Research Examines a New Tool for Discovering Cancer-Driving Structural Variations

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An advanced software tool for analyzing DNA sequences from tumor samples has uncovered new, likely cancer-driving genes. In a study, Weill Cornell Medicine researchers designed the software, known as CSVDriver, to map and analyze the locations of large mutations, known as structural variants, in tumor DNA data sets. They then applied the tool to a data set of 2,382 genomes from 32 different cancer types, analyzing the cancer genomes from different organ systems separately. The results confirmed the likely cancer-driving roles of 47 genes, tentatively linked several of these genes to certain cancer types for the first time, and pointed to 26 other genes as likely cancer drivers—even though they had never been linked to cancer before. The findings of the study were published by Martinez-Fundichely et al in Nature Communications.

“Our results show that CSVDriver could be broadly useful for the cancer research community, providing new insights into cancer development as well as potential new targets for treatment,” said study senior author Ekta Khurana, PhD, an Associate Professor of Physiology and Biophysics, Co-Leader of the Cancer Genetics and Epigenetics Program at the Meyer Cancer Center, and the WorldQuant Foundation Research Scholar at Weill Cornell Medicine.

In the past few decades, cancer biologists have catalogued hundreds of cancer-driving mutations, and many are now the targets of drug treatments. Yet, the discovery of cancer-driving mutations is far from complete.

The vast majority of mutations in cancerous cells are not driver mutations; they are so-called passenger or background mutations that do not enhance tumor growth or survival. These passenger mutations are distributed across the genome, and it can be challenging to distinguish driver mutations amid all that “background noise.” Researchers have made considerable progress in sifting drivers from passengers in the simplest class of DNA mutations: point mutations, also known as single-nucleotide variants. But they have made less progress with structural variants , which are larger, more complex mutations including deletions and extra copies of sometimes lengthy DNA segments.

More on CSVDriver

In the new study, the researchers developed CSVDriver to analyze data sets of structural variants in cancer genomes to uncover likely cancer drivers.

“The general idea here was to model the distribution of background mutations that we would expect for a given cancer type, and then identify, as candidate driver locations, regions where mutations occur more often than expected in a large fraction of patients,” said first study author Alexander Martinez-Fundichely, PhD, an Instructor in Physics and Biophysics as well as Computational Biomedicine at Weill Cornell Medicine, and a member of the Khurana Laboratory.

CSVDriver represents an advance on previous efforts in this area because it models the expected structural variant background in a way that accounts for tissue-specific factors that can influence this background, such as the three-dimensional folding of DNA.

The analysis, in all, identified the following as suspected cancer drivers within the large structural variant data set: 53 protein-coding genes, three segments of DNA that encode regulatory RNAs, and 24 sites known as “enhancers” because they attract transcription factor proteins that can boost the activity of other genes. Many of these suspects were already known to be cancer drivers from prior research; therefore, the results validated the algorithm.

However, CSVDriver also demonstrated its worth as a discovery tool by uncovering some known cancer-linked genes as likely drivers of cancers to which they had not been previously linked; for example, the gene DMD in esophageal cancer, and NF1 in ovarian cancer. Moreover, the results also highlighted 26 genes that had not previously been identified as likely cancer drivers.

“These are results that can be followed up with further wet-lab and animal-model studies to explore the impacts of mutations in these genes, and that, in turn, could lead to the development of new cancer treatments targeting these mutations,” said Dr. Khurana.

Most of the genomes analyzed in the study were from primary cancers, but Dr. Khurana, Dr. Martinez-Fundichely, and their colleagues now plan to use CSVDriver to uncover drivers of advanced, metastatic cancers, which bring the worst prognoses and have few effective treatments.

Disclosure: CSVDriver can be downloaded at For full disclosures of the study authors, visit

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