Clinicians have long dreamed of being able to target cancer cells exclusively while sparing their normal brethren. But this can be very difficult. Now, researchers in the laboratories of Stanford hematologist Ravi Majeti, MD, PhD, and computer scientist David Dill, PhD, have developed a way to sift through mountains of mutational data from human tumors to identify weaknesses for drug targeting. They published their findings today in Nature Communications.
The scientists, including research associate Subarna Sinha, PhD, and postdoctoral scholar Daniel Thomas, PhD, used a computer algorithm they designed called MiSL (pronounced "missile") to identify pairs of genes known as "synthetic lethals." Cells with a mutation in just one member of the pair are fine, but when both members are mutated the cell dies.
As I described in our release:
The researchers capitalized on the fact that cancer cells are often a genomic hot mess. As they proliferate out of control, they play fast and loose with the normal rules for DNA duplication and cellular division. It's not uncommon for genes to be summarily deleted from the genome or, conversely, to be 'amplified' so that they occur two, three or more times in the cells' DNA.
In this study, the researchers taught the computer a simple 'if this, then that' concept to help them identify pairs of genes whose expression levels were co-dependent -- a hallmark of synthetic lethals.
Their reasoning? If one member of the pair of genes is mutated in cancer, inactivating its synthetic lethal partner should kill the cell while sparing non-cancerous cells. Using MiSL, Sinha and Thomas investigated over 3,000 cancer-associated mutations in 12 types of human tumors and identified thousands of new genetic partnerships that could be amenable to drug treatment -- including 17 for a well-known, leukemia-associated mutation that are likely to be susceptible to drugs that are either already clinically available or are under development.
The collaboration between the Majeti and Dill labs arose through Stanford's Center for Cancer Systems Biology, which aims to identify broad biological patterns in the methods cancer cells use to evade the immune system. Marrying biological know-how with powerful computational strategies is a key component of advances in oncology and other medical fields, the researchers stress.
As Majeti described:
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.
Previously: Cheap Data! Stanford scientists' "opposites attract" algorithm plunders public databases, scores surprising drug-disease hookups, Cardiologist Eric Topel on why we need to map the human body and "go deep" with big data and "Predict, prevent and cure precisely," Stanford Medicine's Lloyd Minor urges
Photo by shell belle
