Journal article
Tumor mutational load predicts time to first treatment in chronic lymphocytic leukemia (CLL) and monoclonal B-cell lymphocytosis beyond the CLL international prognostic index
American journal of hematology, Vol.95(8), pp.906-917
08/2020
DOI: 10.1002/ajh.25831
PMCID: PMC7409825
PMID: 32279347
Abstract
Next-generation sequencing identified about 60 genes recurrently mutated in chronic lymphocytic leukemia (CLL). We examined the additive prognostic value of the total number of recurrently mutated CLL genes (i.e., tumor mutational load [TML]) or the individually mutated genes beyond the CLL international prognostic index (CLL-IPI) in newly diagnosed CLL and high-count monoclonal B-cell lymphocytosis (HC MBL). We sequenced 59 genes among 557 individuals (112 HC MBL/445 CLL) in a multi-stage design, to estimate hazard ratios (HR) and 95% confidence intervals (CI) for time-to-first treatment (TTT), adjusted for CLL-IPI and sex. TML was associated with shorter TTT in the discovery and validation cohorts, with a combined estimate of continuous HR = 1.27 (CI:1.17-1.39, P = 2.6 × 10
; c-statistic = 0.76). When stratified by CLL-IPI, the association of TML with TTT was stronger and validated within low/intermediate risk (combined HR = 1.54, CI:1.37-1.72, P = 7.0 × 10
). Overall, 80% of low/intermediate CLL-IPI cases with two or more mutated genes progressed to require therapy within 5 years, compared to 24% among those without mutations. TML was also associated with shorter TTT in the HC MBL cohort (HR = 1.53, CI:1.12-2.07, P = .007; c-statistic = 0.71). TML is a strong prognostic factor for TTT independent of CLL-IPI, especially among low/intermediate CLL-IPI risk, and a better predictor than any single gene. Mutational screening at early stages may improve risk stratification and better predict TTT.
Details
- Title: Subtitle
- Tumor mutational load predicts time to first treatment in chronic lymphocytic leukemia (CLL) and monoclonal B-cell lymphocytosis beyond the CLL international prognostic index
- Creators
- Geffen Kleinstern - Mayo ClinicDaniel R O'Brien - Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USAXing Li - Mayo ClinicShulan Tian - Mayo ClinicBrian F Kabat - Mayo ClinicKari G Rabe - Mayo ClinicAaron D Norman - Mayo ClinicHuihuang Yan - Mayo ClinicCeline M Vachon - Mayo ClinicNicholas J Boddicker - Mayo ClinicTimothy G Call - Mayo ClinicSameer A Parikh - Mayo ClinicLaura Bruins - Division of Hematology /Oncology Mayo Clinic Scottsdale Arizona USACecilia Bonolo de Campos - Division of Hematology /Oncology, Mayo Clinic, Scottsdale, Arizona, USAJose F Leis - Mayo ClinicTait D Shanafelt - Stanford UniversityWei Ding - Mayo ClinicJames R Cerhan - Mayo ClinicNeil E Kay - Mayo ClinicSusan L Slager - Mayo ClinicEsteban Braggio - Mayo Clinic
- Resource Type
- Journal article
- Publication Details
- American journal of hematology, Vol.95(8), pp.906-917
- DOI
- 10.1002/ajh.25831
- PMID
- 32279347
- PMCID
- PMC7409825
- NLM abbreviation
- Am J Hematol
- ISSN
- 0361-8609
- eISSN
- 1096-8652
- Grant note
- R25 CA092049 / NCI NIH HHS R01 CA197120 / NCI NIH HHS P30 CA086862 / NCI NIH HHS R01 CA235026 / NCI NIH HHS P50 CA097274 / NCI NIH HHS
- Language
- English
- Date published
- 08/2020
- Academic Unit
- Epidemiology
- Record Identifier
- 9984368086202771
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