Four distinct genetic subtypes of multiple myeloma with different prognoses have been identified, a finding that might lead to the ability to individualize chemotherapy and direct future research, according to an article in the April issue of Cancer Cell.

The American team used a new computational tool based on an algorithm designed to recognize human faces to distinguish the four gene patterns out of many DNA alterations in the myeloma genome.
These results "define new disease subgroups of multiple myeloma that can be correlated with different clinical outcomes," wrote the authors, led by Ronald DePinho, MD, director of Dana-Farber’s Center for Applied Cancer Science.

Not only do the findings pave the way for treatments tailored to a patient's specific disease type, they also narrow areas of the chromosomes in myeloma cells likely to contain undiscovered genetic flaws that drive disease and which might be vulnerable to targeted drugs.

Kenneth Anderson, MD, a coauthor, said the findings "allow us to predict how patients will respond to current treatments based on a genetic analysis of their disease." In addition, the findings "identify many new genes implicated in the cause and progression of myeloma, and the product of those genes can be targeted with novel therapies."

About 50,000 people in the United States are living with the disease, and an estimated 16,000 new cases are diagnosed annually. Despite improvements in therapy, the five-year survival rate in multiple myeloma is only 32 percent and durable responses are rare.

Previously, scientists had identified two genetic subtypes of myeloma. One, called hyperdiploid multiple myeloma, is characterized by extra copies of entire chromosomes, and patients with this subtype appear to have better prognoses. The non-hyperdiploid form instead has abnormal rearrangements between different chromosomes, and prognosis is generally worse.

The collaborating researchers sought to cast a wide net to capture as many of the genetic flaws in myeloma cells as possible, creating a comprehensive atlas of the genome. First, they used a technique called high-resolution array comparative genomic hybridization to analyze samples from 67 newly diagnosed patients. The technique compared the genomes of a normal blood cell with various myeloma cells in search of differences. The goal was to identify recurrent copy number alterations - hotspots on the chromosomes where genes were abnormally duplicated or lost across many different tumors.

The analysis netted a large number of areas showing such alterations. Then the scientists asked whether any specific pattern or combination of these aberrations in an individual patient might help predict how aggressive the disease would be.

For the deeper analysis, the researchers created an algorithm based on a recently developed computational method designed to recognize individuals by facial features. It is called non-negative matrix factorization. In the myeloma study, the algorithm was used to group the results in a way that yielded distinctive genomic features from hybridization data.

Four distinct myeloma subtypes based on genetic patterns emerged: Two corresponded to the non-hyperdiploid and hyperdiploid types; the latter had two subdivisions called k1 and k2 When the subgroups were checked against the records of patients from whom samples were taken, it showed that patients with the k1 pattern had a longer survival than those with k2.

Digging still deeper, the scientists found evidence suggesting that certain molecular signatures within the subgroups are responsible for the differences in outcomes, providing a clear and productive path for further research.

This narrowing down of potential genes and proteins within the subgroups "is a huge advance," said DePinho. "If you know that a certain gene is driving the disease and influences the clinical behavior of the disease in humans, it immediately goes to the top of the list as a prime candidate for drug development."