Combined population dynamics and entropy modelling supports patient stratification in chronic myeloid leukemia

Modelling the parameters of multistep carcinogenesis is key for a better understanding of cancer progression, biomarker identification and the design of individualized therapies. Using chronic myeloid leukemia (CML) as a paradigm for hierarchical disease evolution we show that combined population dynamic modelling and CML patient biopsy genomic analysis enables patient stratification at unprecedented resolution. Linking CD34+ similarity as a disease progression marker to patientderived gene expression entropy separated established CML progression stages and uncovered additional heterogeneity within disease stages. Importantly, our patient data informed model enables quantitative approximation of individual patients’ disease history within chronic phase (CP) and significantly separates “early” from “late” CP. Our findings provide a novel rationale for personalized and genome-informed disease progression risk assessment that is independent and complementary to conventional measures of CML disease burden and prognosis

Advocating the need of a systems biology approach for personalised prognosis and treatment of B-CLL patients

The clinical course of B-CLL is heterogeneous. This heterogeneity leads to a clinical dilemma: can we identify those patients who will benefit from early treatment and predict the survival? In recent years, mathematical modelling has contributed significantly in understanding the complexity of diseases. In order to build a mathematical model for determining prognosis of B-CLL one has to identify, characterise and quantify key molecules involved in the disease. Here we discuss the need and role of mathematical modelling in predicting B-CLL disease pathogenesis and suggest a new systems biology approach for a personalised therapy of B-CLL patients

Memory NK cell features exploitable in anticancer immunotherapy

Besides their innate ability to rapidly produce effector cytokines and kill virus-infected or transformed cells, natural killer (NK) cells display a strong capability to adapt to environmental modifications and to differentiate into long-lived, hyperfunctional populations, dubbed memory or memory-like NK cells. Despite significant progress in the field of NK cell-based immunotherapies, some factors including their short life span and the occurrence of a tumor-dependent functional exhaustion have limited their clinical efficacy so that strategies aimed at overcoming these limitations represent one of the main current challenges in the field. In this scenario, the exploitation of NK cell memory may have a considerable potential. This article summarizes recent evidence in the literature on the peculiar features that render memory NK cells an attractive tool for antitumor immunotherapy, including their long-term survival and in vivo persistence, the resistance to tumor-dependent immunosuppressive microenvironment, the amplified functional responses to IgG-opsonized tumor cells, and in vitro expansion capability. Along with highlighting these issues, we speculate that memory NK cell-based adoptive immunotherapy settings would greatly take advantage from the combination with tumor-targeting therapeutic antibodies (mAbs), as a strategy to fully unleash their clinical efficacy

Mathematical modeling to elucidate brain tumor abrogation by immunotherapy with T11 target structure

T11 Target structure (T11TS), a membrane glycoprotein isolated from sheep erythrocytes, reverses the immune suppressed state of brain tumor induced animals by boosting the functional status of the immune cells. This study aims at aiding in the design of more efficacious brain tumor therapies with T11 target structure. We propose a mathematical model for brain tumor (glioma) and the immune system interactions, which aims in designing efficacious brain tumor therapy. The model encompasses considerations of the interactive dynamics of macrophages, cytotoxic T lymphocytes, glioma cells, TGF-$\beta$, IFN-$\gamma$ and the T11TS. The system undergoes sensitivity analysis, that determines which state variables are sensitive to the given parameters and the parameters are estimated from the published data. Computer simulations were used for model verification and validation, which highlight the importance of T11 target structure in brain tumor therapy

Stem Cell Transplantation As A Dynamical System: Are Clinical Outcomes Deterministic?

Outcomes in stem cell transplantation (SCT) are modeled using probability theory. However the clinical course following SCT appears to demonstrate many characteristics of dynamical systems, especially when outcomes are considered in the context of immune reconstitution. Dynamical systems tend to evolve over time according to mathematically determined rules. Characteristically, the future states of the system are predicated on the states preceding them, and there is sensitivity to initial conditions. In SCT, the interaction between donor T cells and the recipient may be considered as such a system in which, graft source, conditioning and early immunosuppression profoundly influence immune reconstitution over time. This eventually determines clinical outcomes, either the emergence of tolerance or the development of graft versus host disease. In this paper parallels between SCT and dynamical systems are explored and a conceptual framework for developing mathematical models to understand disparate transplant outcomes is proposed.Comment: 23 pages, 4 figures. Updated version with additional data, 2 new figures and editorial revisions. New authors adde