State-transition modeling of blood transcriptome predicts disease evolution and treatment response in chronic myeloid leukemia

Chronic myeloid leukemia (CML) is initiated and maintained by BCR::ABL which is clinically targeted using tyrosine kinase inhibitors (TKIs). TKIs can induce long-term remission but are also not curative. Thus, CML is an ideal system to test our hypothesis that transcriptome-based state-transition models accurately predict cancer evolution and treatment response. We collected time-sequential blood samples from tetracycline-off (Tet-Off) BCR::ABL-inducible transgenic mice and wild-type controls. From the transcriptome, we constructed a CML state-space and a three-well leukemogenic potential landscape. The potential's stable critical points defined observable disease states. Early states were characterized by anti-CML genes opposing leukemia; late states were characterized by pro-CML genes. Genes with expression patterns shaped similarly to the potential landscape were identified as drivers of disease transition. Re-introduction of tetracycline to silence the BCR::ABL gene returned diseased mice transcriptomes to a near healthy state, without reaching it, suggesting parts of the transition are irreversible. TKI only reverted the transcriptome to an intermediate disease state, without approaching a state of health; disease relapse occurred soon after treatment. Using only the earliest time-point as initial conditions, our state-transition models accurately predicted both disease progression and treatment response, supporting this as a potentially valuable approach to time clinical intervention, before phenotypic changes become detectable.

State-transition Modeling of Blood Transcriptome Predicts Disease Evolution and Treatment Response in Chronic Myeloid Leukemia

Chronic myeloid leukemia (CML) is initiated and maintained by BCR::ABL which is clinically targeted using tyrosine kinase inhibitors (TKIs). TKIs can induce long-term remission but are also not curative. Thus, CML is an ideal system to test our hypothesis that transcriptome-based state-transition models accurately predict cancer evolution and treatment response. We collected time-sequential blood samples from tetracycline-off (Tet-Off) BCR::ABL-inducible transgenic mice and wild-type controls. From the transcriptome, we constructed a CML state-space and a three-well leukemogenic potential landscape. The potential's stable critical points defined observable disease states. Early states were characterized by anti-CML genes opposing leukemia; late states were characterized by pro-CML genes. Genes with expression patterns shaped similarly to the potential landscape were identified as drivers of disease transition. Re-introduction of tetracycline to silence the BCR::ABL gene returned diseased mice transcriptomes to a near healthy state, without reaching it, suggesting parts of the transition are irreversible. TKI only reverted the transcriptome to an intermediate disease state, without approaching a state of health; disease relapse occurred soon after treatment. Using only the earliest time-point as initial conditions, our state-transition models accurately predicted both disease progression and treatment response, supporting this as a potentially valuable approach to time clinical intervention even before phenotypic changes become detectable.

Abstract 2735: An information theoretic approach to investigate AML development using time-series-omics

Introductory Statement: Using our previously published state-transition model applied to times-series micro-RNA (miRNA) expression samples, we construct a miRNA-based transcriptomic landscape of AML development to discover how miRNA dynamics contribute to leukemogenesis.

Experimental Procedures: We used a genetic mouse model that mimics development of inv(16) AML through the induction of a leukemogenic fusion gene CBFB-MYH11 (CM). From monthly blood draws on both CM and control mice, we produced time-series messenger RNA (mRNA) and micro-RNA (miRNA) transcriptomic data. We have recently reported a state-transition model using the mRNA transcriptome data. We model the development of AML as a particle undergoing Brownian motion in a potential well with three critical points. Here, investigate whether the miRNA transcriptome can also be used to model AML development. Our current work is to extend the state-transition model using information theoretic approach to explore both the regulatory relationship between mRNA and miRNA and how a biological system transitions to a disease state.

Summary of Results: Our state-transition model detects early perturbations of the system before it can be detected using cKit expression, the marker of AML blasts in this model. Since individual mice develop AML stochastically in time, we used the state-transition model to align the mice based on the phenotypic disease state rather than chronological time. Four distinct expression dynamics of miRNAs were identified with hierarchical clustering: persistent increase or decrease in expression; and two nonlinear patterns of expression. The nonlinear dynamic groups showed changes in expression at the critical transition point between health and AML. Additionally, the nonlinear expression dynamic patterns occurred at the boundary between the health and AML states was associated with cytokines and systemic inflammation signaling pathways, suggesting an anti-leukemic restorative miRNA program. Using regulatory dynamics in the state-transition model, we observed unique mRNA-miRNA regulatory interactions which may suggest miRNA gene targets at critical transition points in leukemia progression.

Conclusions: Our information theoretic approach combined with our state-transition model is a promising approach to study AML development. This framework provides a method to integrate different -omic data without prior knowledge of the relationship between a gene and its regulatory features. Additionally, our approach can investigate the inter-individual heterogeneity of disease progression which has important implications for precision medicine. The state-transition analysis framework is a promising approach to explore the underlying principles that govern biological systems.

Citation Format: David E. Frankhouser, Sergio Branciamore, Denis O'Meally, Lisa Uechi, Guido Marcucci, Ya-Huei Kuo, Russell Rockne. An information theoretic approach to investigate AML development using time-series-omics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2735.

Abstract 5065: Predicting response to chemotherapy in a mouse model of acute myeloid leukemia

We have previously shown that state transition theory is applicable to acute myeloid leukemia (AML) to predict disease evolution. This mathematical model represents AML evolution from health to disease as a state-transition of the transcriptome represented as a particle undergoing Brownian motion in a double-well quasi-potential, where critical points define states of perturbed hematopoiesis (c1), transition to AML (c2), and overt AML (c3). We successfully demonstrated that the state transition model predicted AML onset. We now test the applicability of AML state-transition model to predict disease response to chemotherapy. To this end, we performed a time-series experiment using the conditional Cbfb-MYH11 (CM) knock-in (Cbfb+/56M/Mx1Cre) mouse model recapitulating inv(16) AML. CM leukemic mice were treated with a combination of cytarabine (50mg/kg/day; 5 days) and daunorubicin (1.5mg/kg/day, 3 days) after detection of overt leukemia which is monitored by circulating leukemia blast (cKit+ > 20%) to model the 7+3 standard of care treatment for newly diagnosed AML. A total of 110 peripheral blood samples from 7 CM mice were collected weekly before, during, and following chemotherapy and subjected to RNA-sequencing. The singular value decomposition was used to construct the transcriptome state-space and identified dynamics of AML (c3) after administration of chemotherapy treatment. Gene expression profiles following treatment revealed dynamics consistent with the state-transition model, with the transcriptome particle moving from leukemia (c3) towards a state of perturbed hematopoiesis (c1), before eventual relapse, re-crossing the transition point (c2) back to overt AML (c3). All 7 CM mice achieved a partial response, defined as the transcriptome particle crossing the c2 critical point, with a mean time to relapse of 5 weeks, defined as the time of the first observation after the particle crosses back over c2 towards the leukemic state c3. Mean arrival time analysis was used to accurately predict the extent of response, defined by the transcriptome particle in the state-space, and the time to relapse. We successfully applied state-transition mathematical model to predict treatment response and the time to relapse in all CM mice, confirming the applicability of the model to post-chemo therapy disease dynamics. This predictive model has implications to improve therapeutic strategies by targeting transcriptome state-transition critical points in human AML.

Citation Format: Lisa Uechi, Sergio Branciamore, David E. Frankhouser, Denis O'Meally, Lianjun Zhang, Ying-Chieh Chen, Man Li, Guido Marcucci, Ya-Huei Kuo, Russell Rockne. Predicting response to chemotherapy in a mouse model of acute myeloid leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5065.

Dynamic patterns of microRNA expression during acute myeloid leukemia state-transition

MicroRNAs (miRNAs) have been shown to hold prognostic value in acute myeloid leukemia (AML); however, the temporal dynamics of miRNA expression in AML are poorly understood. Using serial samples from a mouse model of AML to generate time-series miRNA sequencing data, we are the first to show that the miRNA transcriptome undergoes state-transition during AML initiation and progression. We modeled AML state-transition as a particle undergoing Brownian motion in a quasi-potential and validated the AML state-space and state-transition model to accurately predict time to AML in an independent cohort of mice. The critical points of the model provided a framework to align samples from mice that developed AML at different rates. Our mathematical approach allowed discovery of dynamic processes involved during AML development and, if translated to humans, has the potential to predict an individual's disease trajectory.

Dietary omega-3 fatty acid intake impacts peripheral blood DNA methylation -anti-inflammatory effects and individual variability in a pilot study

Omega-3 or n-3 polyunsaturated fatty acids (PUFAs) are widely studied for health benefits that may relate to anti-inflammatory activity. However, mechanisms mediating an anti-inflammatory response to n-3 PUFA intake are not fully understood. Of interest is the emerging role of fatty acids to impact DNA methylation (DNAm) and thereby modulate mediating inflammatory processes. In this pilot study, we investigated the impact of n-3 PUFA intake on DNAm in inflammation-related signaling pathways in peripheral blood mononuclear cells (PBMCs) of women at high risk of breast cancer. PBMCs of women at high risk of breast cancer (n=10) were obtained at baseline and after 6 months of n-3 PUFA (5 g/d EPA+DHA dose arm) intake in a previously reported dose finding trial. DNA methylation of PBMCs was assayed by reduced representation bisulfite sequencing (RRBS) to obtain genome-wide methylation profiles at the single nucleotide level. We examined the impact of n-3 PUFA on genome-wide DNAm and focused upon a set of candidate genes associated with inflammation signaling pathways and breast cancer. We identified 24,842 differentially methylated CpGs (DMCs) in gene promoters of 5507 genes showing significant enrichment for hypermethylation in both the candidate gene and genome-wide analyses. Pathway analysis identified significantly hypermethylated signaling networks after n-3 PUFA treatment, such as the Toll-like Receptor inflammatory pathway. The DNAm pattern in individuals and the response to n-3 PUFA intake are heterogeneous. PBMC DNAm profiling suggests a mechanism whereby n-3 PUFAs may impact inflammatory cascades associated with disease processes including carcinogenesis.

Vascularity and Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging

Angiogenesis is a key step in the initiation and progression of an invasive breast cancer. High microvessel density by morphological characterization predicts metastasis and poor survival in women with invasive breast cancers. However, morphologic characterization is subject to variability and only can evaluate a limited portion of an invasive breast cancer. Consequently, breast Magnetic Resonance Imaging (MRI) is currently being evaluated to assess vascularity. Recently, through the new field of radiomics, dynamic contrast enhanced (DCE)-MRI is being used to evaluate vascular density, vascular morphology, and detection of aggressive breast cancer biology. While DCE-MRI is a highly sensitive tool, there are specific features that limit computational evaluation of blood vessels. These include (1) DCE-MRI evaluates gadolinium contrast and does not directly evaluate biology, (2) the resolution of DCE-MRI is insufficient for imaging small blood vessels, and (3) DCE-MRI images are very difficult to co-register. Here we review computational approaches for detection and analysis of blood vessels in DCE-MRI images and present some of the strategies we have developed for co-registry of DCE-MRI images and early detection of vascularization.

Treatment-induced arteriolar revascularization and miR-126 enhancement in bone marrow niche protect leukemic stem cells in AML

BACKGROUND

During acute myeloid leukemia (AML) growth, the bone marrow (BM) niche acquires significant vascular changes that can be offset by therapeutic blast cytoreduction. The molecular mechanisms of this vascular plasticity remain to be fully elucidated. Herein, we report on the changes that occur in the vascular compartment of the FLT3-ITD+ AML BM niche pre and post treatment and their impact on leukemic stem cells (LSCs).

METHODS

BM vasculature was evaluated in FLT3-ITD+ AML models (MllPTD/WT/Flt3ITD/ITD mouse and patient-derived xenograft) by 3D confocal imaging of long bones, calvarium vascular permeability assays, and flow cytometry analysis. Cytokine levels were measured by Luminex assay and miR-126 levels evaluated by Q-RT-PCR and miRNA staining. Wild-type (wt) and MllPTD/WT/Flt3ITD/ITD mice with endothelial cell (EC) miR-126 knockout or overexpression served as controls. The impact of treatment-induced BM vascular changes on LSC activity was evaluated by secondary transplantation of BM cells after administration of tyrosine kinase inhibitors (TKIs) to MllPTD/WT/Flt3ITD/ITD mice with/without either EC miR-126 KO or co-treatment with tumor necrosis factor alpha (TNFα) or anti-miR-126 miRisten.

RESULTS

In the normal BM niche, CD31+Sca-1high ECs lining arterioles have miR-126 levels higher than CD31+Sca-1low ECs lining sinusoids. We noted that during FLT3-ITD+ AML growth, the BM niche lost arterioles and gained sinusoids. These changes were mediated by TNFα, a cytokine produced by AML blasts, which induced EC miR-126 downregulation and caused depletion of CD31+Sca-1high ECs and gain in CD31+Sca-1low ECs. Loss of miR-126high ECs led to a decreased EC miR-126 supply to LSCs, which then entered the cell cycle and promoted leukemia growth. Accordingly, antileukemic treatment with TKI decreased the BM blast-produced TNFα and increased miR-126high ECs and the EC miR-126 supply to LSCs. High miR-126 levels safeguarded LSCs, as shown by more severe disease in secondary transplanted mice. Conversely, EC miR-126 deprivation via genetic or pharmacological EC miR-126 knock-down prevented treatment-induced BM miR-126high EC expansion and in turn LSC protection.

CONCLUSIONS

Treatment-induced CD31+Sca-1high EC re-vascularization of the leukemic BM niche may represent a LSC extrinsic mechanism of treatment resistance that can be overcome with therapeutic EC miR-126 deprivation.

State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Development of Acute Myeloid Leukemia

Temporal dynamics of gene expression inform cellular and molecular perturbations associated with disease development and evolution. Given the complexity of high-dimensional temporal genomic data, an analytic framework guided by a robust theory is needed to interpret time-sequential changes and to predict system dynamics. Here we model temporal dynamics of the transcriptome of peripheral blood mononuclear cells in a two-dimensional state-space representing states of health and leukemia using time-sequential bulk RNA-seq data from a murine model of acute myeloid leukemia (AML). The state-transition model identified critical points that accurately predict AML development and identifies stepwise transcriptomic perturbations that drive leukemia progression. The geometry of the transcriptome state-space provided a biological interpretation of gene dynamics, aligned gene signals that are not synchronized in time across mice, and allowed quantification of gene and pathway contributions to leukemia development. Our state-transition model synthesizes information from multiple cell types in the peripheral blood and identifies critical points in the transition from health to leukemia to guide interpretation of changes in the transcriptome as a whole to predict disease progression. SIGNIFICANCE: These findings apply the theory of state transitions to model the initiation and development of acute myeloid leukemia, identifying transcriptomic perturbations that accurately predict time to disease development.See related commentary by Kuijjer, p. 3072 GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/15/3157/F1.large.jpg.

Abstract 4222: Omega-3 fatty acids produce DNA methylation changes in inflammation-related genes and pathways with implication of toll-like receptor signaling

Introduction Omega-3 polyunsaturated fatty acids (n-3 PUFA) have been studied for potential health benefits in many diseases including breast cancer. The preventive effects of n-3 PUFAs may relate in part to inhibition of chronic, low-grade inflammation. Of interest is the role of n-3 PUFAs in modulating pro-inflammatory gene expression via epigenetic factors such as DNA methylation (DNAm).

Methods DNA methylation of PBMCs obtained at 0 and 6 months of n-3 PUFA supplementation was assessed via reduced representation bisulfite sequencing (RRBS). PBMC samples were obtained from women at high risk for breast cancer during a randomized clinical trial investigating the effects of different n-3 PUFA doses. The dosing arm selected for this study was 5 g of EPA+DHA/day, with baseline and 6 month samples (n=10). DNAm was quantified using Bismark from trimmed, pass filter reads and analyzed with MethylKit to determine n-3 PUFA treatment specific DNAm changes.

Results Global DNAm showed no change after 6 months of n-3 PUFA treatment; however, we detected 30,974 differentially methylated CpGs (DMCs) across the genome. DMCs, both genome-wide and in gene promoters, where DNAm can regulate gene expression, were significantly enriched for hypermethylation events after treatment. Focusing the analysis on pro-inflammatory signaling mediators led to identification of candidate gene promoter DMCs involved in several inflammation-related pathways. Four pathways were significantly enriched for both DMCs and DMC hypermethylation events even when accounting for the genome-wide trend toward hypermethylated DMCs (hypergeometric test p-value < 0.001). The Toll-Like Receptor Pathway (TLR) genes harbored the most DNAm changes post n-3 PUFA treatment.

Conclusion Our findings demonstrate that n-3 PUFA supplementation can result in inflammation-related changes stemming from PBMC methylome perturbation. The results suggest the TLR pathway as a potential mechanism for the anti-inflammatory effects of EPA and DHA. Functional studies are needed to confirm our current observation.

Citation Format: David E. Frankhouser, Sarah Woelke, Michael Sovic, Ralf Bundschuh, Pearlly Yan, Lisa D. Yee. Omega-3 fatty acids produce DNA methylation changes in inflammation-related genes and pathways with implication of toll-like receptor signaling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4222.

FuSpot: a web-based tool for visual evaluation of fusion candidates

BACKGROUND

Gene fusions often occur in cancer cells and in some cases are the main driver of oncogenesis. Correct identification of oncogenic gene fusions thus has implications for targeted cancer therapy. Recognition of this potential has led to the development of a myriad of sequencing-based fusion detection tools. However, given the same input, many of these detectors will find different fusion points or claim different sets of supporting data. Furthermore, the rate at which these tools falsely detect fusion events in data varies greatly. This discrepancy between tools underscores the fact that computation algorithms still cannot perfectly evaluate evidence; especially when provided with small amounts of supporting data as is typical in fusion detection. We assert that when evidence is provided in an easily digestible form, humans are more proficient in identifying true positives from false positives.

RESULTS

We have developed a web tool that, given the genomic coordinates of a candidate fusion breakpoint, will extract fusion and non-fusion reads adjacent to the fusion point from partner transcripts, and color code reads by transcript origin and read orientation for ease of intuitive inspection by the user. Fusion partner transcript read alignments are performed using a novel variant of the Smith-Waterman algorithm.

CONCLUSIONS

Combined with dynamic filtering parameters, the visualization provided by our tool introduces a powerful new investigative step that allows researchers to comprehensively evaluate fusion evidence. Additionally, this allows quick identification of false positives that may deceive most fusion detectors, thus eliminating unnecessary gene fusion validation. We apply our visualization tool to publicly available datasets and provide examples of true as well as false positives reported by open source fusion detection tools.

Preclinical Investigation of the Novel Histone Deacetylase Inhibitor AR-42 in the Treatment of Cancer-Induced Cachexia

BACKGROUND

Cancer cachexia is a debilitating condition that impacts patient morbidity, mortality, and quality of life and for which effective therapies are lacking. The anticachectic activity of the novel HDAC inhibitor AR-42 was investigated in murine models of cancer cachexia.

METHODS

The effects of AR-42 on classic features of cachexia were evaluated in the C-26 colon adenocarcinoma and Lewis lung carcinoma (LLC) models. Effects on survival in comparison with approved HDAC inhibitors (vorinostat, romidepsin) were determined. The muscle metabolome and transcriptome (by RNA-seq), as well as serum cytokine profile, were evaluated. Data were analyzed using mixed effects models, analysis of variance, or log-rank tests. All statistical tests were two-sided.

RESULTS

In the C-26 model, orally administered AR-42 preserved body weight (23.9±2.6 grams, AR-42-treated; 20.8±1.3 grams, vehicle-treated; P = .005), prolonged survival (P < .001), prevented reductions in muscle and adipose tissue mass, muscle fiber size, and muscle strength and restored intramuscular mRNA expression of the E3 ligases MuRF1 and Atrogin-1 to basal levels (n = 8). This anticachectic effect, confirmed in the LLC model, was not observed after treatment with vorinostat and romidepsin. AR-42 suppressed tumor-induced changes in inflammatory cytokine production and multiple procachexia drivers (IL-6, IL-6Rα, leukemia inhibitory factor, Foxo1, Atrogin-1, MuRF1, adipose triglyceride lipase, uncoupling protein 3, and myocyte enhancer factor 2c). Metabolomic analysis revealed cachexia-associated changes in glycolysis, glycogen synthesis, and protein degradation in muscle, which were restored by AR-42 to a state characteristic of tumor-free mice.

CONCLUSIONS

These findings support further investigation of AR-42 as part of a comprehensive therapeutic strategy for cancer cachexia.

Statistical methods for detecting differentially methylated regions based on MethylCap-seq data

DNA methylation is a well-established epigenetic mark, whose pattern throughout the genome, especially in the promoter or CpG islands, may be modified in a cell at a disease stage. Recently developed probabilistic approaches allow distributing methylation signals at nucleotide resolution from MethylCap-seq data. Standard statistical methods for detecting differential methylation suffer from 'curse of dimensionality' and sparsity in signals, resulting in high false-positive rates. Strong correlation of signals between CG sites also yields spurious results. In this article, we review applicability of high-dimensional mean vector tests for detection of differentially methylated regions (DMRs) and compare and contrast such tests with other methods for detecting DMRs. Comprehensive simulation studies are conducted to highlight the performance of these tests under different settings. Based on our observation, we make recommendations on the optimal test to use. We illustrate the superiority of mean vector tests in detecting cancer-related canonical gene pathways, which are significantly enriched for acute myeloid leukemia and ovarian cancer.

Quality Control for RNA-Seq (QuaCRS): An Integrated Quality Control Pipeline

QuaCRS (Quality Control for RNA-Seq) is an integrated, simplified quality control (QC) system for RNA-seq data that allows easy execution of several open-source QC tools, aggregation of their output, and the ability to quickly identify quality issues by performing meta-analyses on QC metrics across large numbers of samples in different studies. It comprises two main sections. First is the QC Pack wrapper, which executes three QC tools: FastQC, RNA-SeQC, and selected functions from RSeQC. Combining these three tools into one wrapper provides increased ease of use and provides a much more complete view of sample data quality than any individual tool. Second is the QC database, which displays the resulting metrics in a user-friendly web interface. It was designed to allow users with less computational experience to easily generate and view QC information for their data, to investigate individual samples and aggregate reports of sample groups, and to sort and search samples based on quality. The structure of the QuaCRS database is designed to enable expansion with additional tools and metrics in the future. The source code for not-for-profit use and a fully functional sample user interface with mock data are available at http://bioserv.mps.ohio-state.edu/QuaCRS/.

PrEMeR-CG: inferring nucleotide level DNA methylation values from MethylCap-seq data

MOTIVATION

DNA methylation is an epigenetic change occurring in genomic CpG sequences that contribute to the regulation of gene transcription both in normal and malignant cells. Next-generation sequencing has been used to characterize DNA methylation status at the genome scale, but suffers from high sequencing cost in the case of whole-genome bisulfite sequencing, or from reduced resolution (inability to precisely define which of the CpGs are methylated) with capture-based techniques.

RESULTS

Here we present a computational method that computes nucleotide-resolution methylation values from capture-based data by incorporating fragment length profiles into a model of methylation analysis. We demonstrate that it compares favorably with nucleotide-resolution bisulfite sequencing and has better predictive power with respect to a reference than window-based methods, often used for enrichment data. The described method was used to produce the methylation data used in tandem with gene expression to produce a novel and clinically significant gene signature in acute myeloid leukemia. In addition, we introduce a complementary statistical method that uses this nucleotide-resolution methylation data for detection of differentially methylated features.