The delay associated with the appearance of immunosuppressive effects is a subtle but important aspect of tumor immunosuppression and is a common criticism of in vivo models that are used to test the efficacy of immunotherapies [ ]. In this phenotypic screening assay, we recreate the dynamic appearance of paracrine immunosuppression. Assuming that the factors responsible for the observed immunosuppressive effects are constitutively secreted by the B16F0 cells, we used a proteomic workflow to identify these secreted proteins.
Collectively, the results from the phenotypic screening assay suggest that B16F0 cells use a variety of mechanisms to suppress the bioactivity of IL12 locally, as summarized in Fig. One of the mechanisms is the over expression of one component of the IL receptor, IL12RB2, by B16 tumor cells and also by exosomes secreted by B16F0 cells, and suggests that these receptors create a local cytokine sink for IL12 [ ].
As the phenotypic screening assay identified a number of potentially novel local mechanisms of immunosuppression, I combined data obtained from the Cancer Genome Atlas with computer simulation to identify whether gene expression patterns consistent with the mechanism of immunosuppression identified in vitro are observed in human cancers and to identify particular patient subgroups where these mechanisms may be relevant [ , ].
While many retrospective studies aim to discover biomarkers that are correlated with differential clinical outcomes e. Over-expression of this receptor subunit and the corresponding reduction in IL12 in B16F0-conditioned media suggest that these malignant cells create a local cytokine sink for IL Using the TCGA data as a guide to select appropriate cell lines, differential expression of the components of the IL12 receptor is also observed on breast cancer cell lines that are associated with patient groups that exhibit enhanced anti-tumor immunity [ ].
Collectively, these observations suggest that differential IL12 receptor expression is a remnant of immunoediting during somatic evolution of malignant cells and that this differential expression remains despite adaptation to in vitro culture. The second pattern that we observed in the phenotypic screening assay was that tumor derived WISP1 has a paracrine effect to inhibit the bioactivity of IL In an initial survey, I observed that WISP1 was upregulated in essentially all patients with invasive breast cancer, as shown in Fig.
As recently illustrated in vivo [ 64 ], the absence of IL12 skews T cell polarization towards a type 2 phenotype characterized by an increase in the transcription factor GATA3. PCA allows for a lower dimensional representation of gene expression in terms of an individual genes expression within the entire data set attributed to each of the principal coordinates vectors. The genes with high loadings associated with a particular principal coordinate PC vector can be used to interpret a principal coordinate from a biological perspective.
The PC vectors are ordered such that PC vector 1 captures the most variation in the gene expression data and additional PC vectors capture progressively less information. WISP1 is upregulated in tumor tissue samples obtained from patients with invasive breast cancer. Principal coordinate analysis was applied to expression data for a subset of immune-related genes obtained from the invasive breast cancer arm of the Cancer Genome Atlas see Figure S3 in [ ]. Projections of the genes along the first two principal coordinate PC directions a and the third and fourth PC directions b. PC 1 can be interpreted as a type 1 immune signature and PC 2 is interpreted as a signature of oncogenic transformation in invasive breast cancer.
As principal coordinates are independent, the projection of a gene along the corresponding axes indicates the degree to which the expression of two genes are related and the distance from the origin indicates the strength of the covariation within the data set. The remaining principal coordinates capture progressively less variance in the data and provide little additional information, as illustrated by the distribution of the genes along PC 4, except HLA.
The colored ovals radiating out from the origin indicate principal coordinate values that can not be distinguished from random noise, that is a null hypothesis, with increasing levels of statistical stringency.
These colored ovals were obtained using a simulation approach called bootstrap resampling. However, the main question in this retrospective study is whether a gene expression pattern exists within the data that is consistent with our observation that WISP1 is a paracrine inhibitor of IL To address this question, an inferential statistics approach is required. While conventional hypothesis testing is difficult to do in this focused context, the central idea in hypothesis testing is to protect against the possibility that the observed effect can be explained by random noise associated with the experimental assay or the underlying biology [ ].
By focusing on the particular genes associated with host immunity, I narrowed the universe of possible outcomes using prior knowledge about immune-related genes. To minimize concerns about cognitive biases, genes associated with pro-tumor in addition to anti-tumor host immunity and data from normal tissue samples were included in the analysis. From a hypothesis testing perspective, one can formulate the study question in terms of two hypotheses or models M i: Considering just these two alternative hypotheses, the inference problem can be expressed in a Bayesian framework:. A gene expression pattern consistent with random noise can be constructed using computer simulation, that is by performing the analysis thousands of times on an equivalent synthetic data set constructed for each analysis by randomly sampling with replacement of the entire set of gene expression values.
The potential PC projections of genes that could be explained by random noise are contained within the color ovals around the origin in Fig. The negative correlation of GATA3 with type 1-related immune genes in PC vector 1; which includes perforin, granzyme, CD8 , and IFNG ; suggests that the strongest signal within the data set corresponds to a type 1 immune response.
Moreover, the association of GATA3 with the type 1 immune signature suggests that the GATA3 signature is derived from immune cells and not epithelial differentiation. Collectively, the first three PC vectors all provide gene expression patterns consistent with WISP1 as a paracrine inhibitor of IL12 and that these patterns are very different from random noise. Assuming that the two hypotheses are equally plausible, the evidence supports the alternative hypothesis.
Finally, a pre-clinical mouse model can be used to further validate the role that these identified proteins play in immunosuppression. Collectively, if these local mechanisms to suppress IL12 activity act similarly in humans, knowledge derived from these integrated wet and dry-lab studies could be used to stratify patients based on the prevalence of specific cross-talk mechanisms present within the tumor and guide selecting patient-specific immunotherapies.
In a clinical trial setting, mechanism-based biomarkers that stratify patient populations could be used to improve the statistical power of less expensive clinical trials. In an adjuvant setting, mitigating these cross-talk mechanisms in conjunction with immune checkpoint modulators could expand the differential therapeutic window between productive local anti-tumor immunity and toxic peripheral effects due to auto-immunity, thereby broadening the clinical benefit of existing immunotherapies. While cancer immunotherapy is experiencing incredible clinical success, sustaining progress and maximizing the return to the community from the investment in human and financial capital requires a strategy for identifying new mechanisms of action.
Despite a brisk pace of basic biomedical research, validating new mechanisms of action in humans has been identified as a key pinch point in the pharmaceutical research and development pipeline. Quantitative and systems pharmacology has been proposed as a new conceptual approach to address this challenge.
Here, I have focused on a network-centric view of biology and the integration of mechanistic modeling and simulation with quantitative experimental studies as two central themes that help distinguish QSP as a new discipline. To illustrate the approach, two examples were chosen as they represent extremes of a spectrum of modeling complexity. The first example describes a hierarchical modeling approach where a large mechanistic mathematical model provided a framework to integrate a wide variety of experimental data, from in vitro observations to clinical trial results.
Drawing from experiences using PhysioLab platforms, large-scale mechanistic models that integrate phenotypic signaling-, cellular-, and tissue-level behaviors have the potential to lower many of the translational hurdles associated with cancer immunotherapy. These include prioritizing immunotherapies, assessing the safety of proposed phase I clinical trials, developing mechanistic biomarkers that stratify patient populations and that reflect the underlying strength and dynamics of a protective host immune response, and sharing our understanding of the underlying biology using mechanistic models as clear cubes.
Symposium: Advances in cancer drug discovery and development | BioMelbourne Network
Given the complexity of the models, they required significant resources to develop and rely on a modular approach to modeling physiology, which assumes that the biological networks remain similar in health and disease. To identify how cancer re-wires intercellular networks that locally regulate host immunity, the second example describes a flat modeling approach where the mechanistic models are more intimately connected to specific experimental data, which are acquired to inform the dynamic quantitative nature of the modeled biological system.
As a consequence, the predictive power of the mechanistic model is more focused on a specific question but can be developed more rapidly. Here, this QSP-inspired phenotypic screening approach was used to identify local mechanisms that malignant cells use to suppress IL12 activity. Overall, these two mechanistic modeling approaches each have their strengths and weaknesses in how they can help lower the translational hurdles associated with cancer immunotherapy. To paraphrase George E. Box [ ], it is important to remember that, in some regards, all models are wrong but some may be useful for enabling one to think more clearly about the dynamic relationships among components of biological networks.
While typically associated with mathematical modeling, this statement applies equally to biological and mathematical models. Inevitably, developing a model of a system involves abstraction, where key elements that are thought to be important in governing system behavior and their interactions are included while other components are left out to minimize confounding influences. The data are then used to inform the strength of the interactions included in the model.
For instance, a common framework for modeling cell signaling networks is to assume that signaling events occur in the context of an average cell of constant volume.
This framework neglects the impact of cell proliferation, which can reduce the concentration of activated species in the system through dilution alone. In such case, modeling interactions that regulate the activity of signaling proteins under conditions where cell proliferation may be important, such as during immune-mediated tumor regression [ ], may lead to incorrect conclusions.
Similarly, testing immunotherapies in mouse models prior to the establishment of immunosuppressive networks may lead to inappropriate inference as to the therapeutic potential of modulating a particular node [ ]. Whether a component and its associated interactions are included or left out places conceptual boundaries on the specific questions that can be asked of a model.
The value of model-based inference depends clearly on understanding these conceptual boundaries. The focus on mechanistic modeling within QSP is to use mathematical frameworks to be more explicit about these conceptual boundaries. The goal of QSP is then to facilitate a rational discussion about target prioritization and to use modeling and simulation methods to lower many of the translational hurdles associated with cancer immunotherapy.
Data from [ ] and www.
Advances in cancer drug discovery and development Symposium
The alternative is a black box, where the underlying mathematical details associated with a simulation platform are not made available for critique and review. Changes in the distribution of cells between these two states independent from changes in signaling activation would complicate the interpretation of the data. National Center for Biotechnology Information , U. Journal List J Immunother Cancer v.
Published online Jun David J Klinke, II. Received Jan 14; Accepted Apr This article has been cited by other articles in PMC. Abstract Recent clinical successes of immune checkpoint modulators have unleashed a wave of enthusiasm associated with cancer immunotherapy. Introduction Following the early clinical observations of William B.
Review Contemporary drug discovery and development While the pace of basic biomedical research has been brisk, translating preclinical discoveries into meaningful clinical benefit using cancer immunotherapies has been difficult and mirrors the broader pharmaceutical industry [ 6 ]. Invigorating drug discovery and development using quantitative and systems pharmacology In , the National Institutes of Health organized an industrial and academic working group to study the challenges associated with drug discovery and development.
Open in a separate window. Integrate mechanistic modeling and simulation with quantitative wet-lab studies With the exponential increase in computational power, computer-aided modeling and simulation has transformed industries ranging from financial portfolio management to the aerospace industry. The Entelos PhysioLab platforms: An integrated phenotypic screening approach for cancer immunotherapy Recent genomic sequencing studies of cancer reinforce the idea that cancer arises through repeated rounds of mutation and selection, that is it is a process of somatic evolution [ 49 - 51 ].
A QSP-inspired case study: Identifying local network re-wiring associated with Interleukin As summarized in Fig. Conclusion While cancer immunotherapy is experiencing incredible clinical success, sustaining progress and maximizing the return to the community from the investment in human and financial capital requires a strategy for identifying new mechanisms of action.
- LInvitation au voyage, Invitation to the Journey.
- Advances in cancer drug discovery and development Symposium -.
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- Rational Combination Cancer Immunotherapy.
Footnotes Competing interests The author declares that he has no competing interests. Breakthrough of the year Preclinical development of molecular-targeted agents for cancer. Nat Rev Clin Oncol. The Business and Medicine Report. Defining the critical hurdles in cancer immunotherapy. Getz K, Kaitin KI. Why is the pharmaceutical and biotechnology industry struggling? Best Practices for Streamlining the Development Process.
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Anti-programmed-death-receptor-1 treatment with pembrolizumab in ipilimumab-refractory advanced melanoma: Survival, durable tumor remission, and long-term safety in patients with advanced melanoma receiving nivolumab. Practical management of immune-related adverse events from immune checkpoint protein antibodies for the oncologist.
The economics of follow-on drug research and development: Nat Rev Drug Discov. The patents-based pharmaceutical development process: Vicini P, van der Graaf PH. Systems pharmacology for drug discovery and development: Hanahan D, Weinberg RA. Purvis JE, Lahav G. Encoding and decoding cellular information through signaling dynamics.
Phenotypic screening of small molecule libraries by high throughput cell imaging. An WF, Tolliday N. Cell-based assays for high-throughput screening. In vivo discovery of immunotherapy targets in the tumour microenvironment. Swinney DC, Anthony J. How were new medicines discovered? A dynamic, computer-based mathematical model of atopic asthma. Integrating epidemiological data into a mechanistic model of type 2 diabetes: The pathogenesis and natural history of type 1 diabetes. Cold Spring Harb Perspect Med.
Extent of beta cell destruction is important but insufficient to predict the onset of type 1 diabetes mellitus. Age-corrected beta cell mass following onset of type 1 diabetes mellitus correlates with plasma C-peptide in humans. A comprehensive review of interventions in the NOD mouse and implications for translation. Dosing and timing effects of anti-CD40L therapy: Virtual optimization of nasal insulin therapy predicts immunization frequency to be crucial for diabetes protection.
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Preexisting autoantibodies predict efficacy of oral insulin to cure autoimmune diabetes in combination with anti-CD3. The prioritization of cancer antigens: Mechanistic systems modeling to guide drug discovery and development. Systems biology for battling rheumatoid arthritis: Syst Biol Stevenage ; 4: Treatment with sitagliptin or metformin does not increase body weight despite predicted reductions in urinary glucose excretion.
J Diabetes Sci Technol. A whole-cell computational model predicts phenotype from genotype. Quantifying crosstalk among Interferon-gamma, Interleukin and Tumor Necrosis Factor signaling pathways within a Th1 cell model. Cancer as an evolutionary and ecological process.
Greaves M, Maley CC. Clonal evolution in cancer. An evolutionary perspective on anti-tumor immunity. The biology of cancer. Motz GT, Coukos G. Deciphering and reversing tumor immune suppression. Cellular constituents of immune escape within the tumor microenvironment. Immune resistance orchestrated by the tumor microenvironment. Chen DS, Mellman I. Demonstration of an interferon gamma-dependent tumor surveillance system in immunocompetent mice. Increased susceptibility to tumor initiation and metastasis in TNF-related apoptosis-inducing ligand-deficient mice. Lack of il12rb2 signaling predisposes to spontaneous autoimmunity and malignancy.
Cancer immunotherapy strategies based on overcoming barriers within the tumor microenvironment. Interleukin and the regulation of innate resistance and adaptive immunity. Il12b genotype and clinical phenotype of 13 patients from six kindreds. Am J Hum Genet.
Low penetrance, broad resistance, and favorable outcome of Interleukin 12 receptor beta1 deficiency: Twelve immunotherapy drugs that could cure cancers. Phase I evaluation of intravenous recombinant human interleukin 12 in patients with advanced malignancies. Phase I trial of twice-weekly intravenous interleukin 12 in patients with metastatic renal cell cancer or malignant melanoma: Colombo MP, Trinchieri G. Interleukin in anti-tumor immunity and immunotherapy. Cytokine Growth Factor Rev. Alum with interleukin augments immunity to a melanoma peptide vaccine: Immunization with Melan-A peptide-pulsed peripheral blood mononuclear cells plus recombinant human interleukin induces clinical activity and T-cell responses in advanced melanoma.
A phase i study of interleukin 12 with trastuzumab in patients with human epidermal growth factor receptoroverexpressing malignancies: Tumor-targeted T cells modified to secrete IL eradicate systemic tumors without need for prior conditioning. IL triggers a programmatic change in dysfunctional myeloid-derived cells within mouse tumors. Depletion of myeloid-derived suppressor cells during interleukin immunogene therapy does not confer a survival advantage in experimental malignant glioma.
IL delivered intratumorally by multilamellar liposomes reactivates memory T cells in human tumor microenvironments. A phase 1 study of AS, a novel antibody-cytokine fusion protein, in patients with malignant melanoma or renal cell carcinoma. Conditional interleukin gene therapy promotes safe and effective antitumor immunity.
Efficacy of oncolytic adenovirus expressing suicide genes and interleukin in preclinical model of prostate cancer. B16 as a mouse model for human melanoma. Curr Protoc Immunol Chapter. Establishment of an ILresponsive T cell clone: A screening assay to identify agents that enhance T-cell recognition of human melanomas. Assay Drug Dev Technol.
GITR pathway activation abrogates tumor immune suppression through loss of regulatory T cell lineage stability. Differential secretome analysis of cancer-associated fibroblasts and bone marrow-derived precursors to identify microenvironmental regulators of colon cancer progression. Exploring intercellular signaling by proteomic approaches. Join a breakout discussion group. These are informal, moderated discussions with brainstorming and interactive problem solving, allowing participants from diverse backgrounds to exchange ideas and experiences and develop future collaborations around a focused topic.
Details on the topics and moderators are below. We have determined that the combination of an anti-tumor antibody A , IL-2 I , anti-PD-1 antibody P , and a therapeutic vaccine V is a highly efficacious and well-tolerated immunotherapy against established syngeneic tumors in mice.
Symposium: Advances in cancer drug discovery and development
ADCs have shown favorable results in numerous clinical trials, improving the tolerability of potent, broadly acting cytotoxins. The same properties that make ADCs attractive as single agents also enable them to be preferred partners for combinations with immunotherapy. This talk will provide an overview of the preclinical and clinical results of these combinations. Paula Miliani de Marval, Ph. These models offer unique tools to assess the anti-tumor response to immune-checkpoint inhibitors in animals bearing a human immune environment. The work presented here shows that NCG mice successfully engraft with hPBMC providing a suitable model for pre-clinical immune-oncology studies.
The results from these studies will be discussed in this presentation. Such proliferation of development efforts to a single target is unprecedented in pharma. Focusing on gyneocological cancers, we apply genomic and molecular pathology approaches to define the mechanisms by which the immune system responds to the evolving tumor genome over space and time. We find that optimal anti-tumor immunity requires both T cells and antibody-producing B-lineage cells. We have identified three immune response patterns with distinct implications for immunotherapy.
These insights are being translated to clinical trials of adoptive T cell therapy. This approach empowers antibodies by genetically fusing them to tumor cell-killing cytokines that can also activate host immune system locally in the TME. This presentation details preclinical and clinical updates of FIT technology. Despite the encouraging response rate of variety of malignancies to anti-PD-1 and PD-L1 antibodies resistance do occur in most patients.
We will explore hypotheses for this resistance and potential combinations that may overcome such resistance. Immunotherapy has rapidly been developed as a metastatic treatment for several cancers, but its greatest potential may be as part of neo adjuvant strategies in patients with localized and curable disease. Immunotherapy strategies in neo adjuvant prostate cancer will be discussed as a means to provide a template for similar studies in other cancers. Immune responses and imaging correlates will be discussed as possible biomarkers in such studies.
Despite the impact of these agents, however, only a minority of patients respond to therapy.
Mechanistic and real world implications of these findings will be discussed. Immunotherapy modern companion diagnostics are marked by the urging need for increased sensitivity for the detection of target biomarkers, the constellation of tumor and stromal cells participating in immunoediting, as well as their functional status.
The double-edged interaction of tumor microenvironment with therapeutic regimens conventional or immune-targeting creates a dynamic continuum that needs close follow-up.
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