Clonal hematopoiesis predicts development of therapy-related myeloid neoplasms post-autologous stem cell transplantation.

Therapy-related myeloid neoplasms (tMN) develop after exposure to cytotoxic and radiation therapy, and due to their adverse prognosis, it is of paramount interest to identify patients at high risk. The presence of clonal hematopoiesis has been shown to increase the risk of developing tMN. The value of analyzing hematopoietic stem cells harvested at leukapheresis before autologous stem cell transplantation (ASCT) with next-generation sequencing and immunophenotyping represents potentially informative parameters that have yet to be discovered. We performed a nested case-control study to elucidate the association between clonal hematopoiesis, mobilization potential, and aberrant immunophenotype in leukapheresis products with the development of tMN after ASCT. A total of 36 patients with nonmyeloid disease who were diagnosed with tMN after treatment with ASCT were included as case subjects. Case subjects were identified from a cohort of 1130 patients treated with ASCT and matched with 36 control subjects who did not develop tMN after ASCT. Case subjects were significantly poorer mobilizers of CD34+ cells at leukapheresis (P = .016), indicating that these patients possess inferior bone marrow function. Both clonal hematopoiesis (odds ratio, 5.9; 95% confidence interval, 1.8-19.1; P = .003) and aberrant expression of CD7 (odds ratio, 6.6; 95% confidence interval, 1.6-26.2; P = .004) at the time of ASCT were associated with an increased risk of developing tMN after ASCT. In conclusion, clonal hematopoiesis, present at low variant allele frequencies, and aberrant CD7 expression on stem cells in leukapheresis products from patients with nonmyeloid hematologic cancer hold potential for the early identification of patients at high risk of developing tMN after ASCT.

Novel scripts for improved annotation and selection of variants from whole exome sequencing in cancer research.

Sequencing the exome is quickly becoming the preferred method for discovering disease-inducing mutations. While obtaining data sets is a straightforward procedure, the subsequent analysis and interpretation of the data is a limiting step for clinical applications. Thus, while the initial mutation and variant calling can be performed by a bioinformatician or trained researcher, the output from robust packages such as MuTect and GATK is not directly informative for the general life scientists. In attempt to obviate this problem we have created complementary Wolfram scripts, which enable easy downstream annotation and selection, presented here in the perspective of hematological relevance. It also provides the researcher with the opportunity to extend the analysis by having a full-fledged programming and analysis environment of Mathematica at hand. In brief, post-processing is performed by: •Mapping of germ line and somatic variants to coding regions, and defining variant sets within Mathematica.•Processing of variants in variant effect predictor.•Extended annotation, relevance scoring and defining focus areas through the provided functions.

Sensitive ligand-based protein quantification using immuno-PCR: A critical review of single-probe and proximity ligation assays.

Quantitative PCR (qPCR) of reverse-transcribed mRNA has revolutionized gene expression analyses. qPCR analysis is based on the prevalent assumption that mRNA transcript numbers provide an adequate measure of specific biomarker expression. However, taking the complexity of protein turnover into account, there is a need to correlate qPCR-derived transcriptional patterns with protein translational patterns so as to not leave behind important pathobiological details. One emerging approach in protein analysis is PCR-coupled protein quantification, often denoted as immuno-PCR (iPCR), which targets soluble proteins. Here we review recent trends and applications in iPCR assays that may bridge the gap between classical enzyme-linked immunosorbent assays and mass spectrometry methodologies in terms of sensitivity and multiplexing.

Delineation of known and new transcript variants of the SETMAR (Metnase) gene and the expression profile in hematologic neoplasms.

SET domain and mariner transposase fusion gene (SETMAR), also known as Metnase, has previously been shown to suppress the formation of chromosomal translocation in mouse fibroblasts. Despite the fact that hematologic malignancies are often characterized by chromosomal rearrangements, no studies have hitherto investigated the expression pattern of the gene in these disorders. We hypothesized that a high expression of SETMAR protected the cells from chromosomal rearrangements; thus, we examined the mRNA expression of SETMAR transcript variants in hematologic patients. We identified six transcript variants (var1, var2, var5, varA, varB, varC), of which three had not been reported previously. Expression levels were quantified by transcript-specific quantitative polymerase chain reaction in 15 healthy individuals, 70 acute myeloid leukemia (AML) patients (translocation positive, n= 30 [AML(TPos)], translocation negative, n = 40 [AML(TNeg)]), seven patients with mantle cell lymphoma (t [11,14] positive), and 13 patients with chronic myeloid leukemia (t [9,22] positive). All variants were significantly overexpressed in both subgroups of AML compared with healthy individuals (var1 and var2: p < 0.00001 for both AML subgroups, varA and varB: p = 0.0002, var5: p = 0.0008, and varC: p = 0.0001 for AML(TNeg); varA: p = 0.0048, varB and var5: p = 0.0001, varC: p = 0.0017). When comparing the expression in AML(TNeg) and AML(TPos), we found a significantly increased expression of the full length SETMAR in AML(TNeg) (var1: p = 0.047), suggesting a protective effect of high SETMAR expression on formation of chromosomal translocations. In conclusion, we have found known and novel SETMAR splice variants to be significantly increased in AML. To our knowledge, this is the first study that describes an expression profile of SETMAR in subgroups of hematologic malignancies, which can be linked to the incidence of chromosomal rearrangements.