Immune cell proportions correlate with clinicogenomic features and ex vivo drug responses in acute myeloid leukemia

IntroductionThe implementation of small-molecule and immunotherapies in acute myeloid leukemia (AML) has been challenging due to genetic and epigenetic variability amongst patients. There are many potential mechanisms by which immune cells could influence small-molecule or immunotherapy responses, yet, this area remains understudied.MethodsHere we performed cell type enrichment analysis from over 560 AML patient bone marrow and peripheral blood samples from the Beat AML dataset to describe the functional immune landscape of AML.ResultsWe identify multiple cell types that significantly correlate with AML clinical and genetic features, and we also observe significant correlations of immune cell proportions with ex vivo small-molecule and immunotherapy responses. Additionally, we generated a signature of terminally exhausted T cells (Tex) and identified AML with high monocytic proportions as strongly correlating with increased proportions of these immunosuppressive T cells.DiscussionOur work, which is accessible through a new “Cell Type” module in our visualization platform (Vizome; http://vizome.org/), can be leveraged to investigate potential contributions of different immune cells on many facets of the biology of AML

High throughput sequencing in mice: a platform comparison identifies a preponderance of cryptic SNPs

<p>Abstract</p> <p>Background</p> <p>Allelic variation is the cornerstone of genetically determined differences in gene expression, gene product structure, physiology, and behavior. However, allelic variation, particularly cryptic (unknown or not annotated) variation, is problematic for follow up analyses. Polymorphisms result in a high incidence of false positive and false negative results in hybridization based analyses and hinder the identification of the true variation underlying genetically determined differences in physiology and behavior. Given the proliferation of mouse genetic models (e.g., knockout models, selectively bred lines, heterogeneous stocks derived from standard inbred strains and wild mice) and the wealth of gene expression microarray and phenotypic studies using genetic models, the impact of naturally-occurring polymorphisms on these data is critical. With the advent of next-generation, high-throughput sequencing, we are now in a position to determine to what extent polymorphisms are currently cryptic in such models and their impact on downstream analyses.</p> <p>Results</p> <p>We sequenced the two most commonly used inbred mouse strains, DBA/2J and C57BL/6J, across a region of chromosome 1 (171.6 – 174.6 megabases) using two next generation high-throughput sequencing platforms: Applied Biosystems (SOLiD) and Illumina (Genome Analyzer). Using the same templates on both platforms, we compared realignments and single nucleotide polymorphism (SNP) detection with an 80 fold average read depth across platforms and samples. While public datasets currently annotate 4,527 SNPs between the two strains in this interval, thorough high-throughput sequencing identified a total of 11,824 SNPs in the interval, including 7,663 new SNPs. Furthermore, we confirmed 40 missense SNPs and discovered 36 new missense SNPs.</p> <p>Conclusion</p> <p>Comparisons utilizing even two of the best characterized mouse genetic models, DBA/2J and C57BL/6J, indicate that more than half of naturally-occurring SNPs remain cryptic. The magnitude of this problem is compounded when using more divergent or poorly annotated genetic models. This warrants full genomic sequencing of the mouse strains used as genetic models.</p

A genome-wide CRISPR screen identifies regulators of MAPK and MTOR pathways that mediate resistance to sorafenib in acute myeloid leukemia

Drug resistance impedes the long-term effect of targeted therapies in acute myeloid leukemia (AML), necessitating the identification of mechanisms underlying resistance. Approximately 25% of AML patients carry FLT3 mutations and develop post-treatment insensitivity to FLT3 inhibitors, including sorafenib. Using a genomewide CRISPR screen, we identified LZTR1, NF1, TSC1 and TSC2, negative regulators of the MAPK and MTOR pathways, as mediators of resistance to sorafenib. Analyses of ex vivo drug sensitivity assays in samples from patients with FLT3-ITD AML revealed that lower expression of LZTR1, NF1, and TSC2 correlated with sensitivity to sorafenib. Importantly, MAPK and/or MTOR complex 1 (MTORC1) activity was upregulated in AML cells made resistant to several FLT3 inhibitors, including crenolanib, quizartinib, and sorafenib. These cells were sensitive to MEK inhibitors, and the combination of FLT3 and MEK inhibitors showed enhanced efficacy, suggesting the effectiveness of such treatment in AML patients with FLT3 mutations and those with resistance to FLT3 inhibitors

Clinical resistance to crenolanib in acute myeloid leukemia due to diverse molecular mechanisms.

FLT3 mutations are prevalent in AML patients and confer poor prognosis. Crenolanib, a potent type I pan-FLT3 inhibitor, is effective against both internal tandem duplications and resistance-conferring tyrosine kinase domain mutations. While crenolanib monotherapy has demonstrated clinical benefit in heavily pretreated relapsed/refractory AML patients, responses are transient and relapse eventually occurs. Here, to investigate the mechanisms of crenolanib resistance, we perform whole exome sequencing of AML patient samples before and after crenolanib treatment. Unlike other FLT3 inhibitors, crenolanib does not induce FLT3 secondary mutations, and mutations of the FLT3 gatekeeper residue are infrequent. Instead, mutations of NRAS and IDH2 arise, mostly as FLT3-independent subclones, while TET2 and IDH1 predominantly co-occur with FLT3-mutant clones and are enriched in crenolanib poor-responders. The remaining patients exhibit post-crenolanib expansion of mutations associated with epigenetic regulators, transcription factors, and cohesion factors, suggesting diverse genetic/epigenetic mechanisms of crenolanib resistance. Drug combinations in experimental models restore crenolanib sensitivity.This work was supported in part by The Leukemia & Lymphoma Society Beat AML Program, the V Foundation for Cancer Research, the Gabrielle’s Angel Foundation for Cancer Research and the National Cancer Institute (1R01CA183947–01; 1U01CA217862–01; 1U54CA224019-01; 3P30CA069533-18S5). H.Z. received a Collins Medical Trust research grant. S.D.B. was supported by the National Cancer Institute (5R01CA138744-08)

Identification of β-catenin binding regions in colon cancer cells using ChIP-Seq

Deregulation of the Wnt/β-catenin signaling pathway is a hallmark of colon cancer. Mutations in the adenomatous polyposis coli (APC) gene occur in the vast majority of colorectal cancers and are an initiating event in cellular transformation. Cells harboring mutant APC contain elevated levels of the β-catenin transcription coactivator in the nucleus which leads to abnormal expression of genes controlled by β-catenin/T-cell factor 4 (TCF4) complexes. Here, we use chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-Seq) to identify β-catenin binding regions in HCT116 human colon cancer cells. We localized 2168 β-catenin enriched regions using a concordance approach for integrating the output from multiple peak alignment algorithms. Motif discovery algorithms found a core TCF4 motif (T/A–T/A–C–A–A–A–G), an extended TCF4 motif (A/T/G–C/G–T/A–T/A–C–A–A–A–G) and an AP-1 motif (T–G–A–C/T–T–C–A) to be significantly represented in β-catenin enriched regions. Furthermore, 417 regions contained both TCF4 and AP-1 motifs. Genes associated with TCF4 and AP-1 motifs bound β-catenin, TCF4 and c-Jun in vivo and were activated by Wnt signaling and serum growth factors. Our work provides evidence that Wnt/β-catenin and mitogen signaling pathways intersect directly to regulate a defined set of target genes

Multiple Wnt/ß-Catenin Responsive Enhancers Align with the MYC Promoter through Long-Range Chromatin Loops

Inappropriate activation of c-Myc (MYC) gene expression by the Wnt/ß-catenin signaling pathway is required for colorectal carcinogenesis. The elevated MYC levels in colon cancer cells are attributed in part to ß-catenin/TCF4 transcription complexes that are assembled at proximal Wnt/ß-catenin responsive enhancers (WREs). Recent studies suggest that additional WREs that control MYC expression reside far upstream of the MYC transcription start site. Here, I report the characterization of five novel WREs that localize to a region over 400 kb upstream from MYC. These WREs harbor nucleosomes with post-translational histone modifications that demarcate enhancer and gene promoter regions. Using quantitative chromatin conformation capture, I show that the distal WREs are aligned with the MYC promoter through large chromatin loops. The chromatin loops are not restricted to colon cancer cells, but are also found in kidney epithelial and lung fibroblast cell lines that lack de-regulated Wnt signaling and nuclear ß-catenin/TCF4 complexes. While each chromatin loop is detected in quiescent cells, the positioning of three of the five distal enhancers with the MYC promoter is induced by serum mitogens. These findings suggest that the architecture of the MYC promoter is comprised of distal elements that are juxtaposed through large chromatin loops and that ß-catenin/TCF4 complexes utilize this conformation to activate MYC expression in colon cancer cells

A novel outbred mouse model of 2009 pandemic influenza and bacterial co-infection severity

Influenza viruses pose a significant health risk and annually impose a great cost to patients and the health care system. The molecular determinants of influenza severity, often exacerbated by secondary bacterial infection, are largely unclear. We generated a novel outbred mouse model of influenza virus, Staphylococcus aureus, and coinfection utilizing influenza A/CA/07/2009 virus and S. aureus (USA300). Outbred mice displayed a wide range of pathologic phenotypes following influenza virus or co-infection ranging broadly in severity. Influenza viral burden positively correlated with weight loss although lung histopathology did not. Inflammatory cytokines including IL-6, TNF-α, G-CSF, and CXCL10 positively correlated with both weight loss and viral burden. In S. aureus infection, IL-1β, G-CSF, TNF-α, and IL-6 positively correlated with weight loss and bacterial burden. In co-infection, IL-1β production correlated with decreased weight loss suggesting a protective role. The data demonstrate an approach to identify biomarkers of severe disease and to understand pathogenic mechanisms in pneumonia. © 2013 McHugh et al

Expression Quantitative Trait Loci for Extreme Host Response to Influenza A in Pre-Collaborative Cross Mice

Outbreaks of influenza occur on a yearly basis, causing a wide range of symptoms across the human population. Although evidence exists that the host response to influenza infection is influenced by genetic differences in the host, this has not been studied in a system with genetic diversity mirroring that of the human population. Here we used mice from 44 influenza-infected pre-Collaborative Cross lines determined to have extreme phenotypes with regard to the host response to influenza A virus infection. Global transcriptome profiling identified 2671 transcripts that were significantly differentially expressed between mice that showed a severe (“high”) and mild (“low”) response to infection. Expression quantitative trait loci mapping was performed on those transcripts that were differentially expressed because of differences in host response phenotype to identify putative regulatory regions potentially controlling their expression. Twenty-one significant expression quantitative trait loci were identified, which allowed direct examination of genes associated with regulation of host response to infection. To perform initial validation of our findings, quantitative polymerase chain reaction was performed in the infected founder strains, and we were able to confirm or partially confirm more than 70% of those tested. In addition, we explored putative causal and reactive (downstream) relationships between the significantly regulated genes and others in the high or low response groups using structural equation modeling. By using systems approaches and a genetically diverse population, we were able to develop a novel framework for identifying the underlying biological subnetworks under host genetic control during influenza virus infection