Targeting CCL2/CCR2 Signaling Overcomes MEK Inhibitor Resistance in Acute Myeloid Leukemia.

PURPOSE: Emerging evidence underscores the critical role of extrinsic factors within the microenvironment in protecting leukemia cells from therapeutic interventions, driving disease progression, and promoting drug resistance in acute myeloid leukemia (AML). This finding emphasizes the need for the identification of targeted therapies that inhibit intrinsic and extrinsic signaling to overcome drug resistance in AML. EXPERIMENTAL DESIGN: We performed a comprehensive analysis utilizing a cohort of ∼300 AML patient samples. This analysis encompassed the evaluation of secreted cytokines/growth factors, gene expression, and ex vivo drug sensitivity to small molecules. Our investigation pinpointed a notable association between elevated levels of CCL2 and diminished sensitivity to the MEK inhibitors (MEKi). We validated this association through loss-of-function and pharmacologic inhibition studies. Further, we deployed global phosphoproteomics and CRISPR/Cas9 screening to identify the mechanism of CCR2-mediated MEKi resistance in AML. RESULTS: Our multifaceted analysis unveiled that CCL2 activates multiple prosurvival pathways, including MAPK and cell-cycle regulation in MEKi-resistant cells. Employing combination strategies to simultaneously target these pathways heightened growth inhibition in AML cells. Both genetic and pharmacologic inhibition of CCR2 sensitized AML cells to trametinib, suppressing proliferation while enhancing apoptosis. These findings underscore a new role for CCL2 in MEKi resistance, offering combination therapies as an avenue to circumvent this resistance. CONCLUSIONS: Our study demonstrates a compelling rationale for translating CCL2/CCR2 axis inhibitors in combination with MEK pathway-targeting therapies, as a potent strategy for combating drug resistance in AML. This approach has the potential to enhance the efficacy of treatments to improve AML patient outcomes.

Axial spondyloarthritis patients have altered mucosal IgA response to oral and fecal microbiota.

Axial spondyloarthritis (axSpA) is an inflammatory arthritis involving the spine and the sacroiliac joint with extra-articular manifestations in the eye, gut, and skin. The intestinal microbiota has been implicated as a central environmental component in the pathogenesis of various types of spondyloarthritis including axSpA. Additionally, alterations in the oral microbiota have been shown in various rheumatological conditions, such as rheumatoid arthritis (RA). Therefore, the aim of this study was to investigate whether axSpA patients have an altered immunoglobulin A (IgA) response in the gut and oral microbial communities. We performed 16S rRNA gene (16S) sequencing on IgA positive (IgA+) and IgA negative (IgA-) fractions (IgA-SEQ) from feces (n=17 axSpA; n=14 healthy) and saliva (n=14 axSpA; n=12 healthy), as well as on IgA-unsorted fecal and salivary samples. PICRUSt2 was used to predict microbial metabolic potential in axSpA patients and healthy controls (HCs). IgA-SEQ analyses revealed enrichment of several microbes in the fecal (Akkermansia, Ruminococcaceae, Lachnospira) and salivary (Prevotellaceae, Actinobacillus) microbiome in axSpA patients as compared with HCs. Fecal microbiome from axSpA patients showed a tendency towards increased alpha diversity in IgA+ fraction and decreased diversity in IgA- fraction in comparison with HCs, while the salivary microbiome exhibits a significant decrease in alpha diversity in both IgA+ and IgA- fractions. Increased IgA coating of Clostridiales Family XIII in feces correlated with disease severity. Inferred metagenomic analysis suggests perturbation of metabolites and metabolic pathways for inflammation (oxidative stress, amino acid degradation) and metabolism (propanoate and butanoate) in axSpA patients. Analyses of fecal and salivary microbes from axSpA patients reveal distinct populations of immunoreactive microbes compared to HCs using the IgA-SEQ approach. These bacteria were not identified by comparing their relative abundance alone. Predictive metagenomic analysis revealed perturbation of metabolites/metabolic pathways in axSpA patients. Future studies on these immunoreactive microbes may lead to better understanding of the functional role of IgA in maintaining microbial structure and human health.

Integrative analysis of drug response and clinical outcome in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML.

Reversible suppression of T cell function in the bone marrow microenvironment of acute myeloid leukemia.

Acute myeloid leukemia (AML) is the most common acute leukemia in adults, with approximately four new cases per 100,000 persons per year. Standard treatment for AML consists of induction chemotherapy with remission achieved in 50 to 75% of cases. Unfortunately, most patients will relapse and die from their disease, as 5-y survival is roughly 29%. Therefore, other treatment options are urgently needed. In recent years, immune-based therapies have led to unprecedented rates of survival among patients with some advanced cancers. Suppression of T cell function in the tumor microenvironment is commonly observed and may play a role in AML. We found that there is a significant association between T cell infiltration in the bone marrow microenvironment of newly diagnosed patients with AML and increased overall survival. Functional studies aimed at establishing the degree of T cell suppression in patients with AML revealed impaired T cell function in many patients. In most cases, T cell proliferation could be restored by blocking the immune checkpoint molecules PD-1, CTLA-4, or TIM3. Our data demonstrate that AML establishes an immune suppressive environment in the bone marrow, in part through T cell checkpoint function.

Illuminating biological pathways for drug targeting in head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) remains a morbid disease with poor prognosis and treatment that typically leaves patients with permanent damage to critical functions such as eating and talking. Currently only three targeted therapies are FDA approved for use in HNSCC, two of which are recently approved immunotherapies. In this work, we identify biological pathways involved with this disease that could potentially be targeted by current FDA approved cancer drugs and thereby expand the pool of potential therapies for use in HNSCC treatment. We analyzed 508 HNSCC patients with sequencing information from the Genomic Data Commons (GDC) database and assessed which biological pathways were significantly enriched for somatic mutations or copy number alterations. We then further classified pathways as either "light" or "dark" to the current reach of FDA-approved cancer drugs using the Cancer Targetome, a compendium of drug-target information. Light pathways are statistically enriched with somatic mutations (or copy number alterations) and contain one or more targets of current FDA-approved cancer drugs, while dark pathways are enriched with somatic mutations (or copy number alterations) but not currently targeted by FDA-approved cancer drugs. Our analyses indicated that approximately 35-38% of disease-specific pathways are in scope for repurposing of current cancer drugs. We further assess light and dark pathways for subgroups of patient tumor samples according to HPV status. The framework of light and dark pathways for HNSCC-enriched biological pathways allows us to better prioritize targeted therapies for further research in HNSCC based on the HNSCC genetic landscape and FDA-approved cancer drug information. We also highlight the importance in the identification of sub-pathways where targeting and cross targeting of other pathways may be most beneficial to predict positive or negative synergy with potential clinical significance. This framework is ideal for precision drug panel development, as well as identification of highly aberrant, untargeted candidates for future drug development.

CSF1R inhibitors exhibit antitumor activity in acute myeloid leukemia by blocking paracrine signals from support cells.

To identify new therapeutic targets in acute myeloid leukemia (AML), we performed small-molecule and small-interfering RNA (siRNA) screens of primary AML patient samples. In 23% of samples, we found sensitivity to inhibition of colony-stimulating factor 1 (CSF1) receptor (CSF1R), a receptor tyrosine kinase responsible for survival, proliferation, and differentiation of myeloid-lineage cells. Sensitivity to CSF1R inhibitor GW-2580 was found preferentially in de novo and favorable-risk patients, and resistance to GW-2580 was associated with reduced overall survival. Using flow cytometry, we discovered that CSF1R is not expressed on the majority of leukemic blasts but instead on a subpopulation of supportive cells. Comparison of CSF1R-expressing cells in AML vs healthy donors by mass cytometry revealed expression of unique cell-surface markers. The quantity of CSF1R-expressing cells correlated with GW-2580 sensitivity. Exposure of primary AML patient samples to a panel of recombinant cytokines revealed that CSF1R inhibitor sensitivity correlated with a growth response to CSF1R ligand, CSF1, and other cytokines, including hepatocyte growth factor (HGF). The addition of CSF1 increased the secretion of HGF and other cytokines in conditioned media from AML patient samples, whereas adding GW-2580 reduced their secretion. In untreated cells, HGF levels correlated significantly with GW-2580 sensitivity. Finally, recombinant HGF and HS-5-conditioned media rescued cell viability after GW-2580 treatment in AML patient samples. Our results suggest that CSF1R-expressing cells support the bulk leukemia population through the secretion of HGF and other cytokines. This study identifies CSF1R as a novel therapeutic target of AML and provides a mechanism of paracrine cytokine/growth factor signaling in this disease.

Integrated functional and mass spectrometry-based flow cytometric phenotyping to describe the immune microenvironment in acute myeloid leukemia.

A hallmark of the development of cancer is its ability to avoid detection and elimination by the immune system. There are many identified mechanisms of this immune evasion that can be measured both phenotypically and functionally. Functional studies directly show the ability of the tumor microenvironment to suppress immune responses, typically measured as lymphocyte proliferation, cytokine production or killing ability. While a direct measurement of function is ideal, these assays require ex vivo activation which may not accurately mimic in vivo conditions. Phenotypic assays can directly measure the distribution and activation of immune cell types rapidly after isolation, preserving the conditions present in the patient. While conventional flow cytometry is a rapid and well established assay, it currently allows for measurement of only 12-14 parameters. Mass spectrometry-based flow cytometry, or CyTOF, offers the ability to measure 3-fold more parameters than conventional optical-based modalities providing an advantage in depth of analysis that can be crucial for precious human samples. The goal of this report is to describe the system our group has developed to measure both the phenotype and function of immune cells in the bone marrow of patients with acute myeloid leukemia. We hope to explain our system in the context of previous studies aimed at measuring immune status in tumors and to inform the reader as to some experimental approaches our group has found useful in developing the basic data required to rationally pursue immune-based therapies for patients with cancer.

Between pathways and networks lies context: implications for precision medicine.

Precision medicine, broadly defined as considering individual variability in genes, environment, and lifestyle for each person in disease prevention and selection of suitable medical intervention, shows strong promise in the treatment of cancer Selecting therapies is complicated by multiple routes to gene dysregulation, which manifest in the individual patient within the many different types of genomic measurements. Additionally, multiple mutations exist in patients, aphenomenon known as oncogenic collaboration, which further complicates the selection of therapy. In this article, we discuss current approaches using biological pathways and networks to unify the many types of OMICs data. We argue that a contextual approach combining cancer pathways and networks could lead to a proper understanding of the biology of this significant disease.

The consensus molecular subtypes of colorectal cancer.

Colorectal cancer (CRC) is a frequently lethal disease with heterogeneous outcomes and drug responses. To resolve inconsistencies among the reported gene expression-based CRC classifications and facilitate clinical translation, we formed an international consortium dedicated to large-scale data sharing and analytics across expert groups. We show marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMSs) with distinguishing features: CMS1 (microsatellite instability immune, 14%), hypermutated, microsatellite unstable and strong immune activation; CMS2 (canonical, 37%), epithelial, marked WNT and MYC signaling activation; CMS3 (metabolic, 13%), epithelial and evident metabolic dysregulation; and CMS4 (mesenchymal, 23%), prominent transforming growth factor-ß activation, stromal invasion and angiogenesis. Samples with mixed features (13%) possibly represent a transition phenotype or intratumoral heterogeneity. We consider the CMS groups the most robust classification system currently available for CRC-with clear biological interpretability-and the basis for future clinical stratification and subtype-based targeted interventions.

A Network-Based Model of Oncogenic Collaboration for Prediction of Drug Sensitivity.

Tumorigenesis is a multi-step process, involving the acquisition of multiple oncogenic mutations that transform cells, resulting in systemic dysregulation that enables proliferation, invasion, and other cancer hallmarks. The goal of precision medicine is to identify therapeutically-actionable mutations from large-scale omic datasets. However, the multiplicity of oncogenes required for transformation, known as oncogenic collaboration, makes assigning effective treatments difficult. Motivated by this observation, we propose a new type of oncogenic collaboration where mutations in genes that interact with an oncogene may contribute to the oncogene's deleterious potential, a new genomic feature that we term "surrogate oncogenes." Surrogate oncogenes are representatives of these mutated subnetworks that interact with oncogenes. By mapping mutations to a protein-protein interaction network, we determine the significance of the observed distribution using permutation-based methods. For a panel of 38 breast cancer cell lines, we identified a significant number of surrogate oncogenes in known oncogenes such as BRCA1 and ESR1, lending credence to this approach. In addition, using Random Forest Classifiers, we show that these significant surrogate oncogenes predict drug sensitivity for 74 drugs in the breast cancer cell lines with a mean error rate of 30.9%. Additionally, we show that surrogate oncogenes are predictive of survival in patients. The surrogate oncogene framework incorporates unique or rare mutations from a single sample, and therefore has the potential to integrate patient-unique mutations into drug sensitivity predictions, suggesting a new direction in precision medicine and drug development. Additionally, we show the prevalence of significant surrogate oncogenes in multiple cancers from The Cancer Genome Atlas, suggesting that surrogate oncogenes may be a useful genomic feature for guiding pancancer analyses and assigning therapies across many tissue types.

Computational detection of alternative exon usage.

BACKGROUND: With the advent of the GeneChip Exon Arrays, it is now possible to extract "exon-level" expression estimates, allowing for detection of alternative splicing events, one of the primary mechanisms of transcript diversity. In the context of (1) a complex trait use case and (2) a human cerebellum vs. heart comparison on previously validated data, we present a transcript-based statistical model and validation framework to allow detection of alternative exon usage (AEU) between different groups. To illustrate the approach, we detect and confirm differences in exon usage in the two of the most widely studied mouse genetic models (the C57BL/6J and DBA/2J inbred strains) and in a human dataset. RESULTS: We developed a computational framework that consists of probe level annotation mapping and statistical modeling to detect putative AEU events, as well as visualization and alignment with known splice events. We show a dramatic improvement (∼25 fold) in the ability to detect these events using the appropriate annotation and statistical model which is actually specified at the transcript level, as compared with the transcript cluster/gene-level annotation used on the array. An additional component of this workflow is a probe index that allows ranking AEU candidates for validation and can aid in identification of false positives due to single nucleotide polymorphisms. DISCUSSION: Our work highlights the importance of concordance between the functional unit interrogated (e.g., gene, transcripts) and the entity (e.g., exon, probeset) within the statistical model. The framework we present is broadly applicable to other platforms (including RNAseq).

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

BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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.

TandTRAQ: an open-source tool for integrated protein identification and quantitation.

UNLABELLED: Integrating qualitative protein identification with quantitative protein analysis is non-trivial, given incompatibility in output formats. We present TandTRAQ, a standalone utility that integrates results from i-Tracker, an open-source iTRAQ quantitation program with the search results from X?Tandem, an open-source proteome search engine. The utility runs from the command-line and can be easily integrated into a pipeline for automation. AVAILABILITY: The TandTRAQ Perl scripts are freely available for download at http://www.ohsucancer.com/isrdev/tandtraq/

Consensus framework for exploring microarray data using multiple clustering methods.

The large variety of clustering algorithms and their variants can be daunting to researchers wishing to explore patterns within their microarray datasets. Furthermore, each clustering method has distinct biases in finding patterns within the data, and clusterings may not be reproducible across different algorithms. A consensus approach utilizing multiple algorithms can show where the various methods agree and expose robust patterns within the data. In this paper, we present a software package - Consense, written for R/Bioconductor - that utilizes such an approach to explore microarray datasets. Consense produces clustering results for each of the clustering methods and produces a report of metrics comparing the individual clusterings. A feature of Consense is identification of genes that cluster consistently with an index gene across methods. Utilizing simulated microarray data, sensitivity of the metrics to the biases of the different clustering algorithms is explored. The framework is easily extensible, allowing this tool to be used by other functional genomic data types, as well as other high-throughput OMICS data types generated from metabolomic and proteomic experiments. It also provides a flexible environment to benchmark new clustering algorithms. Consense is currently available as an installable R/Bioconductor package (http://www.ohsucancer.com/isrdev/consense/).

The melting of pulmonary surfactant monolayers.

Monomolecular films of phospholipids in the liquid-expanded (LE) phase after supercompression to high surface pressures (pi), well above the equilibrium surface pressure (pi(e)) at which fluid films collapse from the interface to form a three-dimensional bulk phase, and in the tilted-condensed (TC) phase both replicate the resistance to collapse that is characteristic of alveolar films in the lungs. To provide the basis for determining which film is present in the alveolus, we measured the melting characteristics of monolayers containing TC dipalmitoyl phosphatidylcholine (DPPC), as well as supercompressed 1-palmitoyl-2-oleoyl phosphatidylcholine and calf lung surfactant extract (CLSE). Films generated by appropriate manipulations on a captive bubble were heated from < or =27 degrees C to > or =60 degrees C at different constant pi above pi(e). DPPC showed the abrupt expansion expected for the TC-LE phase transition, followed by the contraction produced by collapse. Supercompressed CLSE showed no evidence of the TC-LE expansion, arguing that supercompression did not simply convert the mixed lipid film to TC DPPC. For both DPPC and CLSE, the melting point, taken as the temperature at which collapse began, increased at higher pi, in contrast to 1-palmitoyl-2-oleoyl phosphatidylcholine, for which higher pi produced collapse at lower temperatures. For pi between 50 and 65 mN/m, DPPC melted at 48-55 degrees C, well above the main transition for bilayers at 41 degrees C. At each pi, CLSE melted at temperatures >10 degrees C lower. The distinct melting points for TC DPPC and supercompressed CLSE provide the basis by which the nature of the alveolar film might be determined from the temperature-dependence of pulmonary mechanics.

Persistence of metastability after expansion of a supercompressed fluid monolayer.

Fluid monolayers of 1-palmitoyl-2-oleoyl-phosphatidylcholine collapse from an air/water interface to form a three-dimensional bulk phase at the equilibrium spreading pressure (pie) of approximately 47 mN/m. This phase transition limits access to higher surface pressures under equilibrium conditions or during slow continuous compressions. We have shown previously that these films avoid collapse and become metastable when compressed on a captive bubble to surface pressures above 60 mN/m and that the metastability persists during expansion at least to pie. Here, we first documented the extent of this persistent metastability. Rates of isobaric collapse during expansion of the metastable films were up to 3 orders of magnitude slower than those during the initial compression to high surface pressures. Recovery of the ability to collapse depended on the surface pressure to which the films were expanded and how long they were held there. Films reverted after brief exposure to 20 mN/m and after 1 h at 35 mN/m. At pie, films remained capable of reaching high surface pressures during slow compressions after 65 h, although an increase in compressibility above 55 mN/m suggested somewhat increased rates of collapse. We also determined if the films remained metastable when they acquired sufficient free area to allow reinsertion of collapsed material. Faster isobaric expansion in the presence of more collapsed material and with further deviation below pie supported the existence of reinsertion. The persistence of metastability to pie shows that films with sufficient free area to allow reinsertion remain resistant to collapse. Observations that suggest heterogeneous reinsertion, however, argue that free area may be distributed heterogeneously and leave open the possibility that metastability persists because significant regions retain a restricted free area.

Metastability of a supercompressed fluid monolayer.

Previous studies showed that monomolecular films of extracted calf surfactant collapse at the equilibrium spreading pressure during quasi-static compressions but become metastable at much higher surface pressures when compressed faster than a threshold rate. To determine the mechanism by which the films become metastable, we studied single-component films of 1-palmitoyl-2-oleoyl phosphatidylcholine (POPC). Initial experiments confirmed similar metastability of POPC if compressed above a threshold rate. Measurements at different surface pressures then showed that rates of collapse, although initially increasing above the equilibrium spreading pressure, reached a sharply defined maximum and then slowed considerably. When heated, rapidly compressed films recovered their ability to collapse with no discontinuous change in area, arguing that the metastability does not reflect transition of the POPC film to a new phase. These observations indicate that in several respects, the supercompression of POPC monolayers resembles the supercooling of three-dimensional liquids toward a glass transition.