Gene fusions and oncogenic mutations in MLH1 deficient and BRAFV600E wild-type colorectal cancers

Gene fusions can act as oncogenic drivers and offer targets for cancer therapy. Since fusions are rare in colorectal cancer (CRC), their universal screening seems impractical. Our aim was to investigate gene fusions in 62 CRC cases with deficient MLH1 (dMLH1) and BRAFV600E wild-type (wt) status from a consecutive real-life series of 2079 CRCs. First, gene fusions were analysed using a novel FusionPlex Lung v2 RNA-based next-generation sequencing (NGS) panel, and these results were compared to a novel Idylla GeneFusion assay and pan-TRK immunohistochemistry (IHC). NGS detected seven (7/62, 11%) NTRK1 fusions (TPM3::NTRK1, PLEKHA6::NTRK1 and LMNA::NTRK1, each in two cases, and IRF2BP2::NTRK1 in one case). In addition, two ALK, four RET and seven BRAF fusions were identified. Idylla detected seven NTRK1 expression imbalances, in line with the NGS results (overall agreement 100%). Furthermore, Idylla detected the two NGS-identified ALK rearrangements as one specific ALK fusion and one ALK expression imbalance, whilst only two of the four RET fusions were discovered. However, Idylla detected several expression imbalances of ALK (n = 7) and RET (n = 1) that were found to be fusion negative with the NGS. Pan-TRK IHC showed clearly detectable, fusion partner-dependent staining patterns in the seven NTRK1 fusion cases. Overall agreement for pan-TRK antibody clone EPR17341 was 98% and for A7H6R 100% when compared to the NGS. Of the 62 CRCs, 43 were MLH1 promoter hypermethylated (MLH1ph) and 39 were RASwt. All fusion cases were both MLH1ph and RASwt. Our results show that kinase fusions (20/30, 67%) and most importantly targetable NTRK1 fusions (7/30, 23%) are frequent in CRCs with dMLH1/BRAFV600Ewt/MLH1ph/RASwt. NGS was the most comprehensive method in finding the fusions, of which a subset can be screened by Idylla or IHC, provided that the result is confirmed by NGS.Peer reviewe

Tool evaluation for the detection of variably sized indels from next generation whole genome and targeted sequencing data

Insertions and deletions (indels) in human genomes are associated with a wide range of phenotypes, including various clinical disorders. High-throughput, next generation sequencing (NGS) technologies enable the detection of short genetic variants, such as single nucleotide variants (SNVs) and indels. However, the variant calling accuracy for indels remains considerably lower than for SNVs. Here we present a comparative study of the performance of variant calling tools for indel calling, evaluated with a wide repertoire of NGS datasets. While there is no single optimal tool to suit all circumstances, our results demonstrate that the choice of variant calling tool greatly impacts the precision and recall of indel calling. Furthermore, to reliably detect indels, it is essential to choose NGS technologies that offer a long read length and high coverage coupled with specific variant calling tools.Author summaryThe development of next generation sequencing (NGS) technologies and computational algorithms enabled the large scale, simultaneous detection of a wide range of genetic variants, such as single nucleotide variants as well as insertions and deletions (indels), which may confer potential clinical significance. Recently, many studies have been conducted to evaluate variant calling tools for indel calling. However, the optimal indel size range for different variant calling tools remains unclear. A good benchmarking dataset for indel calling evaluation should contain biologically representative high-confident indels with a wide size range and preferably come from various sequencing settings. In this article, we created a semi-simulated whole genome sequencing dataset where the sequencing data were computationally generated. The indels in the semi-simulated genome were incorporated from a real human sample to represent biologically realistic indels and to avoid the inclusion of variants due to potential technical sequencing errors. Furthermore, we used three real-world NGS datasets generated by whole genome or targeted sequencing to further evaluate our candidate tools. Our results demonstrated that variant calling tools vary greatly in calling different sizes of indels. Deletion calling and insertion calling also showed differences among the tools. The sequencing settings in coverage and read length also had a great impact on indel calling. Our results suggest that the accuracy of indel calling was dependent on the combination of a variant calling tool, indel size range, and sequencing settings.</p

Chronic Myeloid Leukemia Patients in Prolonged Remission following Interferon-alpha Monotherapy Have Distinct Cytokine and Oligoclonal Lymphocyte Profile

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Mono/oligoclonal T and NK cells are common in chronic myeloid leukemia patients at diagnosis and expand during dasatinib therapy

In a proportion of patients with chronic myeloid leukemia (CML) being treated with dasatinib, we recently observed large granular lymphocyte (LGL) expansions carrying clonal T-cell receptor (TCR) gamma/delta gene rearrangements. To assess the prevalence and role of clonal lymphocytes in CML, we collected samples from patients (n = 34) at the time of diagnosis and during imatinib and dasatinib therapies and analyzed lymphocyte clonality with a sensitive polymerase chain reaction-based method of TCR gamma and delta genes. Surprisingly, at CML diagnosis, 15 of 18 patients (83%) had a sizeable clonal, BCR-ABL1 negative lymphocyte population, which was uncommon in healthy persons (1 of 12; 8%). The same clone persisted at low levels in most imatinib-treated patients. In contrast, in a distinct population of dasatinib-treated patients, the diagnostic phase clone markedly expanded, resulting in absolute lymphocytosis in blood. Most patients with LGL expansions (90%) had TCR delta rearrangements, which were uncommon in patients without an LGL expansion (10%). The TCR delta clones were confined to gamma delta(+) T- or natural killer-cell compartments and the TCR gamma clones to CD4(+)/CD8(+) alpha beta(+) fractions. The functional importance of clonal lymphocytes as a part of leukemia immune surveillance and the putative anergy- reversing role of dasatinib require further evaluation. (Blood. 2010; 116(5): 772- 782

Arpp19 Promotes Myc and Cip2a Expression and Associates with Patient Relapse in Acute Myeloid Leukemia

Disease relapse from standard chemotherapy in acute myeloid leukemia (AML) is poorly understood. The importance of protein phosphatase 2A (PP2A) as an AML tumor suppressor is emerging. Therefore, here, we examined the potential role of endogenous PP2A inhibitor proteins as biomarkers predicting AML relapse in a standard patient population by using three independent patient materials: cohort1 (n = 80), cohort2 (n = 48) and The Cancer Genome Atlas Acute Myeloid Leukemia (TCGA LAML) dataset (n = 160). Out of the examined PP2A inhibitors (CIP2A, SET, PME1, ARPP19 and TIPRL), expression of ARPP19 mRNA was found to be independent of the current AML risk classification. Functionally, ARPP19 promoted AML cell viability and expression of oncoproteins MYC, CDK1, and CIP2A. Clinically, ARPP19 mRNA expression was significantly lower at diagnosis (p = 0.035) in patients whose disease did not relapse after standard chemotherapy. ARPP19 was an independent predictor for relapse both in univariable (p = 0.007) and in multivariable analyses (p = 0.0001) and gave additive information to EVI1 expression and risk group status (additive effect, p = 0.005). Low ARPP19 expression was also associated with better patient outcome in the TCGA LAML cohort (p = 0.019). In addition, in matched patient samples from diagnosis, remission and relapse phases, ARPP19 expression was associated with disease activity (p = 0.034), indicating its potential usefulness as a minimal residual disease (MRD) marker. Together, these data demonstrate the oncogenic function of ARPP19 in AML and its risk group independent role in predicting AML patient relapse tendency

Generation of Reference Values for Cardiac Enzymes from Hospital Admission Laboratory Data

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