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.

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.  

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.

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. 

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. 

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.

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).

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.

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.