A presentation at the all-virtual 62nd ASH Annual Meeting & Exposition revealed a new immune score model of the bone marrow micro-environment, which the researchers said can accurately predict survival in patients with chronic lymphocytic leukemia (CLL).
“Most prognostic and predictive models for survival in patients with CLL are based on clinical and laboratory covariates and have areas under the curve (AUCs) of 0.65 to 0.74, indicating considerable inaccuracy,” said Yang Liang, MD, of the Department of Hematologic Oncology at Sun Yat-sen University Cancer Center in Guangzhou, China. “No current prognostic model uses data on immune state of the bone marrow micro-environment to predict survival.”
To fill that gap, the researchers undertook a study using clinical data and survival information from the International Cancer Genome Consortium (training cohort, n=485) and Gene Expression Omnibus (validation cohort, n=195). They built a survival prediction model based on the expression of immune-related genes in the bone marrow micro-environment.
In the training cohort, the study found an immune signature based on nine immune-related genes, which were then tested in the validation cohort. AUCs were as follows:
- One-year survival: 0.83 in the training cohort and 0.66 in the validation cohort
- Three year-survival: 0.79 in the training cohort and 0.60 in the validation cohort
- Five-year survival: 0.82 in the training cohort and 0.66 in the validation cohort
The immune signature identified patients as having either low or high risk of mortality. The patients identified as being in the high-risk cohort had significantly worse five-year survival than those in low‐risk cohort.
Compared with those in the high-risk cohort, subjects identified as having a low-risk immune signature had:
- Higher proportion of CD4-postive T cells
- Activated natural killer cells
“These data suggest a role for immune cells in the bone marrow micro-environment on survival. The immune score we describe can be combined with other prediction models to improve accuracy,” said Dr. Liang.