Session: 641. CLL: Biology and Pathophysiology, excluding Therapy: Poster III
Hematology Disease Topics & Pathways:
Biological Processes, genomics, immune mechanism, microenvironment
Methods: We downloaded relevant data from International Cancer Genome Consortium (CLL-ES; N = 485) and Gene Expression Omnibus (GSE22762; N = 195) with survival data. We used a LASSO Cox regression model to build a survival prediction model based on the expression of immune-related genes in the bone marrow micro-environment. Immune cells corresponding to these immune genes were deduced Using CIBERSORT and compared in cohorts with different survival probabilities.
Results: An immune signature based on 9 immune-related genes were constructed in the training cohort (CLL-ES) and tested in a validation cohort (GSE22762). The AUCs of 1, 3 and 5 -year survivals were 0.83 [95% Confidence Interval, 0.63, 0.97], 0.79 [0.67, 0.89] and 0.82 [0.73, 0.90] in training and 0.66 [0.55, 0.79], 0.60 [0.50, 0.69] and 0.66 [0.58, 0.76]in the validation cohort. Subjects in the high‐risk cohort identified by immune signature had significantly worse 5-year survival compared with those in low‐risk cohort group (training cohort: 91% [87, 95%] vs. 99% [98, 100%], P <0.001; validation cohort: 61% [50, 71%] vs 81% [80, 82%], P = 0.003). Subjects with a low-risk immune signature had a higher proportion of CD4-postive T-cells, activated NK-cells compared with those in the high-risk cohort (p < 0.05).
Conclusion: The immune score model of the bone marrow micro-environment we developed accurately predicts survival of persons with CLL. 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.
ICGC, GEO, chronic lymphocytic leukemia, immunity, prognostic model
Disclosures: No relevant conflicts of interest to declare.
See more of: Oral and Poster Abstracts