causes a dramatic drop in the expression of a particular protein.

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Automated, Single Cell RNA-Seq Enables Research on Tumor Ecology

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.

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Scientists Quantify Genetic Connections between Cancer and Developmental Disorders

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.”

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Method for Determining Protein Function Opens Opportunities for Precision Cancer Medicine

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.”

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Study of Glioblastoma Tumor Evolution Reveals Strategies Against Advanced Disease

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.

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