Lung adenocarcinomas without driver genes converge to common adaptive strategies through diverse genetic, epigenetic, and niche construction evolutionary pathways.

Somatic evolution selects cancer cell phenotypes that maximize survival and proliferation in dynamic environments. Although cancer cells are molecularly heterogeneous, we hypothesized convergent adaptive strategies to common host selection forces can be inferred from patterns of epigenetic and genetic evolutionary selection in similar tumors. We systematically investigated gene mutations and expression changes in lung adenocarcinomas with no common driver genes (n = 313). Although 13,461 genes were mutated in at least one sample, only 376 non-synonymous mutations evidenced positive evolutionary selection with conservation of 224 genes, while 1736 and 2430 genes exhibited ≥ two-fold increased and ≥ 50% decreased expression, respectively. Mutations under positive selection are more frequent in genes with significantly altered expression suggesting they often "hardwire" pre-existing epigenetically driven adaptations. Conserved genes averaged 16-fold higher expression in normal lung tissue compared to those with selected mutations demonstrating pathways necessary for both normal cell function and optimal cancer cell fitness. The convergent LUAD phenotype exhibits loss of differentiated functions and cell-cell interactions governing tissue organization. Conservation with increased expression is found in genes associated with cell cycle, DNA repair, p53 pathway, epigenetic modifiers, and glucose metabolism. No canonical driver gene pathways exhibit strong positive selection, but extensive down-regulation of membrane ion channels suggests decreased transmembrane potential may generate persistent proliferative signals. NCD LUADs perform niche construction generating a stiff, immunosuppressive microenvironment through selection of specific collagens and proteases. NCD LUADs evolve to a convergent phenotype through a network of interconnected genetic, epigenetic, and ecological pathways.

Drug-resilient Cancer Cell Phenotype Is Acquired via Polyploidization Associated with Early Stress Response Coupled to HIF2α Transcriptional Regulation.

Therapeutic resistance and recurrence remain core challenges in cancer therapy. How therapy resistance arises is currently not fully understood with tumors surviving via multiple alternative routes. Here, we demonstrate that a subset of cancer cells survives therapeutic stress by entering a transient state characterized by whole-genome doubling. At the onset of the polyploidization program, we identified an upregulation of key transcriptional regulators, including the early stress-response protein AP-1 and normoxic stabilization of HIF2α. We found altered chromatin accessibility, ablated expression of retinoblastoma protein (RB1), and enrichment of AP-1 motif accessibility. We demonstrate that AP-1 and HIF2α regulate a therapy resilient and survivor phenotype in cancer cells. Consistent with this, genetic or pharmacologic targeting of AP-1 and HIF2α reduced the number of surviving cells following chemotherapy treatment. The role of AP-1 and HIF2α in stress response by polyploidy suggests a novel avenue for tackling chemotherapy-induced resistance in cancer. SIGNIFICANCE: In response to cisplatin treatment, some surviving cancer cells undergo whole-genome duplications without mitosis, which represents a mechanism of drug resistance. This study presents mechanistic data to implicate AP-1 and HIF2α signaling in the formation of this surviving cell phenotype. The results open a new avenue for targeting drug-resistant cells.

AlphaML: A clear, legible, explainable, transparent, and elucidative binary classification platform for tabular data.

Leveraging the potential of machine learning and recognizing the broad applications of binary classification, it becomes essential to develop platforms that are not only powerful but also transparent, interpretable, and user friendly. We introduce alphaML, a user-friendly platform that provides clear, legible, explainable, transparent, and elucidative (CLETE) binary classification models with comprehensive customization options. AlphaML offers feature selection, hyperparameter search, sampling, and normalization methods, along with 15 machine learning algorithms with global and local interpretation. We have integrated a custom metric for hyperparameter search that considers both training and validation scores, safeguarding against under- or overfitting. Additionally, we employ the NegLog2RMSL scoring method, which uses both training and test scores for a thorough model evaluation. The platform has been tested using datasets from multiple domains and offers a graphical interface, removing the need for programming expertise. Consequently, alphaML exhibits versatility, demonstrating promising applicability across a broad spectrum of tabular data configurations.

The contribution of evolvability to the eco-evolutionary dynamics of competing species.

Evolvability is the capacity of a population to generate heritable variation that can be acted upon by natural selection. This ability influences the adaptations and fitness of individual organisms. By viewing this capacity as a trait, evolvability is subject to natural selection and thus plays a critical role in eco-evolutionary dynamics. Understanding this role provides insight into how species respond to changes in their environment and how species coexistence can arise and be maintained. Here, we create a G-function model of competing species, each with a different evolvability. We analyze population and strategy (= heritable phenotype) dynamics of the two populations under clade initiation (when species are introduced into a population), evolutionary tracking (constant, small changes in the environment), adaptive radiation (availability of multiple ecological niches), and evolutionary rescue (extreme environmental disturbances). We find that when species are far from an eco-evolutionary equilibrium, faster-evolving species reach higher population sizes, and when species are close to an equilibrium, slower-evolving species are more successful. Frequent, minor environmental changes promote the extinction of species with small population sizes, regardless of their evolvability. When several niches are available for a species to occupy, coexistence is possible, though slower-evolving species perform slightly better than faster-evolving ones due to the well-recognized inherent cost of evolvability. Finally, disrupting the environment at intermediate frequencies can result in coexistence with cyclical population dynamics of species with different rates of evolution.

A mathematical investigation of polyaneuploid cancer cell memory and cross-resistance in state-structured cancer populations.

The polyaneuploid cancer cell (PACC) state promotes cancer lethality by contributing to survival in extreme conditions and metastasis. Recent experimental evidence suggests that post-therapy PACC-derived recurrent populations display cross-resistance to classes of therapies with independent mechanisms of action. We hypothesize that this can occur through PACC memory, whereby cancer cells that have undergone a polyaneuploid transition (PAT) reenter the PACC state more quickly or have higher levels of innate resistance. In this paper, we build on our prior mathematical models of the eco-evolutionary dynamics of cells in the 2N+ and PACC states to investigate these two hypotheses. We show that although an increase in innate resistance is more effective at promoting cross-resistance, this trend can also be produced via PACC memory. We also find that resensitization of cells that acquire increased innate resistance through the PAT have a considerable impact on eco-evolutionary dynamics and extinction probabilities. This study, though theoretical in nature, can help inspire future experimentation to tease apart hypotheses surrounding how cross-resistance in structured cancer populations arises.

The impact of sedative and vasopressor agents on cerebrovascular reactivity in severe traumatic brain injury.

BACKGROUND: The aim of this study is to evaluate the impact of commonly administered sedatives (Propofol, Alfentanil, Fentanyl, and Midazolam) and vasopressor (Dobutamine, Ephedrine, Noradrenaline and Vasopressin) agents on cerebrovascular reactivity in moderate/severe TBI patients. Cerebrovascular reactivity, as a surrogate for cerebral autoregulation was assessed using the long pressure reactivity index (LPRx). We evaluated the data in two phases, first we assessed the minute-by-minute data relationships between different dosing amounts of continuous infusion agents and physiological variables using boxplots, multiple linear regression and ANOVA. Next, we assessed the relationship between continuous/bolus infusion agents and physiological variables, assessing pre-/post- dose of medication change in physiology using a Wilcoxon signed-ranked test. Finally, we evaluated sub-groups of data for each individual dose change per medication, focusing on key physiological thresholds and demographics. RESULTS: Of the 475 patients with an average stay of 10 days resulting in over 3000 days of recorded information 367 (77.3%) were male with a median Glasgow coma score of 7 (4-9). The results of this retrospective observational study confirmed that the infusion of most administered agents do not impact cerebrovascular reactivity, which is confirmed by the multiple linear regression components having p value > 0.05. Incremental dose changes or bolus doses in these medications in general do not lead to significant changes in cerebrovascular reactivity (confirm by Wilcoxon signed-ranked p value > 0.05 for nearly all assessed relationships). Within the sub-group analysis that separated the data based on LPRx pre-dose, a significance between pre-/post-drug change in LPRx was seen, however this may be more of a result from patient state than drug impact. CONCLUSIONS: Overall, this study indicates that commonly administered agents with incremental dosing changes have no clinically significant influence on cerebrovascular reactivity in TBI (nor do they impair cerebrovascular reactivity). Though further investigation in a larger and more diverse TBI patient population is required.

Updating the Definition of Cancer.

Most definitions of cancer broadly conform to the current NCI definition: "Cancer is a disease in which some of the body's cells grow uncontrollably and spread to other parts of the body." These definitions tend to describe what cancer "looks like" or "does" but do not describe what cancer "is" or "has become." While reflecting past insights, current definitions have not kept pace with the understanding that the cancer cell is itself transformed and evolving. We propose a revised definition of cancer: Cancer is a disease of uncontrolled proliferation by transformed cells subject to evolution by natural selection. We believe this definition captures the essence of the majority of previous and current definitions. To the simplest definition of cancer as a disease of uncontrolled proliferation of cells, our definition adds in the adjective "transformed" to capture the many tumorigenic processes that cancer cells adopt to metastasize. To the concept of uncontrolled proliferation of transformed cells, our proposed definition then adds "subject to evolution by natural selection." The subject to evolution by natural selection modernizes the definition to include the genetic and epigenetic changes that accumulate within a population of cancer cells that lead to the lethal phenotype. Cancer is a disease of uncontrolled proliferation by transformed cells subject to evolution by natural selection.

Distinct Cholesterol Localization in Glioblastoma Multiforme Revealed by Mass Spectrometry Imaging.

Glioblastoma multiforme (GBM) is the most common and aggressive brain tumor in adults and is highly resistant to chemo- and radiotherapies. GBM has been associated with alterations in lipid contents, but lipid metabolism reprogramming in tumor cells is not fully elucidated. One of the key hurdles is to localize the lipid species that are correlated with tumor growth and invasion. A better understanding of the localization of abnormal lipid metabolism and its vulnerabilities may open up to novel therapeutic approaches. Here, we use time-of-flight secondary ion mass spectrometry (ToF-SIMS) to spatially probe the lipid composition in a GBM biopsy from two regions with different histopathologies: one region with most cells of uniform size and shape, the homogeneous part, and the other with cells showing a great variation in size and shape, the heterogeneous part. Our results reveal elevated levels of cholesterol, diacylglycerols, and some phosphatidylethanolamine in the homogeneous part, while the heterogeneous part was dominated by a variety of fatty acids, phosphatidylcholine, and phosphatidylinositol species. We also observed a high expression of cholesterol in the homogeneous tumor region to be associated with large cells but not with macrophages. Our findings suggest that ToF-SIMS can distinguish in lipid distribution between parts within a human GBM tumor, which can be linked to different molecular mechanisms.

Modeling cancer's ecological and evolutionary dynamics.

In this didactic paper, we present a theoretical modeling framework, called the G-function, that integrates both the ecology and evolution of cancer to understand oncogenesis. The G-function has been used in evolutionary ecology, but has not been widely applied to problems in cancer. Here, we build the G-function framework from fundamental Darwinian principles and discuss how cancer can be seen through the lens of ecology, evolution, and game theory. We begin with a simple model of cancer growth and add on components of cancer cell competition and drug resistance. To aid in exploration of eco-evolutionary modeling with this approach, we also present a user-friendly software tool. By the end of this paper, we hope that readers will be able to construct basic G function models and grasp the usefulness of the framework to understand the games cancer plays in a biologically mechanistic fashion.

Roadmap to the study of gene and protein phylogeny and evolution-A practical guide.

Developments in sequencing technologies and the sequencing of an ever-increasing number of genomes have revolutionised studies of biodiversity and organismal evolution. This accumulation of data has been paralleled by the creation of numerous public biological databases through which the scientific community can mine the sequences and annotations of genomes, transcriptomes, and proteomes of multiple species. However, to find the appropriate databases and bioinformatic tools for respective inquiries and aims can be challenging. Here, we present a compilation of DNA and protein databases, as well as bioinformatic tools for phylogenetic reconstruction and a wide range of studies on molecular evolution. We provide a protocol for information extraction from biological databases and simple phylogenetic reconstruction using probabilistic and distance methods, facilitating the study of biodiversity and evolution at the molecular level for the broad scientific community.

Rapid marine oxygen variability: Driver of the Late Ordovician mass extinction.

The timing and connections between global cooling, marine redox conditions, and biotic turnover are underconstrained for the Late Ordovician. The second most severe mass extinction occurred at the end of the Ordovician period, resulting in ~85% loss of marine species between two extinction pulses. As the only "Big 5" extinction that occurred during icehouse conditions, this interval is an important modern analog to constrain environmental feedbacks. We present a previously unexplored thallium isotope records from two paleobasins that record global marine redox conditions and document two distinct and rapid excursions suggesting vacillating (de)oxygenation. The strong temporal link between these perturbations and extinctions highlights the possibility that dynamic marine oxygen fluctuations, rather than persistent, stable global anoxia, played a major role in driving the extinction. This evidence for rapid oxygen changes leading to mass extinction has important implications for modern deoxygenation and biodiversity declines.

Origin and evolution of animal multicellularity in the light of phylogenomics and cancer genetics.

The rise of animals represents a major but enigmatic event in the evolutionary history of life. In recent years, numerous studies have aimed at understanding the genetic basis of this transition. However, genome comparisons of diverse animal and protist lineages suggest that the appearance of gene families that were previously considered animal specific indeed preceded animals. Animals' unicellular relatives, such as choanoflagellates, ichthyosporeans, and filastereans, demonstrate complex life cycles including transient multicellularity as well as genetic toolkits for temporal cell differentiation, cell-to-cell communication, apoptosis, and cell adhesion. This has warranted further exploration of the genetic basis underlying transitions in cellular organization. An alternative model for the study of transitions in cellular organization is tumors, which exploit physiological programs that characterize both unicellularity and multicellularity. Tumor cells, for example, switch adhesion on and off, up- or downregulate specific cell differentiation states, downregulate apoptosis, and allow cell migration within tissues. Here, we use insights from both the fields of phylogenomics and tumor biology to review the evolutionary history of the regulatory systems of multicellularity and discuss their overlap. We claim that while evolutionary biology has contributed to an increased understanding of cancer, broad investigations into tissue-normal and transformed-can also contribute the framework for exploring animal evolution.

A life history model of the ecological and evolutionary dynamics of polyaneuploid cancer cells.

Therapeutic resistance is one of the main reasons for treatment failure in cancer patients. The polyaneuploid cancer cell (PACC) state has been shown to promote resistance by providing a refuge for cancer cells from the effects of therapy and by helping them adapt to a variety of environmental stressors. This state is the result of aneuploid cancer cells undergoing whole genome doubling and skipping mitosis, cytokinesis, or both. In this paper, we create a novel mathematical framework for modeling the eco-evolutionary dynamics of state-structured populations and use this framework to construct a model of cancer populations with an aneuploid and a PACC state. Using in silico simulations, we explore how the PACC state allows cancer cells to (1) survive extreme environmental conditions by exiting the cell cycle after S phase and protecting genomic material and (2) aid in adaptation to environmental stressors by increasing the cancer cell's ability to generate heritable variation (evolvability) through the increase in genomic content that accompanies polyploidization. In doing so, we demonstrate the ability of the PACC state to allow cancer cells to persist under therapy and evolve therapeutic resistance. By eliminating cells in the PACC state through appropriately-timed PACC-targeted therapies, we show how we can prevent the emergence of resistance and promote cancer eradication.

Stochastic models of Mendelian and reverse transcriptional inheritance in state-structured cancer populations.

Recent evidence suggests that a polyaneuploid cancer cell (PACC) state may play a key role in the adaptation of cancer cells to stressful environments and in promoting therapeutic resistance. The PACC state allows cancer cells to pause cell division and to avoid DNA damage and programmed cell death. Transition to the PACC state may also lead to an increase in the cancer cell's ability to generate heritable variation (evolvability). One way this can occur is through evolutionary triage. Under this framework, cells gradually gain resistance by scaling hills on a fitness landscape through a process of mutation and selection. Another way this can happen is through self-genetic modification whereby cells in the PACC state find a viable solution to the stressor and then undergo depolyploidization, passing it on to their heritably resistant progeny. Here, we develop a stochastic model to simulate both of these evolutionary frameworks. We examine the impact of treatment dosage and extent of self-genetic modification on eco-evolutionary dynamics of cancer cells with aneuploid and PACC states. We find that under low doses of therapy, evolutionary triage performs better whereas under high doses of therapy, self-genetic modification is favored. This study generates predictions for teasing apart these biological hypotheses, examines the implications of each in the context of cancer, and provides a modeling framework to compare Mendelian and non-traditional forms of inheritance.

Mapping the Chemistry of Hair Strands by Mass Spectrometry Imaging-A Review.

Hair can record chemical information reflecting our living conditions, and, therefore, strands of hair have become a potent analytical target within the biological and forensic sciences. While early efforts focused on analyzing complete hair strands in bulk, high spatial resolution mass spectrometry imaging (MSI) has recently come to the forefront of chemical hair-strand analysis. MSI techniques offer a localized analysis, requiring fewer de-contamination procedures per default and making it possible to map the distribution of analytes on and within individual hair strands. Applying the techniques to hair samples has proven particularly useful in investigations quantifying the exposure to, and uptake of, toxins or drugs. Overall, MSI, combined with optimized sample preparation protocols, has improved precision and accuracy for identifying several elemental and molecular species in single strands of hair. Here, we review different sample preparation protocols and use cases with a view to make the methodology more accessible to researchers outside of the field of forensic science. We conclude that-although some challenges remain, including contamination issues and matrix effects-MSI offers unique opportunities for obtaining highly resolved spatial information of several compounds simultaneously across hair surfaces.

It doesn't always pay to be fit: success landscapes.

Landscapes play an important role in many areas of biology, in which biological lives are deeply entangled. Here we discuss a form of landscape in evolutionary biology which takes into account (1) initial growth rates, (2) mutation rates, (3) resource consumption by organisms, and (4) cyclic changes in the resources with time. The long-term equilibrium number of surviving organisms as a function of these four parameters forms what we call a success landscape, a landscape we would claim is qualitatively different from fitness landscapes which commonly do not include mutations or resource consumption/changes in mapping genomes to the final number of survivors. Although our analysis is purely theoretical, we believe the results have possibly strong connections to how we might treat diseases such as cancer in the future with a deeper understanding of the interplay between resource degradation, mutation, and uncontrolled cell growth.

Hypoxia inducible factor-2α importance for migration, proliferation, and self-renewal of trunk neural crest cells.

BACKGROUND: The neural crest is a transient embryonic stem cell population. Hypoxia inducible factor (HIF)-2α is associated with neural crest stem cell appearance and aggressiveness in tumors. However, little is known about its role in normal neural crest development. RESULTS: Here, we show that HIF-2α is expressed in trunk neural crest cells of human, murine, and avian embryos. Knockdown as well as overexpression of HIF-2α in vivo causes developmental delays, induces proliferation, and self-renewal capacity of neural crest cells while decreasing the proportion of neural crest cells that migrate ventrally to sympathoadrenal sites. Reflecting the in vivo phenotype, transcriptome changes after loss of HIF-2α reveal enrichment of genes associated with cancer, invasion, epithelial-to-mesenchymal transition, and growth arrest. CONCLUSIONS: Taken together, these results suggest that expression levels of HIF-2α must be strictly controlled during normal trunk neural crest development and that dysregulated levels affects several important features connected to stemness, migration, and development.

Oxygen-sensing mechanisms across eukaryotic kingdoms and their roles in complex multicellularity.

Oxygen-sensing mechanisms of eukaryotic multicellular organisms coordinate hypoxic cellular responses in a spatiotemporal manner. Although this capacity partly allows animals and plants to acutely adapt to oxygen deprivation, its functional and historical roots in hypoxia emphasize a broader evolutionary role. For multicellular life-forms that persist in settings with variable oxygen concentrations, the capacity to perceive and modulate responses in and between cells is pivotal. Animals and higher plants represent the most complex life-forms that ever diversified on Earth, and their oxygen-sensing mechanisms demonstrate convergent evolution from a functional perspective. Exploring oxygen-sensing mechanisms across eukaryotic kingdoms can inform us on biological innovations to harness ever-changing oxygen availability at the dawn of complex life and its utilization for their organismal development.

Poly-aneuploid cancer cells promote evolvability, generating lethal cancer.

Cancer cells utilize the forces of natural selection to evolve evolvability allowing a constant supply of heritable variation that permits a cancer species to evolutionary track changing hazards and opportunities. Over time, the dynamic tumor ecosystem is exposed to extreme, catastrophic changes in the conditions of the tumor-natural (e.g., loss of blood supply) or imposed (therapeutic). While the nature of these catastrophes may be varied or unique, their common property may be to doom the current cancer phenotype unless it evolves rapidly. Poly-aneuploid cancer cells (PACCs) may serve as efficient sources of heritable variation that allows cancer cells to evolve rapidly, speciate, evolutionarily track their environment, and most critically for patient outcome and survival, permit evolutionary rescue, therapy resistance, and metastasis. As a conditional evolutionary strategy, they permit the cancer cells to accelerate evolution under stress and slow down the generation of heritable variation when conditions are more favorable or when the cancer cells are closer to an evolutionary optimum. We hypothesize that they play a critical and outsized role in lethality by their increased capacity for invasion and motility, for enduring novel and stressful environments, and for generating heritable variation that can be dispensed to their 2N+ aneuploid progeny that make up the bulk of cancer cells within a tumor, providing population rescue in response to therapeutic stress. Targeting PACCs is essential to cancer therapy and patient cure-without the eradication of the resilient PACCs, cancer will recur in treated patients.