Acute myeloblastic leukemia (M0) with an unusual chromosomal abnormality: translocation (1;14)(p13;q32)

A Case of Acute Myeloid Leukemia with a Previously Unreported Translocation (14; 15) (q32; q13)

Background. We hereby describe what we believe to be the first reported case of t (14; 15) (q32; q13) associated with acute myeloid leukemia (AML). Methods. PubMed, Embase, and OVID search engines were used to review the related literature and similar published cases. Case. A47-year-old female presented in December 2011 with AML (acute myelomonocytic leukemia) with normal cytogenetics; molecular testing revealed FLT-3 internal tandem duplication (ITD) mutation, while no mutations involving FLT3 D385/I836, NPM1 exon 12, or KIT exons 8 and 17 were detected. She was induced with 7 + 3 (cytarabine + idarubicin) and achieved complete remission after a second induction with high-dose cytarabine (HiDAC) followed by uneventful consolidation. She presented 19 months after diagnosis with relapsed disease. Of note, at relapse cytogenetic analysis revealed t (14; 15) (q32; q13), while FLT-3 analysis showed a codon D835 mutation (no ITD mutation was detected). She proved refractory to the initial clofarabine-based regimen, so FLAG-idarubicin then was used. She continued to have persistent disease, and she was discharged on best supportive care. Conclusion. Based on this single case of AML with t (14; 15) (q32; q13), this newly reported translocation may be associated with refractory disease.

Critical assessment of automated flow cytometry data analysis techniques

In this analysis, the authors directly compared the performance of flow cytometry data processing algorithms to manual gating approaches. The results offer information of practical utility about the performance of the algorithms as applied to different data sets and challenges.

Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.