The somatic molecular evolution of cancer: Mutation, selection, and epistasis

Cell-cell fusion in cancer: The next cancer hallmark?

In this review, we consider the role of cell-cell fusion in cancer development and progression through an evolutionary lens. We begin by summarizing the origins of fusion proteins (fusogens), of which there are many distinct classes that have evolved through convergent evolution. We then use an evolutionary framework to highlight how the persistence of fusion over generations and across different organisms can be attributed to traits that increase fitness secondary to fusion; these traits map well to the expanded hallmarks of cancer. By studying the tumor microenvironment, we can begin to identify the key selective pressures that may favor higher rates of fusion compared to healthy tissues. The paper concludes by discussing the increasing number of research questions surrounding fusion, recommendations for how to answer them, and the need for a greater interest in exploring cell fusion and evolutionary principles in oncology moving forward.

Signaling, cancer cell plasticity, and intratumor heterogeneity

Cancer's complexity is in part due to the presence of intratumor heterogeneity and the dynamic nature of cancer cell plasticity, which create substantial obstacles in effective cancer management. Variability within a tumor arises from the existence of diverse populations of cancer cells, impacting the progression, spread, and resistance to treatments. At the core of this variability is the concept of cellular plasticity - the intrinsic ability of cancer cells to alter their molecular and cellular identity in reaction to environmental and genetic changes. This adaptability is a cornerstone of cancer's persistence and progression, making it a formidable target for treatments. Emerging studies have emphasized the critical role of such plasticity in fostering tumor diversity, which in turn influences the course of the disease and the effectiveness of therapeutic strategies. The transformative nature of cancer involves a network of signal transduction pathways, notably those that drive the epithelial-to-mesenchymal transition and metabolic remodeling, shaping the evolutionary path of cancer cells. Despite advancements, our understanding of the precise molecular machinations and signaling networks driving these changes is still evolving, underscoring the necessity for further research. This editorial presents a series entitled "Signaling Cancer Cell Plasticity and Intratumor Heterogeneity" in Cell Communication and Signaling, dedicated to unraveling these complex processes and proposing new avenues for therapeutic intervention.

G-quadruplex-mediated genomic instability drives SNVs in cancer

G-quadruplex (G4s) DNA structures have been implicated in inducing genomic instability and contributing to cancer development. However, the relationship between G4s and cancer-related single nucleotide variants (cSNVs) in clinical settings remains unclear. In this large-scale study, we integrated experimentally validated G4s with genomic cSNVs from 13480 cancer patients to investigate the spatial association of G4s with the cellular cSNV landscape. Our findings demonstrate an increase in local genomic instability with increasing local G4 content in cancer patients, suggesting a potential role for G4s in driving cSNVs. Notably, we observed distinct spatial patterns of cSNVs and common single nucleotide variants (dbSNVs) in relation to G4s, implying different mechanisms for their generation and accumulation. We further demonstrate large, cancer-specific differences in the relationship of G4s and cSNVs, which could have important implications for a new class of G4-stabilizing cancer therapeutics. Moreover, we show that high G4-content can serve as a prognostic marker for local cSNV density and patient survival rates. Our findings underscore the importance of considering G4s in cancer research and highlight the need for further investigation into the underlying molecular mechanisms of G4-mediated genomic instability, especially in the context of cancer.

Pairwise and higher-order epistatic effects among somatic cancer mutations across oncogenesis

Cancer occurs as a consequence of multiple somatic mutations that lead to uncontrolled cell growth. Mutual exclusivity and co-occurrence of mutations imply-but do not prove-that mutations exert synergistic or antagonistic epistatic effects on oncogenesis. Knowledge of these interactions, and the consequent trajectories of mutation and selection that lead to cancer has been a longstanding goal within the cancer research community. Recent research has revealed mutation rates and scaled selection coefficients for specific recurrent variants across many cancer types. However, there are no current methods to quantify the strength of selection incorporating pairwise and higher-order epistatic effects on selection within the trajectory of likely cancer genotoypes. Therefore, we have developed a continuous-time Markov chain model that enables the estimation of mutation origination and fixation (flux), dependent on somatic cancer genotype. Coupling this continuous-time Markov chain model with a deconvolution approach provides estimates of underlying mutation rates and selection across the trajectory of oncogenesis. We demonstrate computation of fluxes and selection coefficients in a somatic evolutionary model for the four most frequently variant driver genes (TP53, LRP1B, KRAS and STK11) from 565 cases of lung adenocarcinoma. Our analysis reveals multiple antagonistic epistatic effects that reduce the possible routes of oncogenesis, and inform cancer research regarding viable trajectories of somatic evolution whose progression could be forestalled by precision medicine. Synergistic epistatic effects are also identified, most notably in the somatic genotype TP53 LRP1B for mutations in the KRAS gene, and in somatic genotypes containing KRAS or TP53 mutations for mutations in the STK11 gene. Large positive fluxes of KRAS variants were driven by large selection coefficients, whereas the flux toward LRP1B mutations was substantially aided by a large mutation rate for this gene. The approach enables inference of the most likely routes of site-specific variant evolution and estimation of the strength of selection operating on each step along the route, a key component of what we need to know to develop and implement personalized cancer therapies.

Cytotoxic activity of quinolinequinones in cancer: In vitro studies, molecular docking, and ADME/PK profiling

Lead molecules containing 1,4-quinone moiety are intriguing novel compounds that can be utilized to treat cancer owing to their antiproliferative activities. Nine previously reported quinolinequinones (AQQ1-9) were studied to better understand their inhibitory profile to produce potent and possibly safe lead molecules. The National Cancer Institute (NCI) of Bethesda chose all quinolinequinones (AQQ1-9) based on the NCI Developmental Therapeutics Program and tested them against a panel of 60 cancer cell lines. At a single dose and five further doses, AQQ7 significantly inhibited the proliferation of all leukemia cell lines and some breast cancer cell lines. We investigated the in vitro cytotoxic activities of the most promising compounds, AQQ2 and AQQ7, in MCF7 and T-47D breast cancer cells, DU-145 prostate cancer cells, HCT-116 and COLO 205 colon cancer cell lines, and HaCaT human keratinocytes using the MTT assay. AQQ7 showed particularly high cytotoxicity against MCF7 cells. Further analysis showed that AQQ7 exhibits anticancer activity through the induction of apoptosis without causing cell cycle arrest or oxidative stress. Molecular docking simulations for AQQ2 and AQQ7 were conducted against the COX, PTEN, and EGFR proteins, which are commonly overexpressed in breast, cervical, and prostate cancers. The in vitro ADME and in vivo PK profiling of these compounds have also been reported.

Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools

Cancer originates from somatic cells that have accumulated mutations. These mutations alter the phenotype of the cells, allowing them to escape homeostatic regulation that maintains normal cell numbers. The emergence of malignancies is an evolutionary process in which the random accumulation of somatic mutations and sequential selection of dominant clones cause cancer cells to proliferate. The development of technologies such as high-throughput sequencing has provided a powerful means to measure subclonal evolutionary dynamics across space and time. Here, we review the patterns that may be observed in cancer evolution and the methods available for quantifying the evolutionary dynamics of cancer. An improved understanding of the evolutionary trajectories of cancer will enable us to explore the molecular mechanism of tumorigenesis and to design tailored treatment strategies.

Computational Biology Helps Understand How Polyploid Giant Cancer Cells Drive Tumor Success

Precision and organization govern the cell cycle, ensuring normal proliferation. However, some cells may undergo abnormal cell divisions (neosis) or variations of mitotic cycles (endopolyploidy). Consequently, the formation of polyploid giant cancer cells (PGCCs), critical for tumor survival, resistance, and immortalization, can occur. Newly formed cells end up accessing numerous multicellular and unicellular programs that enable metastasis, drug resistance, tumor recurrence, and self-renewal or diverse clone formation. An integrative literature review was carried out, searching articles in several sites, including: PUBMED, NCBI-PMC, and Google Academic, published in English, indexed in referenced databases and without a publication time filter, but prioritizing articles from the last 3 years, to answer the following questions: (i) "What is the current knowledge about polyploidy in tumors?"; (ii) "What are the applications of computational studies for the understanding of cancer polyploidy?"; and (iii) "How do PGCCs contribute to tumorigenesis?"

Molecular characteristics of incidental lower-grade glioma for treatment decision-making

OBJECTIVE

Several limitations are associated with the early diagnosis and treatment of incidental lower-grade glioma (iLGG), and due to its unknown molecular features, its management is categorized as either the "wait-and-see" strategy or immediate treatment. Therefore, in this study the authors explored iLGG's clinical and molecular landscape to improve its management.

METHODS

The authors retrospectively assessed the differences between the molecular and clinical characteristics of iLGG and symptomatic lower-grade glioma (sLGG) samples filtered based on symptom data corresponding to The Cancer Genome Atlas cohort with mutations. Thereafter, genomic and transcriptomic analysis was performed.

RESULTS

There was no significant difference between iLGG and sLGG with respect to mutation status; however, there was an increase in the interaction between major mutations in sLGG, depending on the histological subtype and the IDH1 mutation status. Furthermore, the IDH1 mutation characteristics corresponding to wild-type glioma were much more obvious in sLGG than in iLGG. Additionally, in sLGG, genes associated with malignancy, including cell proliferation-related, cell migration-related, epithelial-to-mesenchymal transition-related, and negative regulation of cell death-related genes, were significantly upregulated, and groups showing higher expression levels of these genes were associated with worse prognosis. Also, 8 of the 75 identified upregulated genes showed positive correlation with resistance to the drugs that are normally used for glioma treatment, including procarbazine, carmustine, vincristine, and temozolomide.

CONCLUSIONS

The new insights regarding the different molecular features of iLGG and sLGG indicated that the immediate management of iLGG could result in better prognosis than the wait-and-see strategy.

High Levels of Organochlorines Are Associated with Induction of ABL1 Promoter Methylation in Children with Acute Lymphoblastic Leukemia

Exposure to organochlorines is associated with epigenetic changes, including methylation change in the promoter of tumor suppressor genes, thereby leading to cancer induction. The aim of this study was to investigate the relationship between organochlorine pesticides (OCPs) and ABL1 promoter methylation in child patients with acute lymphoblastic leukemia (ALL) and the control group. The methylation rate of the ABL1 promoter was evaluated using the methylation-specific polymerase chain reaction method, and the level of OCPs in patients with ALL and healthy children was measured using gas chromatography. ABL1 promoter hypermethylation was observed in 64% of ALL patients and 28.5% of children in the control group. The level of OCPs in children with methylated ABL1 promoters was significantly higher than that in children with nonmethylated ABL1 promoters (p < 0.05). Our findings suggest that OCPs, especially alpha-hexachlorocyclohexane, beta-hexachlorocyclohexane, gamma-hexachlorocyclohexane, 2,4 dichlorodiphenyldichloroethylene, and 4,4 dichlorodiphenyltrichloroethane may induce methylation at the ABL1 promoter level, thereby preventing the normal expression of the ABL1 gene. As a result, the reduced expression of ABL1 (a tumor suppressor) gene due to the hypermethylation of its promoter leads to the disruption of normal biological processes, thus making cells vulnerable to oncogenic factors.

Cancer Risk and Mutational Patterns Following Organ Transplantation

The rapid development of medical technology and widespread application of immunosuppressive drugs have improved the success rate of organ transplantation significantly. However, the use of immunosuppressive agents increases the frequency of malignancy greatly. With the prospect of “precision medicine” for tumors and development of next-generation sequencing technology, more attention has been paid to the application of high-throughput sequencing technology in clinical oncology research, which is mainly applied to the early diagnosis of tumors and analysis of tumor-related genes. All generations of cancers carry somatic mutations, meanwhile, significant differences were observed in mutational signatures across tumors. Systematic sequencing of cancer genomes from patients after organ transplantation can reveal DNA damage and repair processes in exposed cancer cells and their precursors. In this review, we summarize the application of high-throughput sequencing and organoids in the field of organ transplantation, the mutational patterns of cancer genomes, and propose a new research strategy for understanding the mechanism of cancer following organ transplantation.

Cancer-related Mutations with Local or Long-range Effects on an Allosteric Loop of p53

The tumor protein 53 (p53) is involved in transcription-dependent and independent processes. Several p53 variants related to cancer have been found to impact protein stability. Other variants, on the contrary, might have little impact on structural stability and have local or long-range effects on the p53 interactome. Our group previously identified a loop in the DNA binding domain (DBD) of p53 (residues 207-213) which can recruit different interactors. Experimental structures of p53 in complex with other proteins strengthen the importance of this interface for protein-protein interactions. We here characterized with structure-based approaches somatic and germline variants of p53 which could have a marginal effect in terms of stability and act locally or allosterically on the region 207-213 with consequences on the cytosolic functions of this protein. To this goal, we studied 1132 variants in the p53 DBD with structure-based approaches, accounting also for protein dynamics. We focused on variants predicted with marginal effects on structural stability. We then investigated each of these variants for their impact on DNA binding, dimerization of the p53 DBD, and intramolecular contacts with the 207-213 region. Furthermore, we identified variants that could modulate long-range the conformation of the region 207-213 using a coarse-grain model for allostery and all-atom molecular dynamics simulations. Our predictions have been further validated using enhanced sampling methods for 15 variants. The methodologies used in this study could be more broadly applied to other p53 variants or cases where conformational changes of loop regions are essential in the function of disease-related proteins.