Using data from human tumor samples, Stanford scientists have developed a new computer algorithm to identify pairs of genes that cause cancer. Their research aims to identify alternative ways to target cancer-causing mutations that have thus far evaded effective clinical treatment.

The study, which was published this week in Nature Communications, was led by senior authors Dr. Ravi Majeti and Dr. David Dill and included two CIRM Bridges interns Damoun Torabi and David Cruz Hernandez.

Identifying Partners in Crime

Cancer cells are notorious for acquiring genetic mutations due to the instability of their genomes and errors in the machinery that repairs DNA. Sometimes these errors create what are called synthetic lethal genes. These are pairs of genes that can cause a cell to die if both genes are defective due to acquired mutations, but a defect in only one of the genes allows a cell to live.

Cancer cells rely on pairs of genes with similar functions for their survival. If one gene is mutated, then the cancer cell depends on the other functional gene, aka its “partner in crime”, to keep it doing its mischief. Scientist are interested in targeting this second partner gene in synthetic lethal pairs in the hopes of developing less toxic cancer therapies that only kill cancer cells instead of healthy ones too.

The Stanford team went on the hunt for synthetic lethal partner genes in data from 12 different human cancers using an algorithm they developed called Mining Synthetic Lethals (MiSL). David Dill explained their strategy in a Stanford Medicine news release:

“We were looking for situations in which, if gene A is mutated, gene Y is amplified to compensate for the loss of function of gene A. Conversely, gene Y is only ever deleted in cells in which gene A is not mutated.”

David Dill. (Credit: L.A. Cicero/Stanford News Service)

They identified a total of 3,120 cancer-causing mutations and over 145,000 potential synthetic lethal partner genes associated with these mutations. Some of these partnerships were identified in other studies, validating MiSL as an effective tool for their purposes, while other partnerships were novel.

Targeting Partners in Crime

One of the new partnerships they discovered was between a mutation in the IDH1 gene, which is associated with acute myeloid leukemia, and a gene called ACACA. The team validated this pair with experiments in the lab proving that defects in both IDH1 and ACACA blocked leukemia cell growth. MiSL identified 89 potential synthetic lethal partners for the leukemia-causing IDH1 mutation, 17 of which they believe could be targeted by existing cancer drugs.

The authors concluded that using computer algorithms to sift through mountains of biological data is a powerful strategy for identifying genetic relationships leveraged by tumors and could advance drug development for different types of cancers.

Ravi Majeti concluded,

“We’re entering a new era of precision health. Using data from real human tumors gives us important, fundamental advantages over using cancer cell lines that often don’t display the same mutation profiles. We’ve found that, although many known cancer-associated mutations are difficult to target clinically, their synthetic lethal partners may be much more druggable.”

Ravi Majeti (Credit: Steve Fisch)