Autocrine signaling can explain the emergence of Allee effects in cancer cell populations

In many human cancers, the rate of cell growth depends crucially on the size of the tumour cell population. Low, zero, or negative growth at low population densities is known as the Allee effect; this effect has been studied extensively in ecology, but so far lacks a good explanation in the cancer setting. Here, we formulate and analyze an individual-based model of cancer, in which cell division rates are increased by the local concentration of an autocrine growth factor produced by the cancer cells themselves. We show, analytically and by simulation, that autocrine signaling suffices to cause both strong and weak Allee effects. Whether low cell densities lead to negative (strong effect) or reduced (weak effect) growth rate depends directly on the ratio of cell death to proliferation, and indirectly on cellular dispersal. Our model is consistent with experimental observations from three patient-derived brain tumor cell lines grown at different densities. We propose that further studying and quantifying population-wide feedback, impacting cell growth, will be central for advancing our understanding of cancer dynamics and treatment, potentially exploiting Allee effects for therapy

MultiCellDS : a community-developed standard for curating microenvironment-dependent multicellular data

Exchanging and understanding scientific data and their context represents a significant barrier to advancing research, especially with respect to information siloing. Maintaining information provenance and providing data curation and quality control help overcome common concerns and barriers to the effective sharing of scientific data. To address these problems in and the unique challenges of multicellular systems, we assembled a panel composed of investigators from several disciplines to create the MultiCellular Data Standard (MultiCellDS) with a use-case driven development process. The standard includes (1) digital cell lines, which are analogous to traditional biological cell lines, to record metadata, cellular microenvironment, and cellular phenotype variables of a biological cell line, (2) digital snapshots to consistently record simulation, experimental, and clinical data for multicellular systems, and (3) collections that can logically group digital cell lines and snapshots. We have created a MultiCellular DataBase (MultiCellDB) to store digital snapshots and the 200+ digital cell lines we have generated. MultiCellDS, by having a fixed standard, enables discoverability, extensibility, maintainability, searchability, and sustainability of data, creating biological applicability and clinical utility that permits us to identify upcoming challenges to uplift biology and strategies and therapies for improving human health

MultiCellDS: a community-developed standard for curating microenvironment-dependent multicellular data

Exchanging and understanding scientific data and their context represents a significant barrier to advancing research, especially with respect to information siloing. Maintaining information provenance and providing data curation and quality control help overcome common concerns and barriers to the effective sharing of scientific data. To address these problems in and the unique challenges of multicellular systems, we assembled a panel composed of investigators from several disciplines to create the MultiCellular Data Standard (MultiCellDS) with a use-case driven development process. The standard includes (1) digital cell lines, which are analogous to traditional biological cell lines, to record metadata, cellular microenvironment, and cellular phenotype variables of a biological cell line, (2) digital snapshots to consistently record simulation, experimental, and clinical data for multicellular systems, and (3) collections that can logically group digital cell lines and snapshots. We have created a MultiCellular DataBase (MultiCellDB) to store digital snapshots and the 200+ digital cell lines we have generated. MultiCellDS, by having a fixed standard, enables discoverability, extensibility, maintainability, searchability, and sustainability of data, creating biological applicability and clinical utility that permits us to identify upcoming challenges to uplift biology and strategies and therapies for improving human health

CHARACTERIZATION OF SURVIVAL ASSOCIATED GENE INTERACTIONS AND LYMPHOCYTE HETEROGENEITY IN CANCER

Cancer is the second leading cause of death globally. Tumors form intricate ecosystems in which malignant and immune cells interact to shape disease progression. Yet, the molecular underpinnings of tumorigenesis and immunological responses to tumors are poorly understood, limiting their manipulation to elicit favorable clinical outcomes. This thesis lays conceptual frameworks for investigating the molecular interactions taking place in tumors as well as the diversity of the immune response to cancer. In the molecular level of individual cancer cells, the phenotypic effect of perturbing a gene’s activity depends on the activity level of other genes, reflecting the notion that phenotypes are emergent properties of a network of functionally interacting genes. In the context of cancer, contemporary investigations have primarily focused on just one type of functional genetic interaction (GI) – synthetic lethality (SL). However, there may be additional types of GIs whose systematic identification would enrich the molecular and functional characterization of cancer. This thesis describes a novel data-driven approach called EnGIne, that applied to large-scale cancer data identifies 71,946 GIs spanning 12 distinct types, only a small minority of which are SLs. The detected GIs explain cancer driver genes’ tissue- specificity and differences in patients’ response to drugs, and stratify breast cancer tumors into refined subtypes. These results expand the scope of cancer GIs and lay a conceptual and computational basis for future studies of additional types of GIs and their translational applications. Furthermore, tumor growth is continuously shaped by the immune response. However, T cells typically adopt a dysfunctional phenotype may be reversed using immunotherapy strategies. Most current tumor immunotherapies leverage cytotoxic CD8+ T cells to elicit an effective anti-tumor response. Despite evidence for clinical potential of CD4+ tumor-infiltrating lymphocytes (TILs), their functional diversity has limited our ability to harness their anti-tumor activity. To address this issue, we have used single-cell mRNA sequencing (scRNAseq) to analyze the response of CD4+ T cells specific for a defined recombinant tumor antigen, both in the tumor microenvironment and draining lymph nodes (dLN). New computational approaches to characterize subpopulations identified TIL transcriptomic patterns strikingly distinct from those elicited by responses to infection, and dominated by diversity among T-bet-expressing T helper type 1 (Th1)-like cells. In contrast, the dLN response includes Follicular helper (Tfh)-like cells but lacks Th1 cells. We identify an interferon-driven signature in Th1-like TILs, and show that it is found in human liver cancer and melanoma, in which it is negatively associated with response to checkpoint therapy. Our study unveils unsuspected differences between tumor and virus CD4+ T cell responses, and provides a proof-of-concept methodology to characterize tumor- control CD4+ T cell effector programs. Targeting these programs should help improve immunotherapy strategies

The interaction of the immune system with skeletal muscle during respiratory viral infection of the elderly

The population of the world is ageing. Respiratory syncytial virus (RSV) is emerging as a leading cause of severe respiratory tract infection in the elderly. Loss of muscle mass occurs naturally with age, but can be exacerbated by inflammation, inactivity, or chronic disease, leading to increased risk of morbidity and mortality. If and how RSV infection promotes muscle wasting in the elderly is unknown. This study has developed an aged mouse model to investigate muscle wasting after RSV infection. 12-week-old and 80-week-old female C57BL/6 mice were infected with the same dose of RSV A2. Compared to young mice, elderly mice displayed enhanced RSV disease, including increased weight loss, viral load, and cellular airway infiltration. Elderly, but not young, mice displayed signs of muscle wasting following RSV infection, including decreased tibialis anterior muscle weight, increased expression of muscle atrophy-promoting enzymes, decreased muscle fibre size, and a failure to upregulate muscle protein synthesis. Elderly mice also displayed an impaired antibody response as evidenced by decreased anti-RSV IgG titres, but this was not due to reduced numbers of RSV-specific Tfh cells or germinal centre B cells. Blocking GDF-15, a TGF-β superfamily cytokine associated with muscle wasting and loss of appetite, which was produced in the elderly lung following RSV infection, unexpectedly led to signs of enhanced muscle wasting in elderly mice infected with RSV, suggesting a tissue-protective effect of GDF-15. Blocking IL-6R did not have consistent effects in elderly mice infected with RSV, potentially due to counteracting effects on systemic inflammation, the antibody response, and skeletal muscle. These results demonstrate that RSV infection promotes muscle wasting in an age-dependent manner, potentially regulated by GDF-15. This model is a useful tool for mechanistic studies and could be used in the future for the development of vaccines and treatments for RSV for the elderly.Open Acces

Additional file 1: of Quantifying differences in cell line population dynamics using CellPD

Example of CellPD’s outputs. This folder contains two examples of the outputs generated by CellPD (using the data from Fig. 2 and Additional file 6). (ZIP 36462 kb