Cancer×

News

 

DSB Retreat
Members of the Dennis Vitkup Lab, from l to r: Konstatine Tchourine, German Plata and Jon Chang (Credit: Sandra Squarcia); Photo Gallery of the retreat.

Innovative research projects were highlighted at the Department of Systems Biology’s annual retreat, held October 5, at Wave Hill Public Garden and Cultural Center in Riverdale, NY. The retreat, attended by 160 faculty, staff, post-doctoral scientists, students and guests, also provided an opportunity for young investigators to showcase their work during a poster competition. 

Andrea Califano , Dr., chair of the department, opened the day’s sessions with welcome remarks, as the retreat also served as a site visit by the National Cancer Institute for the Columbia University Center for Cancer Systems Therapeutics (CaST) . CaST, co-directors Drs. Califano and Barry Honig , vice-chair of the department, was established in 2016 as one of the key centers in the NCI’s Cancer Systems Biology Consortium (CSBC). The initiative behind CSBC is heavily grounded on innovation—bringing together interdisciplinary teams of clinical and basic cancer researchers with physical scientists, engineers, mathematicians and computer scientists who collaborate to tackle major questions in cancer biology from a novel out-of-the-box point of view. 

 

Andrea Califano
Andrea Califano, Dr.

Andrea Califano , Dr., a pioneer in the field of systems biology and founding chair of the Department of Systems Biology at Columbia University Irving Medical Center (CUIMC), has been elected to the National Academy of Medicine (NAM) . Membership in the NAM is considered one of the highest honors in the fields of health and medicine and recognizes individuals who have demonstrated outstanding professional achievements and commitment to service.  

A physicist by training, Dr. Califano has taken innovative, systematic approaches to identify the molecular factors that lead to cancer progression and to the emergence of drug resistance at the single-cell level. Directing the conversation about cancer research away from focusing solely on gene mutations, Dr. Califano examines the complex and tumor-specific molecular interaction networks that determine cancer cell behavior. Using information theoretic approaches, analysis of these networks can precisely pinpoint master regulator proteins that are mechanistically responsible for supporting tumorigenesis and for implementing tumor cell homeostasis. Dr. Califano and his lab have shown that master regulators represent critical drivers and tumor dependencies, despite the fact that they are rarely mutated or differentially expressed, thus establishing them as a bona fide new class of therapeutic targets.

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.

Hyundai $2.5M Grant to Columbia
Julia Glade Bender, MD, (center) at the Hyundai Hope on Wheels announcement Mar. 29 during the New York International Auto Show at the Jacob Javitz Center. (Photo courtesy of HHOW)

A team of researchers at Columbia University Irving Medical Center (CUIMC) has recently been awarded a five-year $2.5 million grant from Hyundai Hope on Wheels (HHOW) to fund innovative pediatric cancer research. 

The team at Columbia is being led by principal investigator (PI), Julia Glade Bender , MD, associate professor of pediatrics at CUIMC, with co-PIs Andrea Califano , Dr, chair of Columbia’s Department of Systems Biology and Darrell Yamashiro , MD, PhD, director of pediatric hematology, oncology and stem cell transplantation, along with researchers from Memorial Sloan Kettering, University of San Francisco Children’s and Dana-Farber Cancer Center. 

The Quantum Collaboration award from HHOW is aimed at funding research focused on childhood cancers with poor prognosis. At Columbia, the team will target osteosarcoma, the most commonly diagnosed bone tumor in children and adolescents. No new treatment approaches have successfully been introduced for osteosarcoma in nearly 40 years, and patients with the disease have not benefited from recent breakthroughs like immunotherapy or DNA sequencing and require a shift in the understanding and approach to therapy. 

Michael Shen, PhD
Michael Shen, PhD (Image Courtesy of the Shen Lab)

The Bladder Cancer Advocacy Network (BCAN) has awarded Professor Michael Shen, PhD, the 2018 Bladder Cancer Research Innovation Award. The honor is given to scientists whose novel, creative research has great potential to produce breakthroughs in the management of bladder cancer.  

Dr. Shen, who is professor of medicine, genetics & development, urology and systems biology at Columbia University, has used new techniques of 3D cell culture to establish “organoids” from primary bladder tumors obtained from patients. These personalized laboratory models, which the Shen lab can create in a matter of weeks, provide a new, innovative way to study the molecular mechanisms associated with drug response and drug resistance in bladder cancer patients. 

The BCAN award supports the Shen lab’s efforts in furthering their work in patient-derived bladder tumor organoids

“We will employ these organoid lines to examine how specific oncogenic drivers may regulate the invasiveness and metastatic ability of muscle-invasive bladder cancer (MIBC), both in cell culture and in mouse models,” says Dr. Shen. “Our goal is to use these new experimental approaches to provide molecular insights into the lethal properties of human MIBC, which will hopefully lead to improved therapeutic approaches.”

Bladder cancer is the fifth most common cancer in the United States, and the primary treatment of the disease is surgery. Overall, this new project will examine central questions of bladder cancer biology using Dr. Shen’s innovative approach involving patient-derived tumor organoids, and may provide the basis for future therapies for metastatic bladder cancer.

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.”

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. 

Project GENIE
Richard Carvajal (left) and Raul Rabadan are lead PIs on Project GENIE

Columbia University Irving Medical Center (CUIMC) has recently joined 11 new institutions to collaborate on Project GENIE, an ambitious consortium organized by the  American Association for Cancer Research . An international cancer registry built through data sharing,  Project GENIE , which stands for Genomics Evidence Neoplasia Information Exchange, brings together leading institutions in cancer research and treatment in order to provide the statistical power needed to improve clinical decision-making, particularly in the case of rare cancers and rare genetic variants in common cancers. Additionally, the registry, established in 2016, is powering novel clinical and translational research. In its first two years, Project GENIE has been able to accumulate and make public more than 39,000 cancer genomic records, de-identified to maintain patient privacy. 

May 7, 2018

From Code to Cure

Columbia Magazine

Published Spring 2018 cover story , Columbia Magazine

As reported by David J. Craig, senior editor at Columbia Magazine , we are living in the age of big data, and with every link we click, every message we send, and every movement we make, we generate torrents of information. In the past two years, the world has produced more than 90 percent of all the digital data that has ever been created. New technologies churn out an estimated 2.5 quintillion bytes per day. 

Today, researchers at Columbia University Irving Medical Center (CUIMC) are using the power of data to identify previously unrecognized drug side effects; they are predicting outbreaks of infectious diseases by monitoring Google search queries and social-media activity; and they are developing novel cancer treatments by using predictive analytics to model the internal dynamics of diseased cells. These ambitious projects, many of which involve large interdisciplinary teams of computer scientists, engineers, statisticians, and physicians, represent the future of academic research.

Craig covers Dr. Nicholas Tatonetti's work involving prescription drug safety and his innovative use of digital health and clinical records and Dr. Andrea Califano's unconventional computational approaches in advancing cancer research.

To read the full article , visit the online issue of Columbia Magazine

Raul Rabadan
Raul Rabadan

Systems Biology Professor Raul Rabadan, Phd , has been awarded a Philip A. Sharp Innovation in Collaboration award from Stand Up to Cancer (SU2C) , a group established by film and media leaders to fund cancer research projects that have the potential to quickly deliver new therapies to patients. Dr. Rabadan has received the award jointly with collaborator Dan A. Landau, MD, PhD, of Weill Cornell Medicine.

A theoretical physicist whose expertise lies in the cross section of mathematical genomics, tumor evolution, and cancer research, Dr. Rabadan will work together with Dr. Landau on their winning project, “Cupid-seq—high throughput transcriptomic spatial mapping of immune-tumor interactions in the micro-environment.”  The investigators will devise a novel sequencing technique and computational method for better understanding immune recognition mechanism in glioblastoma. Dr. Rabadan is currently a principal investigator on the SU2C-National Science Foundation Drug Combination Convergence Team and Dr. Lau is a 2016 recipient of a SU2C Innovative Research Grant.

The idea behind the Sharp Awards is to fund projects that involved SU2C researchers who have not yet worked together, including collaborations between members of Dream Teams, Research Teams, and Convergence teams, and between SU2C members and recipients of its innovative research grants. The latter are typically early-career investigators. The Sharp Awards are named after Sharp, Nobel laureate and institute professor at MIT and the Koch Institute for Integrated Cancer Research, to honor the emphasis he has placed on the importance of collaborative research. 

Dr. Rabadan, who also is professor of biomedical informatics at Columbia University, is one of 10 recipients of this year’s Sharp Awards; investigators hail from the U.S., Canada, and the Netherlands, and projects are being funded from a pool of $1.25 million. 

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.

Feb 7-8 Cancer Genomics Symposium

Pictured above, Adolfo Ferrando (left), professor of pediatrics and of pathology and cell biology at Columbia, with Luis Arnes, associate research scientist and first-place winner of the symposium's poster competition; For photos from the symposium, visit the gallery page . Credit: Lydia Lee Photography

A multidisciplinary team of researchers across Columbia University have been busy addressing the complex challenges in basic and translational cancer research. Faculty and investigators are bridging their expertise in fields ranging from mathematics, biology, and engineering to physics, genomics, and chemistry to develop innovative approaches to better understand, for instance, cancer disease progression, drug resistance, and the systems-wide network of tumor evolution.

Central to this ongoing work is research grounded in cancer genomics and mathematical data analysis explored during a two-day conference Feb. 7-8 co-hosted by the National Cancer Institute (NCI) centers at Columbia University Medical Center, Cornell University, and Memorial Sloan Kettering Cancer Center. (Visit the Rabadan Lab YouTube Channel for video of the symposium).

"Genomics is becoming an important tool for the quantitative study of biological systems,” says Raul Rabadan, PhD , professor of systems biology at Columbia and director of the Center for Topology of Cancer Evolution and Heterogeneity and of the Program for Mathematical Genomics . “This meeting organized by four different NCI centers addressed some of the important challenges and new perspectives on the quantitative understanding of cancer using genomics tools.”

Two new precision medicine tests, born out of research from the Califano Lab, that look beyond cancer genes to identify novel therapeutic targets have just received New York State Department of Health approval and are now available to both oncologists and cancer researchers for use at the front lines of patient care. As reported by Columbia University Irving Medical Center (CUIMC), the tests are based on research conducted by CUIMC investigators—and could pave the way for a more precise approach to cancer therapy and help find effective drugs when conventional approaches to precision medicine have failed.

“This means that the vast majority of cancer patients who do not have actionable mutations, or have not responded to, or have relapsed after chemotherapy or targeted therapy, now have access to additional tests that can help their oncologist select the treatment best suited to their specific tumor,” says the tests’ lead developer, Andrea Califano, Dr., chair of systems biology at Columbia University Vagelos College of Physicians and Surgeons.

The two tests, DarwinOncoTreat and DarwinOncoTarget, are available exclusively through the Laboratory of Personalized Genomic Medicine in the Department of Pathology and Cell Biology at Columbia University Vagelos College of Physicians and Surgeons. The tests were developed by DarwinHealth, a Manhattan-based biotech firm founded in 2015 by Dr. Califano and colleague, Gideon Bosker, MD.

For the complete article, visit the CUIMC Newsroom.

Courtesy of The Olive Lab

Shown here, a human pancreatic tumor stained with Masson's trichrome; Image credit: Dr. Kenneth Olive

The Lustgarten Foundation has awarded Columbia University’s Herbert Irving Comprehensive Cancer Center (HICCC) a three-year grant, as part of its Translational Clinical Program, to test a new precision medicine approach to the treatment of metastatic pancreatic cancer.

“The prevailing model in personalized cancer treatment is to attack the DNA mutations that are believed to be driving an individual patient’s tumor,” says principal investigator Kenneth P. Olive, PhD , assistant professor of medicine and pathology & cell biology at HICCC. “While this approach has been astonishingly effective for a handful of rare cancers, we expect it will only work for a very small fraction of patients with the most common types of cancer.”

Pancreatic ductal carcinoma (PDA)—the most common form of pancreatic cancer—is a case in point. Researchers have identified few genetic drivers in pancreatic tumors, and the most common driver ( KRAS ) is not easily targeted. Conservatively, only about 15 percent of PDA patients are likely to benefit from conventional DNA mutation-based precision medicine therapies and most of these will either not respond or will relapse with a drug-resistant form of the disease.

“Our study takes an entirely new approach,” says Dr. Olive. “Instead of looking at the mutations encoded in a tumor’s DNA, we analyze the tumor’s RNA. Since RNA is the tissue-specific ‘working copy’ of a cancer cell’s DNA, it’s a more accurate reflection of the genetic programs that are active in a tumor and critical for its survival. We can then match the patient to approved and investigational drugs that inhibit those programs.”

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.

HTS
Research scientist Hai Li holds up a 384-well plate, pictured in front of Columbia Genome Center's Hamilton Star automation system for HTS; Image credit: Systems Biology

Drug screening and analysis is critical in advancing research and discovery of cancer therapeutics. To this end, a Systems Biology-led team of investigators has recently developed PLATE-Seq, a new technique for low-cost, bulk mRNA sequencing. Coupled with genome-wide regulatory network analysis, the novel PLATE-Seq method advances the goal of providing cancer patients with personalized treatment.

Developed by the labs of Peter Sims and Andrea Califano , PLATE-Seq stands for “pooled library amplification for transcriptome expression” sequencing, and enables genome-wide mRNA profiling specifically designed to complement high-throughput screening assays. High-throughput screening, or HTS, represents a key component of drug discovery and technology used widely in biomedical research. Due to cost or complexity, most screens are still performed using low-complexity reporters, such as cell viability, protein-protein interactions and cell growth, for example, but there is a growing need to couple this screening protocol with genome-wide reporters, to measure the activity of many proteins across the genome.

“Our PLATE-Seq method helps us generate a more comprehensive portrait of drug activity,” said Dr. Sims, assistant professor of systems biology, known for his innovative work in single-cell RNA sequencing. “We’ve been able to show that our technique cuts the cost for gene expression profiling considerably, by incorporating a method we devised for ‘barcoding’ cDNA samples and combining this with computational methods from the Califano Lab that are highly effective on low-coverage sequencing data. This method allows us to sequence 96 samples per plate and 768 samples per sequencing run.”

On average, PLATE-Seq reduces the cost of genome-wide screening from around $400 per sample down to approximately $25 per sample. Genome-wide sequencing is important in advancing our understanding and prediction of disease and in identifying potential treatments.

Topology data analysis of cancer samples

Shown here, topology data analysis of cancer samples; Image credit: The Rabadan Lab

The new Program for Mathematical Genomics (PMG) is aiming to address a growing—and much-needed—area of research. Launched in the fall of 2017 by Raul Rabadan , a theoretical physicist in the Department of Systems Biology, the new program will serve as a research hub at Columbia University where computer scientists, mathematicians, evolutionary biologists and physicists can come together to uncover new quantitative techniques to tackle fundamental biomedical problems.

"Genomic approaches are changing our understanding of many biological processes, including many diseases, such as cancer," said Dr. Rabadan, professor of systems biology and of biomedical informatics. "To uncover the complexity behind genomic data, we need quantitative approaches, including data science techniques, mathematical modeling, statistical techniques, among many others, that can extract meaningful information in a systematic way from large-scale biological systems." 

This new program is being built upon collaborative research opportunities to explore and develop mathematical techniques for biomedical research, leading to a deeper understanding of areas such as disease evolution, drug resistance and innovative therapies. Inaugural members of the new program include faculty across several disciplines: statistics, computer science, engineering and pathology, to name a few. The program also will provide education and outreach to support and promote members' work, including joint discussion groups, the development of cross-campus courses and scientific meetings. 

In honor of its launch, PMG will co-host a two-day symposium February 7 to 8 on cancer genomics and mathematical data analysis. Guest speakers from Columbia University, Memorial Sloan Kettering and Cornell University will present a comprehensive overview of quantitative methods for the study of cancer through genomic approaches. 

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.

Pages