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.
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.
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.
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.
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.
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.
Advances in Acute Myeloid Leukemia (AML) research have shown that the bone marrow microenvironment may distinctly vary across disease subtypes, and that this variation is associated with disease risk ...and response to conventional therapies. Novel therapies aimed at altering the tumor microenvironment, such as T-cell redirection, CAR-T and checkpoint inhibition, are emerging as promising treatment options for AML patients; however, there remains a critical need to determine how response to immune modulation may vary within different subsets of AML. Thus, in collaboration with the Beat AML Consortium, we carried out comprehensive mass cytometry profiling of patient bone marrow samples of nearly 100 Beat AML subjects and characterized their ex vivo response to several immune modulators.
As a complement to this study, we leveraged the Beat AML Consortium dataset (including next-generation sequencing, functional cell-based assays, small molecule screening and clinical information) to investigate connections between disease subtype, immune function and clinical outcome. The mass cytometry time of flight (CyTOF) immune profiling, combined with matched genomic, cytogenetic, and outcome data from the same subjects, provided a unique opportunity to investigate features of the immune environment at single-cell resolution and test for their association with clinical covariates in a large treatment-naïve cohort.
Interestingly, flow cytometry analysis of T-cells isolated from patient bone marrow showed a distinct subset of AML subjects with highly proliferative T-cells and a group of AML subjects with non-proliferative T-cells. To characterize molecular determinants of T-cell function in the AML microenvironment, we compared the transcriptional profiles of tumor specimens from subjects within these two groups. The data revealed a distinct set of differentially expressed genes associated with T-cell proliferation; pathway enrichment analysis indicated that these genes were involved in leukocyte migration, inflammation and response to hypoxia. Genes related to immune function were also enriched, likely due to the presence of immune cell infiltrates and stromal cells in addition to tumor cells from the AML specimens used for RNA-Seq. To estimate the extent of immune and stromal cells in the AML bone marrow, we next computed the approximate cellularity of the RNA-Seq samples using the xCell algorithm. The results of this analysis indicated enrichment of several types of immune cells in the RNA-Seq specimens from the proliferator group, including monocytes, neutrophils and activated dendritic cells. These observations were validated by preliminary results of the CyTOF immune cell profiling of the same subjects.
Ongoing work is focused on the biological interpretation of CyTOF data collected for these subjects, including evaluating the association of functional marker expression on T-cell and myeloid cell populations with T-cell proliferation. Furthermore, we are exploring the functional impact of variation in T-cell fitness and immune cell composition on response to several immune modulators in a series of ex vivo experiments using Beat AML patient samples. Initial findings suggest that for a subset of patients, low baseline levels of T-cell proliferation did not prevent response to immune modulation. We are interrogating the Beat AML dataset for common molecular features of patients in this responder group. Overall, this study evaluates determinants of immune function and variation within the tumor microenvironment of AML patients to advance current knowledge of AML disease biology and to assess the impact of immune fitness on response to immune modulation. These results will contribute to early target identification and development, and importantly shed light on features of the AML bone marrow environment associated with response to therapy.
Brady:Janssen R&D: Employment. Soong:Janssen R&D: Employment. Lind:Celgene: Research Funding; Monojul: Research Funding; Amgen: Research Funding; Janssen Pharmaceutical R&D: Research Funding; Fluidigm: Honoraria. Schaffer:Janssen Research & Development: Employment, Equity Ownership. Hodkinson:Janssen R&D: Employment. Adams:Janssen Pharmaceutical R&D: Employment. Abraham:Janssen R&D: Employment. Safabakhsh:Janssen R&D: Employment. Tyner:AstraZeneca: Research Funding; Aptose: Research Funding; Array: Research Funding; Genentech: Research Funding; Constellation: Research Funding; Gilead: Research Funding; Incyte: Research Funding; Janssen: Research Funding; Takeda: Research Funding; Vivid Biosciences: Membership on an entity's Board of Directors or advisory committees. Druker:Aileron Therapeutics: Consultancy; MolecularMD: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Oregon Health & Science University: Patents & Royalties; Aptose Therapeutics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Cepheid: Consultancy, Membership on an entity's Board of Directors or advisory committees; McGraw Hill: Patents & Royalties; Vivid Biosciences: Membership on an entity's Board of Directors or advisory committees; GRAIL: Consultancy, Membership on an entity's Board of Directors or advisory committees; Blueprint Medicines: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Bristol-Meyers Squibb: Research Funding; ARIAD: Research Funding; Novartis Pharmaceuticals: Research Funding; ALLCRON: Consultancy, Membership on an entity's Board of Directors or advisory committees; Third Coast Therapeutics: Membership on an entity's Board of Directors or advisory committees; Leukemia & Lymphoma Society: Membership on an entity's Board of Directors or advisory committees, Research Funding; Beta Cat: Membership on an entity's Board of Directors or advisory committees; Millipore: Patents & Royalties; Celgene: Consultancy; Gilead Sciences: Consultancy, Membership on an entity's Board of Directors or advisory committees; Patient True Talk: Consultancy; Amgen: Membership on an entity's Board of Directors or advisory committees; Fred Hutchinson Cancer Research Center: Research Funding; Monojul: Consultancy; Henry Stewart Talks: Patents & Royalties. Huang:Janssen R&D: Employment, Equity Ownership.
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, a phenomenon 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.
Introduction: Immune checkpoint inhibitors (ICIs) have shown significant efficacy in solid malignancies and recently in Hodgkin's disease. However, there are presently no biomarkers that predict ...which cancer types and/or individual patients will benefit from ICIs. Despite intensification of chemotherapeutic regimens, survival rates in acute myeloid leukemia (AML) have plateaued. We are therefore, performing studies to characterize the immune phenotype and genetic profile of AML patients who may respond to ICI therapy. Specifically, we are identifying cell subsets present in the marrow of patients with AML, evaluating their differentiation and activation status, and quantifying the density of immune modifiers. We are also performing functional assays to define the status of T cells in the AML microenvironment by evaluating the responsiveness of these cells to stimulation. Additionally, these studies assess the impact of several ICIs on the proliferative potential and cytokine production capacity of T cells resident in the bone marrow of AML patients.
Methods: Immunophenotyping was performed on the diagnostic bone marrow (Dx BM), diagnostic peripheral blood (Dx PB), and remission bone marrow (CR BM) for a single patient with AML. These studies used a time-of-flight mass cytometer (CyTOF) by staining samples with a panel containing 35 antibodies to surface markers and cytokines (Table 1). T cells were defined and then differentiated based on the presence of CD3 and CD4 or CD8. T cells were further classified into naive, central memory (CM), effector memory (EM), and effector cells. Functional assays measured proliferative capacity and cytokine production profile of bone marrow T cells in response to CD3 stimulation for the Dx BM and Dx PB. Changes to the baseline T cell functional profile in the presence of antibodies against PD1, CTLA4, TIM3, and TIGIT were measured in the Dx BM.
Results: T cell percentages were 3.14%, 5.46%, and 17.1% for the Dx BM, Dx, PB, and CR BM, respectively. CD4:CD8 ratios were 3.5, 2.9, and 4.5, respectively. EM cells made up the largest percentage of CD8+ T cells in the Dx BM (40.99%), Dx PB (57.06%), and CR BM (53.55%). Naïve (52.49%), EM (37.63%) and CM (44.3%) cells made up the largest percentages of CD4+ T cells in the Dx BM, Dx PB, and CR BM, respectively (Figure 1). Checkpoint expression was absent (median intensity < 1) on all T cells except for some CD8+ subsets (Figure 2). Within the Dx BM, the effector (1.93) and EM (4.34) cells expressed TIGIT and the EM cells expressed PD1 (1.92). Within the Dx PB, the effector (1.81) and EM (4.0) cells expressed TIGIT. Within the CR BM, the effector cells (2.3) expressed TIGIT. In response to CD3 stimulation, 57.5% of the Dx PB T cells divided while there was no proliferation observed in the Dx BM. In response to CD3 stimulation and PD1 blockade, 28.2% of the Dx BM T cells divided and there was a 4-fold increase in IFNγ production. There was no division in the Dx BM T cells in response to TIGIT blockade but there was a 10-fold and 121-fold increase in IL-2 and IFNγ production, respectively. There was no significant proliferation or cytokine production in response to TIM3 or CTLA4 blockade.
Conclusion: We observed differential T cell responses to stimulation in the presence of ICIs in this patient. The Dx PB T cells were capable of proliferating in the presence of CD3 stimulation alone whereas the Dx BM T cells were only able to divide in the presence of PD1 blocking antibody. PD1 blockade elicited proliferation and low levels of IFNγ production whereas TIGIT blockade elicited negligible proliferation but high levels of IFNγ production. There was low but differential checkpoint expression on T cells, with PD1 and TIGIT having the highest expression, specifically on CD8+ T cell subsets. The predominant CD8+ T cell subset, along with differential checkpoint expression, persisted into the CR BM but there was a shift in the predominant CD4+ T cell subset. These data suggest the presence of immune suppression in the bone marrow microenvironment that may be amenable to checkpoint inhibition.
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Huang:Janssen Research & Development, LLC: Employment, Equity Ownership. Sasser:Janssen Research & Development, LLC: Employment. Adams:Janssen Research & Development, LLC: Employment. Druker:Agios: Honoraria; Ambit BioSciences: Consultancy; ARIAD: Patents & Royalties, Research Funding; Array: Patents & Royalties; AstraZeneca: Consultancy; Blueprint Medicines: Consultancy, Equity Ownership, Other: travel, accommodations, expenses ; BMS: Research Funding; CTI: Equity Ownership; Curis: Patents & Royalties; Cylene: Consultancy, Equity Ownership; D3 Oncology Solutions: Consultancy; Gilead Sciences: Consultancy, Other: travel, accommodations, expenses ; Lorus: Consultancy, Equity Ownership; MolecularMD: Consultancy, Equity Ownership, Patents & Royalties; Novartis: Research Funding; Oncotide Pharmaceuticals: Research Funding; Pfizer: Patents & Royalties; Roche: Consultancy. Lind:Janssen: Research Funding; Fluidigm: Honoraria.
Introduction: Identification of tumor associated antigens (TAA) in acute myeloid leukemia (AML) will facilitate the use of targeted immunotherapies such as chimeric antigen receptor T cell therapy ...and bispecific antibodies. The characteristics of an ideal TAA include tumor specificity, high expression level, large percentage of positive tumor cells, and evidence for expression on putative cancer stem cells. V-domain Ig Suppressor of T cell Activation (VISTA) is a cell surface protein previously shown to be present on hematologic cells and inhibitory towards T cells. VISTA's role and presence within AML has yet to be defined. We performed detailed studies to characterize VISTA expression on tumor cells and mononuclear cell subsets in patients with AML.
Methods: Immunophenotyping was performed on mononuclear cells from primary AML patient specimens using mass cytometry (CyTOF) by staining samples with a panel containing 34 antibodies to surface markers and cytokines (Table 1). This antibody panel was used to identify blast cells (variable immunophenotype), leukemic stem cells (LSCs) (CD34+, CD38-), T cells (CD3+), monocytes (CD33+, CD14+, CD11b+, HLA-DR+), myeloid derived suppressor cells (MDSCs) (CD33+, CD11b+, HLA-DR-), and M2 macrophages (CD11b+, CD163+). Raw median VISTA expression and percentage of cells expressing VISTA were calculated for all of these cell types. Samples were split into 2 categories based on blast VISTA expression. A sample was considered VISTA high if >10% of the blasts expressed VISTA, and VISTA low if <10% of the blasts expressed VISTA. Similar data was generated from a pool of healthy donors for comparison.
Results: Fourteen bone marrow samples from patients with newly diagnosed AML and 10 from healthy donors were obtained. The average percentage of blasts expressing VISTA was 68% (range 28% - 81%) in the VISTA high group and 4.2% (range 0.3% - 6.8%) in the VISTA low group (p<0.05). VISTA expression was higher in the VISTA high group than in the VISTA low group (17.7 vs -0.1, p<0.05). The VISTA high group had a lower blast percentage in the bone marrow compared to the VISTA low group (56% vs 91%, p<0.05). The VISTA high and low groups had similar percentages of LSCs (2.5% vs 0.6%, p=0.11), but the LSCs in the VISTA high group maintained higher VISTA expression than did those in the VISTA low group (6.81 vs -0.11, p<0.05). The LSCs within the VISTA high group had similar VISTA expression to the blast population (6.81 vs 17.7, p=0.15). Within the non-blast population, VISTA expression was higher on the myeloid cell subsets than on the T cells for both the AML samples (11.5 vs 0.17, p<0.05) and the healthy donor samples (8.07 vs 0.24, p<0.05). VISTA expression was similar between the VISTA high and low groups on T cells (0.36 vs 0.02, p=0.08), monocytes (15.7 vs 19, p=0.7), MDSCs (6.3 vs 4, p=0.26), and M2 macrophages (7.4 vs 16.6, p =0.25). VISTA expression was similar between AML and healthy donor samples for T cells (0.17 vs 0.24, p=0.6), monocytes (15.7 vs 14, p=0.41), MDSCs (6.3 vs 3.6, p=0.22), and M2 macrophages (7.4 vs 6.6, p=0.19). Monocytes had the highest expression of VISTA within the healthy donor samples (p<0.05) and this difference was maintained in the AML samples.
Conclusion: A subset of patients with AML has a large percentage of blasts, including LSCs, with high VISTA expression on their surface. VISTA is expressed on normal myeloid cells in the bone marrow of patients with AML, but at low levels and to a similar degree as in the marrow of healthy donors.
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Huang:Janssen Research & Development, LLC: Employment, Other: I am an employee of Janssen and a stock owner . Sasser:Janssen Research & Development, LLC: Employment. Adams:Janssen Research & Development, LLC: Employment. Druker:Agios: Honoraria; Ambit BioSciences: Consultancy; ARIAD: Patents & Royalties, Research Funding; Array: Patents & Royalties; AstraZeneca: Consultancy; Blueprint Medicines: Consultancy, Equity Ownership, Other: travel, accommodations, expenses ; BMS: Research Funding; CTI: Equity Ownership; Curis: Patents & Royalties; Cylene: Consultancy, Equity Ownership; D3 Oncology Solutions: Consultancy; Gilead Sciences: Consultancy, Other: travel, accommodations, expenses ; Lorus: Consultancy, Equity Ownership; MolecularMD: Consultancy, Equity Ownership, Patents & Royalties; Novartis: Research Funding; Oncotide Pharmaceuticals: Research Funding; Pfizer: Patents & Royalties; Roche: Consultancy. Lind:Janssen: Research Funding; Fluidigm: Honoraria.
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-beta 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.
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/ Contact: laderast@ohsu.edu
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/).
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