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of General Medical Science
NEW YORK—Research has shown that family history is a strong risk factor for developing chronic lymphocytic leukemia (CLL).
First-degree relatives have an 8.5-fold risk of getting CLL and an increased risk of other lymphoproliferative disorders, according to a study published in 2009.
However, despite the strong evidence of a genetic contribution, one expert believes it’s premature to bring genetic testing into the clinic for screening in CLL.
“At this time, we do not recommend genetic screening,” said Susan Slager, PhD, of the Mayo Clinic in Rochester, Minnesota.
“There’s no known relationship between the inherited variants and treatment response,” she explained, and the relatively low incidence of CLL argues against active screening in affected families at present.
Dr Slager discussed genetic and non-genetic factors associated with CLL and the clinical implications of these factors at Lymphoma & Myeloma 2016.
Demographic risk factors
Dr Slager noted that age, gender, and race are risk factors for CLL.
Individuals aged 65 to 74 have the highest incidence of CLL, at 28%, while the risk is almost non-existent for those under age 20, she said.
There is a higher incidence of CLL in males than in females, and the reason for this gender disparity is unknown.
There is a higher incidence of CLL in Caucasians than Asians, for both males and females.
“Again, it’s unknown why there’s this variability in incidence in CLL,” Dr Slager said. “Obviously, age, sex, and race—these are things you can’t modify. You’re stuck with them.”
However, several studies have been undertaken to look at some of the potentially modifiable factors associated with CLL.
Beyond demographic factors
The International Lymphoma Epidemiology Consortium, known as InterLymph, was initiated in 2001 to evaluate the association of risk factors in CLL. Study centers are located primarily in North America and Europe, with one in Australia.
In one of the larger InterLymph studies, investigators evaluated risk factors—lifestyle exposure, reproductive history, medical history, occupational exposures, farming exposure, and family history—in 2440 CLL patients and 15,186 controls.
The investigators found that sun exposure and atopy—allergies, asthma, eczema, and hay fever—have a protective effect in CLL, while serological hepatitis C virus (HCV) infections, farming exposure, and family history carry an increased risk of CLL.
This confirmed an earlier study conducted in New South Wales, Australia, that had uncovered an inverse association between sun exposure and non-Hodgkin lymphoma (NHL) risk, which fell significantly with increasing recreational sun exposure.
Another earlier study from New South Wales revealed a 20% reduction in the risk of NHL for any specific allergy.
However, the investigators of the large, more recent study observed little to no evidence of reduced risk for asthma and eczema.
The underlying biology for atopy or allergies is a hyper-immune system, Dr Slager explained.
“So if you have a hyper-immune system, then we hypothesize that you have protection against CLL,” she said.
Another medical exposure investigators analyzed that impacts CLL risk is HCV. People infected with HCV have an increased risk of CLL, perhaps due to chronic antigen stimulation or possibly disruption of the T-cell function.
Height is also associated with CLL. CLL risk increases with greater height. The concept is that taller individuals have increased exposure to growth hormones that possibly result in cell proliferation.
Another hypothesis supporting the height association is that people of shorter stature experience more infections, which could result in a stronger immune system. And a stronger immune system perhaps protects against NHL.
Investigators consistently observed a 20% increased risk of CLL for people living or working on a farm.
Animal farmers, as opposed to crop farmers, experienced some protection. However, the sample size was too small to be conclusive, with only 29 people across all studies being animal farmers.
Among other occupations evaluated, hairdressers also had an increased risk of CLL, although this too was based on a small sample size.
One of the strongest risk factors for CLL is family history.
Using population-based registry data from Sweden, investigators found that people with a first-degree relative with CLL have an 8.5-fold risk of CLL.
They also have an elevated risk of other lymphoproliferative disorders, including NHL (1.9-fold risk), Waldenström’s macroglobulinemia (4.0-fold risk), hairy cell leukemia (3.3-fold risk), and follicular lymphoma (1.6-fold risk).
GWAS in CLL
Investigators conducted genome-wide association studies (GWAS) to determine what is driving the familial risk.
Dr Slager described these studies as an agnostic approach that looks across the entire genome to determine which regions are associated with a trait of interest.
Typically, many markers are genotyped—somewhere between half a million to 5 million markers—and each is looked at individually with respect to CLL, she said.
Unrelated cases and controls are included in the studies.
The first GWAS study identifying susceptibility loci for CLL was published in 2008. Subsequently, more studies were published with increasing sample sizes—more cases, more controls, and more genetic variants identified.
In the largest meta-analysis for CLL to date (Slager and Houlston et al, not yet published), investigators analyzed 4400 CLL cases and 13,000 controls.
They identified 9 more inherited variances with CLL, for a total of 43 identified to date.
The genes involved follow an apoptosis pathway, the telomere length pathway, and the B-cell lymphocyte development pathway.
“We have to remember, though, that these are non-causal,” Dr Slager cautioned. “We are just identifying the region in the genome that’s associated with CLL . . . . So now we have to dig deeper in these relationships to understand what’s going on.”
Using the identified CLL single-nucleotide polymorphisms, the investigators computed a polygenic risk score. CLL cases in the highest quintile had 2.7-fold increased risk of CLL.
However, the most common GWAS variants explain only 17% of the genetic heritability of CLL, which suggests that more loci are yet to be identified, Dr Slager clarified.
She went on to say that CLL incidence varies by ethnicity. Caucasians have a very high rate of CLL, while Asians have a very low rate. And African Americans have an incidence rate between those of Caucasians and Asians.
Investigators have hypothesized that the differences in incidence are based on the distinct genetic variants that are associated with the ethnicities.
For example, 4 of the variants with more than 20% frequency in Caucasians are quite rare in Chinese individuals and are also quite uncommon in African Americans, with frequencies less than 10%.
Dr Slager suggested that conducting these kinds of studies in Asians and African Americans will take a large sample size and most likely require an international consortium to bring enough CLL cases together.
Impact on clinical practice
Because of the strong genetic risk, patients with CLL naturally want to know about their offspring and their siblings, Dr Slager has found.
Patients who have monoclonal B-cell lymphocytosis (MBL), which is a precursor to CLL, pose the biggest quandary.
MBL is detected in about 5% of people over age 40. However, it’s detected in about 15% to 18% of people with a first-degree relative with CLL.
“These are individuals who have lymphocytosis,” Dr Slager said. “They come to your clinic and have an elevated blood cell count, flow cytometry. [So] you screen them for MBL, and these individuals who have more than 500 cells per microliter, they are the ones who progress to CLL, at 1% per year.”
Individuals who don’t have the elevated blood counts do have the clonal cells, Dr Slager noted.
“They just don’t have the expansion,” she said. “The progression of these individuals to CLL is still yet to be determined.”
For these reasons, Dr Slager believes “it’s still premature to bring genetic testing into clinical practice.”
Future directions include bringing together the non-environmental issues and the inherited issues to create a model that will accurately predict the risk of CLL.