Advanced software tool uncovers new cancer-driving genes

Advanced software tool uncovers new cancer-driving genes

An advanced software tool for analyzing DNA sequences from tumor samples has discovered likely new cancer-driving genes in a study led by researchers at Weill Cornell Medicine.

In the study, published September 26 in Nature Communications, researchers designed software known as CSVDriver to map and analyze the locations of large mutations, known as structural variants (SVs), in tumor DNA datasets. They then applied the tool to a dataset of 2,382 genomes from 32 different cancer types and analyzed the cancer genomes from different organ systems separately. The results confirmed the likely cancer-driving roles of 47 genes, tentatively linked several of them to specific cancers for the first time, and pointed to 26 other genes as likely cancer drivers, even though they had never been linked to cancer before.

“Our results show that CSVDriver could be of great benefit to the cancer research community by providing new insights into cancer evolution as well as potential new targets,” said study lead author Dr. Ekta Khurana, associate professor of physiology and biophysics and co-director of the Cancer Genetics and Epigenetics Program at the Meyer Cancer Center at Weill Cornell Medicine.

The first author of the study was Dr. Alexander Martinez-Fundichely, lecturer in physiology and biophysics at Weill Cornell Medicine and member of the Khurana Laboratory.

Cancers typically arise and progress to greater malignancy when DNA mutations occur in a single cell, effectively removing or overriding the usual brakes on cell division. Cancer biologists have cataloged hundreds of these cancer-causing mutations over the past few decades, and many are now the target of drug treatments. But the discovery of cancer-causing mutations is far from complete.

The vast majority of mutations in cancer cells are not driver mutations. They are so-called passenger or background mutations that do not promote tumor growth or survival. These passenger mutations are spread throughout the genome, and it can be difficult to distinguish driver mutations amid all this “background noise.” Researchers have made significant progress in sorting out drivers and passengers in the simplest class of DNA mutations, point mutations, also known as single nucleotide variants. But they have made less progress on SVs, which are larger, more complex mutations, including deletions and extra copies of sometimes long segments of DNA.

In the new study, researchers developed CSVDriver to analyze datasets of SVs in cancer genomes to uncover likely cancer drivers.

The general idea was to model the distribution of background mutations that we would expect for a given cancer type and then identify, as possible driver sites, regions where mutations occur more frequently than expected in a large proportion of patients.

Dr. Alexander Martinez-Fundichely, Lecturer in Physiology and Biophysics, Weill Cornell Medicine

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CSVDriver represents an advance over previous efforts in this area because it models the expected SV background in a way that takes into account tissue-specific factors that may influence this background, such as: B. the three-dimensional folding of DNA.

Overall, the analysis identified as putative cancer drivers within the large SV data set 53 protein-coding genes, three DNA segments that encode regulatory RNAs, and 24 sites known as “enhancers” because they attract transcription factor proteins that increase the activity of other genes. Many of these suspects were already known to be cancer drivers from previous investigations, so the results validated the algorithm in this respect.

However, CSVDriver also demonstrated its value as a discovery tool by revealing some known cancer-related genes as likely drivers of cancers with which they had not previously been linked, such as the gene DMD in esophageal cancer and NF1 in ovarian cancer. In addition, the results also highlighted 26 genes not previously linked to cancer as likely cancer drivers.

"These are findings that can be followed up with further wet lab and animal model studies to investigate the effects of mutations in these genes, and which in turn could lead to the development of new cancer treatments that target these mutations," said Dr. Khurana, who is also a WorldQuant Foundation Research Fellow at Weill Cornell Medicine.

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

Source:

Weill-Cornell medicine

Reference:

Martinez-Fundichely, A., et al. (2022) Modeling tissue-specific breakpoint proximity of structural variations from whole genomes to identify cancer drivers. Nature communication. doi.org/10.1038/s41467-022-32945-2.

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