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eBriefing

Frontiers in Cancer Immunotherapy

Frontiers in Cancer Immunotherapy
Reported by
Rebecca Delker

Posted June 22, 2018

Presented By

The Cancer Discussion Group

The New York Academy of Sciences

Overview

The recent FDA approval of a gene therapy for leukemia treatment marks a new frontier in medical innovation for the field of cancer immunotherapy. Unlike the more traditional chemotherapy, cancer immunotherapy relies on biologics rather than exogenous chemicals, and ultimately aims to give the immune system an advantage in the battle against cancer by using two complementary strategies: the release of cancer driven immunosuppression, and direct activation of immune cells to mount an attack against the invading cancer.

On April 26 and April 27, 2018, the Cancer Discussion Group at the New York Academy of Sciences hosted Frontiers in Cancer Immunotherapy, a two-day symposium that brought together scientists and physician-scientists from industry and academia for a discussion about the exciting breakthroughs, challenges, and opportunities presented by recent developments in cancer immunotherapy. Speakers covered an array of topics, including: new mechanistic perspectives from basic and preclinical research, results from early phase clinical trials of combination therapies, the durability of treatment responses to CAR-T cell therapies, and managing the known toxicity of effective immunotherapies in clinical trials.

Speakers

Chris Boshoff, MD, PhD, Pfizer
Chris Boshoff, MD, PhD, Pfizer
Dale L. Greiner, PhD, University of Massachusetts Medical School
Dale L. Greiner, PhD, University of Massachusetts Medical School
Priti Hegde, PhD, Genentech
Priti Hegde, PhD, Genentech
Marco Davila, MD, PhD, Moffitt Cancer Center
Marco Davila, MD, PhD, Moffitt Cancer Center
Prasad S. Adusumilli, MD, Memorial Sloan Kettering Cancer Center
Prasad S. Adusumilli, MD, Memorial Sloan Kettering Cancer Center
Christopher Klebanoff, MD, Memorial Sloan Kettering Cancer Center
Christopher Klebanoff, MD, Memorial Sloan Kettering Cancer Center
Maria Fardis, PhD, MBA, Iovance Biotherapeutics
Maria Fardis, PhD, MBA, Iovance Biotherapeutics
David Jenkins, PhD, Tesaro
David Jenkins, PhD, Tesaro
Jeffrey V. Ravetch, MD, PhD, The Rockefeller University
Jeffrey V. Ravetch, MD, PhD, The Rockefeller University
Miriam Merad, MD, PhD, Icahn School of Medicine at Mount Sinai
Miriam Merad, MD, PhD, Icahn School of Medicine at Mount Sinai
Timothy Chan, MD, PhD, Memorial Sloan Kettering Cancer Center
Timothy Chan, MD, PhD, Memorial Sloan Kettering Cancer Center
Renier Brentjens, MD, PhD, Memorial Sloan Kettering Cancer Center
Renier Brentjens, MD, PhD, Memorial Sloan Kettering Cancer Center
Jennifer Wargo, MD, MD Anderson Cancer Center
Jennifer Wargo, MD, MD Anderson Cancer Center
Combining Immune CheckPoint Blockers with Targeted Therapies

Speaker

Chris Boshoff, of Pfizer, opened the conference with an overview of critical points of focus to advance the field of cancer immmunotherapy and develop more universally successful therapeutics. Many of these advances emphasized the necessity of clinical research, and the construction of the necessary infrastructure and data informatics to draw meaning from clinical research to maximize the potential benefit of these therapies. Referencing a 2017 study, which demonstrated that patient-to-patient variability is sufficient to explain the superior efficacy of FDA-approved drug combinations compared to single drug treatments, Boshoff noted the importance of a precision medicine strategy to understand what tumors will and will not respond to the therapy in use. In order to overcome the 20%-25% response rate to single checkpoint therapies, and the fact that a majority of tumors will develop resistance, we need “real world data to understand some of the basic clinical aspects of current therapies,” said Boshoff. This includes the use of novel artificial intelligence and/or neural network platforms to draw meaning from comprehensive data sets of patients receiving immunotherapy treatment; a greater understanding of the connection between the genotype of a tumor and the phenotype of the tumor microenvironment to predict therapeutic response from sequencing data; and finally, a better understanding of how well information gleaned from peripheral blood samples (e.g. tumor mutation burden, T cell repertoire) both correlates with information derived from tumor biopsies, and can predict patient response and personal risk factors.

Inter-patient Variability can explain increased efficacy of combination therapies. Inter-patient variability can explain the increased response rates of many combination therapies. It is not that the combined therapies necessarily have a stronger effect, but that a larger portion of the population now responds to at least one of the applied therapies.

Inter-patient Variability can explain increased efficacy of combination therapies. Inter-patient variability can explain the increased response rates of many combination therapies. It is not that the combined therapies necessarily have a stronger effect, but that a larger portion of the population now responds to at least one of the applied therapies.

Speaker Presentation

Combining Immune CheckPoint Blockers with Targeted Therapies


Chris Boshoff (Pfizer)

Further Readings

Session 1: Innovative Tools for Immunotherapy

Speakers

Current State of Humanized Mouse Models in Cancer Immunotherapy

“There are over 3000 clinical trials in immunotherapies and the expense of that is going to be amazing,” said Dale Greiner, of the University of Massachusetts Medical School. Greiner and colleagues work to find pre-clinical models that can accelerate the pace of discovery. For Greiner, this involves the engraftment of human immune cells into immunodeficient mice to generate a close proxy for the human immune system. Greiner opened with a historical account of the mutations made to generate an immunodeficient mouse, as well as an overview of the methods to engraft human immune cells into this mouse (Figure 2). The “limitations of humanized mice are enormous and you need to design your question properly,” said Greiner, emphasizing that humanized mouse systems are not perfect. Some of these limitations include the rapid onset of graft-versus-host-disease (GVHD) in ‘Hu-PBL-SCID’ mice; the education of human T cells with mouse MHC molecules in ‘Hu-SRC-SCID’ mice; and the knockout of IL2Rg–common to many humanized mouse models, which abrogates cytokine signaling necessary for the development of good germinal centers, follicular dendritic cells, and class-switch recombination in B lymphocytes.

An overview of the methods of engraftment to generate mice with humanized immune systems. Each of these methods has unique benefits and limitations, making the choice of model an important consideration in experimental design.

An overview of the methods of engraftment to generate mice with humanized immune systems. Each of these methods has unique benefits and limitations, making the choice of model an important consideration in experimental design.

Greiner is working toward the development of additional modifications to these mouse models to help overcome some of these limitations. Recognizing that the bulk of GVHD in humanized mouse models stems from targeting mouse MHC class I and II molecules, for example, Greiner and colleagues have generated knockouts of each of these to reduce the lethal effect. Further, as the structure of cytokines does not translate across the species barrier, Greiner is replacing the expression of murine cytokines with their human counterpart. As Greiner is interested in advancing patient-derived xenograft modeling in cancer, he stressed the importance of human cytokine expression for the engraftment of primary human myeloma cells, which require IL6 signaling for growth. He hopes that these mice will be useful for growing human tumors as well as  for testing immunotherapies in the context of an immune system derived from the same patient. This of course, as he noted, relies on a constant effort to validate that tumor engraftments in humanized mice recapitulate their patient counterpart.

Insights into Modes of Action and Resistance to Checkpoint Inhibitors in Cancer

Priti Hegde, of Genentech, presented a comprehensive analysis of the factors that influence response to checkpoint inhibitors. Using data from a large number of phase III clinical trials, Hegde emphasized the importance of two signals—tumor mutation burden (signal 1) and a pre-existing immune response as measured by PDL1 expression and IFNg expression (signal 2)—that positively impact response rate. The information we glean from cancers that respond to checkpoint blockades can be used to design a strategy against tumors that have lower response rates. Artificially enhancing the immunogenicity of the tumor (signal 1) or the immune response (signal 2) can improve the efficacy of immune checkpoint therapies for the classically non-responsive cancers. As an example, Hegde presented a potential combination therapy to enhance signal 2 in bladder cancer. The presence of a pre-existing immune response, referred to as “inflamed cancers” by Hegde, correlates with the localization of immune cells in and near the tumor. While inflamed tumors are infiltrated with immune cells, non-inflamed tumors either exclude immune cells to the surrounding stroma or reside in an immune desert. By analyzing DNA mutation data, gene expression data, and the spatial localization of T cells within tumors of patients with metastatic bladder cancer, Hegde and her team recognized the correlation between TGFbeta signaling and poor response. TGFbeta, which is involved in the formation of collagen fibers, can trap T cells and prevent infiltration. Hegde compared the collagen fibers to “train tracks where T cells are the trains, and they essentially go along those train tracks,” resulting in the exclusion of T cells from the tumor. In fact, immunohistochemistry of patient tumor samples displayed an excluded phenotype. Using a murine model of breast cancer that also displays immune cell excluded tumors, they tested the efficacy of treatment with anti-PDL1 in combination with anti-TGFbetab. In this model, the combination therapy increased T cell infiltration and inhibited tumor growth more than anti-PDL1 alone. Modulating the tumor microenvironment may make it possible to improve the efficacy of therapies already in existence.

Immune Cell Exclusion by TGFbeta. On the left is a schematic of how high TGFbetab signaling can result in the formation of collagen tracks, trapping T cells outside of the tumor and preventing a productive immune response. On the right is a depiction of train tracks as analogous imagery for this phenomenon, as well as a staining of an actual bladder tumor showing CD8+ T cells surrounding, but not infiltrating, the tumor.

Immune Cell Exclusion by TGFbeta. On the left is a schematic of how high TGFbetab signaling can result in the formation of collagen tracks, trapping T cells outside of the tumor and preventing a productive immune response. On the right is a depiction of train tracks as analogous imagery for this phenomenon, as well as a staining of an actual bladder tumor showing CD8+ T cells surrounding, but not infiltrating, the tumor.

Speaker Presentations

Current State of Humanized Mouse Models in Cancer Immunotherapy


Dale L. Greiner (University of Massachusetts Medical School)

Insights into Modes of Action and Resistance to Checkpoint Inhibitors in Cancer


Priti Hegde (Genentech)

Further Readings

Hegde

Chen DS, Mellman I.

Immunity. 2013;39(1):1–10.

Alexandrov LB, Nik-Zainal S, Wedge DC, et al.

Nature. 2013;500(7463):415–421.

Mariathasan S, Turley SJ, Nickles D, et al.

Nature. 2018;554(7693):544–548.

Hegde PS, Karanikas V, Evers S.

Clinical Cancer Research. 2016;22(8):1865–1874.

Session 2: Mechanistic Perspectives driving the Next Generation of Immunotherapies

Speakers

The Next Evolution of CD19-targeted CAR-T Cells

Marco Davila, of the Moffitt Cancer Center, for example, suggested that because the two FDA approved CD19-targeted CAR-T cell therapies utilize different costimulatory domains (CD28 versus 4-1BB), the high relapse rate of both may be due to distinct cellular mechanisms. In fact, Davila presented pre-clinical mouse model data to suggest that 4-1BB based CAR-T cells rely on proliferation and persistence in patients to mount an anti-tumor response. CD28 based CAR-T cells have increased cytotoxicity, and thus rely less on persistence, but also suffer from a low level of tonic signaling that results in the eventual suppression of antigen recognition (aka T Cell exhaustion). A dissection of the signaling pathways downstream of each of these domains enabled Davila and colleagues to design mutant forms of both 4-1BB and CD28 that bypass the aforementioned problems. In a pre-clinical murine model of ALL, they demonstrated increased persistence of mutant 4-1BB CAR-T cells due to enhanced NFkB signaling, and reduced T cell exhaustion in mutant CD28 CAR-T cells, while preserving CAR-T function. Davila’s work indicates the potential benefits of altering the costimulatory domain of the CAR.

CAR-T Cell Therapy for Solid Tumors

Prasad Adusumilli, of Memorial Sloan Kettering Cancer Center, recognizes the importance of the infiltration of immune cells into the tumor for survival. In a series of studies, Adusumilli and colleagues mapped the localization of immune factors within the tumor and stroma and correlated this with survival in patients with thoracic cancers. He and others are working toward developing cellular therapies against solid tumors using CAR-T cell technology. The main challenge is to find a tumor antigen as a target of CAR-T cells that is specific to, or more highly expressed in, tumor cells as compared to healthy tissue to minimize “on-target, off-tumor toxicity.” Adusumilli has generated CAR-T cells against the cell surface protein mesothelin, which is highly expressed in a variety of solid tumors, with low expression in healthy mesothelia cells.

In addition to its high expression across a broad set of cancer types, mesothelin is implicated in promoting cancer aggressiveness, making it an even more favorable antigen.

In pre-clinical murine models of mesothelioma and lung adenocarcinoma, mesothelin-targeted CAR-T cells successfully eliminated tumors in a large fraction of the tested population; and early phase I preclinical trial results demonstrate no associated toxicity, providing hope for the future use of this therapy. Beyond the initial challenge of discovering a suitable antigen to target is the challenge of overcoming the immunosuppressive effects of the tumor microenvironment. In fact, Adusumilli is working to develop an ex vivo culture system to test the efficacy of CAR-T cells in an immunocompetent model instead of mice, which are often made immunodeficient to conduct these experiments. Using patient-derived malignant pleural effusions (MPEs), which contain tumor cells in addition to a full complement of immune cells, he showed that the effector function of CAR-T cells is significantly decreased as compared to the equivalent cells cultured in MPE-free media. This finding not only reinforces the importance of the TME in modulating immune response, but could provide a much more representative model in which to test immunotherapies.

T-Cell Therapy 2.0: Building the Next Generation of Adoptive Cancer Immunotherapies

Christopher Klebanoff, of Memorial Sloan Kettering Cancer Center, has been working on manipulating the intrinsic biology of T cells to improve T cell receptor and CAR-engineered adoptive immunotherapies. One common problem for CAR-T cell therapy is the suppression of an immune response by the tumor microenvironment, despite the T cell having specificity for the tumor antigen. One such way that this can occur is through the induction of T cell apoptosis. Many solid tumors express FAS-ligand, which, when bound to FAS receptor on T cells, can induce T cell death. To counter this, and increase the efficacy of the tumor-targeted T cells, Klebanoff and colleagues designed CAR-T cells with a dominant negative FAS receptor that can still bind FAS-ligand, but does not induce cell death. Pre-clinical murine models demonstrated the ability of this additional mutation to decrease tumor growth and increase survival.

Further, the subtypes of T cells that make up the CAR-T cell therapy infusion material are heterogeneous. Thus, optimizing the efficacy of the infusion material may be accomplished by optimizing the T-cell make up of the infusion bag. Toward this end, Klebanoff presented data focused on the transcription factor, FOXO-1, which regulates central memory T cells. Mutation of FOXO-1 in preclinical studies can bias the contents of the infusion material toward this subset of memory T cells, which have been shown to increase the efficacy of T cell therapies.

Speaker Presentations

The Next Evolution of CD19-targeted CAR-T Cells


Marco Davila (Moffitt Cancer Center)

CAR-T Cell Therapy for Solid Tumors


Prasad S. Adusumilli (Memorial Sloan Kettering Cancer Center)

T-Cell Therapy 2.0: Building the Next Generation of Adoptive Cancer Immunotherapies


Christopher Klebanoff (Memorial Sloan Kettering Cancer Center)

Further Readings

Davila

Maude SL, Frey N, Shaw PA, et al.

N Engl J Med. 2014;371(16):1507–1517.

Adusumilli

Menon S, Shin S, Dy G.

Cancers. 2016;8(12):106.

Morello A, Sadelain M, Adusumilli PS.

Cancer Discov. 2016;6(2):133–146.

Klebanoff

Gattinoni L, Klebanoff CA, Restifo NP.

Nat Rev Cancer. 2012;12(10):671–684.

Klebanoff CA, Scott CD, Leonardi AJ, et al.

Journal of Clinical Investigation. 2016;126(1):318–334.

Kim MV, Ouyang W, Liao W, Zhang MQ, Li MO.

Immunity. 2013;39(2):286–297.

Session 3: Alternative Checkpoints and Cellular Therapies

Speakers

Miriam Merad

Icahn School of Medicine at Mount Sinai

Checkpoint Inhibitor Therapies

The proper functioning of the immune system requires a rather fine-tuned balance between activating and inhibitory signals. Inhibitory signals are critical for limiting the threshold and duration of immune activity, as well as protecting the organism’s healthy tissue from a self-specific immune response. Unfortunately, tumors have hijacked and taken advantage of these built-in inhibition mechanisms to generate an immunosuppressive tumor microenvirownment. Thus, even if T cells—either endogenously or exogenously delivered—show specificity for tumor antigen, the cytotoxic activity of the T cell is blocked. Further, chronic antigen exposure can lead to upregulation of multiple immune checkpoints on T cells, leading to the loss of sensitivity to antigen (T cell exhaustion), and the loss of cytotoxic activity. Efforts to block the immunosuppressive effects of the tumor through the delivery of monoclonal antibodies that block checkpoint inhibition pathways (i.e. checkpoint inhibitor blockades) show efficacy for some cancer types in the clinic, and have been approved for application. These include the anti-CTLA4 therapeutic antibodies that were approved first, and the more recent anti-PD1 and anti-PDL1 therapeutic antibodies, which block the interaction of PDL1 expressed on the surface of tumor cells with PD1 on T cells. While there were promising early results from each of these therapies, there is still much room for improvement. PD1/PDL1 blockades, for example, show a large range of response rates for various cancer types, with the bulk of cancers responding at a rate less than 50%. To tackle the low response rate, researchers are working toward combination therapies that target multiple checkpoints, as well as developing alternative cell therapy approaches, sometimes in combination with checkpoint inhibitor blockades.

PD1/PDL1 Blockade Therapy Response Rate by Tumor Type. A large range of response rates exist for different cancer types, but the bulk of cancers show a response rate less than 50%.

PD1/PDL1 Blockade Therapy Response Rate by Tumor Type. A large range of response rates exist for different cancer types, but the bulk of cancers show a response rate less than 50%.

Developing Tumor Infiltrating Lymphocytes for the Treatment of Cancer

Beyond checkpoint inhibition is a new array of cell therapies, in which immune cells are infused into a patient in the hope that they mount a direct anti-tumor immune response. Genomically engineered CAR-T cells have been the most popular in the media, but other forms of cell therapy are in development. Maria Fardis, of Iovance Biotherapeutics, presented initial findings from a number of clinical trials testing the company’s TIL therapy. TILs, or tumor infiltrating lymphocytes, are a patient’s own T cells that have left the periphery, entered the tumor, and mounted an anti-tumor response. They were first discovered through the observation that some malignant lesions can resolve without therapeutic intervention. Thus, TIL therapy, which extracts T cells from a patient’s tumor, expands the cells in vitro, and then reinfuses these cells back into the patient (after lymphodepletion with chemotherapy), is designed to leverage and enhance the body’s natural defense against cancer. Because there is no selection for antigen-specificity during the expansion process, the TIL infusion product is polyclonal, which means that it can recognize multiple tumor neo-antigens. This is particularly beneficial for solid tumors, which are quite heterogeneous.

With this focus on solid tumors, Iovance has developed a portfolio of clinical trials testing the use of TIL therapy on a variety of tumors including metastatic melanoma, head and neck squamous cell carcinoma (HNSCC), cervical cancer, and non-small cell lung cancer (NSCLC). The results from metastatic melanoma trials demonstrated that despite a relatively low complete response rate (CR = 22%), those who achieved CR did not relapse even nine years later. This occurred regardless of the therapies patients received prior to TIL treatment. This durability of response is incredibly encouraging. Clinical trials investigating the use of TILs on other types of cancer are ongoing, currently with limited evaluable data. Finally, two ongoing studies are testing the efficacy of TIL therapy in combination with either anti-PD1 or anti-PDL1 checkpoint blockade therapy in patients with NSCLC. As with other combination therapies we’ve seen, this aims to infuse the tumor with anti-tumor T cells while simultaneously preventing an immunosuppressive response from the tumor itself.

Building on PD-1: New Combinations to Enhance Therapeutic Activity

David Jenkins, of Tesaro, spoke about his group’s efforts to rationally design combination therapies by focusing on cellular factors that limit the response to immunotherapy. In addition to PD-1/PD-L1 checkpoint inhibition, tumor immunosurveillance is hampered by the expression of TIM-3 and LAG3, additional transmembrane proteins on the surface of T-cells. Both are implicated in T cell exhaustion due to chronic antigen exposure; and TIM-3 is even upregulated in response to anti-PD1 treatment in mice, as well as associated with dysfunctional T cells derived from patient tumors. Thus, tumor resistance to anti-PD-1 treatment may occur through the subsequent upregulation of these additional checkpoints, suggesting that targeting the PD1 pathway in combination with either TIM-3 or LAG-3 may overcome this resistance. In pre-clinical mouse models an anti-TIM3/anti-PD1 combination therapy or an anti-LAG-3/anti-PD-1 demonstrated improved anti-tumor activity as compared to either antibody treatment alone. Based on these positive results, both combination therapies are currently being tested in the clinic.

TIM-3 Regulates Dendritic Cell Function and Anti-Tumor Immunity

TIM-3 is important not only for T cells, but other immune cells as well. Brian Ruffell, of Moffitt Cancer Center, introduced TIM-3 as a target for modulating the activity of tumor-residing dendritic cells (DCs). Using a mouse model of mammary carcinoma—a tumor in which immune cells like macrophages and dendritic cells, but not T cells, express TIM-3—Ruffell and colleagues tested the efficacy of an anti-TIM-3 blockade to restrict tumor growth. Alone, anti-TIM-3 had no effect, but improved the efficacy of the chemotherapeutic agent, Paclitaxel, when delivered in combination. Performing a number of studies in this mouse model to deplete specific subsets of immune cells or block chemokine activity, they were able to discern the requirement of Type 1 DCs, CD8+ T cells, and DC chemokine expression to achieve this effect. These results demonstrate the promising effects of combining more traditional cancer therapeutics with immunotherapies. In this case, it is possible that the increase in tumor cell death due to chemotherapy enhances the propensity of DCs to promote a CD8+ T cell response, which is further enhanced by releasing the DC from TIM-3 mediated inhibition. This style of combination therapies is corroborated by work David Jenkins presented, demonstrating that PARP inhibition, which was typically thought to have a direct cytotoxic effect on tumor cells, may also impact the activity of immune cells, enhancing immune surveillance, and likely benefitting from the added blockade of inhibitory molecules such as PD1.

A proposed mechanism for the combined action of anti-TIM3 antibody therapy and chemotherapy for mammary carcinoma. Paclitaxel, a chemotherapeutic agent, induces cell death, which enhances dendritic cell activation. However, inhibition by TIM-3 prevents DC activation of CD8+ effector T cells unless released by the addition of an anti-TIM3 blockade. This unleashes the cytotoxic activity of T cells, further killing tumor cells.

A proposed mechanism for the combined action of anti-TIM3 antibody therapy and chemotherapy for mammary carcinoma. Paclitaxel, a chemotherapeutic agent, induces cell death, which enhances dendritic cell activation. However, inhibition by TIM-3 prevents DC activation of CD8+ effector T cells unless released by the addition of an anti-TIM3 blockade. This unleashes the cytotoxic activity of T cells, further killing tumor cells.

Keynote Lecture — Next Generation Immunotherapeutics for Cancer: Coupling Innate and Adaptive Immunity

The first keynote speaker of the conference, Jeffrey Ravetch, of The Rockefeller University, gave a comprehensive look at his research on the Fc (i.e. fragment, crystallizable) component of an antibody. Antibodies, produced by B-lymphocytes and important regulators of our adaptive immune response, contain two main components: the Fc fragment, and the Fab (i.e. fragment, antigen binding) fragment, which is where the antigen-binding capacity of the antibody lies. Typically, the latter fragment was considered the “interesting variable” of the antibody. It is where antigen binds, and it is subject to extraordinary diversification through the processes of V(D)J recombination and somatic hypermutation. These two processes enable our immune systems to produce antibodies that can recognize virtually any foreign antigen presented. The Fc, on the other hand, was considered the more stable and uninteresting component of the antibody. However, what Ravetch demonstrated through a number of studies spanning his career, is that the Fc is worthy of equal recognition with regard to the role it plays in regulating immunity. As he pointed out, the Fc contributes to diverse reactions in both our innate and adaptive immune response. He also demonstrated that, though not on par with the Fab fragment, the Fc fragment is subject to a great amount of diversity, generated through class switch recombination (generating different isotypes, such as IgG1 versus IgG3, etc.), as well as through the addition of branched oligosaccharides, both of which greatly affect binding patterns to the family of Fc receptors (FcR) expressed on other cells. In total, there can be greater than 103 different Fc variants for any one selected variable region (Figure 7). Extensive clinical evidence for the impact of Fc modifications exists, but one telling example is the requirement of Fc sialyation for the anti-inflammatory activity of IVIG therapy.

Fc Diversity. A schematic of the amount of diversity that can be generated in the Fc component of an antibody molecule.

Fc Diversity. A schematic of the amount of diversity that can be generated in the Fc component of an antibody molecule.

Ravetch’s work suggests that Fc domains are essential for therapeutic anti-tumor antibodies for which the mechanism was thought to be independent of Fc receptor signaling. This includes Herceptin, which targets Her2 in a subset of breast cancer, as well as Rituxan, which targets CD20 expressed on the surface of lymphomas. In a murine model, the effect of these antibody treatments required binding to FcRs and subsequent recruitment of effector cells. This lead to the subsequent modification of Rituxan to enhance binding to the activating FCRIIIA receptor (i.e. Obinutuzumab), which also increased progression free survival. More recent cancer immunotherapies, most notably checkpoint inhibitor blockades, may also work in more complex ways. While often thought of as a simple blocking mechanism, Ravetch presented data from murine models to show that the absence of FcRs completely abrogates the efficacy of anti-CTLA4 and anti-PDL1 therapeutics. Thus, the Fc fragment of the antibody is necessary, and modulating the Fc receptor can improve the efficacy of the therapy. For example, two CTLA4 antibodies produced, ipilimumab and tremelimumab, differ only in their Fc receptor but the former is effective, while the latter is not. Ravetch stressed that removal of Fc receptors “completely abrogates in vivo activity [of anti CTLA4 antibody]; it is completely dead, even though it is still blocking CTLA4.” Given this evidence, it will be necessary to move beyond the simplistic mechanistic model for how antibody antagonists and agonists work, to a more accurate, yet far more complex, model.

Mechanisms of Therapeutic Antibody Activity. On the left is the simple model, which envisions each antibody simply as an antagonist or agonist of its target. The right depicts a more complex, yet likely more representative, model in which an antibody therapy not only binds to its target but modulates the immune response through additional mechanisms of action.

Mechanisms of Therapeutic Antibody Activity. On the left is the simple model, which envisions each antibody simply as an antagonist or agonist of its target. The right depicts a more complex, yet likely more representative, model in which an antibody therapy not only binds to its target but modulates the immune response through additional mechanisms of action.

Finally, to study human Fc effector function in the mouse model requires the complement of human Fc receptors. Toward this end, Ravetch and his group generated an FcR humanized mouse model, replacing the mouse locus with that of the human. This model should enable the community to test human antibodies in a system that expresses human FcRs in the correct population of cells. As the community moves forward with anti-cancer antibody therapeutics, Ravetch advises people “to consider both ends of the molecule.” That is, focus on the Fc just as much as the Fab.

Keynote Lecture — Tumor Immunology and Immunotherapy

Concurrent with our recent understanding that inter-individual heterogeneity requires a precision medicine approach to disease treatment, is an awareness that this heterogeneity also requires a similar approach to research and discovery. As keynote speaker Miriam Merad, of the Icahn School of Medicine at Mount Sinai, noted, traditionally immunologists generated mouse models that were simply validated in humans at the end of the research process. Now, we are revisiting this paradigm and starting with the human and then refining the mouse models we use, a process many of the speakers referred to as “reverse translation.” This shift in thinking, arguably driven by technological advances, can potentially change the landscape of cancer research as it places a much greater emphasis on data collection from human lesions (untreated and treated) to drive research directions in the lab, and recognizes the power of the human system—revealed to us through new technologies—as a route to discovery.

Reverse Translation. This process involves generating insight from patient samples, and then moving back into a model system to test hypotheses and develop therapies.

Reverse Translation. This process involves generating insight from patient samples, and then moving back into a model system to test hypotheses and develop therapies.

Merad and her team have developed and utilized a number of new technologies to study the contribution of myeloid immune cells to survival following checkpoint inhibition. Merad discussed a series of compelling experiments that demonstrate that a subset of myeloid cell—the antigen-presenting, tissue resident CD103+ (CD141+ in human) dendritic cells (DCs)—are  necessary for response to checkpoint inhibitor blockade. Using a mouse model of melanoma, her lab showed that these dendritic cells carry antigen from the site of tumor to the draining lymph node in order to activate a CD8+ T cell response. A depletion of this cell compartment abrogated the response to PD-1/PD-L1 blockade therapy; and, notably, an expansion enhanced the response. To study this cell type in human lesions, Merad’s group developed a novel multiplex tissue staining protocol to analyze the expression of several immune markers present in the tumor microenvironment on a single slide. This iterative staining and destaining protocol revealed that immune cells are more numerous than tumor cells in the microenvironment, and that high tumor DC content correlated with better tumor outcome in patients with NSCLC. This suggests that in addition to TMB, PDL1 expression, and T cell infiltration, DC content can provide added information in guiding therapeutic intervention.

Merad also discussed the development and application of additional techniques to extensively analyze the myeloid compartment of human NSCLC tumors. These techniques include cyTOF phenotypic profiling of known immune cell markers, and single-cell RNA sequencing to identify immune cells in an unbiased fashion. For example, when paired with new scanning technology developed at Mount Sinai for detecting early lung cancer lesions, these technologies revealed that rapid and significant changes in immune cell composition occur very early after cancer formation. As she said, “very early on, the tumor is using the immune system to progress” and become invasive. Thus, Merad stressed the need to start therapy as early as possible.  Together, her studies emphasize the need to explore the diverse tumor myeloid compartment to identify targets in patient tumors to modulate anti-tumor immunity.

Speaker Presentations

Developing Tumor Infiltrating Lymphocytes for the Treatment of Cancer


Maria Fardis (Iovance Biotherapeutics)

Building on PD-1: New Combinations to Enhance Therapeutic Activity


David Jenkins (Tesaro)

TIM-3 Regulates Dendritic Cell Function and Anti-Tumor Immunity


Brian Ruffell (Moffitt Cancer Center)

Next Generation Immunotherapeutics for Cancer: Coupling Innate and Adaptive Immunity


Jeffrey V. Ravetch (The Rockefeller University)

Further Readings

Fardis

Rosenberg SA, Yang JC, Sherry RM, et al.

Clinical Cancer Research. 2011;17(13):4550–4557.

Jenkins

Nguyen LT, Ohashi PS.

Nat Rev Immunol. 2015;15(1):45–56.

Wherry EJ.

Nat Immunol. 2011;131(6):492–499.

Sakuishi K, Apetoh L, Sullivan JM, Blazar BR, Kuchroo VK, Anderson AC.

Journal of Experimental Medicine. 2010;207(10):2187–2194.

Ruffell

Gardner A, Ruffell B.

Trends Immunol. 2016;37(12):855–865.

de Mingo Pulido Á, Gardner A, Hiebler S, et al.

Cancer Cell. 2018;33(1):60–74.e66.

Ravetch

Nimmerjahn F, Ravetch JV.

Nat Rev Immunol. 2008;8(1):34–47.

Nimmerjahn F, Ravetch JV.

Science. 2005;310(5753):1510–1512.

Clynes RA, Towers TL, Presta LG, Ravetch JV.

Nat Med. 2000;6(4):443–446.

Nimmerjahn F, Ravetch JV.

Journal of Experimental Medicine. 2007;204(1):11–15.

DiLillo DJ, Ravetch JV.

Cell. 2015;161(5):1035–1045.

Simpson TR, Li F, Montalvo-Ortiz W, et al.

Journal of Experimental Medicine. 2013;210(9):1695–1710.

Dahan R, Sega E, Engelhardt J, Selby M, Korman AJ, Ravetch JV.

Cancer Cell. 2015;28(3):285–295.

Merad

Herbst RS, Soria J-C, Kowanetz M, et al.

Nature. 2014;515(7528):563–567.

Hugo W, Zaretsky JM, Sun L, et al.

Cell. 2016;165(1):35–44.

Remark R, Merghoub T, Grabe N, et al.

Sci Immunol. 2016;1(1):aaf6925–aaf6925.

Lavin Y, Kobayashi S, Leader A, et al.

Cell. 2017;169(4):750–757.e15.

Session 4: Beyond Cancer Biology: Learning from Immunity

Speaker

Immunotherapy: What’s Genes Got To Do With It?

Timothy Chan, of Memorial Sloan Kettering Cancer Center, introduced the possibilities available with new technologies to better predict patient response to immunotherapies. His approach is to dissect anti-tumor immunity with large-scale technologies to understand genetic determinants within the tumor and the T Cell population that predict response to checkpoint inhibitor blockades. Understanding the tumor mutational burden, gene expression profile, and the diversity of antigens presented on tumors cells through analysis of the MHC peptidome, as well as analyses of T cell expression patterns and T Cell Receptor repertoire (TCR) can help inform the type of therapy best suited to an individual patient.

Results from a number of clinical studies have demonstrated that tumor mutation burden (TMB) is predictive of better response to checkpoint inhibitor blockade therapies. Chan presented the results from an unsuccessful phase III trial of an anti-PD1 therapy (Nivolumab) versus traditional chemotherapy in patients with non-small cell lung cancer (NSCLC). Strikingly, when the patients were divided by mutational burden, it became clear that those with high TMB responded better to the immunotherapy as compared to chemotherapy; those with low TMB showed the opposite trend. Thus, despite the statistical washout of these positive responders in the whole group analysis, this trial demonstrates the power of using TMB as a diagnostic to better partition patients into the proper treatment pipeline. In fact, TMB is associated with greater survival in response to checkpoint blockade therapy in many cancer types, excluding glioma. Chan and colleagues are now using machine learning to look at the correlation between the expression of specific genes and response rates as well as dive deeper into tumor evolution in response to therapy.

Chan also presented evidence for the connection between HLA diversity and therapeutic response. Patients with heterozygosity at each of their three HLA loci fared better with immunotherapy. This increase in response was furthered by the combination of HLA diversity and high TMB. Not only is this an incredibly interesting biological finding, but it also underscores the need to approach cancer therapy at the level of the individual.

Tumor Mutation Burden as a Predictor of Response. Tumor mutation burden (TMB) is a good predictor of response to immune checkpoint blockade (ICB). In general, higher mutation burden is associated with increased survival post-treatment. The exception to this rule is glioma.

Tumor Mutation Burden as a Predictor of Response. Tumor mutation burden (TMB) is a good predictor of response to immune checkpoint blockade (ICB). In general, higher mutation burden is associated with increased survival post-treatment. The exception to this rule is glioma.

Speaker Presentation

Immunotherapy: What’s Genes Got To Do With It?


Timothy Chan (Memorial Sloan Kettering Cancer Center)

Further Readings

Chan

Rizvi NA, Hellmann MD, Snyder A, et al.

Science. 2015;348(6230):124–128.

Riaz N, Havel JJ, Makarov V, et al.

Cell. 2017;171(4):934–939.e15.

Chowell D, Morris LGT, Grigg CM, et al.

Science. 2018;359(6375):582–587.

Session 7: Clinical Responses and Tumor Dynamics

Speakers

CAR-T Cell Therapy of Cancer

Renier Brentjens, of Memorial Sloan Kettering Cancer Center, presented an early clinical trial that demonstrated that CD19 CAR-T treatment against relapsed/refractory B-ALL resulted in roughly 85% complete remission rate, but had a high associated relapse rate, with only about 50% of patients who exhibited minimal disease burden prior to CAR-T treatment surviving five years post-treatment. Like others, Brentjens was motivated by this data to make additional modifications to CAR-T cells to improve their function. Brentjens and his team have generated what he calls, “armored CAR-T cells,” which contain additional functionalities beyond the specificity for the tumor antigen of interest.

A schematic of ‘Armored CARs.’ Armored CAR-T Cells contain additional modifications beyond the CAR itself. Expression of additional costimulatory ligands, secretion of cytokines, and production of ScFv antibody fragments are depicted.

A schematic of ‘Armored CARs.’ Armored CAR-T Cells contain additional modifications beyond the CAR itself. Expression of additional costimulatory ligands, secretion of cytokines, and production of ScFv antibody fragments are depicted.

Thus far, these functionalities include expression of additional costimulatory ligands, secretion of pro-inflammatory cytokines, and secretion of antibody fragments to block cancer-driven immunosuppression. Each of these additional functionalities has shown promising improvements to CAR-T cell function in pre-clinical mouse models. Expression of the pro-inflammatory cytokine IL-18, for example, increased CAR-T cell persistence through an autocrine loop, and resulted in increased recruitment of non-CAR modified immune cells to mount an anti-tumor response. Co-expression of CD40 ligand on the surface of CAR-T cells also enabled cells to persist longer and control the tumor better, as well as directly enhance cell killing through an interaction with CD40 on tumor cells. Finally, despite the specificity of CAR-T cells for tumor antigen, suppression of activity can occur due to PD-L1 expression on the tumor. Thus, the secretion of an anti-PD-1 scFv antibody fragment has the potential to combine CAR-T based therapies with targeted checkpoint inhibition to overcome this suppression.

The Role of the Microbiome in Response to Cancer Therapy

Finally, Jennifer Wargo, of the MD Anderson Cancer Center, used novel sequencing technology to profile the gut microbiome. As our individual composition of bacterial cells—which vastly outnumber our human cells—has been found to affect our biology in a number of ways, Wargo and others have wondered how our non-human co-residents affect response to cancer immunotherapies. To study this, Wargo and her colleagues collected oral and gut (fecal) microbiome samples, as well as tumor biopsies, from patients with metastatic melanoma before and after the onset of therapy. Using 16s sequencing and/or genome shotgun sequencing, the composition and diversity of a patient’s microbiome was discerned.

For patients enrolled in a trial testing anti-PD-1 therapy, responders tended to have higher diversity in their gut microbiomes as compared to non-responders. Interestingly, there were no differences between the two response groups with regard to the oral microbiome. Further, patients with a high diversity in gut microbiome had significantly prolonged progression-free survival compared with patients with low or intermediate diversity. She noted that administration of antibiotics prior to or during immunotherapy was associated with a less favorable response to antibody treatment. In addition to diversity, Wargo noted the differences in composition of microbiomes between responders and non-responders. Some bacteria, such as Clostridiales, Ruminococcaceae, and Faecalibacterium, were in higher abundance in responders, and others, like Bacteroidales, were higher in non-responders. A connection between these favorable and unfavorable microbiomes could be made with the immune composition of the tumor microenvironment. Patients with a more favorable microbiome profile had tumors that exhibited higher expression of cytolytic T cell markers, and a higher percentage of antigen presenting cells. These studies provide strong evidence for the importance of our microbiome in regulating response to immune checkpoint blockade therapy, so the focus becomes whether or not we can modulate the gut microbiome to enhance response to immunotherapy.  Wargo discussed several potential strategies to do this, including fecal transplant, alterations in diet, and “designer probiotics.” She also discussed an upcoming clinical trial designed to test the effect of microbiome modulation.

It’s evident is that a complex array of tumor phenotypes, immune cell phenotypes, and microbiome phenotypes likely contribute to both the successes and failures of immunotherapies. To untangle this mess of interacting factors will likely require additional personalized research strategies.

Speaker Presentations

CAR-T Cell Therapy of Cancer


Renier Brentjens (Memorial Sloan Kettering Cancer Center)

The Role of the Microbiome in Response to Cancer Therapy


Jennifer Wargo (Anderson Cancer Center)

Further Readings

Brentjens

Park JH, Rivière I, Gonen M, et al.

N Engl J Med. 2018;378(5):449–459.

Sadelain M, Rivière I, Brentjens R.

Nat Rev Cancer. 2003;3(1):35–45.

Wargo

Sivan A, Corrales L, Hubert N, et al.

Science. 2015;350(6264):1084–1089.

Vetizou M, Pitt JM, Daillere R, et al.

Science. 2015;350(6264):1079–1084.

Gopalakrishnan V, Spencer CN, Nezi L, et al.

Science. 2018;359(6371):97–103.

Gopalakrishnan V, Helmink BA, Spencer CN, Reuben A, Wargo JA.

Cancer Cell. 2018;33(4):570–580.