It’s feasible that CLL manifests very early on as MBL or as an asymptomatic disease, and this could induce an immune dysfunction that makes patients more vulnerable to infections. However, the 2-decade time window is also compatible with another possibility: That is, that the immune dysfunction arises first and results in CLL due to impaired immune surveillance. Alternatively, immune dysfunction could pave the way for severe infections, which could then stimulate the development of the malignancy through repeated antigenic stimulation of B cells.

Either way, there’s some evidence that the underlying mechanism is genetically linked. Some genome-wide association studies have flagged several genes key to B-cell development and to CLL.3 In addition, Dr Niemann and his colleagues also discovered a pattern of increased antimicrobial use among CLL patients’ children and grandchildren, suggesting a potential heritability of risk. However, it is less clear whether it’s the CLL/MBL disease risk or the heightened infection risk that’s inherited.

Talal Hilal, MD, assistant professor of medicine at the University of Mississippi Medical Center, who wasn’t involved in the study, said he isn’t surprised by the team’s observations, and suspects that many of the patients who required antimicrobials prior to their CLL diagnosis in fact had MBL or undiagnosed early-stage disease. “I think the greatest trend was a decade prior to diagnosis. Beyond that, it is less clear,” he wrote to Cancer Therapy Advisor in an email. “Another important aspect is the magnitude of the effect. It is quite a small difference (especially for macrolides and antifungals) —  albeit statistically significant — that I would caution against imagining frequent infections.”

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Dr Hilal also highlighted some limitations that are inherent to national registries: It’s not clear what the circumstances are surrounding the period of increased use of antimicrobials, including the type of infection, recurrent nature of infections, or evidence of MBL or CLL, he noted. “We would need more data points collected prospectively to determine which came first: the chicken or the egg.”

To Dr Niemann, the results highlight the possibility of being able to identify earlier time points at which to intervene in the disease. In some of his other research, he and his colleagues have developed a machine learning algorithm to identify patients at high risk of infections at the time of diagnosis.4

Dr Niemann and his colleagues are currently enrolling patients in a phase 2 study to investigate whether short-term combination treatment with the B-cell lymphoma pathway 2–targeting agent venetoclax and the tyrosine kinase inhibitor acalabrutinib can improve infection-free survival ( Identifier: NCT03868722). In doing so, they’re essentially investigating whether by targeting the CLL cells “you could actually turn back the immune dysfunction to a normal functioning immune system,” he explained.

Another approach could be to prevent early infections through vaccinations or prophylactic antibiotics. “We might test [the] prospects in a clinical trial [investigating] whether in high-risk patients with pre-CLL states we could actually change the immune dysfunction and the development of CLL.”

Disclosure: Some of the investigators disclosed various financial ties to the pharmaceutical industry in this study. For a full list of disclosures, please refer to the original paper.


  1. Da Cunha-Bang C, Simonsen J, Rostgaard K, Geisler C, Hjalgrim H, Niemann CU. Improved survival for patients diagnosed with chronic lymphocytic leukemia in the era of chemo-immunotherapy: a Danish population-based study of 10455 patients. Blood Cancer J. 2016;6(11):e499. doi:10.1038/bcj.2016.105[RH1] 
  2. Andersen MA, Rostgaard K, Niemann CU, Hjalgrim H. Antimicrobial use before chronic lymphocytic leukemia: a retrospective cohort study. Leukemia. Published online July 20, 2020. doi:10.1038/s41375-020-0980-0
  3. Law PJ, Berndt SI, Speedy HE, et al. Genome-wide association analysis implicates dysregulation of immunity genes in chronic lymphocytic leukaemia. Nat Commun. 2017;8:14175. doi:10.1038/ncomms14175
  4. Agius R, Brieghel C, Andersen MA, et al. Machine learning can identify newly diagnosed patients with CLL at high risk of infection. Nat Commun. 2020;11:363. doi:10.1038/s41467-019-14225-8