Clonal hematopoiesis of indeterminate potential in patients with immunoglobulin light-chain AL amyloidosis.
Paolo LopedoteBenjamin EvansAlfredo MarchettiTianzeng ChenMaria MoscvinSamuel BoulltNiccolo' BolliGiada BianchiPublished in: Blood advances (2024)
Immunoglobulin light-chain (AL) amyloidosis is characterized by the deposition of misfolded monoclonal free light chains, with cardiac complications accounting for patient mortality. Clonal hematopoiesis of indeterminate potential (CHIP) has been associated with worse cardiovascular outcomes in the general population. Its significance in AL amyloidosis remains unclear. We collected clinical information and outcome data on 76 patients with a diagnosis of AL amyloidosis who underwent deep targeted sequencing for myeloid neoplasia-associated mutations between April 2018 and August 2023. Variant allele frequency was set at 2% to call CHIP-associated mutations. CHIP mutations were present in patients with AL amyloidosis at a higher frequency compared with age-matched control individuals. Sixteen patients (21%) had at least 1 CHIP mutation. DNMT3A was the most frequent mutation (7/16; 44%). Compared with patients without CHIP, patients with CHIP had a higher prevalence of t(11;14) translocation (69% vs 25%, respectively; P = .004). Furthermore, among patients with renal involvement, those with CHIP had a lower Palladini renal stage (P = .001). At a median follow-up of 32.5 months, the presence of CHIP was not associated with worse overall survival or major organ dysfunction progression-free survival. Larger studies and longer follow-up are needed to better define the impact of CHIP in patients with AL amyloidosis.
Keyphrases
- high throughput
- circulating tumor cells
- multiple myeloma
- free survival
- end stage renal disease
- newly diagnosed
- ejection fraction
- risk factors
- prognostic factors
- oxidative stress
- healthcare
- dna methylation
- left ventricular
- climate change
- dendritic cells
- type diabetes
- risk assessment
- coronary artery disease
- cardiovascular events
- cancer therapy
- deep learning
- cardiovascular disease
- artificial intelligence