Control of focal adhesion kinase activation by RUNX1-regulated miRNAs in high-risk AML.

We recently described a 16-gene expression signature for improved risk stratification of acute myeloid leukemia (AML) patients called the AML Prognostic Score (APS). A subset of APS-high-risk AML patients showed increased levels of focal adhesion kinase (FAK), encoded by the Protein Tyrosine Kinase 2 (PTK2) gene, which was correlated with RUNX1 mutations. RUNX1 mutant cells are more sensitive to PTK2 inhibitors. As we were not able to detect RUNX1-binding sites in the PTK2 promoter, we hypothesized that RUNX1 might regulate micro(mi)RNAs that repress PTK2, such that loss-of-function RUNX1 mutations would result in reduced miRNA expression and derepression of PTK2. Examination of paired RNA-seq and miRNA-seq data from 301 AML cases revealed two miRNAs that positively correlated with RUNX1 expression, contained RUNX1-binding sites in their promoters and were predicted to target PTK2. We show that the hsa-let7a-2-3p and hsa-miR-135a-5p promoters are regulated by RUNX1, and that PTK2 is a direct target of both miRNAs. Even in the absence of RUNX1 mutations, hsa-let7a-2-3p and hsa-miR-135a-5p regulate PTK2 expression, and reduced expression of these two miRNAs sensitizes AML cells to PTK2 inhibition. These data explain how RUNX1 regulates PTK2, and identify potential miRNA biomarkers for targeting AML with PTK2 inhibitors.

A clinical transcriptome approach to patient stratification and therapy selection in acute myeloid leukemia.

As more clinically-relevant genomic features of myeloid malignancies are revealed, it has become clear that targeted clinical genetic testing is inadequate for risk stratification. Here, we develop and validate a clinical transcriptome-based assay for stratification of acute myeloid leukemia (AML). Comparison of ribonucleic acid sequencing (RNA-Seq) to whole genome and exome sequencing reveals that a standalone RNA-Seq assay offers the greatest diagnostic return, enabling identification of expressed gene fusions, single nucleotide and short insertion/deletion variants, and whole-transcriptome expression information. Expression data from 154 AML patients are used to develop a novel AML prognostic score, which is strongly associated with patient outcomes across 620 patients from three independent cohorts, and 42 patients from a prospective cohort. When combined with molecular risk guidelines, the risk score allows for the re-stratification of 22.1 to 25.3% of AML patients from three independent cohorts into correct risk groups. Within the adverse-risk subgroup, we identify a subset of patients characterized by dysregulated integrin signaling and RUNX1 or TP53 mutation. We show that these patients may benefit from therapy with inhibitors of focal adhesion kinase, encoded by PTK2, demonstrating additional utility of transcriptome-based testing for therapy selection in myeloid malignancy.

Assessing Limit of Detection in Clinical Sequencing.

Clinical reporting of solid tumor sequencing requires reliable assessment of the accuracy and reproducibility of each assay. Somatic mutation variant allele fractions may be below 10% in many samples due to sample heterogeneity, tumor clonality, and/or sample degradation in fixatives such as formalin. The toolkits available to the clinical sequencing community for correlating assay design parameters with assay sensitivity remain limited, and large-scale empirical assessments are often relied upon due to the lack of clear theoretical grounding. To address this uncertainty, a theoretical model was developed for predicting the expected variant calling sensitivity for a given library complexity and sequencing depth. Binomial models were found to be appropriate when assay sensitivity was only limited by library complexity or sequencing depth, but functional scaling for library complexity was necessary when both library complexity and sequencing depth were co-limiting. This model was empirically validated with sequencing experiments by using a series of DNA input amounts and sequencing depths. Based on these findings, a workflow is proposed for determining the limiting factors to sensitivity in different assay designs, and the formulas for these scenarios are presented. The approach described here provides designers of clinical assays with the methods to theoretically predict assay design outcomes a priori, potentially reducing burden in clinical tumor assay design and validation efforts.

Altered microRNA expression links IL6 and TNF-induced inflammaging with myeloid malignancy in humans and mice.

Aging is associated with significant changes in the hematopoietic system, including increased inflammation, impaired hematopoietic stem cell (HSC) function, and increased incidence of myeloid malignancy. Inflammation of aging ("inflammaging") has been proposed as a driver of age-related changes in HSC function and myeloid malignancy, but mechanisms linking these phenomena remain poorly defined. We identified loss of miR-146a as driving aging-associated inflammation in AML patients. miR-146a expression declined in old wild-type mice, and loss of miR-146a promoted premature HSC aging and inflammation in young miR-146a-null mice, preceding development of aging-associated myeloid malignancy. Using single-cell assays of HSC quiescence, stemness, differentiation potential, and epigenetic state to probe HSC function and population structure, we found that loss of miR-146a depleted a subpopulation of primitive, quiescent HSCs. DNA methylation and transcriptome profiling implicated NF-κB, IL6, and TNF as potential drivers of HSC dysfunction, activating an inflammatory signaling relay promoting IL6 and TNF secretion from mature miR-146a-/- myeloid and lymphoid cells. Reducing inflammation by targeting Il6 or Tnf was sufficient to restore single-cell measures of miR-146a-/- HSC function and subpopulation structure and reduced the incidence of hematological malignancy in miR-146a-/- mice. miR-146a-/- HSCs exhibited enhanced sensitivity to IL6 stimulation, indicating that loss of miR-146a affects HSC function via both cell-extrinsic inflammatory signals and increased cell-intrinsic sensitivity to inflammation. Thus, loss of miR-146a regulates cell-extrinsic and -intrinsic mechanisms linking HSC inflammaging to the development of myeloid malignancy.

Loss of lenalidomide-induced megakaryocytic differentiation leads to therapy resistance in del(5q) myelodysplastic syndrome.

Interstitial deletion of the long arm of chromosome 5 (del(5q)) is the most common structural genomic variant in myelodysplastic syndromes (MDS)1. Lenalidomide (LEN) is the treatment of choice for patients with del(5q) MDS, but half of the responding patients become resistant2 within 2 years. TP53 mutations are detected in ~20% of LEN-resistant patients3. Here we show that patients who become resistant to LEN harbour recurrent variants of TP53 or RUNX1. LEN upregulated RUNX1 protein and function in a CRBN- and TP53-dependent manner in del(5q) cells, and mutation or downregulation of RUNX1 rendered cells resistant to LEN. LEN induced megakaryocytic differentiation of del(5q) cells followed by cell death that was dependent on calpain activation and CSNK1A1 degradation4,5. We also identified GATA2 as a LEN-responsive gene that is required for LEN-induced megakaryocyte differentiation. Megakaryocytic gene-promoter analyses suggested that LEN-induced degradation of IKZF1 enables a RUNX1-GATA2 complex to drive megakaryocytic differentiation. Overexpression of GATA2 restored LEN sensitivity in the context of RUNX1 or TP53 mutations by enhancing LEN-induced megakaryocytic differentiation. Screening for mutations that block LEN-induced megakaryocytic differentiation should identify patients who are resistant to LEN.

Genomic testing in myeloid malignancy.

Clinical genetic testing in the myeloid malignancies is undergoing a rapid transition from the era of cytogenetics and single-gene testing to an era dominated by next-generation sequencing (NGS). This transition promises to better reveal the genetic alterations underlying disease, but there are distinct risks and benefits associated with different NGS testing platforms. NGS offers the potential benefit of being able to survey alterations across a wider set of genes, but analytic and clinical challenges associated with incidental findings, germ line variation, turnaround time, and limits of detection must be addressed. Additionally, transcriptome-based testing may offer several distinct benefits beyond traditional DNA-based methods. In addition to testing at disease diagnosis, research indicates potential benefits of genetic testing both prior to disease onset and at remission. In this review, we discuss the transition from the era of cytogenetics and single-gene tests to the era of NGS panels and genome-wide sequencing-highlighting both the potential and drawbacks of these novel technologies.

Fixation Effects on Variant Calling in a Clinical Resequencing Panel.

Formalin fixation is the standard method for the preservation of tissue for diagnostic purposes, including pathologic review and molecular assays. However, this method is known to cause artifacts that can affect the accuracy of molecular genetic test results. We assessed the applicability of alternative fixatives to determine whether these perform significantly better on next-generation sequencing assays, and whether adequate morphology is retained for primary diagnosis, in a prospective study using a clinical-grade, laboratory-developed targeted resequencing assay. Several parameters relating to sequencing quality and variant calling were examined and quantified in tumor and normal colon epithelial tissues. We identified an alternative fixative that suppresses many formalin-related artifacts while retaining adequate morphology for pathologic review.

Applications of Bayesian network models in predicting types of hematological malignancies.

Network analysis is the preferred approach for the detection of subtle but coordinated changes in expression of an interacting and related set of genes. We introduce a novel method based on the analyses of coexpression networks and Bayesian networks, and we use this new method to classify two types of hematological malignancies; namely, acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Our classifier has an accuracy of 93%, a precision of 98%, and a recall of 90% on the training dataset (n = 366); which outperforms the results reported by other scholars on the same dataset. Although our training dataset consists of microarray data, our model has a remarkable performance on the RNA-Seq test dataset (n = 74, accuracy = 89%, precision = 88%, recall = 98%), which confirms that eigengenes are robust with respect to expression profiling technology. These signatures are useful in classification and correctly predicting the diagnosis. They might also provide valuable information about the underlying biology of diseases. Our network analysis approach is generalizable and can be useful for classifying other diseases based on gene expression profiles. Our previously published Pigengene package is publicly available through Bioconductor, which can be used to conveniently fit a Bayesian network to gene expression data.

A clinically validated diagnostic second-generation sequencing assay for detection of hereditary BRCA1 and BRCA2 mutations.

Individuals who inherit mutations in BRCA1 or BRCA2 are predisposed to breast and ovarian cancers. However, identifying mutations in these large genes by conventional dideoxy sequencing in a clinical testing laboratory is both time consuming and costly, and similar challenges exist for other large genes, or sets of genes, with relevance in the clinical setting. Second-generation sequencing technologies have the potential to improve the efficiency and throughput of clinical diagnostic sequencing, once clinically validated methods become available. We have developed a method for detection of variants based on automated small-amplicon PCR followed by sample pooling and sequencing with a second-generation instrument. To demonstrate the suitability of this method for clinical diagnostic sequencing, we analyzed the coding exons and the intron-exon boundaries of BRCA1 and BRCA2 in 91 hereditary breast cancer patient samples. Our method generated high-quality sequence coverage across all targeted regions, with median coverage greater than 4000-fold for each sample in pools of 24. Sensitive and specific automated variant detection, without false-positive or false-negative results, was accomplished with a standard software pipeline using bwa for sequence alignment and samtools for variant detection. We experimentally derived a minimum threshold of 100-fold sequence depth for confident variant detection. The results demonstrate that this method is suitable for sensitive, automatable, high-throughput sequence variant detection in the clinical laboratory.

Barnacle: detecting and characterizing tandem duplications and fusions in transcriptome assemblies.

BACKGROUND: Chimeric transcripts, including partial and internal tandem duplications (PTDs, ITDs) and gene fusions, are important in the detection, prognosis, and treatment of human cancers. RESULTS: We describe Barnacle, a production-grade analysis tool that detects such chimeras in de novo assemblies of RNA-seq data, and supports prioritizing them for review and validation by reporting the relative coverage of co-occurring chimeric and wild-type transcripts. We demonstrate applications in large-scale disease studies, by identifying PTDs in MLL, ITDs in FLT3, and reciprocal fusions between PML and RARA, in two deeply sequenced acute myeloid leukemia (AML) RNA-seq datasets. CONCLUSIONS: Our analyses of real and simulated data sets show that, with appropriate filter settings, Barnacle makes highly specific predictions for three types of chimeric transcripts that are important in a range of cancers: PTDs, ITDs, and fusions. High specificity makes manual review and validation efficient, which is necessary in large-scale disease studies. Characterizing an extended range of chimera types will help generate insights into progression, treatment, and outcomes for complex diseases.