‘Driver’ mutations are behind chronic leukemia’s growth

New research digs into the varying rates of disease growth in patients with chronic lymphocytic leukemia (CLL).

In a new study, scientists report that CLL growth is apt to follow one of three trajectories: relentlessly upward, steadily level or something in between. The particular course the disease takes is tightly linked to the genetic makeup of the cancer cells, particularly the number of growth-spurring “driver” mutations they contain.

The study contains a further insight: Genetic changes that occur very early in CLL development exert a powerful influence on the growth pattern the CLL cells will ultimately take. This raises the possibility that physicians may one day be able to predict the course of the disease by its molecular features at the time of diagnosis.

“Our findings provide a framework not only for understanding the differing patterns of CLL growth in patients but also for exploring the basic biological mechanisms that underlie these differences,” says co-corresponding author Catherine J. Wu of the Dana-Farber Cancer Institute, the Broad Institute at the Massachussetts Institute of Technology, and Brigham and Women’s Hospital.

“Ultimately, we’d like to be able to tie the genotype of the disease—the particular genetic abnormalities in a patient’s cancer cells—to its phenotype, or how the cancer actually behaves.”

Slow and steady or superfast?

CLL is a useful model for studying the pace of cancer growth because it progresses at widely different rates from one patient to another, Wu says. In many patients, it persists at a low level for many years before advancing to the point where treatment is necessary. In others, it progresses so rapidly that treatment is required shortly after diagnosis.

To see if there were different patterns of CLL growth among patients, researchers drew on data from 107 patients diagnosed with the disease.

Beginning at diagnosis, each patient underwent periodic blood tests to track disease progress over the succeeding months and years, and continued until the disease reached a stage where treatment would begin. Each test consisted of a white blood cell count, which served as a proxy measure of CLL: the greater the number of white cells within a blood sample, the greater the burden of the disease. Researchers conducted the tests over a period ranging from two years in one patient to 19 years in another.

The serial testing allowed researchers to calculate growth rates over time for CLL in each patient. They used a statistical model to determine if the rates were consistent with various patterns of cancer growth.

“We found that some cases of CLL show exponential growth, in which it expands without any apparent limit, while other cases show ‘logistic’ growth, in which it plateaus at a fairly consistent level,” says co-lead author Ivana Bozic, an assistant professor of applied mathematics at the University of Washington.

Researchers classified cases that didn’t fit either category as indeterminate.

Competitive edge

To explore whether genetic differences were at the root of these divergent growth patterns, the researchers performed whole-exome sequencing on several CLL samples collected from each patient prior to receiving therapy. Whole-exome sequencing provides a letter-by-letter readout of the regions of DNA that encode for cellular proteins.

They found that exponentially growing CLL typically carried a large number of driver mutations—those that confer a competitive advantage in growth—and quickly reached the stage that required treatment.

In contrast, logistically growing CLL had fewer genetic alterations and fewer types of alterations and progressed relatively slowly toward the level that requires treatment. Seventy-five percent of patients with exponential growth eventually warranted treatment; by comparison, 21 percent of those with logistic growth and 67 percent of those with indeterminate growth eventually required treatment.

By analyzing patients’ serial blood samples collected over a period of time, researchers found that exponential CLL not only grows faster but also evolves faster, spinning off new subtypes of cancer cells, each with a particular set of genetic abnormalities. Whole-exome sequencing revealed that a great variety of tumor cell types and subtypes marks exponential CLL, while a relatively less diverse collection of tumor cells marks logistic CLL.

Cancer evolution

The information from whole-exome sequencing further allowed researchers to discover the growth rates of those subpopulations of cells within each patient’s leukemia that could be identified on the basis of a subset of mutations, some of them putative driver mutations.

These measurements clearly revealed that many of the mutations, which researchers suspected were centrally involved in CLL growth, did in fact provide subpopulations with preferential growth acceleration compared to populations lacking these putative drivers. Their results further indicate that the eventual course of CLL growth is inscribed in the genes of tumor cells early in the disease’s development.

“If the course of the disease isn’t altered by therapeutic treatment, the rate and pattern of CLL growth over time seems to ‘play out’ according to a predetermined set of genetic instructions,” Wu says.

“The discovery that CLL growth accelerates in the presence of large numbers of driver mutations is compelling evidence that these mutations do, in fact, confer a growth advantage to cells—that they truly do ‘drive’ the disease,” says co-lead author Michaela Gruber of Dana-Farber, the Broad Institute, and the Medical University of Vienna.

Bozic and co-lead authors Ignaty Leshchiner and Dimitri Livitz at the Broad Institute developed methods to jointly model possible phylogenetic relationships of cancer cell subpopulations—which are a description of each subpopulation’s history and relationships to each other during the evolution of the cancer—as well as integrate growth rates with subclone-specific genetic information.

“Combining clinical data with computational and mathematical modeling, we show that the growth of many CLLs seems to follow specific mathematical equations—exponential and logistic—each associated with distinct underlying genetics and clinical outcomes,” Bozic says.

“Integrating tumor burden and whole-exome sequencing data allowed us to quantify the growth rates of different tumor subpopulations in individual CLLs, methodology that could potentially inform personalized therapy in the future.”

The research appears in Nature.

Additional coauthors are from Harvard University; the University of London; Dana-Farber; Brigham and Women’s Hospital; the Moores Cancer Center at the University of California, San Diego; the Broad Institute; the Barts Cancer Institute at the University of London; Hofstra North Shore-LIJ School of Medicine; and the MD Anderson Cancer Center.

The National Cancer Institute; the CLL Global Research Foundation; the National Heart, Lung, and Blood Institute; the European Union; and the Leukemia and Lymphoma Society funded the work.

Source: University of Washington

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