427 research outputs found
Time to Treatment Prediction in Chronic Lymphocytic Leukemia Based on New Transcriptional Patterns
Chronic lymphocytic leukemia (CLL) is the most frequent lymphoproliferative syndrome in
western countries. CLL evolution is frequently indolent, and treatment is mostly reserved
for those patients with signs or symptoms of disease progression. In this work, we used
RNA sequencing data from the International Cancer Genome Consortium CLL cohort
to determine new gene expression patterns that correlate with clinical evolution.We
determined that a 290-gene expression signature, in addition to immunoglobulin heavy
chain variable region (IGHV) mutation status, stratifies patients into four groups with
notably different time to first treatment. This finding was confirmed in an independent
cohort. Similarly, we present a machine learning algorithm that predicts the need for
treatment within the first 5 years following diagnosis using expression data from 2,198
genes. This predictor achieved 90% precision and 89% accuracy when classifying
independent CLL cases. Our findings indicate that CLL progression risk largely correlates
with particular transcriptomic patterns and paves the way for the identification of high-risk
patients who might benefit from prompt therapy following diagnosis.S
The association of germline variants with chronic lymphocytic leukemia outcome suggests the implication of novel genes and pathways in clinical evolution
Background
Chronic Lymphocytic Leukemia (CLL) is the most frequent lymphoproliferative disorder in western countries and is characterized by a remarkable clinical heterogeneity. During the last decade, multiple genomic studies have identified a myriad of somatic events driving CLL proliferation and aggressivity. Nevertheless, and despite the mounting evidence of inherited risk for CLL development, the existence of germline variants associated with clinical outcomes has not been addressed in depth.
Methods
Exome sequencing data from control leukocytes of CLL patients involved in the International Cancer Genome Consortium (ICGC) was used for genotyping. Cox regression was used to detect variants associated with clinical outcomes. Gene and pathways level associations were also calculated.
Results
Single nucleotide polymorphisms in PPP4R2 and MAP3K4 were associated with earlier treatment need. A gene-level analysis evidenced a significant association of RIPK3 with both treatment need and survival. Furthermore, germline variability in pathways such as apoptosis, cell-cycle, pentose phosphate, GNα13 and Nitric oxide was associated with overall survival.
Conclusion
Our results support the existence of inherited conditionants of CLL evolution and points towards genes and pathways that may results useful as biomarkers of disease outcome. More research is needed to validate these findings.S
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