Showing papers by "Timothy L. Ratliff published in 2018"

Dietary Protein Restriction Reprograms Tumor-Associated Macrophages and Enhances Immunotherapy.

Abstract: Purpose: Diet and healthy weight are established means of reducing cancer incidence and mortality. However, the impact of diet modifications on the tumor microenvironment and antitumor immunity is not well defined. Immunosuppressive tumor-associated macrophages (TAMs) are associated with poor clinical outcomes and are potentially modifiable through dietary interventions. We tested the hypothesis that dietary protein restriction modifies macrophage function toward antitumor phenotypes. Experimental Design: Macrophage functional status under different tissue culture conditions and in vivo was assessed by Western blot, immunofluorescence, qRT-PCR, and cytokine array analyses. Tumor growth in the context of protein or amino acid (AA) restriction and immunotherapy, namely, a survivin peptide–based vaccine or a PD-1 inhibitor, was examined in animal models of prostate (RP-B6Myc) and renal (RENCA) cell carcinoma. All tests were two-sided. Results: Protein or AA-restricted macrophages exhibited enhanced tumoricidal, proinflammatory phenotypes, and in two syngeneic tumor models, protein or AA-restricted diets elicited reduced TAM infiltration, tumor growth, and increased response to immunotherapies. Further, we identified a distinct molecular mechanism by which AA-restriction reprograms macrophage function via a ROS/mTOR-centric cascade. Conclusions: Dietary protein restriction alters TAM activity and enhances the tumoricidal capacity of this critical innate immune cell type, providing the rationale for clinical testing of this supportive tool in patients receiving cancer immunotherapies.

Cholesterol Esterification Inhibition Suppresses Prostate Cancer Metastasis by Impairing the Wnt/β-catenin Pathway

Abstract: Dysregulation of cholesterol is a common characteristic of human cancers including prostate cancer. This study observed an aberrant accumulation of cholesteryl ester in metastatic lesions using Raman spectroscopic analysis of lipid droplets in human prostate cancer patient tissues. Inhibition of cholesterol esterification in prostate cancer cells significantly suppresses the development and growth of metastatic cancer lesions in both orthotopic and intracardiac injection mouse models. Gene expression profiling reveals that cholesteryl ester depletion suppresses the metastatic potential through upregulation of multiple regulators that negatively impact metastasis. In addition, Wnt/β-catenin, a vital pathway for metastasis, is downregulated upon cholesteryl ester depletion. Mechanistically, inhibition of cholesterol esterification significantly blocks secretion of Wnt3a through reduction of monounsaturated fatty acid levels, which limits Wnt3a acylation. These results collectively validate cholesterol esterification as a novel metabolic target for treating metastatic prostate cancer. Mol Cancer Res; 16(6); 974-85. ©2018 AACR.

Naturally-Occurring Canine Invasive Urothelial Carcinoma: A Model for Emerging Therapies.

Abstract: The development of targeted therapies and the resurgence of immunotherapy offer enormous potential to dramatically improve the outlook for patients with invasive urothelial carcinoma (InvUC). Optimization of these therapies, however, is crucial as only a minority of patients achieve dramatic remission, and toxicities are common. With the complexities of the therapies, and the growing list of possible drug combinations to test, highly relevant animal models are needed to assess and select the most promising approaches to carry forward into human trials. The animal model(s) should possess key features that dictate success or failure of cancer drugs in humans including tumor heterogeneity, genetic-epigenetic crosstalk, immune cell responsiveness, invasive and metastatic behavior, and molecular subtypes (e.g., luminal, basal). While it may not be possible to create these collective features in experimental models, these features are present in naturally-occurring InvUC in pet dogs. Naturally occurring canine InvUC closely mimics muscle-invasive bladder cancer in humans in regards to cellular and molecular features, molecular subtypes, biological behavior (sites and frequency of metastasis), and response to therapy. Clinical treatment trials in pet dogs with InvUC are considered a win-win scenario; the individual dog benefits from effective treatment, the results are expected to help other dogs, and the findings are expected to translate to better treatment outcomes in humans. This review will provide an overview of canine InvUC, the similarities to the human condition, and the potential for dogs with InvUC to serve as a model to predict the outcomes of targeted therapy and immunotherapy in humans.

Phenotyping Immune Cells in Tumor and Healthy Tissue Using Flow Cytometry Data

Abstract: We present an automated pipeline capable of distinguishing the phenotypes of myeloid-derived suppressor cells (MDSC) in healthy and tumor-bearing tissues in mice using flow cytometry data. In contrast to earlier work where samples are analyzed individually, we analyze all samples from each tissue collectively using a representative template for it. We demonstrate with 43 flow cytometry samples collected from three tissues, naive bone-marrow, spleens of tumor-bearing mice, and intra-peritoneal tumor, that a set of templates serves as a better classifier than popular machine learning approaches including support vector machines and neural networks. Our "interpretable machine learning" approach goes beyond classification and identifies distinctive phenotypes associated with each tissue, information that is clinically useful. Hence the pipeline presented here leads to better understanding of the maturation and differentiation of MDSCs using high-throughput data.

"Super Gene Set" Causal Relationship Discovery from Functional Genomics Data

Abstract: In this article, we present a computational framework to identify “causal relationships” among super gene sets. For “causal relationships,” we refer to both stimulatory and inhibitory regulatory relationships, regardless of through direct or indirect mechanisms. For super gene sets, we refer to “pathways, annotated lists, and gene signatures,” or PAGs. To identify causal relationships among PAGs, we extend the previous work on identifying PAG-to-PAG regulatory relationships by further requiring them to be significantly enriched with gene-to-gene co-expression pairs across the two PAGs involved. This is achieved by developing a quantitative metric based on P AG-to- P AG C o-expressions (PPC), which we use to infer the likelihood that PAG-to-PAG relationships under examination are causal—either stimulatory or inhibitory. Since true causal relationships are unknown, we approximate the overall performance of inferring causal relationships with the performance of recalling known r-type PAG-to-PAG relationships from causal PAG-to-PAG inference, using a functional genomics benchmark dataset from the GEO database. We report the area-under-curve (AUC) performance for both precision and recall being 0.81. By applying our framework to a myeloid-derived suppressor cells (MDSC) dataset, we further demonstrate that this framework is effective in helping build multi-scale biomolecular systems models with new insights on regulatory and causal links for downstream biological interpretations.

“Super Gene Set” Causal Relationship Discovery from Functional Genomics Data

Abstract: In this article, we present a computational framework to identify “causal relationships” among super gene sets. For “causal relationships,” we refer to both stimulatory and inhibitory regulatory relationships, regardless of through direct or indirect mechanisms. For super gene sets, we refer to “pathways, annotated lists, and gene signatures,” or PAGs. To identify causal relationships among PAGs, we extend the previous work on identifying PAG-to-PAG regulatory relationships by further requiring them to be significantly enriched with gene-to-gene co-expression pairs across the two PAGs involved. This is achieved by developing a quantitative metric based on P AG-to- P AG C o-expressions (PPC), which we use to infer the likelihood that PAG-to-PAG relationships under examination are causal—either stimulatory or inhibitory. Since true causal relationships are unknown, we approximate the overall performance of inferring causal relationships with the performance of recalling known r-type PAG-to-PAG relationships from causal PAG-to-PAG inference, using a functional genomics benchmark dataset from the GEO database. We report the area-under-curve (AUC) performance for both precision and recall being 0.81. By applying our framework to a myeloid-derived suppressor cells (MDSC) dataset, we further demonstrate that this framework is effective in helping build multi-scale biomolecular systems models with new insights on regulatory and causal links for downstream biological interpretations.

Abstract 4709: Cell-specific gene program-based small-molecule immunomodulators targeting solid-tumor microenvironments

Abstract: Cancer immunotherapy includes promising strategies based on immune checkpoint blockade (e.g., anti-PD-1/PD-L1, anti-CTLA-4). A limitation of such therapies for solid tumors stems from other immune-suppressive mechanisms mediated by myeloid-derived suppressor cells (MDSC) and tumor-associated macrophages. We hypothesize that molecules that target specific suppressive immune cells in the tumor microenvironment can reprogram the pro-tumor microenvironment towards antitumor immunity. Using our proteome-scale target/antitarget network-based lead optimization method (CANDESIGN), we designed and synthesized cell-specific nontoxic chemical libraries with modular functions for anticancer potency and immunomodulation. We used machine learning iteratively on experimental data to identify cell-specific target/anti-target networks (gene programs) as well as designed and synthesized compound libraries targeting cell-specific programs for desired cancer and immune cell function ex vivo. Specifically, we altered suppressive function of MDSCs by targeting upregulated genes in activated monocytic MDSCs in the tumor microenvironment compared to cells in spleen. We synthesized a potent nontoxic compound that specifically modulates activated MDSC function and corresponding changes in CD4+ and CD8+ T-cell activity in a mouse bladder cancer model. We observed a significant reduction in tumor mass following treatment by oral gavage. Interestingly, the bladder cancer cells used in our mouse model (as well as human and dog bladder cancer cells) are insensitive to our lead compound in vitro, suggesting in vivo antitumor activity via immunomodulation. Our lead decreased frequency of monocytic and granulocytic MDSCs in the ascites and the tumor. Moreover, a decreased frequency of MDSC expressing suppressive functional markers (e.g., Arg1, iNOS, PD-L1) and increased IFNγ+CD8+ T cells were observed in the tumor microenvironment after compound treatment. Finally, we performed a small clinical trial on pet dogs with bladder tumors treated with our compound tablets for 4-5 weeks that resulted in ~50% reduction in tumor volume. We conclude that designing cell-specific compounds perturbing the tumor microenvironment to combat immune suppression gives a selective advantage to the immune system to combat solid tumors via single and combination drug/cell therapies. Citation Format: Erin Kischuk, Joydeb Majumder, Jonathan A. Fine, Travis C. Lantz, Deepika Dhawan, Deborah W. Knapp, Timothy L. Ratliff, Gaurav Chopra. Cell-specific gene program-based small-molecule immunomodulators targeting solid-tumor microenvironments [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4709.