A race between tumor immunoescape and genome maintenance selects for optimum levels of (epi)genetic instability

Immunoediting in SARS-CoV-2: Mutual relationship between the virus and the host

Immunoediting is a well-known concept that occurs in cancer through three steps of elimination, equilibrium, and escape (3Es), where the immune system first suppresses the growth of tumor cells and then promotes them towards the malignancy. This phenomenon has been conceptualized in some chronic viral infections such as HTLV-1 and HIV by obtaining the resistance to elimination and making a persistent form of infected cells especially in untreated patients. Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a heterogeneous disease characterizing from mild/asymptomatic to severe/critical courses with some behavioral aspects in an immunoediting setting. In this context, a coordinated effort between innate and adaptive immune system leads to detection and destruction of early infection followed by equilibrium between virus-specific responses and infected cells, which eventually ends up with an uncontrolled inflammatory response in severe/critical patients. Although the SARS-CoV-2 applies several escape strategies such as mutations in viral epitopes, modulating the interferon response and inhibiting the MHC I molecules similar to the cancer cells, the 3Es hallmark may not occur in all clinical conditions. Here, we discuss how the lesson learnt from cancer immunoediting and accurate understanding of these pathophysiological mechanisms helps to develop more effective therapeutic strategies for COVID-19.

Next-generation computational tools for interrogating cancer immunity

The remarkable success of cancer therapies with immune checkpoint blockers is revolutionizing oncology and has sparked intensive basic and translational research into the mechanisms of cancer-immune cell interactions. In parallel, numerous novel cutting-edge technologies for comprehensive molecular and cellular characterization of cancer immunity have been developed, including single-cell sequencing, mass cytometry and multiplexed spatial cellular phenotyping. In order to process, analyse and visualize multidimensional data sets generated by these technologies, computational methods and software tools are required. Here, we review computational tools for interrogating cancer immunity, discuss advantages and limitations of the various methods and provide guidelines to assist in method selection.

A mathematical model of pre-diagnostic glioma growth

Due to their location, the malignant gliomas of the brain in humans are very difficult to treat in advanced stages. Blood-based biomarkers for glioma are needed for more accurate evaluation of treatment response as well as early diagnosis. However, biomarker research in primary brain tumors is challenging given their relative rarity and genetic diversity. It is further complicated by variations in the permeability of the blood brain barrier that affects the amount of marker released into the bloodstream. Inspired by recent temporal data indicating a possible decrease in serum glucose levels in patients with gliomas yet to be diagnosed, we present an ordinary differential equation model to capture early stage glioma growth. The model contains glioma-glucose-immune interactions and poses a potential mechanism by which this glucose drop can be explained. We present numerical simulations, parameter sensitivity analysis, linear stability analysis and a numerical experiment whereby we show how a dormant glioma can become malignant.

Turning ecology and evolution against cancer

The fight against cancer has drawn researchers from a wide variety of disciplines, ranging from molecular biology to physics, but the perspective of an ecological theorist has been mostly overlooked. By thinking about the cells that make up a tumour as an endangered species, cancer vulnerabilities become more apparent. Studies in conservation biology and microbial experiments indicate that extinction is a complex phenomenon, which is often driven by the interaction of ecological and evolutionary processes. Recent advances in cancer research have shown that tumours, like species striving for survival, harbour intricate population dynamics, which suggests the possibility to exploit the ecology of tumours for treatment.

Cancer quasispecies and stem-like adaptive aneuploidy

In this paper we develop a theoretical frame to understand self-regulation of aneuploidy rate in cancer and stem cells. This is accomplished building upon quasispecies theory, by leaving its formal mathematical structure intact, but by drastically changing the meaning of its objects. In particular, we propose a novel definition of chromosomal master sequence, as a sequence of physically distinct whole or fragmented chromosomes, whose length is taken to be the sum of the copy numbers of each whole or fragmented chromosome. This fundamental change in the functional objects of quasispecies theory allows us to show that previously measured aneuploidy rates in cancer populations are already close to a formally derived aneuploid error threshold, and that any value of aneuploidy rate larger than the aneuploid error threshold would lead to a loss of fitness of a tumor population. Finally, we make a phenomenological analysis of existing experimental evidence to argue that single clone cancer cells, derived from an aneuploid cancer subpopulation, are capable of self-regulating their aneuploidy rate and of adapting it to distinct environments, namely primary and metastatic microenvironments. We also discuss the potential origin of this self-regulatory ability in the wider context of developmental and comparative biology and we hypothesize the existence of a diversification factor, i.e. a cellular mechanism that regulates adaptation of aneuploidy rates, active in all embryo, adult and cancer stem cells.

Plasticity of tumour and immune cells: a source of heterogeneity and a cause for therapy resistance?

Immunotherapies, signal transduction inhibitors and chemotherapies can successfully achieve remissions in advanced stage cancer patients, but durable responses are rare. Using malignant melanoma as a paradigm, we propose that therapy-induced injury to tumour tissue and the resultant inflammation can activate protective and regenerative responses that represent a shared resistance mechanism to different treatments. Inflammation-driven phenotypic plasticity alters the antigenic landscape of tumour cells, rewires oncogenic signalling networks, protects against cell death and reprogrammes immune cell functions. We propose that the successful combination of cancer treatments to tackle resistance requires an interdisciplinary understanding of these resistance mechanisms, supported by mathematical models.

Applying ecological and evolutionary theory to cancer: a long and winding road

Since the mid 1970s, cancer has been described as a process of Darwinian evolution, with somatic cellular selection and evolution being the fundamental processes leading to malignancy and its many manifestations (neoangiogenesis, evasion of the immune system, metastasis, and resistance to therapies). Historically, little attention has been placed on applications of evolutionary biology to understanding and controlling neoplastic progression and to prevent therapeutic failures. This is now beginning to change, and there is a growing international interest in the interface between cancer and evolutionary biology. The objective of this introduction is first to describe the basic ideas and concepts linking evolutionary biology to cancer. We then present four major fronts where the evolutionary perspective is most developed, namely laboratory and clinical models, mathematical models, databases, and techniques and assays. Finally, we discuss several of the most promising challenges and future prospects in this interdisciplinary research direction in the war against cancer.

Bioinformatics for cancer immunology and immunotherapy

Recent mechanistic insights obtained from preclinical studies and the approval of the first immunotherapies has motivated increasing number of academic investigators and pharmaceutical/biotech companies to further elucidate the role of immunity in tumor pathogenesis and to reconsider the role of immunotherapy. Additionally, technological advances (e.g., next-generation sequencing) are providing unprecedented opportunities to draw a comprehensive picture of the tumor genomics landscape and ultimately enable individualized treatment. However, the increasing complexity of the generated data and the plethora of bioinformatics methods and tools pose considerable challenges to both tumor immunologists and clinical oncologists. In this review, we describe current concepts and future challenges for the management and analysis of data for cancer immunology and immunotherapy. We first highlight publicly available databases with specific focus on cancer immunology including databases for somatic mutations and epitope databases. We then give an overview of the bioinformatics methods for the analysis of next-generation sequencing data (whole-genome and exome sequencing), epitope prediction tools as well as methods for integrative data analysis and network modeling. Mathematical models are powerful tools that can predict and explain important patterns in the genetic and clinical progression of cancer. Therefore, a survey of mathematical models for tumor evolution and tumor-immune cell interaction is included. Finally, we discuss future challenges for individualized immunotherapy and suggest how a combined computational/experimental approaches can lead to new insights into the molecular mechanisms of cancer, improved diagnosis, and prognosis of the disease and pinpoint novel therapeutic targets.