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.

Abstract 5942: An integrated approach reveals novel extrinsic pathways and cytokine signatures associated with drug response in acute myeloid leukemia

Background: Acute myeloid leukemia (AML) is a heterogeneous disease with a dismal 5 year survival rate below 30%. Current AML therapies have provided little improvement in achieving complete remission. This is attributed to the development of drug resistance and extrinsic factors from the microenvironment that promote AML progression. Previously, our lab demonstrated that proinflammatory cytokines from the bone marrow microenvironment such as IL-1, promote AML progression. This emphasizes the rationale to design and implement targeted therapies to block extrinsic pathways coupled with intrinsic pathways conferring drug resistance in AML.

Methods: To identify factors that promote drug sensitivity and resistance in AML, we used a cohort of 350 AML patients with various genetic subtypes. We quantified levels of 41 growth factors in plasma samples using a multiplex luminex assay. The same cohort of AML samples were analyzed for drug response to 130 small molecule inhibitors in in vitro functional drug sensitivity assay, for RNA-seq gene expression, and whole exome sequencing. The data was integrated to identify differential pathways and markers for drug response. To model drug response in vitro we created a series of drug resistant AML cell lines by culturing these cells in specific drugs long-term.

Results: Pathway analysis integrating drug sensitivity, transcriptome, and cytokine expression data identified distinct differentially regulated pathways for various inhibitors. We found that protein synthesis pathways were significantly enriched in sorafenib (FLT-3 inhibitor) resistant samples whereas cytokine and immune pathways (such as TLR and MCP1 signaling) were correlated with trametinib (MEK inhibitor) and venetoclax (Bcl-2 inhibitor) resistance. Specifically, venetoclax resistance correlated with augmented levels of cytokines such as IL-1 and IP-10 and growth factors such as PDGF. Further, trametinib resistance in AML samples is correlated with the upregulation of CCL11, MCP1, and TGFα but a downregulation of CCL22 levels. Accordingly, using AML cell line models, we observed an increase in MCP1 and TGFα at the transcript and secreted protein levels in trametinib resistant cell lines. We demonstrated that MCP1 stimulation activates survival pathways by activating pERK and pJNK signaling, thus reducing apoptosis in AML cells. Targeting MCP1 in combination with trametinib reverses these effects, thus offering a novel therapeutic approach to overcome drug resistance.

Conclusion and perspectives: Our data suggest that distinct extrinsic pathways may regulate the response to specific targeted therapy, thus providing a strong basis to design new treatment regimens. Combination treatment incorporating extrinsic pathways would aid in sensitizing AML cells to therapy. Our study offers an integrated approach and a resource to identify such pathways for a large number of tested drugs, and narrows down functionally important pathways associated with drug response.

Citation Format: Rucha V. Modak, Alisa Damnernsawad, Ted Laderas, Guanming Wu, Jeffery W. Tyner, Karin D. Rodland, Shannon K. McWeeney, Anupriya Agarwal. An integrated approach reveals novel extrinsic pathways and cytokine signatures associated with drug response in acute myeloid leukemia [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5942.

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.

Immune checkpoint inhibitors reverse T-cell functional suppression in the bone marrow of a subset of AML patients

Acute Myeloid Leukemia is a blood cancer with poor prognosis despite decades of therapeutic development. Immune checkpoint inhibitors (ICIs) have been shown to be effective in many cancers, but their utility in AML has not been clearly established. To better understand the immune landscape of the AML microenvironment, we performed immunophenotyping on fresh bone marrow cells from AML patients using mass-spec based flow cytometry. In addition, we performed functional assays to evaluate T-cell proliferative capacity and determine whether AML bone marrow T cells can be modified by ICI treatment. Sixty two bone marrow samples from patients with AML were analyzed. We performed T-cell stimulation assays to measure proliferation and cytokine production in the presence and absence of ICIs. Samples were sub-categorized as being proliferators or non-proliferators based on the percent of T-cell division in response to TCR stimulation. Phenotypic analysis showed proliferation status correlated with a specific T-cell profile. Additionally, within the non-proliferator group, 70% overcame immune suppression and responded to at least one of the ICIs tested. In conclusion, we show that T-cell dysfunction is present in a subset of patients with AML. Nonetheless, this suppression can be reversed by ICIs in many cases, supporting the use of ICIs in AML therapy.

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.

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-β 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.

Abstract 603: Consensus molecular subtyping through a community of experts advances unsupervised gene expression-based disease classification and facilitates clinical translation

Background: Gene expression-based subtyping is widely accepted as a relevant source of disease stratification. Despite the widespread use, its translational and clinical utility is hampered by discrepant results, likely related to differences in data processing and algorithms applied to diverse patient cohorts, sample preparation methods, and gene expression platforms. In the absence of a clear methodological gold standard to perform such analyses, a more general framework that integrates and compares multiple strategies is needed to define common disease patterns in a principled, unbiased manner.

Methods: We formed a consortium of 6 independent experts groups - each with a previously published CRC classifier, ranging from 3 to 6 subtypes - to understand similarities and differences of their subtyping systems. Sage Bionetworks functioned as neutral party to aggregate public and proprietary data (Synapse platform) and perform meta-analysis. Each group applied its CRC subtyping signature to the collection of data sets with gene expression (n = 4,151, predominantly stage II and III). Using the resulting subtype labels, we developed a network-based model and applied a Markov cluster algorithm to detect robust network substructures that would indicate recurring subtype patterns and therefore a consensus subtyping system. Correlative analyses using clinico-pathological, genomic and epigenomic features was performed to robustly characterize the identified subtypes.

Results: This analytical framework revealed significant interconnectivity between the six independent classification systems, leading to the identification of four biologically distinct consensus molecular subtypes (CMS) enriched for key pathway traits: CMS1 (MSI Immune), hypermutated, microsatellite unstable, with strong immune activation; CMS2 (Canonical), epithelial, chromosomally unstable, with marked WNT and MYC signaling activation; CMS3 (Metabolic), epithelial, with evident metabolic dysregulation; and CMS4 (Mesenchymal), prominent TGFβ activation, angiogenesis, stromal invasion. Patients diagnosed with MSI Immune tumors had worse survival after relapse and those with mesenchymal tumors had increased risk of metastasis and worse overall survival.

Discussion: We describe a novel methodological paradigm for deriving benchmarks of disease subtyping. Our work represents the first example of a community of experts identifying and advocating for a single reproducible model for cancer subtyping, effectively unifying previous classifiers. In the CRC domain, the uniformity afforded by this new classification system and its application to a large data set revealed important subtype-specific biological associations that were previously unnoticed or marginally significant, supporting a new taxonomy of the disease.

Citation Format: Justin Guinney, Rodrigo Dienstmann, Xin Wang, Aurelien de Reynies, Andreas Schlicker, Charlotte Soneson, Laetitia Marisa, Paul Roepman, Gift Nyamundanda, Paolo Angelino, Brian Bot, Jeffrey S. Morris, Iris Simon, Sarah Gerster, Evelyn Fessler, Felipe de Sousa e Melo, Edoardo Missiaglia, Hena Ramay, David Barras, Krisztian Homicsko, Dipen Maru, Ganiraju Manyam, Bradley Broom, Valerie Boige, Ted Laderas, Ramon Salazar, Joe W. Gray, Josep Tabernero, Rene Bernards, Stephen Friend, Pierre Laurent-Puig, Jan P. Medema, Anguraj Sadanandam, Lodewyk Wessels, Mauro Delorenzi, Scott Kopetz, Louis Vermeulen, Sabine Tejpar. Consensus molecular subtyping through a community of experts advances unsupervised gene expression-based disease classification and facilitates clinical translation. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 603. doi:10.1158/1538-7445.AM2015-603

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.

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

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/).