Systems biology of cancer: entropy, disorder, and selection-driven evolution to independence, invasion and “swarm intelligence”

Our knowledge of the biology of solid cancer has greatly progressed during the last few years, and many excellent reviews dealing with the various aspects of this biology have appeared. In the present review, we attempt to bring together these subjects in a general systems biology narrative. It starts from the roles of what we term entropy of signaling and noise in the initial oncogenic events, to the first major transition of tumorigenesis: the independence of the tumor cell and the switch in its physiology, i.e., from subservience to the organism to its own independent Darwinian evolution. The development after independence involves a constant dynamic reprogramming of the cells and the emergence of a sort of collective intelligence leading to invasion and metastasis and seldom to the ultimate acquisition of immortality through inter-individual infection. At each step, the probability of success is minimal to infinitesimal, but the number of cells possibly involved and the time scale account for the relatively high occurrence of tumorigenesis and metastasis in multicellular organisms.

Introduction
A major transition in the evolution of life has been the passage from unicellular to pluricellular organisms. At this stage, each cell had to transfer its main imperatives (to survive and multiply) from itself to the organism, i.e., to subordinate them to those of the whole. The tumor cell accomplishes more or less completely the reversal of this transfer. In this review, we propose to analyze this inverse pathway. Transformations at any level of cell signaling may uncouple to some extent the tight subordination of a cell to the control of the whole. Over the long history of the organism, the sequential or simultaneous conjunction in cells of many such dysfunctions may lead in one cell to a transition to complete independence: the tumor cell. Some of these, the cancer cells, will evolve into an invasive, metastasizing state and eventually killing inside invader. From the point of view of these cells, the ultimate success is the transmission from one organism to another, i.e., the immortalization of the tumor by inter-organism infection. Besides, those who treat cancers often have the impression of dealing with an organism with its own strategy of survival. This reminds of the collective behavior of swarms of birds, fishes, or ants that, through a few simple reflexes, achieve coherent group behavior: the emergence.
In this assay, we try to present a tentative coherent narrative framework with known but often unrelated features of solid cancer evolution generally reviewed separately. In a systems biology approach, we consider mostly programs, pathways, and phenotypes rather than individual genes or enzymes used as examples. Many of the terms used in this literature are reviewed and defined in Ashworth et al. [1]. We based this review on important results from recent major journal publications. We shall consider, from the point of view of the cell, the causes of oncogenic events, which initiate the programs leading to the characteristic transition to independence of the tumor cell, then the evolution of tumors to cancers, metastases, and infectious cancers. We shall then discuss the concept of emergence of swarm-like behaviors in the progression of cancer. With regard to causes, we shall mainly consider phenomena more elusive than the direct classical viral or mutational carcinogens at the origin of a minority of cancers.
2 Causal events 2.1 Propensity for disorder: entropy and its consequences What is entropy (cf. glossary)? Increasing entropy, the principle that, unless provided with new outside energy, any system will constantly lose usable energy, is a theoretical formulation for progressive irreversible disorder and randomness in the cells. The concept of entropy (structural genomic, transcriptomic, network of signal transduction, etc.) has been repeatedly applied to the characteristics of cancer tissue or cells: A role in the genesis of cancer was suggested [2][3][4][5][6][7], and a causal role was formally proposed in cancerogenesis [8,9]. This aspect will be emphasized in this review. Increased entropy of signaling (or gene interaction networks) has been well studied as a cancer characteristic: Network entropy (NE) increases along with cancer progresses, yielding NE normal <NE tumor <NE metastasis [2,5]. Examples of increasing disorder in the cell will be given in the next subsection. Increased entropy in cell signaling could result from any affected level: genetic, DNA damage repair, epigenetic, transcription control, mRNA editing, miRNA generation, translation, signaling pathways and cross-signaling, tissue structure, etc. [8]. In this regard, DNA damage repair, for example, can be seen as a gatekeeper for increased entropy of signaling, watching over the order in the DNA sequence.
The cell, as any other living system, degrades over time, but it repairs itself partially. It transforms by a phenomenon of graceful or fault-tolerant degradation. The known direct exogenous causes of cancer, X-ray or UV radiation, mutagen exposure, inflammation, etc., which can be thought of as extra-organism sources of disorder, are neutral or deleterious for part of affected cells and may kill them. But they may also in very few cells cause the activation of protooncogenes and inactivation of suppressor genes, leading them on the path to cancer. Similarly, the major part of signaling degradations and related mutations will have more neutral and deleterious than beneficial effects on the cells. Therefore, ultimately, they mostly lead to aging, senescence, and death by apoptosis or necrosis. But, among the few survivor cells with positive effects, only one suffices to initiate a cancer. Entropy of signaling can, in one cell, favor the appearance of oncogenic events in this cell, a competitive advantage and/or a greater probability of further changes, e.g., loss of negative control on cell proliferation. Further positive changes in these cells will further increase the competiveness and so on. The tumor cell will have accumulated enough advantages to become independent of normal physiological regulation (Fig. 1). The diversity of mutation combinations found from one cancer to another, in any type, is compatible with results of such an entropy-driven mechanism. The most classical, clinical, and experimental cancers studied are initiated by one well-defined oncogenic event such as protooncogene activation or tumor suppressor gene inactivation, which are also the very frequent common mutations of many cancers (the drivers in the initial cells) combined with many rare mutations specific for each cancer (passengers or enabling) [10]. Thus, study of genetic defects in human cancers has revealed for each cancer a few major causative genetic events (driving mutations) and Fig. 1 The two extreme cases in the carcinogenetic transformation of a normal cell. Left: stepwise model in which sequential additional genetic events progressively lead to the fully developed, proliferative, and undifferentiated cancer cell (Vogelstein model). Right: catastrophic model in which a mitotic catastrophe (chromothripsis) or widespread mutations around a DNA repair site (kataegis) leads in one step to death of most cells and in one or a few cells to a cancer cell genotype. The two models might sequentially happen in the cells myriads of minor events, peculiar for each cancer. Whereas many of the minor events may be considered as mere passenger mutations, some are plural minor contributing events as illustrated by the many low penetrance polymorphisms linked to several types of cancer [11]. Thus, carcinogenesis could also result from a multitude of additional small genetic changes.
The five to ten driving mutations and hundreds passenger mutations in one tumor are observed in bulk tumor DNA, i.e., in the majority of tumor cells, which indicates a clonal origin [10]. Clonality is demonstrated for most, if perhaps not all, cancers [12]. This clonality indicates that, out of many combinations of stochastic events or changes of signaling, only one event combination has initially led to one tumor. It is not a whole population of cells that progressively became cancerous but one cell with the right combination of events. The derived cancer cells will bear the cancer-causing mutations but also all the random mutations acquired by the initiating cells before cancer initiation [13].
Cancer may also result from non-progressive acute causalities. Chromothripsis or chromoagenesis, the mitotic catastrophes caused by mitotic errors leading to widespread chromosomal rearrangements, induce the death of many target cells but also leads to aneuploidy, enhanced proliferation, and carcinogenesis of a few cells [14][15][16] (Fig. 1). In this case, it is not the accumulation of such effects but one major hot event that leads to cancer. Kataegis whereby point mutations rapidly occur in clusters over hundreds to million bases plays a similar role [17,18]. Viral oncogenes, radiation, carcinogenic agents, and inflammation may also lead to one major tumorigenic event.
Sequential combinations of stepwise and general catastrophic genetic events can also occur.
Regardless of the causing mechanism, if the initial carcinogenetic event bears only on somatic stem cells or pluripotent cells, it will have to occur in this minor fraction of the cell population, i.e., the number of potential initiating cells will be low. Whatever the fraction of the cell population involved, the more cells participate in the process, the greater are the chances that one becomes cancerous. Bigger breasts have higher cancer occurrence rate. On the other hand, dwarf mice (with impaired growth due to PTEN overexpression) are very resistant to cancer [19].

Examples of increasing entropy in the cell
Agents that hinder DNA replication often cause breaks in DNA [20,21]. Transient MAP-2-induced chromosomal instability leads to lung tumor relapse after oncogene withdrawal [22]. Cryptic genetic variation, masked by the robustness of cell phenotype, may also represent an entropy-driven mechanism. It may have, in some circumstances, deleterious effects but in some others allow rapid evolutionary adaptation [23].
Clonal mosaicism (DNA changes) in subpopulations of cells of one individual increases with age and is suggested to be a risk factor for cancer [24,25].
Aneuploidy resulting from faulty mitosis is a common cause of increased entropy of signaling in cancer. It is globally detrimental to cells, slows the rate of cell proliferation and organism growth [26], but, in a minority of cells, is associated with cell proliferation and thus frequently with cancer [27]. This tumorigenic effect is greatly increased when p53 is inactivated [28]. The most probable explanation for these are differences in gene dosage resulting from several mechanisms, genetic recombination, and defective DNA damage repair leading to gain of protooncogenes expression, loss of tumor suppressor genes, accumulation of improperly folded proteins, etc. This drives genomic instability and a mutator phenotype [27,29].
Transcription-associated mutagenesis and recombination imply a direct relation between transcription rate and DNA damage [30]. This may explain in part the strong relation between division rate and malignity of cancers [31,32].
Cells could be protected against minor protein structural defects by heat-shock proteins (HSP) (e.g., HSP90) [33]. Stochastic variations in the HSP expression can allow a minority of cells with protein defects to demonstrate a phenotype [34]. Diverse stress conditions lead to HSP transient inhibition and consequent aneuploidy [35]. Subtle variations in PTEN concentration and presumably other tumor suppressor genes predispose to breast tumors in mice [36].
Cancer cells themselves exhibit a less effective shield against disorder than normal cells, e.g., the disruption of euchromatin versus heterochromatin replication timing [37] transcription infidelity [38] and aneuploidy. Even in one breast cancer cell line, 1 % of the mutations differ from cell to cell [39]. This will cause a faster evolution of tumors and a higher rate of cell death. On the other hand, transcription infidelity will generate mutated proteins that will elicit new antigenic sites and mild but definite antibody production [40].
Entropy also plays a role in cell biology models. In the generation of cell lines and in vitro evolution, signaling pathways which are separate and distinct in physiological differentiated cells become more and more interconnected in the derived cells (e.g., PCCl3, WRT, and FRTL5 rat thyroid cell lines) [41]. The final result of such evolution would be that almost everything does everything on everything [42]: an increased network entropy or a promiscuity of signaling which is the antithesis of tight physiological control.

Noise as an alternative of entropy or a source to it
Noise represents unavoidable spontaneous stochastic and reversible fluctuations from one moment to another or from one cell to another, of any variable (e.g., mRNA, protein, metabolite level, or structure). Such variations may be minor (a few percent) to very significant (whole or none). Noise functions in multicellular development, differentiation, and evolution by facilitating transitions [43]. It is expressed as stochastic bursts of expression, time averaging buffering the burst, propagation, and eventual amplification of fluctuations. Noise has an important role in genetic circuits and in the epigenetic landscape [44] by allowing some randomness in cell behavior [43]. Increased randomness in gene expression allows cells to better select for conditions best suited for a given situation [45].
Cell to cell variability and dynamics in the expression of key transcription factors [46] and at other levels [47] may allow very different responses of identical cells to changing conditions. Noisy splicing driving mRNA isoform diversity in human cells may play a similar role [48].
A consequence of the noise in cell processes is the very important functional heterogeneity at a given time of similar cells in the same conditions. For example, thyroid cells stimulated together with TSH responded, from one cell to another, with delays of minutes to 2 h by acute thyroid hormone secretion [49] or the timing of NFKB activation in 3T3 mammalian cells in culture submitted to the same stimulus TNF-alpha [50] is very different. Similar heterogeneity maintained by heritable epigenetic controls is an important element in malaria parasites and bacteria bet hedging [51,52]. This reversible heterogeneity in bacteria as well as in human cells may take place in time scales from seconds to days involving different mechanisms [53,54]. It is believed to obey deterministic constrains [55].
Fine-tuning is one aspect of cell physiology that appears to be opposed to noise. Increasing noise increases the probability of escaping from existing regulatory constrains; decreasing noise decreases the possibilities of adaptation. On one hand, signaling circuits are robust to mutations and environmental fluctuations [56]. On the other hand, noise is extremely difficult to completely suppress [57]. Whereas noise is reversible and can decrease, entropy, which is cumulative, always increases. Increase of entropy will lead to progressive incremental changes, but a fluctuation may lead to a sudden qualitative change, a binary pattern which just requires a positive feedback to occur [53]. This could cause an irreversible change, potentially oncogenic.
Robustness is the capacity of a system to resist changes. It could be considered as a shield against entropy and noise [56]. Whatever its involved mechanisms, increased robustness of human cells could account for the requirement of more mutations to generate cancers in humans than in mice [58]. One example of the role of noise dampening by negative feedback as a protection against uncontrolled cell growth is the activation of p53 after a short mitogenic stimulus [59] preventing cell cycle entry.
Paradoxically, while robustness characteristics may protect normal cells against transformation, they may also at the tumor stage protect the cancer properties or oncogenes (e.g., oncogenic Raf stabilized by HSP90) [60].

Role of miRNA in the control of noise and entropy
A major mechanism of robustness is the redundant control by miRNA [61,62]. miRNA controls are partial and multidimensional; one miRNA has several mRNA targets, and each mRNA has several controlling miRNA. miRNAs are fine-tuners of the cell biology (canalization) [63][64][65]. miRNA can therefore be considered as ideal guardians against increased entropy: Their moderate and combinatorial action is better suited to fine quantitative adjustment than to qualitative regulation. Their ascribed long half life is also suited to long-term adjustment and to robustness of a system [66]. The role of miRNA against increasing entropy is beautifully illustrated by the very progressive dedifferentiation and loss of tissue structure of mice thyroids in which Dicer gene, coding for the miRNA generating protein, has been inactivated (KO) [67]. A role of some miRNA in limiting the cellular response to E2F activation and thus preventing replicative stress is another example of such fine-tuning negative feedback [68]. The general role of miRNA downregulation in cancer has already been reviewed [69,70]. It is remarkable that general defects in miRNA synthesis progressively lead to transformation and tumorigenesis [71,72]. General miRNA concentration decreases in cancer, and there are many defects at various steps of miRNA synthesis in many cancers [73], mostly by epigenetic silencing [74]. Most miRNA are downregulated in tumors, and low levels of miRNA-processing proteins (Dicer or Drosha) have been associated with the severity of several cancers [71,75]. Also in breast tumor cell lines and breast cancer, a broad set of miRNA are repressed under estrogen proliferation treatment [76]. This general concept should not mask the fact that a minority of miRNAs could be causal agents of tumors [77]. However, loss of Dicer and the consequent depletion of miRNAs do not preclude tumor formation by a sarcoma cell line [78].
Although little is known on the subject, entropic alterations of ncRNA could play the same role as those affecting the miRNAs.

Relation of entropy to aging
Aging of an organism can be defined as the progressive decline and deterioration accompanying age. Entropy and aging are strongly related, as aging is the major consequence of increasing entropy. Gene network integrity declines with age in Caenorhabditis elegans [79]. In nucleotide excision repair disorders, there is a striking correlation between the degree of repair deficiency, aging, and cancer [80]. Analysis of 27 datasets of tissue gene expression in aging mice, rats, and humans demonstrates over-expression signatures of inflammation, immune response, apoptosis, and senescence [81]. Aging mice have increased chromosomal instability and cancer [82]. The aging process thus impairs all cells and increases the probability of cancers in some.
3 Transition to independence (Fig. 2) The definite transition from a normal cell to a tumor cell is a fundamental change of philosophy: The cell and its descendants no longer behave as functional components of the organism submitted to the physiologically perceived requirements of the whole [83]; it becomes independent as a unicellular organism applying to itself and its descendants the ultimate rules of such an organism: to survive and multiply [84]. It is a reversal of the fundamental transition from unicellular to pluricellular organisms. Many of the genes associated in processes of metazoan multicellularity are also implicated in cancer [85].
The asociality of tumor cells even goes as far as ingesting and digesting other tumor cells or even lymphocytes [86,87]. This transition, as well as the further steps in the evolution of the tumor [17,18], may occur stepwise, as in the Vogelstein model, or at once, e.g., after chromothripsis [14], kataegis, or in a sequential combination.
When progressive, the evolution of tumor cells involves several major steps: the independence of growth of the cells (tumor cells) in a benign tumor, the evolution of the tumor cell to cancer cell, the process of invasion and epithelialmesenchymal transition (EMT), the metastatic process with its own various steps, and ultimately inter-organism transfer. Several steps can occur at the same time.
In two landmarks articles synthesizing a huge amount of information on cancer, Hanahan and Weinberg [84,88] outlined the few general properties (hallmarks) that cells must acquire to become cancerous. In fact, one can interpret some of these properties as the relief of constrains that subordinate the normal cells of a pluricellular organism to the physiological requirements of the whole. Two of these hallmarks are the prerequisite for the first step of tumorigenesis, i.e., independence: (1) sustaining proliferation signaling: It involves proliferation as a unicellular organism, without need of initiation by a physiological growth factor, and evading of growth suppressors and loss of contact inhibition [89]; (2) activating invasion involves the relief of physiological tissue constrains, cell-cell adhesion, polarity, anoikis, etc. This relief, which may involve genetic or epigenetic events [90], is also described as field cancerization [91]. It is therefore interesting that the Warburg type of glucose channeling into non-oxidative metabolism confers resistance to anoikis [92].
Sustained proliferation and invasion are together sufficient to initiate a tumor. Both are necessary: If repressed, there is no tumor. For example, cancer cells in a normal tissue matrix in vitro may not proliferate [93,94], and cancer cells inserted in a morula may differentiate in the resulting organism as perfectly normal cells [95]. When mammary cells are cultured in a tissue-like environment, isolated cells expressing one of the most classical oncogenes (ERBB2) fail to develop and proliferate. Only by moving cells out of tissue constrains does ERBB2 allow cell transformation. The negative constrains of pluricellular organisms dominate Fig. 2 The first phase of progressive transformation of an epithelium to an independent tumor: loss of proliferation constrains and loss of tissue constrains in any order [96]. Loss of epithelial tissue constrains may follow inactivation of protein(s) involved in tissue organization, e.g., in gap junctions [97], in tight junctions [98], PAR1 kinase [99], or in cell substratum adhesion (e.g., by metalloproteases) [100]. However, loosening of cell compartmentalization, which is part of the invasion process, is a necessary but not sufficient carcinogenetic event. Most normal epithelial cells die when dissociated from the epithelium. The mechanisms of loss of epithelial cell organization and of escape from its lethal consequences are complex and have become a major research subject [101].
Independent growth induced by cell endogenous oncogene or loss of tumor suppressor gene is the second and best recognized requirement for independent growth. However, this characteristic is often accompanied by several negative effects and feedbacks, which must be overcome. Their mechanisms are multiple and therefore difficult to bypass. They include: oncogenic induced apoptosis, i.e., suicide (e.g., Raf-induced apoptosis), oncogenic-induced senescence, DNA damage induced arrest of cell cycle (checkpoint G1, Gs, G2), or catastrophic mitosis and post-mitotic cell death. Thus, even at this stage, a high proportion of early tumors will fizzle out (e.g., in experimental skin tumors) [102].
The other hallmarks leading to further transition to cancer may be acquired later in the evolution of the tumor. For instance, differentiation characteristics are lost progressively with the aggressiveness of thyroid tumors [103]. In the case of progressive cancerization, the cumulative probability over time for a given cell of having accumulated the necessary complementary events and the consequent hallmarks will increase, along with the growth of cancer incidence with age [104].
Competition as such is a characteristic of tumor cell populations, but it also occurs in normal tissue. If independence is the first necessary step in tumorigenesis, cell-cell competition is not sufficient. Competitive interactions between cells in a compartment or between stem cells in a niche, already select, better endowed, more competitive winner cells and their descendants [105,106]. This represents a physiological mechanism of selection. Live cell delamination, followed by cell death, is a physiological mechanism which counterbalances epithelial growth and limits tissue overcrowding [107,108]. Cell competition, with losers going into apoptosis and winners generating descendants which replace them, exists in non-tumoral conditions with mechanisms generating supercompetitors (e.g., Myc over-expression) and mechanisms protecting for a while losers from apoptosis (transient Sparc expression during development in Drosophila) [109]. A p53inactivating mutation in bronchial epithelium allowed descendent cells to spread in 70 % of the mucosa [110] without generating a cancer. Loss of polarity in Drosophila wing leads to apoptosis and compensatory proliferation [46]. Impairment or the mere heterozygosity of the basolateral membrane organizer SCRIB is sufficient to initiate prostate hyperplasia and further biallelic loss [111].
Tissue constrains exerted through polarity, tight, and adherens junctions restricting cell proliferation are often mediated by the Hippo pathway [112]. This pathway may even eliminate deviant tumor cells generated by inactivating mutations of tumor suppressor genes [113,114]. The Hippo and the TOR pathways regulate the control of normal organ size in opposite direction [112,115].

Mechanisms
Once having acquired its independence, the tumor may progress or not by successive additional steps to acquire all the hallmarks of cancers and to develop the competitiveness of its cells [116]. It could also acquire all these properties at once in a few cells after a catastrophic genome rearrangement (e.g., chromothripsis, kataegis …). We shall mainly consider the progressive model.
"Tumors evolve by a reiterative process of clonal expansion, genetic diversification and clonal selection within the adaptive landscapes of tissue ecosystems. The dynamics are complex with highly variable patterns of genetic diversity and resulting multiclonal architecture" [117][118][119][120]. The process is favored by the mutator phenotype of cancer cells [121], with the mutation rate increasing in more advanced stages [18].
The role of the DNA damage response as an anticancer barrier in early tumorigenesis suggests that new genomic instability is an early phenomenon after independence [122] and contributes to evolution. Among the genetic events, some like those affecting genes of the splicing machinery are frequent and may influence entire programs [123]. The mutations may also involve recurrent hemizygous, sometimes extensive, deletions [124]. The reprogramming of characteristics is therefore largely a result of the reprogramming of the genome itself with the emergence of new genomic systems [125]. Clonal evolution of tumors (e.g., in leukemia) in response to treatment has been demonstrated by whole genome sequencing [126].
This evolution also involves epigenetic and possibly lysogenic-like and transcriptional control mechanisms [40,127,128]. In colon cancers, epigenetic events are far more frequent than genetic events [129], and in lung cancer, they may account for the regression of most differentiation genes [130]. Transcriptional networks, which are able to maintain cellular identity, certainly also have the capacity to play a major role in cancer cell identity [131].
The Darwinian selection will eliminate many less competitive clones [132]. The cells will lose physiological characteristics (i.e., differentiation) that are unnecessary for their survival and reproduction, if not all characteristics of the cell of origin, some of which being used in the new context. The end result is a great diversity and heterogeneity [133,134]. Examples are the greatly increased mRNA heterogeneity in cancers [38] and the great diversity of single-nucleotide mutations from cell to cell in a cancer [135][136][137]. Cancerization is not a linear process either in space or in time.
Just the fact that cancer cells proliferate, vary epigenetically, and mutate allows them to discover myriads strategies to escape attacks by anticancer drugs [138,139]. Noise increases the adaptability in the cancer cell population. Similarly, the faster increasing entropy of cancer cells, e.g., in chromatin replication timing [140], favors the appearance of new mutations, allowing some cells to resist therapies based on existing oncogenic mutations [141]. For example, treatment of B-Raf mutated melanoma selects cells with Ras mutations which will short-circuit the inhibition and re-establish the activation of MAP kinases in the tumor and also in normal cells, thus generating new skin carcinoma [142]. Treatment of cells of an osteosarcoma cell line by nutlin which blocks p53 degradation leads to somatic inactivating mutation of p53 and selects for p53 mutated cells [143]. Inhibition or lack of telomerase activity in human cancers leads to alternative lengthening of telomeres mechanisms [144]. An analogy between the evolution of drug resistance in bacterial communities and malignant tissue has been suggested [145].
On the other hand, some mutations or epigenetic controls while not being beneficial or detrimental per se might introduce a new vulnerability to treatment [146,147].
Moreover, the process of adaptation by mutation is not necessarily infinite, e.g., in bacteria diminishing returns of epistasis among beneficial mutations decelerates adaptation [148] and lead to an equilibrium of maximal efficiency.
The pervasive increasing entropy in tumor cell evolution will, as in the normal cells, generate far more deleterious than favorable genetic and epigenetic events. This will lead to the development of much more uncompetitive than competitive cells. Tubiana had demonstrated this [149]: Only 10 to 20 % divisions in breast cancer cells contribute to tumor growth. The less competitive cells may divide a few times but not infinitely, redifferentiate, and become senescent, quiescent, or even die. The other, more competitive cells may correspond to the so-called cancer stem cells postulated by the cancer stem cell theory that we called cancer stem-tumor-propagating cells [132]. These cells, defined by different sets of properties depending on the systems and the authors, need not be a fixed distinct population of cells, but rather one extreme phenotype in an evolving population [132,150,151].
Thus, the same increase in entropy that generated initially the tumor-initiating cell will impair many cells during the evolution of the tumor but allows new adaptations to arise in a minority.
Due to the stochastic nature of genetic events and their relation with cell divisions it is not astonishing that the best Fig. 3 Schematic view of steps in tumor evolution. From one normal cell, to dominance of this cell, independent benign tumor, invasive tumor, cancer, and metastasis. The width of the arrows suggests the relative importance of the relations prognostic markers of tumor evolution are the Ki67 proliferative index and tumor size [31,32]. The more cell divisions, the more cells involved, and the more mutations, a fraction of which will contribute in the affected cells to positive tumor cell evolution.

Features of the evolution
Besides the properties acquired in the first transition to independence, mobility, autonomy, and escape of tissue constrains, what are the properties progressively acquired by tumor cells in their evolution to cancer cells, the hallmarks of cancer [84]?
These properties include the capacity to react independently to the environment (e.g., O 2 availability, signals from other cells), loss of differentiation, the search for O 2 and substrates; movement, propagation, and exploration behavior; proliferation whenever, wherever it is possible; escape harmful conditions and environment; escape immune reactions; escape of metabolic control; and escape of apoptosis. These correspond to the imperatives of the cancer cell progression and invasion. At the step of invasion, the tumor has acquired the full set of cancer properties, and it is a required step for metastasis. Tumor cells may migrate in bulk as clusters, strands, or sheets (collective migration) or individually. In the latter case, they may, after EMT, either proceed through the extracellular matrix by hydrolyzing and remodeling it or may swim around it as an amoeba [152]. These patterns are reversible depending on the context (e.g., extracellular matrix organization).
The newly acquired properties are not independent; they all reflect the new character of the cell: its cancerishness. A nice illustration of this is the finding that all proposed specific gene expression signatures of breast cancers are related and therefore give redundant prognostic results [32]. The programs and their resulting hallmarks represent a complex interrelated dynamic network in which some hallmarks themselves drive new programs. In this regard, the Warburg reorganization of cell metabolism in cancer is both a consequence and a cause of the cancer process itself [153][154][155]. The Warburg effect confers resistance to anoikis [92]. The invasion programs imply a close and reciprocal interrelation with the extracellular matrix and the various micro-environmental cancer and non-cancer cells. They present a great variety. The dynamic interrelations of the hallmarks are also illustrated by the many mechanisms of immune escape. Cancer vaccination drives Nanogdependent evolution of tumor cells toward an immuneresistant and stem-like phenotype [156]. Hypoxia induces escape from innate immunity through HIF and Adam10 [157] and promotes tolerance via Treg cells [158]. Lactate as such stimulates migration, radioresistance, and immune escape [159]. A large part of the program of embryonic stem cells taken over by cancer cells is the prevention of differentiation.
The evolution of the independent tumor to cancer will involve the acquisition, progressively (evolution) or abruptly (e.g., after chromothripsis), of all those properties, not necessarily in a given sequence. At any step, the progression of a tumor clone may stop, but this will have little general effects at stages when many clones coexist. Also, higher saturation density is increasingly permissive for expression of the neoplastic transformation [94]. This evolution will involve the acquisition of all these properties leading to tumor growth, the required angiogenesis, invasion, and ultimately metastasis. Exaggerated angiogenesis will increase but also restrain tumor access and become a pathological process per se [160]. The evolution will result from branched genetic evolution within the tumor [134] as well as even more frequent epigenetic changes [161][162][163].
For this evolution, the cells will transiently or permanently appropriate programs that improve their competitiveness and growth. The over-expression by human cancers of genes that are specific to a variety of normal human tissues [164] may reflect this adaptation or/and the increasing entropy of cancer cells. Most often, cancer cells do not invent new programs; they co-opt or highjack available programs expressed in some circumstances by certain cells such as, for invasion, the wound program [165]; for growth, the regrowth program of amputated liver; for adaptation, to flux the program of blood cells; for escape of differentiation constrains, programs of embryonic stem cells, etc. One other example is the appropriation of leukocytic trafficking and leukocyte attraction mechanisms for invasion and metastasis [166]. To provide itself with the necessary building blocks for proliferation, the cancer cell activates permanently a temporary program of the normal cell cycle [167], thus generating a permanent Warburg effect [168].
Versatility in the use of such programs confers to the cell a nimbleness that reminds of the embryonic cells. For instance, cMyc oncogene when expressed induces proliferation but suppresses cancer metastasis [169], i.e., negative and positive roles played by over-expressing the same protein.
In a given cancer, there is complementarity, a synergy between amplified and mutated genes in eliciting programs [170]. Many cells need an active PI3K pathway to grow, and when they have no activating mutation of the pathway, they over-express IGF1 receptors [162]. When the pathway is inhibited by therapy, recurrences make use of a Met or cMyc amplification [171]. Similarly, the widespread potential of cancers to adapt to treatment may involve the redundancy of growth factor-regulated signaling cascade [172], heterodimeric activations [173], emergence of new mutations [174], etc.
The respective use of all these programs varies for different cancers and for one cancer at different times and locations, depending on the existing conditions and the cells accessible programs [175]. For instance, telomere dysfunction in the absence of telomerase generates very disadvantageous genomic instability but is often corrected by reexpression of telomerase [126]. EMT transition can suppress major attributes of human epithelial tumor-initiating cells [176]. Plasticity, reciprocity, and evolution are the major characteristics of these processes [175,177].
The evolution of cancer toward independent and maximal growth, and loss of differentiation characteristics leads to a convergence of phenotypes between cancers of same type with very different genotypes [178] and even between different cancer types [178,179], especially for the most dedifferentiated cancers. However, the programs used will often still reflect characteristics of the cell of origin, which are useful for their progression. For instance, transcription factor TTF1 (also called NKX2.1), which is used and necessary in thyroid and lung normal cells proliferation is still used and necessary for pulmonary and thyroid cancer cell lines growth [180].
Not all cancers use all available programs. For instance, basocellular skin carcinoma or brain tumors may not express EMT program nor develop metastasis. Also, not all the cancer cells in a cancer express the same programs, nor do they necessarily express them all the time, e.g., EMT is induced in some peripheral carcinoma cells and is reversible after metastasis [181].
A step of the evolutionary process is the development of the cancer independently of its initial cause, e.g., the skin tumor in response to a mutagenic then progression treatment, which develops independently of the causal oncogenic events thereafter. By contrast, a hyperplastic tumor may develop with a hormonal treatment or with the expression of an oncogene, both of which recede when the stimulus is stopped [182]. In the first case, the tumor cells have acquired an independent property, but, in the other, did not. Oncogenic addiction, the necessary activity of one oncogene for tumor progression characterizes this latter type. Because of its mechanistic simplicity and economy, it is very efficient but offers simple therapeutic targets. The inverse, oncogenic independence may arise following treatment (e.g., by relief of a negative feedback on EGFR in Raf-induced melanoma) [183,184]. It explains some resistances to single target treatments. Non-oncogene addiction may also occur, for example, to heat-shock factors that compensate for protein denaturing stress resulting from the increasing entropy of cancer cells [124,185].
The classical scenario of tumor progression to cancer described so far should not obscure the fact that many tumors do not progress or even regress. This is a well-recognized possible fate of benign tumors such as hemangiomas, lymphangiomas, gliomas, etc. Thyroid microcarcinomas, with all the characteristic of classical papillary carcinomas (e.g., B Raf mutations, RET-PTC rearrangements), are found in up to 30 % systematic autopsies. Most never evolve [186]. Similarly, naevi may be considered as dormant quiescent melanoma [187]. There are many dormant breast carcinomas [188], prostatic intraepithelial neoplasia [189]. Mechanisms of these evolutions are poorly reversible senescence [189] which may result from the uncorrected oncogenic stimulus itself [189] or the control by T cell infiltration [190].

Metastasis program
The generation of metastases is a third step in the evolution of a cancer. It is most important and lethal since it is responsible of 90 % of cancer deaths worldwide.
As shown by mutation patterns of tumors and metastases, metastases can be generated early from the bulk of the tumor as well as late in its evolution from some cells in the tumor [191]. A lot of arguments in favor of each concept show that both are valid. However, the rarity in the primary tumor of clonal genetic events found in metastasis suggests that only rare cells in this tumor have the ability to metastasize [192]. The genealogy of metastases shows that some are early, some are late, and some are even metastases of metastases [193]. Moreover, there are metastases of undetected cancers [194]. Thus, the metastasis program can be independent of other cancer cell programs.
The steps involved in the metastasis process have been analyzed [195,196]. They include invasion, individualization or not of the cells by EMT, migration toward capillaries, intravasation, survival in circulation, extravasation, seeding, and growth as metastasis. EMT which ultimately individualizes the cells, which then acquire a mesenchymal type, implies the loss of cell-cell adhesion and cadherins and a switch of the cytoskeleton proteins from cytokeratins to vimentin [93,152]. It is a major determinant of metastasis [197].
Each metastasis step implies different capacities and programs and a certain frequency of failures. After EMT, extravasation of the cells is certainly facilitated by their attraction to oxygen and by the large but tortuous and leaky vascularization of the tumor [198]. Moreover, cancer cells, by decreasing stiffness and increasing cytoskeletal remodeling of endothelial cells, break down the endothelial barrier [199]. Survival in the circulation certainly requires the hallmark characteristic of escape from anoikis, the apoptosis of cells that lost cell contact.
According to Valastyan [195], once in circulation, survival, extravasation, and seeding are very efficient. Eighty percent of intravenously implanted carcinoma cells succeed in extravasation. These steps do not even require malignancy of the cells as normal mammary cells succeed to achieve seeding if not growth [200]. On the other hand, the attrition rate for metastases is evaluated at 99.98 %, i.e., a success rate of 2/10 4 [201]. As it is difficult to imagine a significant evolution in the few hours or days between intravasation and seeding, either the necessary capacities for metastatic development are stochastically available in a fraction of the intravasating cells or they are acquired in some cells later after seeding. The major limiting factor is the tissue environment, i.e., in part, the composition of extracellular matrix, the niche [202]. Also, the cancer cell may exploit for its implantation surface proteins expressed by chance, e.g., claudin-2-expressing breast cancer cells functioning as unspecialized adhesion molecules to bind breast cancer cells to hepatocytes [203]. Additionally, primary tumor-associated stromal cells can also be seeded in secondary sites and potentiate the metastases formation [204].
It is easy to explain metastases of peripheral organs in the lung where venous blood from the tumor will have to pass, or metastases of visceral organs in the liver where most of their venous blood passes. It is more difficult to conceive how groups of cells of a diameter from 10 to 30 μm can escape the filters represented by the capillaries of lung and liver. However, there are venous shunts in the lung, which allow less than 1 % of the circulating blood to bypass lung capillaries. The necessity of intravenous shunts to bypass liver and lung would imply a great attrition rate at this level. On the other hand, isolated cancer cells, the present favored origin of metastases [205], could flow through the capillaries, as the leukocytes do. In fact, such cells also hijack other leukocyte programs, e.g., for extravasation [166].
Choice of the metastatic site, the organ-specific metastatic tropism, will be conditioned by the environment (e.g., cytokines, etc.) and by the adaptation of the seeded cells to their environment. Those breast cancer cells that succeed to colonize the lung are those which produce tenascin as a metastatic niche complement [202].
The stochastic character of the transition of cancer cells to metastasis is illustrated by the nice relation between size of tumor and number of metastases [149].
The ultimate step in the evolution to total independence of cancer cells is the success of some cancers to transfer from one organism to another, as parasites do: by infection like Tasmanian devils transmissible tumors, which in their path to total independence have even managed to acquire new mitochondria from one transitory host [206]. This cancer has now evolved in several subclones sufficiently different to evade immunity against one of them [207]. Canine transmissible venereal tumor is another example [208,209]. Other examples of interorganism transfer are the cases of maternal transmission to offspring [210], fetus to fetus transmission [211], and transmission with organ transplants [212].
6 Cooperation, "swarm intelligence," and "emergence" in cancer What is emergence (cf. glossary)? To the clinician who treats it, cancer often appears as an intelligent enemy, displaying what is known as swarm intelligence. Swarm intelligence refers to the collective behaviors of a decentralized selforganized system composed of single individuals interacting locally by few signals. It is also called collective intelligence or said to display an emergent property. The interactions between the single entities are stochastic. The equilibrium reached by the whole population is dynamic. In that context, emergence is the generation of swarm intelligence in a collection of such individuals. It must result from the acquisition by Darwinian selection of the appropriate reflexes by the whole population. It is therefore a biological concept [213,214]. Common examples are flocks of migrating birds, of fishes, ant colonies, soil microbes [166], and perhaps packs of cars at the beginning of vacation. In emergence, the different units of an entity display teleonomic, i.e., apparently purposeful behavior [215].
Normal cells display cooperation. Bacteria use few signals and stereotyped responses to cooperate once their density is sufficient (quorum sensing), to develop swarming behavior, luminescence, and other cooperative behaviors [216]. Animal gene regulatory networks by themselves generate emerging properties [217]. The major process of invasion, the collective tumor cell invasion, involves concepts of cell-cell cooperation and collective behavior [205] programs appropriated from embryological development [214].
The complex interplay between genetically distinct populations within a cancer has already been discussed with examples of commensalism (i.e., positive interaction where one clone benefits the others) and mutualism (cooperation between populations) [1].
Hypoxia and angiogenesis represent one striking example of "intelligent cooperation." The response to hypoxia is a remarkable general response of the tumor cells to a deleterious condition, with cells moving toward oxygen and vessels, becoming more malignant, etc. In fact, tumor vessels' therapeutic targeting promotes adaptations in cancer that result in increased malignancy [218].
Hypoxia and HIF1α induce aggressive growth, invasion, angiogenesis, and metastasis in xenografts of human cancers [219], zebrafish tumors [220], and pancreatic cancer cells [221]. The activation of HIF leads to the induction of SNAIL transcription factors conferring EMT and stem cell properties [222] and of transcription factors such as OCT4 conferring stemness properties [223], telomerase, and multidrug transporters. Hypoxia and HIF also promote VEGF secretion and angiogenesis and activate an invasive program by upregulating Met tyrosine kinase [224]. Similarly, lactate levels predict likelihood of metastasis and tumor recurrence in human cervical cancers [225]. The shift to glycolysis from glucose oxidation (Warburg effect) protects cells from anoikis after detachment [92]. Some glioblastoma cells even generate endothelial cells and vessels themselves which irrigate the tumor [21,226]. Thus, hypoxia and HIF induction, early consequences of the process of tumorigenesis, are major drivers of this process [227].
Cancer invasion potential and resistance to therapy are interconnected properties, more or less linked programs that may induce each other [228].
Another example of cooperative collective behavior is illustrated by the tumoral control of the microenvironment. In the invasion process, there is a reciprocal reprogramming of both tumor cells and the surrounding tissue [177]. For example, the breast cancer-associated fibroblasts which induce EMT and chemoresistance [229], stromal fibroblasts surrounding cancer cells, may stretch out in parallel networks letting cancer cells move along them [230]. Tumors may secrete PDGF-BB to stimulate stromal cells to produce erythropoietin which induces angiogenesis and red cell generation, i.e., which improves their oxygen supply [231]. They also recruit mesenchymal stem cells which stimulate growth, metastasis, and resistance to cisplatin [232] and which supply them with cysteine, allowing them to synthesize the necessary glutathione [233]. On the other hand, of course cancer cells compete with normal cells. A role of secretory miRNA of tumoral cells has been proposed in this regard [234].
In experimental models, cooperation may also involve different tumor cells with complementary mutations (e.g., Ras V12 and SCRIB-mutations [192]. Even senescent cancer cells may harness wound healing responses to support the other cancer cells [235]. Inflammation is another cooperative phenomenon. Its role is illustrated by the remarkable slowing down of tumor progression by aspirin treatment [236]. Recognition of tumor-specific antigens by lymphocytes leads to cancer immunoediting, loss of tumor antigen expression or presentation in some cells that will then selectively outgrow the others [237], secretion of immunosuppressive cytokines, lymphocytic inhibitory enzymes (e.g., indoleamine 2,3 dioxygenase), or recruitment of immunosuppressive Treg cells [238]. Tumor acid pH inhibits T cell immunity [239]. Clearance of apoptotic cells, very frequent in cancers, results in the production of anti-inflammatory cytokines [240]. On the other hand, acquisition by melanoma and colon cancer cells of chemokine receptors (CXCl3, CXCR1, CCR7), which in lymphocytes attract the cells to lymph nodes, will lead these cancer cells to lymph node invasion [241].
Cooperation may also be observed as response to therapy. During cancer radiotherapy, tumor cells dying from apoptosis stimulate growth and repopulation of the tumor [242].
Danger signals produced by irradiated tissues also comprise pro-angiogenic molecules, thus protecting the vasculature [243].
An even more daring but still to be substantiated theory would have the cancer-cell-secreting factors that would mobilize bone marrow-derived cells to pre-metastatic sites [244]. Another hypothesis is the transmission of neoplastic properties from transformed to normal human stem cells [245].
All these examples of cell cooperation could represent true emergence, i.e., a collective behavior resulting from reflexes of signaling acquired by a Darwinian selection.
The evolution from drug-sensitive to insensitive cells does not fall into the emergence category as it reflects rescuing mutations in a few cells that take over and regenerate a whole population [246].

Efficiency of the cancer process
Although collectively, cancer cells are deadly, at the level of the individual cells, the carcinogenetic process is remarkably inefficient. At each step of the cascade, the frequency of events deleterious to the cell outnumbers the frequency of positive evolutionary events, but these generate the progression. In mammary cancers, up to 90 % of the cells are doomed [149]. The cancer stem-tumor-propagating cells concept, with only a fraction of the cancer cells being competitive, implies the same conclusion [132].
At each step of the cascade, especially in the early steps, the lack of a supplementary event may stop the process in its track. This explains the high frequency of benign or static tumors (e.g., cancer in situ). At each step of the cascade, tumors can even regress if they have not achieved immortality (e.g., hemangioma). However, at the end of the evolutionary process, one cell is sufficient to generate a xenotransplanted mammary tumor [247].
A gross estimate of cell number at different stages of thyroid cancer evolution gives an appreciation of the efficiency of cancerization from the point of view of the cell. One thyroid (3×10 9 cells) with 7 % annual renewal [248] over 30 years generates one or two nodules in 50 % of the female population, (probability 1/10 10 per cell) of which 5 % are malignant (probability 5×10 −12 per cell). At another level, a cancer of 1 g (±5×10 8 cells) releasing in the blood 50.000 cells per day or 2×10 7 cells per year, generating five metastases in 5 years, represents a probability of 5×10 −8 metastasis per released cancer cell. Even for a release of 500 cells/day, the probability would be 1×10 −6 . Finally, only two cells in history, the Tasmanian devil and the canine transmissible venereal tumor cancer cell, managed to pass regularly from organism to organism, i.e., to become infectious and thus immortal.

Conclusion
Besides the minority of cancers arising from direct exogenous causes such as radiation, carcinogens, viruses, or inflammation, the majority of cancers do not originate from single obvious events but rather from multiple chance events in the normal life of our 3×10 13 cells. We link these events to the increasing entropy in the cell physiology and to the noise in our cell metabolism, which may initiate the process in some cells. This can lead to the major reverse biological transition from cells in pluricellular organisms, which obey constrains of the whole, to unicellular organisms, which independently strive to survive and multiply. After this transition to independence, the same causal mechanisms coupled to a Darwinian selection will cause the evolution to progression, invasion, metastasis, and in a few cases immortality as infectious agents. At the cellular level, this evolution will make use in a dynamic and reversible manner of the various existing normal cells programs. It will involve adaptive cell survival strategies and even apparent cooperation between cancer and normal cells from the microenvironment. This will be accompanied by a profound genomic remodeling. Finally, the cancer process, when considered from the cell point of view, is extremely inefficient at many stages: in the initiation as well as during the progression, when only rare mutations are viable and improve cell competiveness. Due the high number of cells possibly involved at the level of the organism and its lifespan, the probability of cancer and metastasis remains high.
The concept of an evolution of tumor cells to a diverse population of more and more competitive cancer cells through a process of rare consecutive stochastic genetic or epigenetic events suggests several therapeutic considerations.
First, whatever the lesions involved, the removal of a large part of the cell population in which the event takes place, i.e., the surgical approach, will greatly restrict the population in which further oncogenic events will occur. It will therefore remain fundamental.
Second, wherever the tumor has arrived in its evolution, the avoidance of further oncogenic events may delay or stop this oncogenic evolution and is therefore useful. It is well known that 5 years after abstention from smoking, the probability for lung cancer is brought back to a much lower level [249].
Third, whatever the stage of a cancer, a temporary reprieve may be helpful. In a differentiated thyroid carcinoma of a 60-year-old patient, one or two reprieves of 5 to 10 years would allow an almost normal lifespan.
Fourth, the great heterogeneity of tumor cell populations in a tumor, even with a single similar initial oncogenic event and the increase in signaling entropy which multiplies redundant signaling, suggest the interest of broad simultaneous multi-target therapies [127,[250][251][252]. programs (e.g., EMT, proliferation, somatic cell characteristics, etc.)? Are these programs always or randomly expressed, exclusive of each other?

Chromothripsis
Mitotic catastrophe caused by mitotic errors leading to widespread chromosomal rearrangements [14]. Emergence The appearance in a system of swarm intelligence, i.e., a collective behavior of a selforganized system without centralized control, composed of single entities interacting locally by a few signals (e.g., fish swarm). The tumor is composed of single tumoral and stromal cells that are our entities. They interact with signals that depend on their environment, and they undergo selective pressure. They seem to display intelligent behavior through the emergence of a sort of collective intelligence. Intelligence is not a human-restricted notion (e.g., artificial intelligence). Think of bots in video games that are, by design, intelligent entities, understanding their environment and reacting to changes in it to maximize chances of success. Remember that, before being rendered as human-looking pixels on screens, bots are lines of code on hard drives. An intelligent tumor or cell has no consciousness but adapts through selective pressure to its environment by reacting to its changes. Entropy Theoretical formulation for progressive irreversible disorder and randomness: It is a notion used in diverse fields that requires prior definition. The second principle of thermodynamics stipulates that entropy of an isolated system always increases. In familiar terms, unless provided with new outside energy, any system constantly loses usable energy. For example, we might imagine a machine, e.g., an old car, which is machined to support a certain type of action. Whether it is left in a garage or used on a daily basis, a shift may be observable with time from its original behavior. The degradation causing this behavioral shift in machines can be linked to dissipation processes or increased macroscopic entropy and is used in modeling machine reliability. But entropy is also a statistical thermodynamics measure of disorder at a microscopic level and is, so to speak, a generalization of the macroscopic thermodynamic measurement. It is not necessarily linked to loss of usable energy but rather to the number of possible configurations of the components of the system. The cell is our system. Its internal initial order and environment determine its behavior. In the same environment, while losing this order (e.g., by acquiring mutations), the cell may behave differently. This is because of increasing entropies of gene interaction networks (entropy of signaling) that have actually been measured and compared in different publications (see main text). Consequently, increased entropy is used as a metaphor of progressive irreversible and random loss of initial order in the cell; it is strongly linked to aging and will shape cell behavior. Graceful degradation or fault-tolerance Property of a system (the cell) that enables the system to function properly (survive) after failure of one of its component (disorderdriven degradation at any level in the cell).

Kataegis
Sudden appearance of clusters of point mutations in a chromosome after DNA damage and repair [17,18]. Noise Unavoidable spontaneous stochastic fluctuations in space and time of a biological variable.

Robustness
The resistance of a system to noise and disorganization; its capacity to behave normally despite new defects or disorders.


extfieldsofstudy: Biology Medicine

text:
Increased Concentrations of Fructose 2,6-Bisphosphate Contribute to the Warburg Effect in Phosphatase and Tensin Homolog (PTEN)-deficient Cells*

Background: PTEN deficiency leads to increased glycolytic flux characteristic of cancer cells. Results: PTEN-deficient cells have high concentrations of fructose 2,6-bisphosphate due to protein stabilization of PFKFB3 that results from impaired APC/C-Cdh1-dependent degradation. Conclusion: Elevated fructose 2,6-bisphosphate concentrations contribute to the increased rates of glycolysis and proliferation in PTEN-deficient cells. Significance: Fructose 2,6-bisphosphate might be a crucial mediator of tumorigenesis in PTEN-deficient cells. Unlike normal differentiated cells, tumor cells metabolize glucose via glycolysis under aerobic conditions, a hallmark of cancer known as the Warburg effect. Cells lacking the commonly mutated tumor suppressor PTEN exhibit a glycolytic phenotype reminiscent of the Warburg effect. This has been traditionally attributed to the hyperactivation of PI3K/Akt signaling that results from PTEN loss. Here, we propose a novel mechanism whereby the loss of PTEN negatively affects the activity of the E3 ligase APC/C-Cdh1, resulting in the stabilization of the enzyme PFKFB3 and increased synthesis of its product fructose 2,6-bisphosphate (F2,6P2). We discovered that when compared with wild-type cells, PTEN knock-out mouse embryonic fibroblasts (PTEN KO MEF) have 2–3-fold higher concentrations of F2,6P2, the most potent allosteric activator of the glycolytic enzyme phosphofructokinase-1 (PFK-1). Reintroduction of either wild-type or phosphatase mutant PTEN in the PTEN KO cells effectively lowers F2,6P2 to the wild-type levels and reduces their lactate production. PTEN KO cells were found to have high protein levels of PFKFB3, which directly contribute to the increased concentrations of F2,6P2. PTEN enhances interaction between PFKFB3 and Cdh1, and overexpression of Cdh1 down-regulates the PFKFB3 protein level in wild-type, but not in PTEN-deficient cells. Importantly, we found that the degradation of endogenous PFKFB3 in PTEN KO cells occurs at a slower rate than in wild-type cells. Our results suggest an important role for F2,6P2 in the metabolic reprogramming of PTEN-deficient cells that has important consequences for cell proliferation.

are characterized by high rates of glycolysis and metabolize glucose into lactate even in the presence of oxygen, a phenomenon known as the "Warburg effect" or "aerobic glycolysis" (1). Upregulation of glycolysis is proposed to endow cancer cells with several selective advantages, in particular the incorporation of nutrients into biomass to sustain high rates of proliferation (2,3). Deregulation of certain cancer-related genes has been linked to the acquisition of the glycolytic phenotype (4). The phosphatase and tensin homolog, PTEN, 2 is a tumor suppressor most well known for its ability to oppose the PI3K/Akt signaling pathway through the dephosphorylation of phosphatidylinositol-3,4,5-trisphosphate (5). Deletions in the PTEN gene locus have been reported in multiple cancers (6,7). Interestingly, PTEN knock-out cells have been used as cellular models of the Warburg effect, given that they exhibit an enhanced glycolytic phenotype and high proliferative rates (8,9). Excessive PI3K/ Akt signaling as a consequence of PTEN loss can account for the enhanced glycolytic rates of these cells (10 -13). In our study, however, we are proposing a novel role of PTEN in the regulation of the Warburg effect that is independent of its ability to oppose the PI3K/Akt pathway.
It was recently reported that PTEN, in a phosphatase-independent manner, is capable of enhancing the ubiquitination activity of the APC/C-Cdh1 ligase (14). The anaphase-promoting complex/cyclosome (APC/C) is an E3 ubiquitin ligase known to regulate cell cycle progression and to form two complexes with distinct substrate specificity: APC/C-Cdc20 and APC/C-Cdh1 (15). Interestingly, the APC/C-Cdh1 has been demonstrated to mediate the degradation of PFKFB3 (16,17). PFKFB3 is an isozyme from the family of enzymes 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatases (PFK-2/FB-Pase-2) that catalyze the synthesis and degradation of fructose 2,6-bisphosphate (F2,6P 2 ), a known allosteric activator of the key glycolytic enzyme PFK-1 (18). PFKFB3 is ubiquitously expressed in mammalian tissues and has the highest kinase-tobisphosphatase ratio; hence it is virtually dedicated to the synthesis of F2,6P 2 (19). In light of these studies, we hypothesized that PTEN-deficient cells exhibit enhanced glycolytic rates due to increased concentrations of F2,6P 2 , which are the result of impaired degradation of PFKFB3 via the APC/C-Cdh1 E3 ligase.

MATERIALS AND METHODS
Cell Culture and Transfection-Wild-type mouse embryonic fibroblasts (MEF) and PTEN knock-out mouse embryonic fibroblasts (PTEN KO MEF) (a kind gift from Dr. Tak Mak, University of Toronto) were grown at 37°C and 5% CO 2 in Dulbecco's modified Eagle's medium (Invitrogen), supplemented with 10% fetal bovine serum (HyClone), L-glutamine (Invitrogen), and penicillin/streptomycin (Invitrogen Fructose 2,6-Bisphosphate Measurement-Fructose 2,6-bisphosphate was extracted and measured using the method described in Ref. 20. Briefly, cells were lysed in 0.1 M NaOH and heated at 80°C for 5 min. Samples were then centrifuged, and the supernatant was neutralized with ice-cold 1 M acetic acid in the presence of 20 mM Hepes. The mixture was centrifuged again, and F2,6P 2 was assayed in the resultant supernatant through its ability to activate potato tuber pyrophosphate phosphofructokinase-1 (PP i -PFK). In the assay, the following reagents at the specified final concentrations were used: 50 mM Tris/HCl, pH 8, 5 mM MgC1 2 , 0.15 mM NADH, 17.5 mM glucose 6-phosphate, 0.5 mM pyrophosphate, 1 mM fructose 6-phosphate, 50 g/ml aldolase, 1 g/ml triosephosphate isomerase, 10 g/ml glycerol-3-phosphate dehydrogenase. Light absorbance of NADH at 340 nm was measured in a spectrophotometer. F2,6P 2 was quantified using a standard (a kind gift from Dr. Richard Honzatko, Iowa State University) and expressed as pmol of F2,6P 2 /g of cellular protein or as percentage of (or -fold difference when compared with) control cells.
Lactate Measurement-Cells were seeded at low density in 6-well plates and the next day incubated with fresh medium containing 10% dialyzed fetal bovine serum (FBS) to decrease background levels of lactate. After 72 h, 200 l of the medium were collected and assayed with a lactate PAP kit (Biomerieux). Lactate readings were normalized to protein amount, which was assayed with BCA Protein Assay Kit (Pierce).
Glycolytic Flux Measurement-Complete medium containing [5-3 H]glucose (PerkinElmer) at a concentration of 363.63 Ci/mmol glucose was prepared. Cells seeded in 24-well plates were incubated with 300 l of the medium at 37°C for 6 h. In negative control wells, 2-deoxyglucose (Sigma) at final concentration 5 mM was also added. Glass vials with rubber stoppers and hanging wells were set up for each sample, and filter paper (1 ϫ 6 cm) soaked in 200 l of H 2 O was placed inside the hanging well. After a 6-h incubation, 250 l of medium were collected and centrifuged to pellet any cells, and the supernatant was added to the bottom of the glass vial. Vials were sealed and incubated at 37°C for 48 h to allow evaporation of 3 H 2 O. The filter paper was then transferred to a scintillation tube. In addition, the hanging well was washed once with 200 l of water, and this was added to the scintillation tube. 3 H radioactivity count was measured for each sample. Readings were normalized to protein amount.
Statistical Analysis-Measurements are expressed as means Ϯ S.E. Statistical analysis of the results was performed by unpaired Student's t test or by one-way analysis of variance followed by Tukey's honestly significant difference test for pairwise comparison of means. p Ͻ 0.05 was considered significant.

PTEN-deficient Cells Have Higher Concentration of F2,6P 2 Than Wild-type Cells-PTEN-deficient cells have been
reported to exhibit high rates of aerobic glycolysis. To validate this, we measured glycolytic flux through the metabolism of [5-3 H]glucose, wherein traceable 3 H 2 O is produced at the enolase reaction and is released into the culture medium (22). To ensure the specificity of the assay, we co-incubated control wells with the glucose analog 2-deoxyglucose (2-DG), which is converted to 2-DG-P and inhibits hexokinase at the rate-limiting step of glycolysis (23). Results are thus expressed as total [5-3 H]glucose-to-3 H 2 O metabolism minus nonspecific metabolism that occurs during inhibition by 2-DG. Wild-type and PTEN KO MEF cells were assayed this way, and we found that, in accordance with previous studies, the PTEN-deficient cells had a significantly higher glycolytic flux than the wild-type cells (Fig. 1A).
Aerobic glycolysis culminates in the production of lactic acid from pyruvate, which in aqueous solutions dissociates almost completely to lactate and H ϩ . We thus assayed the rate of lactate synthesis in wild-type and PTEN KO MEF cells. In line with the [5-3 H]glucose results, PTEN KO cells had a higher rate of lactate production (Fig. 1B). The good correlation between glycolytic flux and lactate production validated the latter as a good read-out of aerobic glycolysis.
According to the proposed hypothesis, cells lacking PTEN are expected to have higher concentrations of F2,6P 2 than wildtype cells, which in turn may contribute to their increased glycolytic capacity. To investigate this, an enzymatic assay to measure F2,6P 2 was set up and optimized based on the method described in Ref. 20. Cell extracts from wild-type and PTEN KO MEF cells were analyzed in the assay to determine the effects of PTEN deficiency on F2,6P 2 concentration. The results were normalized to the protein concentration to control for variable rates of cell proliferation. Even after accounting for higher cell mass, PTEN KO MEF consistently showed increased concentrations of F2,6P 2 (Fig. 1C), thus supporting our hypothesis.
Reconstitution of the PTEN Gene Decreases the Concentration of F2,6P 2 -The complete loss of a gene, such as PTEN, may trigger compensatory mechanisms to re-establish cellular homeostasis. Hence, we aimed to determine whether the increased concentrations of F2,6P 2 in the PTEN KO cells were indeed the result of the absence of PTEN. Furthermore, we wished to elucidate whether the role of PTEN in regulating F2,6P 2 is dependent on its phosphatase activity. To this end, PTEN KO MEF cells were infected with retroviruses encoding for control GFP, WT PTEN-HA, and C124S PTEN-HA, a mutant form of PTEN that lacks lipid phosphatase activity. Stable cell lines were generated, and their PTEN expression was assessed by Western blotting (Fig. 1D).
Remarkably, reconstitution of both the wild-type and the mutant PTEN in the PTEN KO cells decreased the concentration of F2,6P 2 to a level similar to that of wild-type MEF (Fig.  1E). This result demonstrates a direct role for PTEN in the regulation of F2,6P 2 concentrations. Furthermore, the regulation by PTEN appears to be largely independent of its lipid phosphatase activity. This is in agreement with our hypothesis that PTEN regulates F2,6P 2 via its ability to promote, in a phosphatase-independent manner, the APC/C-Cdh1-mediated degradation of PFKFB3.
The glycolytic capacity of the retrovirus-infected cells followed a trend similar to the F2,6P 2 concentrations. The control GFP-expressing cells maintained a high rate of lactate synthesis, whereas the cells reconstituted with WT and C124S PTEN decreased their lactate production (Fig. 1F). Acidification of the cell culture medium as judged from the red-to-yellow change in color, supported the lactate production results (supplemental Fig. S1A). Of note, the drop in lactate synthesis was statistically more significant in the PTEN WT than the C124S mutant cells. This is to be expected due the role of PTEN phosphatase activity in regulating glycolysis through inhibition of PI3K/Akt signaling.
We further examined whether the regulation of F2,6P 2 concentration by PTEN is a conserved cellular mechanism and not a cell type-dependent effect restricted to the mouse embryonic fibroblasts. Interestingly, silencing of PTEN in HEK293T cells by a dsiRNA oligo that effectively down-regulates PTEN protein levels caused a significant increase in F2,6P 2 concentrations (Fig. 1, G and H). In contrast, silencing with an oligo that has no effect on PTEN protein levels failed to alter F2,6P 2 concentrations (Fig. 1, G and H).
Abundance of PFKFB3 in PTEN KO Cells Contributes to the Elevated F2,6P 2 Concentration-Having shown that PTEN deficiency results in elevated F2,6P 2 , we next addressed the premise that PFKFB3 protein abundance is responsible for the increased concentrations of this metabolite. Wild-type and PTEN KO MEF cells were analyzed for PFKFB3 protein abundance by Western blotting in several independent experiments, and the average densitometry values were plotted ( Fig. 2A). PTEN KO cells were consistently observed to have higher PFKFB3 protein levels than wild-type MEF cells.
To validate the role of PFKFB3 in contributing to the high F2,6P 2 concentrations, we screened two possible dsiRNA oligos against PFKFB3 and found oligo 2 to be effective (Fig. 2B). Silencing of PFKFB3 with oligo 2 in the PTEN KO cells resulted in a dramatic decrease in the F2,6P 2 concentration so that F2,6P 2 levels resembled that of wild-type cells (Fig. 2C). In contrast, knockdown of PFKFB3 in the wild-type MEF cells had only a minor effect on the concentration of F2,6P 2 (Fig. 2C). To further substantiate these observations, we performed a complementary experiment wherein human PFKFB3 was overexpressed in wild-type MEF cells. Following overexpression, the levels of PFKFB3 protein in wild-type cells rose to the endogenous levels in the PTEN KO cells (Fig. 2D, lower panel), and this effectively abolished the difference in F2,6P 2 concentrations between the wild-type and knock-out conditions (Fig. 2D,  upper panel). These data validate the reported regulation of F2,6P 2 by PFKFB3 and suggest that the abundance of PFKFB3 protein in PTEN KO cells contributes to their elevated F2,6P 2 concentrations. In addition, silencing of PFKFB3 resulted in decreased lactate production, and this effect was more evidently observed in the PTEN KO cells (Fig. 2E).
The enzymatic activity of PFKFB3 is regulated by phosphorylation (24). In view of our hypothesis, we aimed to rule out the involvement of enhanced enzymatic activity of PFKFB3 in PTEN KO cells as opposed to the premise of increased protein abundance. We constructed phospho-mutant and phosphomimic forms of PFKFB3 at sites Ser-461 and Ser-478 and tested their ability to up-regulate F2,6P 2 synthesis. Interestingly, the mutants were as capable as wild-type PFKFB3 of up-regulating F2,6P 2 (supplemental Fig. S1, B and C), suggesting that phosphorylation of PFKFB3 is unlikely to play a major role in the elevated synthesis of F2,6P 2 , in PTEN KO cells.
AMP-activated protein kinase, PKC, and PKA have been reported to phosphorylate PFKFB3 at the Ser-461 site (24), whereas the residue Ser-478 is a conserved site for Akt phosphorylation. We therefore treated wild-type and PTEN KO MEF cells with inhibitors of these kinases and examined the levels of F2,6P 2 (supplemental Fig. S1C). Blockage of the PI3K/ Akt signaling pathway with LY294002 lowered the F2,6P 2 concentration in PTEN KO but not in wild-type MEF cells, indicating a PTEN KO-specific effect. However, the degree of inhibition was subject to high variability, and the concentration of F2,6P 2 in the PTEN KO cells did not decrease to wild-type levels. Therefore, an elevation in PI3K/Akt signaling activity can only partially account for the high levels of F2,6P 2 in PTEN KO cells. In contrast, the inhibition of AMP-activated protein DECEMBER 13, 2013 • VOLUME 288 • NUMBER 50 JOURNAL OF BIOLOGICAL CHEMISTRY 36023 kinase, mTOR complex I (mTORC1), PKC, and PKA did not result in PTEN KO-specific decreases in F2,6P 2 concentrations (supplemental Fig. S1C) .

F2,6P 2 Contributes to Warburg Effect in PTEN KO Cells
PFKFB3 Degradation via the APC/C-Cdh1 Ligase Is Impaired in PTEN KO Cells-According to the proposed hypothesis, upregulation of PFKFB3 in PTEN KO cells may be attributed to decreased PFKFB3 protein degradation via the APC/C-Cdh1 ligase. Hence, we first confirmed the previous work by Song et al. (14) reporting the impaired function of APC/C-Cdh1 in PTEN-deficient cells. To this end, PTEN KO and wild-type cells were treated with cycloheximide, a protein synthesis inhibitor, and the half-life of two known APC/C ligase substrates, Geminin and PLK-1, was followed in a time course lysis experiment. These substrates were chosen because they are specifically recognized by the substrate receptor Cdh1 and not by Cdc20. Analysis by Western blotting showed that both Geminin and PLK-1 appear to be degraded at a slower rate in the PTEN KO cells (Fig. 3A), confirming that the function of the APC/C-Cdh1 ligase is impaired in PTEN-deficient cells.
Next, as proof of principle, we examined the relationship of PTEN, PFKFB3, and Cdh1 at the level of protein interaction. As observed in Fig. 3B, immunoprecipitation of Cdh1 resulted in specific pulldown of co-transfected PFKFB3, suggesting that PFKFB3 and Cdh1 interact directly and supporting the notion of PFKFB3 as an APC/C-Cdh1 substrate. Importantly, co-overexpression of PTEN was observed to increase the binding of PFKFB3 to Cdh1 (Fig. 3B).
To test more directly the physiological role of PTEN in enhancing the degradation of PFKFB3 by the APC/C-Cdh1 ligase, HEK293 cells were treated with PTEN dsiRNA or negative control dsiRNA and then transfected with wild-type PFKFB3 or a KEN box mutant form of PFKFB3 (KEN to AAA). The KEN box is a protein motif recognized by the APC/C-Cdh1 ligase for ubiquitination of its substrates (25). In cells treated with negative control dsiRNA, co-overexpression of Cdh1 was capable of decreasing the protein levels of PFKFB3 WT but had no effect on the KEN box mutant (Fig. 3C). This is in agreement with the reported role of the APC/C-Cdh1 ligase in mediating PFKFB3 degradation (16,17). In contrast, Cdh1 overexpression failed to affect the levels of PFKFB3 WT in cells where PTEN had been knocked down (Fig. 3C), supporting the premise that PTEN is required for the efficient degradation of PFKFB3 by the APC/C-Cdh1 ligase. Although the overall lower protein levels of PFKFB3 in the PTEN-silenced cells seem contradictory, we found that this is due to lower transfection efficiency in these cells when compared with those treated with control dsiRNA (supplemental Fig. S1D).
Subsequently, we addressed whether the protein stability of endogenous PFKFB3 is affected in PTEN KO cells when compared with wild-type cells. We thus treated cells with cycloheximide and monitored the protein level of PFKFB3 over time. In wild-type MEF cells, PFKFB3 was practically undetectable after 10 h of treatment. In contrast, PFKFB3 remained relatively constant in the PTEN KO cells throughout the 10 h (Fig. 3D), suggesting that the degradation of PFKFB3 is compromised in cells lacking PTEN.
In virtue of the known cell cycle-dependent regulation of PFKFB3 by the APC/C-Cdh1 ligase (17,26,27), we deemed it relevant to investigate any possible cell cycle-specific differences in PFKFB3 stabilization between the PTEN KO and wildtype cells. Cells were synchronized and released at the G 2 /M boundary with nocodazole. Cdh1 protein levels were observed to drop progressively, denoting transition through mid G 1 -phase, whereas appearance of the APC/C-Cdh1 substrate Geminin at the later time points indicated entry into S-phase (Fig. 3E). In the wild-type cells, there was a nice correlation between Cdh1 protein level drop and rise of PFKFB3 protein, as has been previously reported (25). On the other hand, PFKFB3 levels remained fairly stable in the PTEN KO cells and did not appear to be affected by the fluctuation of Cdh1 (Fig. 3E). Taken together, all these results provide evidence to support the hypothesis that PFKFB3 degradation via the APC/C-Cdh1 ligase is impaired in PTEN-deficient cells.
PFKFB3 Contributes to the High Proliferative Rate of PTEN KO Cells-We showed that PFKFB3 down-regulation in PTEN-deficient cells translates into decreased cellular lactate production, thus suggesting a role for F2,6P 2 as a mediator of aerobic glycolysis. One of the proposed advantages that the Warburg effect confers to cancer cells is the ability to sustain high proliferative rates (2). Accordingly, we questioned plasmids, and the lysates were subjected to Cdh1-Myc immunoprecipitation. C, HEK293 cells were transfected with the mentioned dsiRNA oligos and incubated in 0.5% serum medium for 48 h. 24 h prior to the end of serum starvation, cells were transfected with the indicated DNA plasmids. Following incubation with complete medium (containing 10% serum), cells were lysed at time 16 h. E, PTEN KO and wild-type MEF were synchronized at the G 2 /M boundary of the cell cycle with 40 ng/ml nocodazole for 12 h and were released by mitotic shake-off and PBS washing. Cells were harvested at the indicated times following release and were subjected to immunoblotting. DECEMBER 13, 2013 • VOLUME 288 • NUMBER 50 whether metabolic reprogramming of PTEN KO cells via PFKFB3-mediated synthesis of F2,6P 2 may contribute to the high proliferative capacity of these cells. To address this, we silenced PFKFB3 in both wild-type and PTEN KO MEF cells and assessed their proliferative capacity over a 3-day period. Monitoring of the cells by microscopy showed that PTEN KO cells transfected with negative control dsiRNA proliferated significantly faster than equally treated wild-type cells (Fig. 4A), an observation that has been reported extensively in the literature for wild-type and PTEN KO cells (28). Remarkably, transfection of cells with PFKFB3-targeting dsiRNA caused a dramatic decrease in cell proliferation, particularly in the PTEN KO cells (Fig. 4A). These results were confirmed by Trypan blue exclusion counting of similarly treated PTEN KO and wild-type cells at days 0,1, and 2 (Fig. 4B).

F2,6P 2 Contributes to Warburg Effect in PTEN KO Cells
The fact that cell proliferation was more evidently affected in the PTEN KO cells is consistent with our previous results regarding the up-regulated levels of PFKFB3 in these cells. Furthermore, these data suggest that the contribution of F2,6P 2 to the glycolytic phenotype of PTEN KO cells has a significant impact on cell proliferation, conforming to prevalent ideas about the physiological relevance of the Warburg effect.

DISCUSSION
PTEN deficiency is known to result in a glycolytic phenotype and an increased cellular proliferation rate. In this study, we demonstrate that elevated F2,6P 2 concentrations are important in mediating the increased rates of glycolysis and proliferation in PTEN KO MEF cells. Furthermore, we show that the phosphofructokinase-2 isoform PFKFB3 is responsible for the F2,6P 2 increase in PTEN-deficient cells. Finally, we provide mechanistic evidence that PFKFB3 protein stabilization as a result of impaired APC/C-Cdh1-dependent degradation is a major contributing factor to the increase in F2,6P 2 concentration. These findings are of great significance given that elevations in glycolytic flux play an important role in tumorigenesis and PTEN is one of the most frequently mutated tumor suppressor genes in cancer.
Enhanced glycolytic rates in PTEN-deficient cells have been previously attributed to increased PI3K/Akt-dependent signaling and activation of the mTORC1. In their study, Tandon et al. (29) proposed the mTORC1 substrate ribosomal protein S6 kinase as a major activator of glucose metabolism. However, they showed that silencing of S6 kinase affected glycolytic rate and viability in both control and PTEN knockdown cells. In contrast, we provide evidence that regulation of glycolysis and cell proliferation via PFKFB3 is largely specific for PTEN-deficient cells. We also observed that treatment of cells with a PI3K inhibitor partially reversed the F2,6P 2 increase in PTEN knockout cells, whereas inhibition of other candidate protein kinases was without significant effect. This is consistent with studies that implicate the PI3K/Akt signaling pathway in the regulation of cellular F2,6P 2 synthesis (11,30). Notwithstanding, a lipid phosphatase-deficient mutant of PTEN was able to reduce the F2,6P 2 concentration and glycolytic rate in PTEN-deficient cells to a similar degree as wild-type PTEN. This strongly argues for a role of PTEN in regulating cellular F2,6P 2 synthesis via a mechanism that is independent of PI3K/Akt signaling.
Here, we show that elevated F2,6P 2 concentrations in PTEN KO cells are a consequence of PFKFB3 protein stabilization due to impaired APC/C-Cdh1 ligase function. PTEN was recently reported to accelerate the degradation of various APC/C-Cdh1 substrates in a phosphatase-independent manner (14). We confirmed such a role of PTEN upon observing a slower rate of degradation of APC/C-Cdh1 substrates, Geminin and PLK-1, in the PTEN KO MEF cells. Importantly, PFKFB3 was observed to follow a similar pattern of decreased protein degradation in the PTEN-deficient cells. At the level of protein interaction, we showed that PTEN augments the binding between Cdh1 and PFKFB3. In the study by Song et al. (14), PTEN was demonstrated to enhance the association of Cdh1 with the APC ligase complex, resulting in more efficient assembly of the ligase and ubiquitination of protein substrates. Consequently, the increased binding of PFKFB3 to Cdh1 in the presence of PTEN is likely to result from enhanced assembly of the APC/C-Cdh1 complex and concomitant recruitment of PFKFB3 to the functional ligase. Finally, we showed that only in the presence of PTEN can overexpression of Cdh1 effectively down-regulate PFKFB3 protein levels. This is dependent on the presence of the KEN box motif on PFKFB3, consistent with previous studies regarding the importance of the KEN box for Cdh1-mediated degradation of PFKFB3 (16,17). Systemic overexpression of PTEN in transgenic mice was recently shown to reduce PFKFB3 protein levels (31). This demonstrates that PTEN can regulate PFKFB3 protein stability under in vivo conditions. However, our results highlight the up-regulation of PFKFB3 and the resultant change in glycolytic flux as a consequence of endogenous PTEN loss, which is commonly observed in cancer (6,7).
The Warburg effect has been proposed to support the proliferative demands of cancer cells (2). In accordance, we showed that PFKFB3 directly contributes to the proliferation rate of MEF cells, particularly in the absence of PTEN. Tudzarova et al. (27) showed that PFKFB3 silencing prevents G 1 /S transition of the cell cycle by down-regulating glycolytic flux at a nutrientsensitive restriction point. Interestingly, PTEN KO embryonic stem cells have been reported to have accelerated G 1 /S transition (28). It would thus be interesting to investigate whether PTEN-deficient cells exploit their elevated PFKFB3 protein levels to bypass the G 1 /S restriction point and accelerate their progression through the cell cycle. Furthermore, the therapeutic potential of targeting PFKFB3 and F2,6P 2 as metabolic mediators of proliferation in PTEN-deficient cancers is worthy of consideration.


s2fieldsofstudy:
Biology
Computer Science

text:
YHap: software for probabilistic assignment of Y haplogroups from population re-sequencing data

Y haplogroup analyses are an important component of genealogical reconstruction, population genetic analyses, medical genetics and forensics. These fields are increasingly moving towards use of low-coverage, high throughput sequencing. However, there is as yet no software available for using sequence data to assign Y haplogroup groups probabilistically, such that the posterior probability of assignment fully reflects the information present in the data, and borrows information across all samples sequenced from a population. YHap addresses this problem.

INTRODUCTION
The non-recombining portion of haploid chromosome Y is passed intact from father to son with a mutation rate several times greater than autosomes (Xue et al. 2009). As such, patterns of variation in Y are widely used to uncover historical patterns of human migration; are important in genealogical reconstruction and have application in forensic analyses.
The Y Chromosome Consortium (YCC) published revised Y-chromosome DNA haplogroup tree in 2008, consisting of approximately 600 markers, which can be used to characterize 20 major global haplogroups, labeled A-T, as well as sub-classification into a total of 311 haplogroups at the finest level of resolution. Different major haplogroups have been found at high frequencies in different geographical regions, for example the E clade in Africa, and the O clade in Eastern Asia. Particular fine-level haplogroups are found in multiple locations, such as R1a in Eastern Europe, South Asia and Central Asia, indicating migration of R1a from Eurasian Steppes to the new world. The C3 haplogroup, found at high frequency throughout Asia is commonly interpreted as genealogical remnants of the empire of Genghis Khan 2 (Zerjal et al. 2003).
Y haplogroup assignment has traditionally been carried out by targeted genotyping using a combination of short tandem repeat typing, multi-plex PCR and minisequencing (Sanchez et al. 2003, Zerjal et al. 2002, often using a hierarchical strategy in order to first refine the major haplogroup, and subsequently genotype markers within that haplogroup which illuminate finer levels of resolution. Such a procedure requires substantial amount of wet-lab analysis, requires stringent replication and quality control to eliminate errors which can arise due to the limited amount of information collected at each step. More recently, personal genetic companies have included specific Y chromosome markers on custom genotyping arrays (Turner et al. 2008). Nevertheless, the resolution available from genotyping arrays is limited by markers included on the chip.
Very high coverage high throughput sequencing has the potential to capture all single nucleotide and insertion/deletion variation, and as such provide near-perfect assignment of individuals to Y haplogroups. As high coverage sequencing of large population samples remains expensive, low coverage population sequencing, in which each individual is sequenced at less than 2x haploid coverage is an attractive alternative, but this will not capture all individual-level variation. However, given the sharing of haplogroups within an ethnically homogenous population, it should be possible to borrow information across individuals within a population in order to improve haplogroup assignment.
In this application note, we present the YHap tool, which has been primarily designed for assigning haplogroups to low-coverage population re-sequencing data. YHap borrows information across all samples to assign samples to haplogroups probabilistically, thus providing an accurate representation of the inference which can be made from the data collected. YHap is a complete solution and can also be applied to high-coverage sequence data, as well as data from genotyping arrays.

METHODS
We use the set of haplogroups and mutations defined in (Karafet et al. 2008). We map the forward and reverse primers described in this manuscript to identify the reported strand of the variation in the GRCh37 reference. After strand correction, we identify whether the mutant allele is the equal to the alternative or reference allele, so that we can subsequently work in reference/alternative allele space on the forward strand, consistent with conventional genotype calling schema. Next, we map each mutation to its position on the pre-defined Y phylogenetic tree T. Finally we create a haplogroup matrix H of size Nref*M where M is the total number of nodes in T (including leaf and internal nodes) and Nref is the number of pre-defined Y markers. Each entry Hij = {Hijg} is a probability distribution vector expressing the probability of a sampled individual from the clade below node j carries allele g (in this case either the reference or alternate allele).
At leaf nodes, this probability vector is either {0,1} or {1,0}, and at internal nodes, it is the proportion of descendant leaf nodes with reference or alternate alleles, respectively.
To assign a sequenced individual to a specific haplogroup, we obtained genotype likelihoods at each putative variant site (inclusive of all markers in H) from chrY VCF files of 1000 genome project. This results in a matrix S of size L*N where L is the number of sequenced samples, N is the number of putative variants, and Gij = {Gijg} is a vector of genotype likelihoods. We then generated an augmented H* matrix by adding in extra sites in G but not H with probability vector H*ij = {0.5,0.5}. The pipeline is similar for genotype data, except that the genotype likelihoods are taken to be either 1, if , or 0 otherwise. We can calculate the assignment of each individual using (1) Where and = Gijg We can then calculate the posterior probability of each haplogroup amongst a set of haplogroups, where prior haplogroup probability distribution P(H * .l) is set to the uniform distribution, By restricting the set of haplogroups considered in equation (2), YHap can be customized to either only assign to within the major haplogroups (A through to T), or all possible haplogroups at the finest level of classification.
While this model is sufficient for assigning Y haplogroups individually, it does not capture shared information between sequenced samples adequately, particularly for low coverage sequencing. Given that a population sample will share individuals from the same haplogroup, and while none of these individuals may have enough depth at informative Y haplogroup markers, there is enough information across the pooled reads from all samples from the same haplogroup. However, we do not know a-priori which samples can be pooled as coming from the same haplogroup.
In order to pool information between samples, we treat the allele probability distribution {H*ijg} at markers present in the sequence data but not present as haplogroup markers, as parameters in our model. We update these parameters using expectation maximization, in which we first calculate the posterior probability assigning each sample j to each haplogroup l using equation 2, and then update the {H*ilg} to reflect the average of genotypes assigned to haplogroup l at position i, weighted by this posterior probability of assignment. In this way, the model learns which alleles are characteristic of the pre-defined haplogroups, and is thus able to more accurately assign individuals which may not have good coverage at those sites, but which show similarity to other individuals across the Y chromosome. The probabilities P(H * .l) are also updated at each step to reflect the proportion of haplogroups assigned in the population.

RESULTS
We applied YHap to low-coverage sequencing data generated in the pilot phase of the 1000 genome consortium which were also part of the Hapmap project, including 19 YRI, 16 JPT, 21 CEU and 9 CHB samples (Abecasis et al. 2010). Major Y haplogroups have been previously assigned to these samples as part of the Hapmap project. The average sequencing depth of these samples is 1.67X as described in 1000 genome Y chromosome analysis report. Compared to haplogroups previously obtained from the Hapmap project (Altshuler et al. 2010), YHap showed perfect assignment accuracy (Table 1). We also used YHap on the Hapmap combined phase 1,2,3 Y genotype data and obtained the same assignments previously reported with this data.
In order to investigate the ability of YHap to assign finer-level haplogroups we compared YHap results obtained at complete resolution (i.e. considering all haplogroup leaf nodes on the pre-defined Y phylogenetic tree) on both Hapmap genotype data and also 1000 genomes low coverage sequence data (Suppl. Table 1). We see that there is complete concordance at the major haplogroup level, and there is increasing uncertainty in assignment as the resolution of assignment increases, particularly using dense genotype data. We also observe that accuracy remains high amongst those assignments which YHap assigns high confidence.
Finally, in order to investigate the relationshis between sequencing depth and assignment accuracy, we randomly downsampled the original bam files from 1000 genome to 0.6X. For simplicity, we chose JPT to run the test. We see that downsampling increases the uncertainty of assignment (Suppl. Table 2), but YHap accuracy remains high amongst those assignments which are made with high posterior probability. This demonstrates that as the underlying amount of information decreases, YHap is still able to extract inference and accurately represent the uncertainty of this inference.
The total complexity for the whole procedure is O(N 2 T), conventionally, when using defaut settings, it will take almost 10min to locate 10~20 individuals and approximately 200 Mb memory. Numbers indicate the percentage of samples which had an assignment with a given lower bound on certainty from 1000 genomes sequence data (horizontal) and Hapmap genotype data (vertical), and different levels. Level 1 indicates main haplogroup assignment (A-T), while levels 2-5 indicate increasing levels of precision. Numbers in brackets indicate the concordance rate between predictions made by sequence and genotype data within each bin.