AI Article Synopsis

  • - A new prognostic algorithm for chronic lymphocytic leukemia (CLL) has been developed, using genetic mutations and chromosomal abnormalities from 1274 samples to classify four risk groups based on their survival rates over 10 years.
  • - The risk groups are: high-risk (29% survival) with TP53/BIRC3 abnormalities, intermediate-risk (37% survival) with NOTCH1/SF3B1 mutations or del11q22-q23, low-risk (57% survival) with +12 or normal genetics, and very low-risk (69.3% survival) with only del13q14.
  • - This integrated model not only predicts survival more accurately than previous methods but also accounts for clonal

Article Abstract

The identification of new genetic lesions in chronic lymphocytic leukemia (CLL) prompts a comprehensive and dynamic prognostic algorithm including gene mutations and chromosomal abnormalities and their changes during clonal evolution. By integrating mutational and cytogenetic analysis in 1274 CLL samples and using both a training-validation and a time-dependent design, 4 CLL subgroups were hierarchically classified: (1) high-risk, harboring TP53 and/or BIRC3 abnormalities (10-year survival: 29%); (2) intermediate-risk, harboring NOTCH1 and/or SF3B1 mutations and/or del11q22-q23 (10-year survival: 37%); (3) low-risk, harboring +12 or a normal genetics (10-year survival: 57%); and (4) very low-risk, harboring del13q14 only, whose 10-year survival (69.3%) did not significantly differ from a matched general population. This integrated mutational and cytogenetic model independently predicted survival, improved CLL prognostication accuracy compared with FISH karyotype (P < .0001), and was externally validated in an independent CLL cohort. Clonal evolution from lower to higher risk implicated the emergence of NOTCH1, SF3B1, and BIRC3 abnormalities in addition to TP53 and 11q22-q23 lesions. By taking into account clonal evolution through time-dependent analysis, the genetic model maintained its prognostic relevance at any time from diagnosis. These findings may have relevant implications for the design of clinical trials aimed at assessing the use of mutational profiling to inform therapeutic decisions.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3578955PMC
http://dx.doi.org/10.1182/blood-2012-09-458265DOI Listing

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