Cellular Darwinism: Regulatory networks, stochasticity, and selection in cancer development

Amitotic Cell Division, Malignancy, and Resistance to Anticancer Agents: A Tribute to Drs. Walen and Rajaraman

Cell division is crucial for the survival of living organisms. Human cells undergo three types of cell division: mitosis, meiosis, and amitosis. The former two types occur in somatic cells and germ cells, respectively. Amitosis involves nuclear budding and occurs in cells that exhibit abnormal nuclear morphology (e.g., polyploidy) with increased cell size. In the early 2000s, Kirsten Walen and Rengaswami Rajaraman and his associates independently reported that polyploid human cells are capable of producing progeny via amitotic cell division, and that a subset of emerging daughter cells proliferate rapidly, exhibit stem cell-like properties, and can contribute to tumorigenesis. Polyploid cells that arise in solid tumors/tumor-derived cell lines are referred to as polyploid giant cancer cells (PGCCs) and are known to contribute to therapy resistance and disease recurrence following anticancer treatment. This commentary provides an update on some of these intriguing discoveries as a tribute to Drs. Walen and Rajaraman.

The Spiral Model of Evolution: Stable Life Forms of Organisms and Unstable Life Forms of Cancers

If one must prioritize among the vast array of contributing factors to cancer evolution, environmental-stress-mediated chromosome instability (CIN) should easily surpass individual gene mutations. CIN leads to the emergence of genomically unstable life forms, enabling them to grow dominantly within the stable life form of the host. In contrast, stochastic gene mutations play a role in aiding the growth of the cancer population, with their importance depending on the initial emergence of the new system. Furthermore, many specific gene mutations among the many available can perform this function, decreasing the clinical value of any specific gene mutation. Since these unstable life forms can respond to treatment differently than stable ones, cancer often escapes from drug treatment by forming new systems, which leads to problems during the treatment for patients. To understand how diverse factors impact CIN-mediated macroevolution and genome integrity-ensured microevolution, the concept of two-phased cancer evolution is used to reconcile some major characteristics of cancer, such as bioenergetic, unicellular, and multicellular evolution. Specifically, the spiral of life function model is proposed, which integrates major historical evolutionary innovations and conservation with information management. Unlike normal organismal evolution in the microevolutionary phase, where a given species occupies a specific location within the spiral, cancer populations are highly heterogenous at multiple levels, including epigenetic levels. Individual cells occupy different levels and positions within the spiral, leading to supersystems of mixed cellular populations that exhibit both macro and microevolution. This analysis, utilizing karyotype to define the genetic networks of the cellular system and CIN to determine the instability of the system, as well as considering gene mutation and epigenetics as modifiers of the system for information amplification and usage, explores the high evolutionary potential of cancer. It provides a new, unified understanding of cancer as a supersystem, encouraging efforts to leverage the dynamics of CIN to develop improved treatment options. Moreover, it offers a historically contingent model for organismal evolution that reconciles the roles of both evolutionary innovation and conservation through macroevolution and microevolution, respectively.

Metabolic adaptation pilots the differentiation of human hematopoietic cells

A continuous supply of energy is an essential prerequisite for survival and represents the highest priority for the cell. We hypothesize that cell differentiation is a process of optimization of energy flow in a changing environment through phenotypic adaptation. The mechanistic basis of this hypothesis is provided by the established link between core energy metabolism and epigenetic covalent modifications of chromatin. This theory predicts that early metabolic perturbations impact subsequent differentiation. To test this, we induced transient metabolic perturbations in undifferentiated human hematopoietic cells using pharmacological inhibitors targeting key metabolic reactions. We recorded changes in chromatin structure and gene expression, as well as phenotypic alterations by single-cell ATAC and RNA sequencing, time-lapse microscopy, and flow cytometry. Our observations suggest that these metabolic perturbations are shortly followed by alterations in chromatin structure, leading to changes in gene expression. We also show that these transient fluctuations alter the differentiation potential of the cells.

Chronic arsenic exposure induces malignant transformation of human HaCaT cells through both deterministic and stochastic changes in transcriptome expression

Biological processes are inherently stochastic, i.e., are partially driven by hard to predict random probabilistic processes. Carcinogenesis is driven both by stochastic and deterministic (predictable non-random) changes. However, very few studies systematically examine the contribution of stochastic events leading to cancer development. In differential gene expression studies, the established data analysis paradigms incentivize expression changes that are uniformly different across the experimental versus control groups, introducing preferential inclusion of deterministic changes at the expense of stochastic processes that might also play a crucial role in the process of carcinogenesis. In this study, we applied simple computational techniques to quantify: (i) The impact of chronic arsenic (iAs) exposure as well as passaging time on stochastic gene expression and (ii) Which genes were expressed deterministically and which were expressed stochastically at each of the three stages of cancer development. Using biological coefficient of variation as an empirical measure of stochasticity we demonstrate that chronic iAs exposure consistently suppressed passaging related stochastic gene expression at multiple time points tested, selecting for a homogenous cell population that undergo transformation. Employing multiple balanced removal of outlier data, we show that chronic iAs exposure induced deterministic and stochastic changes in the expression of unique set of genes, that populate largely unique biological pathways. Together, our data unequivocally demonstrate that both deterministic and stochastic changes in transcriptome-wide expression are critical in driving biological processes, pathways and networks towards clonal selection, carcinogenesis, and tumor heterogeneity.

Karyotype as code of codes: An inheritance platform to shape the pattern and scale of evolution

Organismal evolution displays complex dynamics in phase and scale which seem to trend towards increasing biocomplexity and diversity. For over a century, such amazing dynamics have been cleverly explained by the apparently straightforward mechanism of natural selection: all diversification, including speciation, results from the gradual accumulation of small beneficial or near-neutral alterations over long timescales. However, although this has been widely accepted, natural selection makes a crucial assumption that has not yet been validated. Specifically, the informational relationship between small microevolutionary alterations and large macroevolutionary changes in natural selection is unclear. To address the macroevolution-microevolution relationship, it is crucial to incorporate the concept of organic codes and particularly the "karyotype code" which defines macroevolutionary changes. This concept piece examines the karyotype from the perspective of two-phased evolution and four key components of information management. It offers insight into how the karyotype creates and preserves information that defines the scale and phase of macroevolution and, by extension, microevolution. We briefly describe the relationship between the karyotype code, the genetic code, and other organic codes in the context of generating evolutionary novelties in macroevolution and imposing constraints on them as biological routines in microevolution. Our analyses suggest that karyotype coding preserves many organic codes by providing system-level inheritance, and similar analyses are needed to classify and prioritize a large number of different organic codes based on the phases and scales of evolution. Finally, the importance of natural information self-creation is briefly discussed, leading to a call to integrate information and time into the relationship between matter and energy.

Profiling and targeting cancer stem cell signaling pathways for cancer therapeutics

Tumorigenic cancer stem cells (CSCs) represent a subpopulation of cells within the tumor that express genetic and phenotypic profiles and signaling pathways distinct from the other tumor cells. CSCs have eluded many conventional anti-oncogenic treatments, resulting in metastases and relapses of cancers. Effectively targeting CSCs' unique self-renewal and differentiation properties would be a breakthrough in cancer therapy. A better characterization of the CSCs' unique signaling mechanisms will improve our understanding of the pathology and treatment of cancer. In this paper, we will discuss CSC origin, followed by an in-depth review of CSC-associated signaling pathways. Particular emphasis is given on CSC signaling pathways' ligand-receptor engagement, upstream and downstream mechanisms, and associated genes, and molecules. Signaling pathways associated with regulation of CSC development stand as potential targets of CSC therapy, which include Wnt, TGFβ (transforming growth factor-β)/SMAD, Notch, JAK-STAT (Janus kinase-signal transducers and activators of transcription), Hedgehog (Hh), and vascular endothelial growth factor (VEGF). Lastly, we will also discuss milestone discoveries in CSC-based therapies, including pre-clinical and clinical studies featuring novel CSC signaling pathway cancer therapeutics. This review aims at generating innovative views on CSCs toward a better understanding of cancer pathology and treatment.

Constrained disorder principle-based variability is fundamental for biological processes: Beyond biological relativity and physiological regulatory networks

The constrained disorder principle (CDP) defines systems based on their degree of disorder bounded by dynamic boundaries. The principle explains stochasticity in living and non-living systems. Denis Noble described the importance of stochasticity in biology, emphasizing stochastic processes at molecular, cellular, and higher levels in organisms as having a role beyond simple noise. The CDP and Noble's theories (NT) claim that biological systems use stochasticity. This paper presents the CDP and NT, discussing common notions and differences between the two theories. The paper presents the CDP-based concept of taking the disorder beyond its role in nature to correct malfunctions of systems and improve the efficiency of biological systems. The use of CDP-based algorithms embedded in second-generation artificial intelligence platforms is described. In summary, noise is inherent to complex systems and has a functional role. The CDP provides the option of using noise to improve functionality.

Making use of noise in biological systems

Disorder and noise are inherent in biological systems. They are required to provide systems with the advantages required for proper functioning. Noise is a part of the flexibility and plasticity of biological systems. It provides systems with increased routes, improves information transfer, and assists in response triggers. This paper reviews recent studies on noise at the genome, cellular, and whole organ levels. We focus on the need to use noise in system engineering. We present some of the challenges faced in studying noise. Optimizing the efficiency of complex systems requires a degree of variability in their functions within certain limits. Constrained noise can be considered a method for improving system robustness by regulating noise levels in continuously dynamic settings. The digital pill-based artificial intelligence (AI)-based platform is the first to implement second-generation AI comprising variability-based signatures. This platform enhances the efficacy of the therapeutic regimens. Systems requiring variability and mechanisms regulating noise are mandatory for understanding biological functions.

Genome Chaos, Information Creation, and Cancer Emergence: Searching for New Frameworks on the 50th Anniversary of the "War on Cancer"

The year 2021 marks the 50th anniversary of the National Cancer Act, signed by President Nixon, which declared a national “war on cancer.” Powered by enormous financial support, this past half-century has witnessed remarkable progress in understanding the individual molecular mechanisms of cancer, primarily through the characterization of cancer genes and the phenotypes associated with their pathways. Despite millions of publications and the overwhelming volume data generated from the Cancer Genome Project, clinical benefits are still lacking. In fact, the massive, diverse data also unexpectedly challenge the current somatic gene mutation theory of cancer, as well as the initial rationales behind sequencing so many cancer samples. Therefore, what should we do next? Should we continue to sequence more samples and push for further molecular characterizations, or should we take a moment to pause and think about the biological meaning of the data we have, integrating new ideas in cancer biology? On this special anniversary, we implore that it is time for the latter. We review the Genome Architecture Theory, an alternative conceptual framework that departs from gene-based theories. Specifically, we discuss the relationship between genes, genomes, and information-based platforms for future cancer research. This discussion will reinforce some newly proposed concepts that are essential for advancing cancer research, including two-phased cancer evolution (which reconciles evolutionary contributions from karyotypes and genes), stress-induced genome chaos (which creates new system information essential for macroevolution), the evolutionary mechanism of cancer (which unifies diverse molecular mechanisms to create new karyotype coding during evolution), and cellular adaptation and cancer emergence (which explains why cancer exists in the first place). We hope that these ideas will usher in new genomic and evolutionary conceptual frameworks and strategies for the next 50 years of cancer research.

Further illusions: On key evolutionary mechanisms that could never fit with Modern Synthesis

In the light of illusions of the Modern Synthesis (MS) listed by Noble (2021a), its key concept, that gradual accumulation of gene mutations within microevolution leads to macroevolution, requires reexamination. In this article, additional illusions of the MS are identified as being caused by the absence of system information and correct history. First, the MS lacks distinction among the two basic types of information: genome-defined system and gene-defined parts-information, as its treatment was based mostly on gene information. In contrast, it is argued here that system information is maintained by species-specific karyotype code, and macroevolution is based on the whole genome information package rather than on specific genes. Linking the origin of species with system information shows that the creation and accumulation of the latter in evolution is the fundamental question omitted from the MS. Second, modern evidence eliminates the MS's preferred theory that present evolutionary events can be linearly extrapolated to the past to reconstruct Life's history, wrongly assuming that most of the fossil record supports the gradual change while ignoring the true karyotype/genome patterns. Furthermore, stasis, as the most prominent pattern of the deep history of Life, remains a puzzle to the MS, but can be explained by the mechanism of karyotype-preservation-via-sex. Consequently, the concept of system-information is smoothly integrated into the two-phased evolutionary model that paleontology requires (Eldredge and Gould, 1972). Finally, research on genome-level causation of evolution, which does not fit the MS, is summarized. The availability of alternative concepts further illustrates that it is time to depart from the MS.

Intercellular communication through extracellular vesicles in cancer and evolutionary biology

Extracellular vesicles (EVs) are nano-sized membrane enclosed vesicles that are released by cells. While initially thought to be cellular detritus or particles involved in eliminating waste from cells, EVs have been recognised as important mediators of intercellular communication by transferring their bioactive cargoes. Notably, over the last two decades, a substantial research effort has been undertaken to understand the role of EVs in cancer. It is now understood that tumour derived EVs can transfer their contents to influence metastatic behaviour, as well as establish favourable microenvironments and pre-metastatic niches that support cancer development and progression. EV-mediated intercellular communication in cancer will be of importance to understanding the emerging paradigm which views cancer as the establishment of a new species within the host organism. Here, we provide a concise overview of EVs and the current understanding of their role and application in cancer. In addition, we explore the potential wider role of EVs in the transfer of inherited characteristics and evolutionary biology.