Immunosurveillance encounters cancer metabolism

Tumor cells reprogram nutrient acquisition and metabolic pathways to meet their energetic, biosynthetic, and redox demands. Similarly, metabolic processes in immune cells support host immunity against cancer and determine differentiation and fate of leukocytes. Thus, metabolic deregulation and imbalance in immune cells within the tumor microenvironment have been reported to drive immune evasion and to compromise therapeutic outcomes. Interestingly, emerging evidence indicates that anti-tumor immunity could modulate tumor heterogeneity, aggressiveness, and metabolic reprogramming, suggesting that immunosurveillance can instruct cancer progression in multiple dimensions. This review summarizes our current understanding of how metabolic crosstalk within tumors affects immunogenicity of tumor cells and promotes cancer progression. Furthermore, we explain how defects in the metabolic cascade can contribute to developing dysfunctional immune responses against cancers and discuss the contribution of immunosurveillance to these defects as a feedback mechanism. Finally, we highlight ongoing clinical trials and new therapeutic strategies targeting cellular metabolism in cancer.

The Evolutionary Landscape of Colorectal Tumorigenesis: Recent Paradigms, Models, and Hypotheses

Using colorectal cancer as a model, we review some of the insights into cancer evolution afforded by cancer sequencing. These include nonlinear and neutral evolution; polyclonality of driver mutations and parallel evolution in adenomas, although these are rare in carcinomas; the ability of mutational processes to shape evolution against the force of selection; the presence of rare driver genes that function in the same signaling pathways as the longstanding canonical drivers; and the existence of selective windows that constrain the functional effects of cancer driver mutations within limits. Many of these nascent evolutionary paradigms are potentially important for treating colorectal cancers as well as understanding their development.

J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments

Background

The combined effects of biological variability and measurement-related errors on cancer sequencing data remain largely unexplored. However, the spatio-temporal simulation of multi-cellular systems provides a powerful instrument to address this issue. In particular, efficient algorithmic frameworks are needed to overcome the harsh trade-off between scalability and expressivity, so to allow one to simulate both realistic cancer evolution scenarios and the related sequencing experiments, which can then be used to benchmark downstream bioinformatics methods.

Result

We introduce a Julia package for SPAtial Cancer Evolution (J-SPACE), which allows one to model and simulate a broad set of experimental scenarios, phenomenological rules and sequencing settings.Specifically, J-SPACE simulates the spatial dynamics of cells as a continuous-time multi-type birth-death stochastic process on a arbitrary graph, employing different rules of interaction and an optimised Gillespie algorithm. The evolutionary dynamics of genomic alterations (single-nucleotide variants and indels) is simulated either under the Infinite Sites Assumption or several different substitution models, including one based on mutational signatures. After mimicking the spatial sampling of tumour cells, J-SPACE returns the related phylogenetic model, and allows one to generate synthetic reads from several Next-Generation Sequencing (NGS) platforms, via the ART read simulator. The results are finally returned in standard FASTA, FASTQ, SAM, ALN and Newick file formats.

Conclusion

J-SPACE is designed to efficiently simulate the heterogeneous behaviour of a large number of cancer cells and produces a rich set of outputs. Our framework is useful to investigate the emergent spatial dynamics of cancer subpopulations, as well as to assess the impact of incomplete sampling and of experiment-specific errors. Importantly, the output of J-SPACE is designed to allow the performance assessment of downstream bioinformatics pipelines processing NGS data. J-SPACE is freely available at: https://github.com/BIMIB-DISCo/J-Space.jl.

Chromosomal instability as a source of genomic plasticity

Chromosomal instability (CIN) is a hallmark of the most aggressive malignancies. Features of these tumors include complex genomic rearrangements, the presence of mis-segregated chromosomes in micronuclei, and extrachromosomal DNA (ecDNA) formation. Here, we review the development of CIN, and examine CIN in the context of cancer evolution, tumor genomic evolution, and therapeutic resistance. We also discuss the role of whole-genome duplications, breakage-fusion-bridge cycles, ecDNA or double minutes in gene amplification promoting tumor evolution.

Evolution under spatially heterogeneous selection in solid tumors

Spatial genetic and phenotypic diversity within solid tumors has been well documented. Nevertheless, how this heterogeneity affects temporal dynamics of tumorigenesis has not been rigorously examined because solid tumors do not evolve as the standard population genetic model due to the spatial constraint. We therefore, propose a neutral spatial (NS) model whereby the mutation accumulation increases toward the periphery; the genealogical relationship is spatially determined and the selection efficacy is blunted (due to kin competition). In this model, neutral mutations are accrued and spatially distributed in manners different from those of advantageous mutations. Importantly, the distinctions could be blurred in the conventional model. To test the NS model, we performed a three-dimensional multiple microsampling of two hepatocellular carcinomas. Whole-genome sequencing (WGS) revealed a 2-fold increase in mutations going from the center to the periphery. The operation of natural selection can then be tested by examining the spatially determined clonal relationships and the clonal sizes. Due to limited migration, only the expansion of highly advantageous clones can sweep through a large part of the tumor to reveal the selective advantages. Hence, even multiregional sampling can only reveal a fraction of fitness differences in solid tumors. Our results suggest that the NS patterns are crucial for testing the influence of natural selection during tumorigenesis, especially for small solid tumors.

Exploiting vulnerabilities in cancer signalling networks to combat targeted therapy resistance

Drug resistance remains one of the greatest challenges facing precision oncology today. Despite the vast array of resistance mechanisms that cancer cells employ to subvert the effects of targeted therapy, a deep understanding of cancer signalling networks has led to the development of novel strategies to tackle resistance both in the first-line and salvage therapy settings. In this review, we provide a brief overview of the major classes of resistance mechanisms to targeted therapy, including signalling reprogramming and tumour evolution; our discussion also focuses on the use of different forms of polytherapies (such as inhibitor combinations, multi-target kinase inhibitors and HSP90 inhibitors) as a means of combating resistance. The promise and challenges facing each of these polytherapies are elaborated with a perspective on how to effectively deploy such therapies in patients. We highlight efforts to harness computational approaches to predict effective polytherapies and the emerging view that exceptional responders may hold the key to better understanding drug resistance. This review underscores the importance of polytherapies as an effective means of targeting resistance signalling networks and achieving durable clinical responses in the era of personalised cancer medicine.