Raising the bar during early immunotherapy for cancer: simple mathematical models may help distinguish temporary vs. ultimate progression

Background: The apparent success of immunotherapy depends on the duration of follow up, sometimes with little evidence of efficacy during the first 4 to 8 months and often some degree of “pseudoprogression”. Differentiating transient pseudoprogression from true progression that would require a change in therapy can be challenging. The present study uses mathematical modeling and simulation to account for the unique kinetics and delayed clinical effects of immunotherapy and suggests improved approaches to predict efficacy and patient response from imaging studies. Methods: A mathematical model of tumor cell-immunocyte interaction is exercised to simulate a large number of individual patients and to derive surrogate endpoints for success or failure from the ratio of tumor diameter at 2, 4, 6, 8, 10, or 12 months follow up to initial tumor diameter. The simplified predator-prey model includes 4 lumped parameters: net tumor growth rate, g; immune cell killing efficiency, k; immune cell signaling, ; and immune cell half-life decay, μ. Differential equations, dT/dt = gT – kL and dL/dt = LT – μL, for numbers of tumor cells, T, (the prey) and immunocytes, L, (the predators) are solved numerically as functions of time, t , with ranges of g, k, , and initial conditions estimated from clinically available data. Tumor diameters, d, are proportional to the cube root of T + L. Apparent progression is defined when the time-varying diameter ratio, d/d0, exceeds a pre-defined, adjustable threshold. True progression is defined as d/d0 \u3e 1 at 24 months follow up or T/T0 \u3e 10 at any time. Results: Depending on initial conditions, the model equations predict either simple or complex dynamics, including cyclic increases in tumor cell numbers prior to a population crash to zero, apparent cure with late recurrence, and better long-term outcome with initially smaller lymphocyte numbers. Simulations of 4000 such complex cases show that d/d0 \u3e 1.0 at 2 to 6 months is a poor predictor of true progression, and often signals pseudoprogression. However, raising the bar or threshold for defining progressive disease from d/d0 \u3e 1.0 to d/d0 \u3e 2.0 during the first 6 months of immunotherapy and lowering the bar to d/d0 \u3e 0.5 after 6 months can eliminate most instances of pseudoprogression and lead to better over-all outcomes. Conclusions: Mathematical models can account for the complex dynamics of immune-tumor cell interactions that make accurate clinical decisions to continue or discontinue treatment difficult. The present model and approach can be adapted and calibrated to data for different types and stages of cancer and help to optimize treatment success

Cancer Immunotherapy: a Review

BACKGROUND: The goals of treating patients with cancer are to cure the disease, prolong survival, and improve quality of life. Immune cells in the tumor microenvironment have an important role in regulating tumor progression. Therefore, stimulating immune reactions to tumors can be an attractive therapeutic and prevention strategy.CONTENT: During immune surveillance, the host provides defense against foreign antigens, while ensuring it limits activation against self antigens. By targeting surface antigens expressed on tumor cells, monoclonal antibodies have demonstrated efficacy as cancer therapeutics. Recent successful antibody-based strategies have focused on enhancing antitumor immune responses by targeting immune cells, irrespective of tumor antigens. The use of antibodies to block pathways inhibiting the endogenous immune response to cancer, known as checkpoint blockade therapy, has stirred up a great deal of excitement among scientists, physicians, and patients alike. Clinical trials evaluating the safety and efficacy of antibodies that block the T cell inhibitory molecules cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and programmed cell death 1 (PD-1) have reported success in treating subsets of patients. Adoptive cell transfer (ACT) is a highly personalized cancer therapy that involve administration to the cancer-bearing host of immune cells with direct anticancer activity. In addition, the ability to genetically engineer lymphocytes to express conventional T cell receptors or chimeric antigen receptors has further extended the successful application of ACT for cancer treatment.SUMMARY: For cancer treatment, 2011 marked the beginning of a new era. The underlying basis of cancer immunotherapy is to activate a patient's own T cells so that they can kill their tumors. Reports of amazing recoveries abound, where patients remain cancer-free many years after receiving the therapy. The idea of harnessing immune cells to fight cancer is not new, but only recently have scientists amassed enough clinical data to demonstrate what a game-changer cancer immunotherapy can be. This field is no stranger to obstacles, so the future looks very promising indeed

A tug-of-war between driver and passenger mutations in cancer and other adaptive processes

Cancer progression is an example of a rapid adaptive process where evolving new traits is essential for survival and requires a high mutation rate. Precancerous cells acquire a few key mutations that drive rapid population growth and carcinogenesis. Cancer genomics demonstrates that these few 'driver' mutations occur alongside thousands of random 'passenger' mutations-a natural consequence of cancer's elevated mutation rate. Some passengers can be deleterious to cancer cells, yet have been largely ignored in cancer research. In population genetics, however, the accumulation of mildly deleterious mutations has been shown to cause population meltdown. Here we develop a stochastic population model where beneficial drivers engage in a tug-of-war with frequent mildly deleterious passengers. These passengers present a barrier to cancer progression that is described by a critical population size, below which most lesions fail to progress, and a critical mutation rate, above which cancers meltdown. We find support for the model in cancer age-incidence and cancer genomics data that also allow us to estimate the fitness advantage of drivers and fitness costs of passengers. We identify two regimes of adaptive evolutionary dynamics and use these regimes to rationalize successes and failures of different treatment strategies. We find that a tumor's load of deleterious passengers can explain previously paradoxical treatment outcomes and suggest that it could potentially serve as a biomarker of response to mutagenic therapies. Collective deleterious effect of passengers is currently an unexploited therapeutic target. We discuss how their effects might be exacerbated by both current and future therapies

CCL2 produced by the glioma microenvironment is essential for the recruitment of regulatory T cells and myeloid-derived suppressor cells

In many aggressive cancers, such as glioblastoma multiforme (GBM), progression is enabled by local immunosuppression driven by the accumulation of regulatory T cells (Treg) and myeloid-derived suppressor cells (MDSC). However, the mechanistic details of how Treg and MDSC are recruited in various tumors is not yet well understood. Here we report that macrophages and microglia within the glioma microenvironment produce CCL2, a chemokine that is critical for recruiting both CCR4+ Treg and CCR2+Ly-6C+ monocytic MDSC in this disease setting. In murine gliomas, we established novel roles for tumor-derived CCL20 and osteoprotegerin in inducing CCL2 production from macrophages and microglia. Tumors grown in CCL2 deficient mice failed to maximally accrue Treg and monocytic MDSC. In mixed-bone marrow chimera assays, we found that CCR4-deficient Treg and CCR2-deficient monocytic MDSC were defective in glioma accumulation. Further, administration of a small molecule antagonist of CCR4 improved median survival in the model. In clinical specimens of GBM, elevated levels of CCL2 expression correlated with reduced overall survival of patients. Lastly, we found that CD163-positive infiltrating macrophages were a major source of CCL2 in GBM patients. Collectively, our findings show how glioma cells influence the tumor microenvironment to recruit potent effectors of immunosuppression that drive progression

Single-Cell Transcriptomics in Cancer Immunobiology: The Future of Precision Oncology.

Cancer is a heterogeneous and complex disease. Tumors are formed by cancer cells and a myriad of non-cancerous cell types that together with the extracellular matrix form the tumor microenvironment. These cancer-associated cells and components contribute to shape the progression of cancer and are deeply involved in patient outcome. The immune system is an essential part of the tumor microenvironment, and induction of cancer immunotolerance is a necessary step involved in tumor formation and growth. Immune mechanisms are intimately associated with cancer progression, invasion, and metastasis; as well as to tumor dormancy and modulation of sensitivity to drug therapy. Transcriptome analyses have been extensively used to understand the heterogeneity of tumors, classifying tumors into molecular subtypes and establishing signatures that predict response to therapy and patient outcomes. However, the classification of the tumor cell diversity and specially the identification of rare populations has been limited in these transcriptomic analyses of bulk tumor cell populations. Massively-parallel single-cell RNAseq analysis has emerged as a powerful method to unravel heterogeneity and to study rare cell populations in cancer, through unsupervised sampling and modeling of transcriptional states in single cells. In this context, the study of the role of the immune system in cancer would benefit from single cell approaches, as it will enable the characterization and/or discovery of the cell types and pathways involved in cancer immunotolerance otherwise missed in bulk transcriptomic information. Thus, the analysis of gene expression patterns at single cell resolution holds the potential to provide key information to develop precise and personalized cancer treatment including immunotherapy. This review is focused on the latest single-cell RNAseq methodologies able to agnostically study thousands of tumor cells as well as targeted single-cell RNAseq to study rare populations within tumors. In particular, we will discuss methods to study the immune system in cancer. We will also discuss the current challenges to the study of cancer at the single cell level and the potential solutions to the current approaches