Do cell-autonomous and non-cell-autonomous effects drive the structure of tumor ecosystems?

Transmission of unfolded protein response-a regulator of disease progression, severity, and spread in virus infections

The unfolded protein response (UPR) is a cell-autonomous stress response aimed at restoring homeostasis due to the accumulation of misfolded proteins in the endoplasmic reticulum (ER). Viruses often hijack the host cell machinery, leading to an accumulation of misfolded proteins in the ER. The cell-autonomous UPR is the immediate response of an infected cell to this stress, aiming to restore normal function by halting protein translation, degrading misfolded proteins, and activating signaling pathways that increase the production of molecular chaperones. The cell-non-autonomous UPR involves the spreading of UPR signals from initially stressed cells to neighboring unstressed cells that lack the stressor. Though viruses are known modulators of cell-autonomous UPR, recent advancements have highlighted that cell-non-autonomous UPR plays a critical role in elucidating how local infections cause systemic effects, thereby contributing to disease symptoms and progression. Additionally, by utilizing cell-non-autonomous UPR, viruses have devised novel strategies to establish a pro-viral state, promoting virus spread. This review discusses examples that have broadened the understanding of the role of UPR in virus infections and disease progression by looking beyond cell-autonomous to non-autonomous processes and mechanistic details of the inducers, spreaders, and receivers of UPR signals.

Untangling the web of intratumour heterogeneity

Intratumour heterogeneity (ITH) is a hallmark of cancer that drives tumour evolution and disease progression. Technological and computational advances have enabled us to assess ITH at unprecedented depths, yet this accumulating knowledge has not had a substantial clinical impact. This is in part due to a limited understanding of the functional relevance of ITH and the inadequacy of preclinical experimental models to reproduce it. Here, we discuss progress made in these areas and illuminate future directions.

Going with the Flow: Modeling the Tumor Microenvironment Using Microfluidic Technology

Simple Summary

The clinical success of cancer immunotherapy targeting immune checkpoints (e.g., PD-1, CTLA-4) has ushered in a new era of cancer therapeutics aimed at promoting antitumor immunity in hopes of offering durable clinical responses for patients with advanced, metastatic cancer. This success has also reinvigorated interest in developing tumor model systems that recapitulate key features of antitumor immune responses to complement existing in vivo tumor models. Patient-derived tumor models have emerged in recent years to facilitate study of tumor–immune dynamics. Microfluidic technology has enabled development of microphysiologic systems (MPSs) for the evaluation of the tumor microenvironment, which have shown early promise in studying tumor–immune dynamics. Further development of microfluidic-based “tumor-on-a-chip” MPSs to study tumor–immune interactions may overcome several key challenges currently facing tumor immunology.

Abstract

Recent advances in cancer immunotherapy have led a paradigm shift in the treatment of multiple malignancies with renewed focus on the host immune system and tumor–immune dynamics. However, intrinsic and acquired resistance to immunotherapy limits patient benefits and wider application. Investigations into the mechanisms of response and resistance to immunotherapy have demonstrated key tumor-intrinsic and tumor-extrinsic factors. Studying complex interactions with multiple cell types is necessary to understand the mechanisms of response and resistance to cancer therapies. The lack of model systems that faithfully recapitulate key features of the tumor microenvironment (TME) remains a challenge for cancer researchers. Here, we review recent advances in TME models focusing on the use of microfluidic technology to study and model the TME, including the application of microfluidic technologies to study tumor–immune dynamics and response to cancer therapeutics. We also discuss the limitations of current systems and suggest future directions to utilize this technology to its highest potential.

From cells to tissue: How cell scale heterogeneity impacts glioblastoma growth and treatment response

Glioblastomas are aggressive primary brain tumors known for their inter- and intratumor heterogeneity. This disease is uniformly fatal, with intratumor heterogeneity the major reason for treatment failure and recurrence. Just like the nature vs nurture debate, heterogeneity can arise from intrinsic or environmental influences. Whilst it is impossible to clinically separate observed behavior of cells from their environmental context, using a mathematical framework combined with multiscale data gives us insight into the relative roles of variation from different sources. To better understand the implications of intratumor heterogeneity on therapeutic outcomes, we created a hybrid agent-based mathematical model that captures both the overall tumor kinetics and the individual cellular behavior. We track single cells as agents, cell density on a coarser scale, and growth factor diffusion and dynamics on a finer scale over time and space. Our model parameters were fit utilizing serial MRI imaging and cell tracking data from ex vivo tissue slices acquired from a growth-factor driven glioblastoma murine model. When fitting our model to serial imaging only, there was a spectrum of equally-good parameter fits corresponding to a wide range of phenotypic behaviors. When fitting our model using imaging and cell scale data, we determined that environmental heterogeneity alone is insufficient to match the single cell data, and intrinsic heterogeneity is required to fully capture the migration behavior. The wide spectrum of in silico tumors also had a wide variety of responses to an application of an anti-proliferative treatment. Recurrent tumors were generally less proliferative than pre-treatment tumors as measured via the model simulations and validated from human GBM patient histology. Further, we found that all tumors continued to grow with an anti-migratory treatment alone, but the anti-proliferative/anti-migratory combination generally showed improvement over an anti-proliferative treatment alone. Together our results emphasize the need to better understand the underlying phenotypes and tumor heterogeneity present in a tumor when designing therapeutic regimens.

Modelling Cooperative Tumorigenesis in Drosophila

The development of human metastatic cancer is a multistep process, involving the acquisition of several genetic mutations, tumour heterogeneity, and interactions with the surrounding microenvironment. Due to the complexity of cancer development in mammals, simpler model organisms, such as the vinegar fly, Drosophila melanogaster, are being utilized to provide novel insights into the molecular mechanisms involved. In this review, we highlight recent advances in modelling tumorigenesis using the Drosophila model, focusing on the cooperation of oncogenes or tumour suppressors, and the interaction of mutant cells with the surrounding tissue in epithelial tumour initiation and progression.

Non-cell-autonomous effects yield lower clonal diversity in expanding tumors

Recent cancer research has investigated the possibility that non-cell-autonomous (NCA) driving tumor growth can support clonal diversity (CD). Indeed, mutations can affect the phenotypes not only of their carriers (“cell-autonomous”, CA effects), but also sometimes of other cells (NCA effects). However, models that have investigated this phenomenon have only considered a restricted number of clones. Here, we designed an individual-based model of tumor evolution, where clones grow and mutate to yield new clones, among which a given frequency have NCA effects on other clones’ growth. Unlike previously observed for smaller assemblages, most of our simulations yield lower CD with high frequency of mutations with NCA effects. Owing to NCA effects increasing competition in the tumor, clones being already dominant are more likely to stay dominant, and emergent clones not to thrive. These results may help personalized medicine to predict intratumor heterogeneity across different cancer types for which frequency of NCA effects could be quantified.

Germline Proliferation Is Regulated by Somatic Endocytic Genes via JNK and BMP Signaling in Drosophila

Signals derived from the microenvironment contribute greatly to tumorigenesis . The underlying mechanism requires thorough investigation. Here, we use Drosophila testis as a model system to address this question, taking the advantage of the ease to distinguish germline and somatic cells and to track the cell numbers. In an EMS mutagenesis screen, we identified Rab5, a key factor in endocytosis, for its nonautonomous role in germline proliferation. The disruption of Rab5 in somatic cyst cells, which escort the development of germline lineage, induced the overproliferation of underdifferentiated but genetically wild-type germ cells. We demonstrated that this nonautonomous effect was mediated by the transcriptional activation of Dpp [the fly homolog of bone morphogenetic protein (BMP)] by examining the Dpp-reporter expression and knocking down Dpp to block germline overgrowth. Consistently, the protein levels of Bam, the germline prodifferentiation factor normally accumulated in the absence of BMP/Dpp signaling, decreased in the overproliferating germ cells. Further, we discovered that the JNK signaling pathway operated between Rab5 and Dpp, because simultaneously inhibiting the JNK pathway and Rab5 in cyst cells prevented both dpp transcription and germline tumor growth. Additionally, we found that multiple endocytic genes, such as avl, TSG101, Vps25, or Cdc42, were required in the somatic cyst cells to restrict germline amplification. These findings indicate that when the endocytic state of the surrounding cells is impaired, genetically wild-type germ cells overgrow. This nonautonomous model of tumorigenesis provides a simple system to dissect the relation between tumor and its niche.