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

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

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

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

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

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

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

Andrew Anzalone and Sakellarios ZairisMD/PhD students Andrew Anzalone and Sakellarios Zairis combined approaches based in chemical biology, synthetic biology, and computational biology to develop a new method for protein engineering.

The ribosome is a reliable machine in the cell, precisely translating the nucleotide code carried by messenger RNAs (mRNAs) into the polypeptide chains that form proteins. But although the ribosome typically reads this code with uncanny accuracy, translation has some unusual quirks. One is a phenomenon called -1 programmed ribosomal frameshifting (-1 PRF), in which the ribosome begins reading an mRNA one nucleotide before it should. This hiccup bumps translation “out of frame,” creating a different sequence of three-nucleotide-long codons. In essence, -1 PRF thus gives a single gene the unexpected ability to code for two completely different proteins.

Recently Andrew Anzalone, an MD/PhD student in the laboratory of Virginia Cornish, set out to explore whether he could take advantage of -1 PRF to engineer cells capable of producing alternate proteins. Together with Sakellarios Zairis, another MD/PhD student in the Columbia University Department of Systems Biology, the two developed a pipeline for identifying RNA motifs capable of producing this effect, as well as a method for rationally designing -1 PRF “switches.” These switches, made up of carefully tuned strands of RNA bound to ligand-sensing aptamers, can react to the presence of a specific small molecule and reliably modulate the ratio in the production of two distinct proteins from a single mRNA. The technology, they anticipate, could offer a variety of exciting new applications for synthetic biology. A paper describing their approach and findings has been published in Nature Methods.

cQTLs modify TF binding

Cofactors work with transcription factors (TFs) to enable efficient transcription of a TF's target gene. The Bussemaker Lab showed that genetic alterations in the cofactor gene (cQTLs) change the nature of this interaction, affecting the connectivity between the TF and its target gene. This, combined with other factors called aQTLs that affect the availability of the TF in the nucleus, can lead to downstream changes in gene expression.

When different people receive the same drug, they often respond to it in different ways — what is highly effective in one patient can often have no benefit or even cause dangerous side effects in another. From the perspective of systems biology, this is because variants in a person’s genetic code lead to differences in the networks of genes, RNA, transcription factors (TFs), and other proteins that implement the drug’s effects inside the cell. These multilayered networks are much too complex to observe directly, and so systems biologists have been developing computational methods to infer how subtle differences in the genome sequence produce these effects. Ultimately, the hope is that this knowledge could improve scientists’ ability to identify drugs that would be most effective in specific patients, an approach called precision medicine.

In a paper published in the Proceedings of the National Academy of Sciences, a team of Columbia University researchers led by Harmen Bussemaker proposes a novel approach for discovering some critical components of this molecular machinery. Using statistical methods to analyze biological data in a new way, the researchers identified genetic alterations they call connectivity quantitative trait loci (cQTLs), a class of variants in transcription cofactors that affect the connections between specific TFs and their gene targets.

Staphylococcus epidermis
Interactions between human cells and the bacteria that inhabit our bodies can affect health. Here, Staphylococcus epidermis binds to nasal epithelial cells. (Image courtesy of Sheetal Trivedi and Sean Sullivan.)

Launched in 2014 by investigators in the Mailman School of Public Health, the CUMC Microbiome Working Group brings together basic, clinical, and population scientists interested in understanding how the human microbiome—the ecosystems of bacteria that inhabit and interact with our tissues and organs—affects our health. Computational biologists in the Department of Systems Biology have become increasingly involved in this interdepartmental community, contributing expertise in analytical approaches that make it possible to make sense of the large data sets that microbiome studies generate.