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Updated: May 8, 2025

Immunoglobulin Gene Sequence Analysis In Chronic Lymphocytic Leukemia: From Patient Material To Sequence Interpretation
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Integration of Germline and Somatic Variation Improves Chronic Lymphocytic Leukemia Risk Stratification.

Aubrey K Hubbard1, Derek W Brown1,2, Jie Liu3

  • 1Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland.

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|April 30, 2025
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Summary

Integrating genetic and blood cell data improves chronic lymphocytic leukemia (CLL) risk prediction. Combining germline genetic variation, polygenic risk scores (PGS), and clonal hematopoiesis (CH) enhances identification of individuals at high risk for CLL.

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Area of Science:

  • Hematology
  • Genetics
  • Cancer Epidemiology

Background:

  • Chronic lymphocytic leukemia (CLL) risk is influenced by both inherited genetic variations and acquired mutations.
  • Current risk prediction models for CLL can be improved by integrating diverse risk factors.

Purpose of the Study:

  • To assess if incorporating a CLL-associated polygenic score (PGS) and markers of clonal hematopoiesis (CH), specifically autosomal mosaic chromosomal alterations (mCAs) and CH of indeterminate potential (CHIP), can enhance CLL risk stratification.
  • To evaluate the discriminative ability of integrative models combining germline, somatic, and clinical data for CLL risk prediction.

Main Methods:

  • Analysis of 436,784 participants from the UK Biobank and 35,382 from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial.
  • Investigated associations between specific autosomal mCAs, CHIP mutations in lymphoid driver genes, and CLL risk.
  • Developed integrative models incorporating sex, age, smoking status, blood cell traits, genetic similarity, CLL PGS, autosomal mCAs, and CHIP.

Main Results:

  • Individual mCAs and CHIP mutations were significantly associated with CLL risk.
  • Integrative models incorporating CLL PGS and CH demonstrated the highest discriminative ability for CLL risk stratification.
  • The predictive utility of CH waned approximately five years after measurement.
  • Sensitivity analyses confirmed persistent increased discriminative ability even after excluding individuals with abnormal blood cell counts or CH.

Conclusions:

  • Integrating germline genetic data (PGS) with somatic data (CH) significantly improves the ability to identify individuals at high risk for CLL.
  • The combined assessment of genetic predisposition and clonal hematopoiesis offers enhanced stratification for CLL risk.
  • Peripheral blood data, encompassing germline and somatic information, is crucial for advanced CLL risk identification.