Fre ´de ´ric Thomas
email: frederic.thomas2@ird.fr

Antoine M Dujon

Beata Ujvari

Catherine Alix-Panabie

Bridging the Gap Nocturnal circulating tumor cells: The ultimate selective filter in cancer progression?

The survival duration of circulating tumor cells (CTCs) in the vasculature is a critical parameter in the establishment of the metastatic cascade. Diamantopoulou and colleagues demonstrate that the metastatic capacity of CTCs is strongly influenced by circadian rhythms, suggesting a rationale for the time-controlled interrogation and treatment of metastatic cancers.

Cancer growth is initiated by genetic and epigenetic alterations that lead to local uncontrolled expansion of cells that thrive in specific microenvironments (e.g., chronic inflammation). Once the malignant cells spread through the body, i.e., the metastatic cascade, cancer progressively becomes a systemic and ultimately lethal disease. Metastasis formation is the main cause of cancer-related death in patients with solid tumors. At the beginning of this process, cancer cells escape from the primary tumor to the vascular system, where they become circulating tumor cells (CTCs). Only a small subgroup of CTCs will survive during the harsh journey in the blood and colonize distant sites. [START_REF] Eslami-S | Functional analysis of circulating tumour cells: the KEY to understand the biology of the metastatic cascade[END_REF] The most critical parameter governing the formation of clinical metastases (i.e., the key step of the metastatic cascade) is the survival duration of CTCs, 2 and thus therapies reducing the survival of CTCs in the vascular system could significantly reduce the risk of metastasis.

Using evolutionary ecology reasoning, Corte ´s-Herna ´ndez et al. predicted that malignant cells, including CTCs, should behave differently between day and night. [START_REF] Cortes-Hernandez | Do malignant cells sleep at night?[END_REF] Following the recent work of Diamantopoulou et al., [START_REF] Diamantopoulou | The metastatic spread of breast cancer accelerates during sleep[END_REF] this is now a proven fact for CTCs, and this discovery will undoubtedly have major medical implications. Would this suggest that we have so far missed a major adaptive feature of the metastatic process?

The main findings by Diamantopoulou et al. [START_REF] Diamantopoulou | The metastatic spread of breast cancer accelerates during sleep[END_REF] include the following: (1) a correlation between the circadian rhythm and CTC intravasation. Diamantopoulou and colleagues identified a striking and unexpected pattern of CTC-generation dynamics in both patients with breast cancer (n = 30; 21 patients were diagnosed with early breast cancer-no metastasis-and 9 were diagnosed with stage IV metastatic disease) as well as in four different mouse models. Their results showed that CTCs are not shed continuously during tumor progression, but the greatest release and intravasation of single and clustered CTCs occurs during sleep (4:00 a.m.). Diamantopoulou et al. also discovered (2) a metastatic ability of CTCs during sleep. CTCs isolated during the rest phase (4:00 a.m.) are highly prone to metastasize, whereas CTCs generated during the active phase are devoid of metastatic ability. Indeed, when isolated during the night and compared with those isolated during the day, CTC clusters and CTC-white blood cell clusters seem to have greater metastasis-forming properties than those of single CTCs. This observation suggests that most of the rest-phase-dependent metastatic spread of breast cancer could be ascribed to both homotypic and heterotypic CTC clusters. Another discovery is (3) a timedependent gene expression in CTCs. A marked upregulation of mitotic genes exclusively during the rest phase (4:00 a.m.) in both patients and mouse models seems to enable metastasis proficiency. Molecular gene expression analysis of CTCs from patients and mouse models isolated during the rest and active phases highlights very distinct gene-expression patterns. During sleep (4:00 a.m.), gene expression is dominated by cell division and mitosis, whereas during the active phase (10:00 a.m.), there is high ribosome-biogenesis activity. This oscillatory proliferation timing is observed not only in CTCs, but also in the primary tumor, suggesting this as a general phenomenon occurring in breast cancer cells during disease progression. Lastly, Diamantopoulou et al. found (4) regulators of CTC intravasation. Proliferation and intravasation of breast cancer cells seem to be dictated by daily oscillations in key circadian-rhythm-regulated hormones, such as melatonin, testosterone, and glucocorticoids, whose action influences breast cancer metastasis dynamics.

The authors concluded that the spontaneous generation of CTCs with high metastatic capacity does not occur continuously but rather is concentrated during the night, providing a new rationale for time-controlled interrogation and treatment of metastatic cancers. The CTC dynamic between day and night may change how clinicians will treat cancer patients in the future. However, the study by Diamantopoulou et al. used in vivo models and a relatively low number (n = 30) of breast cancer patients. Thus, further analyses and validations in a larger cohort of patients with breast cancer will be required prior to the introduction of nocturnal liquid biopsy in clinical practice. It will be of utmost interest to assess whether nocturnal CTCs also play a key role in other solid cancers (e.g., colon, prostate, and lung cancers as well as melanoma) to confirm whether the observed patterns are specific to breast cancer or a common hallmark of solid cancers. In this aspect, the European Liquid Biopsy Society (ELBS, www.elbs.eu) will help to answer these important questions for the future of liquid biopsy and precision medicine for cancer patients. [START_REF] Alix-Panabieres | The future of liquid biopsy[END_REF] Here, we also argue that the complex links evidenced by Diamantopoulou et al. between malignant cells and circadian rhythm provide an additional reason to adopt a Darwinian approach in cancer research. The biology of most, if not all, living things is regulated by biological rhythms, in particular the circadian rhythm, from whole organisms to their constituent cells, their microbiota, and parasites. Given their parasitic lifestyle, malignant cells are also influenced by the circadian rhythm for many reasons. [START_REF] Cortes-Hernandez | Do malignant cells sleep at night?[END_REF] However, an important difference between parasitic organisms sensu lato and cancer cells is that, in the majority of cases, the latter are not transmitted between hosts. Even if cancer is an ancient disease that appeared at the dawn of multicellularity a billion years ago, each cancer must "reinvent the wheel" because the evolved adaptations selected during tumorigenesis disappear when their host dies. Thus, malignant cells are only under selective pressure for a few years at most, and sophisticated adaptive responses, as seen in organisms exposed to natural selection over evolutionary times, are unlikely to exist. Neverthe-less, due to the extreme diversity of cells typically observed in tumors, natural selection acting on cells and/or groups of cells 6 may also promote the emergence of clones that exhibit, for each stage of tumorigenesis, the best characteristics for a malignant lifestyle given the tissue in which they proliferate, thus allowing the tumor to continue its progression to the metastatic stage. [START_REF] Tissot | Metastasis and the evolution of dispersal[END_REF] The above evolutionary considerations suggest that, of the likely wide variety of tumors capable of releasing CTCs, only those with the characteristics found by Diamantopoulou et al. will be able to metastasize efficiently. Only in these "successful" tumors can adaptations related to the host circadian rhythm be detected. In other words, many CTCproducing neoplasms in the bodies of multicellular organisms lack the conditions required to produce metastasis. Diamantopoulou et al. have identified a novel selective filter in tumorigenesis, maybe the last one, and this is undoubtedly a major discovery.

These findings open the door to explore different research directions, as it is crucial to determine if, when, and how malignant cells acquire adaptive features that allow metastasis. Because many non-genetic variables are involved during tumorigenesis, it seems crucial to determine whether the exploitation of circadian rhythms in malignant cells is due to phenotypic plasticity or the result of genetic adaptation. Another promising direction arising from the work of Diamantopoulou et al. is the hypothesis that some tumors, instead of releasing CTCs primarily at night, are selected to manipulate the circadian rhythms of their hosts, as parasitic organisms sometimes do. [START_REF] Cortes-Hernandez | Do malignant cells sleep at night?[END_REF] Sleep disturbances are common in people affected by cancer, and for reasons that are so far unclear, many cancer patients often exhibit reduced distinction between daytime and nighttime activities. [START_REF] Tissot | Host manipulation by cancer cells: Expectations, facts, and therapeutic implications[END_REF][START_REF] Buttner-Teleaga | Sleep Disorders in cancer-A Systematic Review[END_REF] Sleep disturbances have a variety of causes, but clearly one that has so far been little explored is a direct tumor effect on sleep cycles; for instance, through the secretion of cytokines that modulate the sleep/wake cycle. Because sleep alterations are nonspecific symptoms, however, it must be rigorously determined whether cancer cells are directly promoted by selection to disrupt sleep and/or whether this is simply a pathological side effect with incidental positive consequences for carcinogenesis. We believe that exploring the proximal mechanisms used by parasitic organisms that manipulate their host's circadian rhythm is promising, as cancer cell strategies may rely on similar biochemical precursors. In this context, it would be promising to test whether therapies that target manipulative activities in parasites could be equally effective in preventing tumors from metastasizing. Finally, given the wide diversity of mammalian species that develop metastatic cancers, which are monitored by veterinarians in zoos, [START_REF] Vincze | Cancer risk across mammals[END_REF] an original direction would be to explore the behavior and activity of CTCs in nocturnal species, predicting that an opposite pattern should be observed compared to diurnal species. Similarly, knowing that large, long-lived animals nonintuitively do not have more cancers than other species (a phenomenon called Peto's paradox [START_REF] Vincze | Cancer risk across mammals[END_REF] ), the results of Diamantopoulou et al. are an invitation to explore nocturnal anticancer defense in these species.

In conclusion, the fascinating discovery by Diamantopoulou et

  al. opened the door for further research including interdisciplinary approaches that consider different types of cancer not only in humans, but also in other species with varied cancer susceptibility. Such approaches would be needed to identify the typical time when organisms are most at risk of invasive cancer cells to emerge. Once these general patterns are identified, new preventive strategies can be developed. from the European Union Horizon 2020 Research and Innovation program under the Marie Skłodowska-Curie grant agreement no. 765492, The National Institute of Cancer (INCa, http://www.ecancer.fr), SIRIC Montpellier Cancer Grant INCa_Inserm_DGOS_12553, the ERA-NET TRANSCAN 2 JTC 2016 PROLIPSY, la Fondation ARC pour la Recherche sur le cancer, and les Fonds de dotation AFER pour la recherche me ´dicale.

ACKNOWLEDGMENTS F.T. is supported by the MAVA foundation. C.A.-P. is supported by funding

DECLARATION OF INTERESTS


Krishna Prasad Shrestha

Raj Binod
email: binod.giri@kaust.edu.sa

Pelé Giri

Mohammad Ronan

Khalid Adil

Ali Aljohani

Pierre Elkhazraji

Fabien Brequigny

Christine Halter

Mounaïm-Rousselle

Giri

Ronan Pelé

Mohammad Adil

Khalid Aljohani

Ali Elkhazraji

Pierre Brequigny

Fabien Halter

Christine Mounaïm- Rousselle

An experimental and kinetic modeling study of the combustion behaviour of ammonia blended with methanol and ethanol at engine relevant conditions

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Despite several benefits of ammonia (NH 3 ) as a promising carbon-free fuel, it has some barriers which hinder its effective utilization in combustion devices. These barriers include but are not limited to narrow flammability limits (16-25% by volume in air), high autoignition temperature, relatively low energy density, and pollutants. Most of these barriers can be overcome by blending ammonia with high energy density and/or high reactive fuels. Hence, co-firing NH 3 with renewable biofuels appears to be a good strategy. To this end, methanol (CH 3 OH) and ethanol (C 2 H 5 OH) can be promising candidates to promote NH 3 combustion. These simple alcohols are long considered a clean fuel for spark ignition engines because of their high-octane rating and significantly low emission. Due to their remarkable combustion properties, they are attractive fuels to promote the combustion behaviour of ammonia. In our previous work, we have extensively studied the blends of ammonia with the combustion promoters such as dimethyl ether (DME), dimethoxy methane (DMM), and diethyl ether (DEE) in various proportions [1,2]. Our works revealed the critical role of cross-reactions between the carbon and nitrogen family to accurately predict the combustion behaviour of the blends [2]. We found that the reactions of amino (NH 2 ) radicals with fuels and intermediates significantly affect the low-temperature oxidation kinetics of NH 3 /oxygenate blends. Our recent theoretical work [3] demonstrated the critical importance of NH 2 reactions with CH 3 OH and C 2 H 5 OH, particularly affecting the low temperature combustion behaviour of NH3-alcohol blends. The calculated reaction rate constants from this work have been implemented in our kinetic model to explore the blending effect of CH 3 OH and C 2 H 5 OH on the combustion behaviour of NH 3 . The main aim of this work is to develop a comprehensive kinetic model for the NH 3 /CH 3 OH and NH 3 /C 2 H 5 OH blends at engine relevant conditions. The developed model has been extensively validated against the newly experimental data from this work and the available literature data for NH 3 -C 1 /C 2 alcohols chemical systems.

Experimental Details

Laminar flame speeds of NH 3 /CH 3 OH and NH 3 /C 2 H 5 OH blends were measured using the constant volume spherical flame methodology as fully described in Halter et al. [4]. The measurement of the laminar flame speed of expanding spherical flames is obtained by using both pressure and flame radius growth, based on the hypothesis of an isentropic compression of the unburned gases following the ignition: thus, resulting in a simultaneous increase of both pressure and temperature within the vessel. Therefore, as each flame propagation is submitted to simultaneous pressure and temperature increase, the flame speed values are a function of the couple (P, T). The laminar flame speed were measured for χ CH3OH/C2H5OH = 0, 25, 50 and 100% (χ CH3OH/C2H5OH = (by mol)).

Ignition delay time (IDT) measurements of NH 3 /ethanol blends were conducted utilizing the high-and low-pressure shock tubes described in [5,6]. The IDT were measured for χ C2H5OH = 10 and 30% at 1, 10, and 20 bar and  = 1.0, respectively. Additionally, simultaneous multi-species (CO, NO, and C 2 H 5 OH) measurements were carried out by employing the mid-IR laser absorption technique. Temperature and pressure dependence of the absorption-cross section of these species were also measured to accurately determine the species concentration in the rapidly changing thermodynamic properties of the chemical system.

Chemical Kinetic Modeling

This work is an ongoing effort aiming to build a comprehensive kinetic model for C/H/N/O chemical systems. For a few years, there has been a constant update on the theoretical rates of elementary chemistry in the ammonia sub-set. Despite this, there is still high uncertainty in ammonia sub-set chemistry. The knowledge of several important elementary reactions is still in its infancy. In several cases, the predictions of the kinetic model exhibit a significant discrepancy with the experimental data. The existing discrepancy motivated us to conduct the present study. The current kinetic model presents a major update in the ammonia kinetic model compared to all our past published models [1,7]. The new experimental data from this study and the newly available theoretical study from the literature motivated us to make a major update in the ammonia model. However, the main aim of this work is also to develop the kinetic model for

Results and Discussions

Conclusions

We measured the laminar flame speeds of NH 3 and its blends CH

NH 3 /

 3 CH 3 OH and NH 3 /C 2 H 5 OH blends. The current kinetic model updates our recent modeling work characterizing the oxidation behavior of NH 3 /DEE blends [2]. Several kinetic parameters have been updated in the present work following suggestions from the recently published experimental, theoretical, and modeling work. The updates were made on NH, NH 2 , NH 3 , N x H y , H 2 NO, HONO, HNO 2, and HNO sub-mechanism. The cross-reactions between the nitrogen and carbon families relevant to NH 3 /CH 3 OH and NH 3 /C 2 H 5 OH systems have also been included.

Figure 1

 1 Figure 1 shows the laminar flame speed of NH 3 /air as a function of pressure and temperature at ϕ = 1.0 (a) and ignition delay time of NH 3 / C 2 H 5 OH blends at ϕ = 1.0 and 20 bar (b). Fig. 1a displays the model predictions with the present work (blue solid line) and model prediction with our previous model (blue dashed lines); whereas other lines represent the model predictions using literature models. Clearly, there is a large discrepancy between different models. However, the present model performs better than all other models, and our model prediction is within the experimental uncertainty range. Likewise, Figure 1b compares the current model predictions with the IDT experimental data. As seen, for both χ C2H5OH = 10 and 30% blends, our model captured the experimental data remarkably. Black solid lines show the model predictions for neat NH 3 . A small addition of C 2 H 5 OH (10%) can significantly shorten the IDT of NH 3 . Further increase of C 2 H 5 OH to 20% doesn't enhance the ignition with the same magnitude.

Figure 1 :

 1 Figure 1: Laminar flame speed of ammonia/air as a function of pressure and temperature at ϕ = 1.0 (left) and ignition delay time of NH 3 /ethanol blends (right) at ϕ = 1.0 and 20 bar. Symbols: present experimental work. (a): solid blue line (this model), rest (literature model). (b): lines (this model).

  3 OH using the combustion vessel at ϕ = 1.0, 2 -7 bar, 508 -610 K for NH 3 /air, 2 -9.6 bar, 506 -645 K, for NH 3 /CH 3 OH/air, and 2 -11.3 bar, 503 -645 K, for NH 3 /C 2 H 5 OH/air. We found that adding CH 3 OH and C 2 H 5 OH to NH 3 significantly enhanced the laminar flame speed of ammonia. We also determined IDTs and measured multi-species in real time for various blends of NH 3 /C 2 H 5 OH in shock tubes over a wide range of conditions. The addition of C 2 H 5 OH significantly improved the reactivity of NH 3 , leading to the shortening of autoignition timing.


Clément Chalet
email: clement.chalet@gmail.com

Ekaterina Darii

Denis Lesage

Estelle Rathahao-Paris

Yves Gimbert

Annelaure Damont

Alain Perret

François Fenaille

Christophe Junot

Jean-Claude Tabet

Sandra Alves

STEREOSPECIFIC RECOGNITION OF AMINO ACIDS IN QUATERNARY COPPERED COMPLEXES VIA ENANTIOSELECTIVE REDUCTION

 Mixtures of D AA or L AA and Cu II salt were prepared in H 2 O/MeOH (1:1) at 20 µM and directly infused in an Orbitrap Elite mass spectrometer (Thermo Fisher Scientific) in positive ESI mode.  QCCs were subjected to resonant activation using collision-induced dissociation (CID) while varying collision energy (CE) and activation time (ActT). The resulting energy-resolved mass spectrometry (ERMS) data was represented via the survival yield (SY) method.  CID experiments were simulated within the frame of RRKM theory using MassKinetics Scientific Demo software [4]. METHODS  Chirality poses a unique challenge in life sciences since individual enantiomers may exhibit different biological activity and toxicity. Efficient chiral analytical methods are thus required.  Enantiomeric distinction of amino acids ( D/L AA) based on the relative rate constants of competitive fragmentations of quaternary coppered complexes (QCCs) is an efficient method for chiral differentiation [1,2].  Phenylalanine (Phe) was used as a model compound with proline (Pro) and non-natural AA phenylglycine (PhG) as chiral selectors [3] for a detailed study of the enantioselective reduction of homochiral or heterochiral QCCs [Cu II ,( D/L Phe, D PhG, D Pro-H)] + in gas phase (see adjacent panel). Additional in silico simulations were carried out for further kinetic insights.

INTRODUCTION

RESULTS

Enantioselective reduction

 Enantioselective reduction in [Cu II ,(Phe,PhG,Pro-H)] + QCCs would be a consequence of the instability of heterochiral complexes, which would favour direct cleavage and the product ion [Cu II ,(Phe,Pro-H)] + m/z 342, rather than the result of an increase in the formation of the reduced product ion [Cu I ,(Phe,Pro)] + m/z 343 in homochiral QCCs.

 A putative second form of the precursor QCC was found, which hints at a possibly distinct conformation but was not identified.

CONCLUSION

These results can be described using the RRKM theory [5]:

• Rearrangements (such as the formation of the product ion m/z 343) have low entropy of activation and low critical energy E 0 , and are favoured at low CE and long ActT.

• Conversely, direct cleavage (i.e. formation of the product ion m/z 342), characterized with higher entropy and critical energy, is favoured at high CE and short ActT.

Preliminary simulations using MassKinetics were performed to explore these kinetic findings.

 Enantioselective reduction describes the difference in relative abundance of the reduced product ion m/z 343 [Cu I ,(Phe,Pro)] + in precursor QCCs with different chiralities.

 Enantioselective reduction is increased in homochiral complexes (DDD or LLL) and limited in heterochiral complexes (such as LDD) (see also Fig. 1e and1f). (2) The critical energy E 0 values used in the simulation for the formation of the reduced ion m/z 343 are comparable in DDD and LDD complexes (0.85 eV). However, the formation of the product ion m/z 342 requires a lower E 0 in LDD (1.12 eV) compared to DDD complexes (1.20 eV) (Fig. 3a and3b).

The product ion m/z 342 is thus preferentially formed in heterochiral QCCs, rather than the reduced ion m/z 343 being favored in homochiral QCCs.

(3) The increase of R 343/342 followed by its decrease would be an artefact caused the presence of prompt product ions at 0% CE (Fig. 3c), likely formed by the isolation waveform in the ion trap. Without these preliminary dissociations, the abundance of the reduced ion m/z 343 would always be higher than the product ion m/z 342 at low CE and R 343/342 would only decrease (Fig. 3d).

 The main fragmentation pathway of [Cu II ,(Phe, PhG, Pro-H)] + consists in the loss of PhG, which yields the product ion [Cu II ,(Phe,Pro-H)] + (m/z 342), and the loss of (PhG-H)• with the concomitant reduction of Cu II to Cu I , resulting in the product ion [Cu I ,(Phe,Pro)] + (m/z 343).

(1) The extent of the reduction, i.e. the abundance of the product ion m/z 343, is enhanced when increasing ActT (Fig. 1e-1f).

(2) LDD complexes are less stable than DDD complexes (Fig. 1a-1b).

(3) Surprisingly, the abundance of the reduced ion m/z 343 was found to decrease when increasing CE. The ratio of the product ions m/z 343 over m/z 342 R 343/342 follows a similar pattern. The switch between the two forms was accompanied by an unexpected m/z shift of about 3 ppm (Fig. 5). Additional experiments using an FT-ICR at very high resolution (1-2.10 6 at m/z 500) could neither evidence a second form nor the m/z shift.

Under certain circumstances, the abundance of the QCC precursor ion was found to plateau before its complete disappearance (Fig. 4). This phenomenon hints that at least two forms of QCC co-exist, the most abundant of which leads to the chiral effects described above.

The second form was isolated using in-source fragmentation (SF) to remove the most abundant form (Fig. 4). Sequential MS experiments on this second form show different fragmentation patterns, most notably the fragment [M-H 2 O] + m/z 475, which suggest two distinct structures.

The second form could not be observed using ion-mobility (TIMS-ToF, Bruker), either because the CCS of both forms are similar or because the source of the instrument does not allow the appearance of the second form. (1) ERMS curves simulated with MassKinetics are able to show the influence of ActT on the dissociation of the precursor ion [Cu II ,(Phe,PhG,Pro-H)] + (Fig. 2). In particular, the unexpected behavior of the reduced ion m/z 343 can be reproduced within the simulation.

  I ,(Phe,Pro)] + [Cu II ,(Phe,Pro-H)] + [Cu II ,( D/L Phe, D PhG, D Pro-H)] +

Fig. 1 :Fig. 3 :Fig. 5 :

 135 Fig. 1: ERMS of DDD and LDD [Cu II ,(Phe,PhG,Pro-H)] + obtained at ActT of 10, 100, 1000, 5000 and 10000 ms; precursor ion [Cu II ,(Phe,PhG,Pro-H)] + m/z 493 (a and b), product ion [Cu II ,(Phe,Pro-H)] + m/z 342 (c and d), product ion [Cu I ,(Phe,Pro)] + m/z 343 (e and f), ratio of product ions m/z 343 over m/z 342, R 343/342 (g and h).

Fig. 4 :

 4 Fig. 4: ERMS of DDD [Cu II ,(Phe,PhG,Pro-H)] + obtained at an ActT of 100 ms with (SF, square) and without in-source fragmentation (circle).

Fig. 2 :

 2 Fig. 2: Simulated ERMS of DDD [Cu II ,(Phe,PhG,Pro-H)] + for ActT of 10, 100, 1000, 5000 and 10000 ms (precursor ion m/z 493 (blue), product ions m/z 342 (orange) and m/z 343 (green)).

ACKNOWLEDGMENTS


Jean-Pascal Capp
email: capp@insa-toulouse.fr

Frédéric Thomas

Prospects &

Keywords: atavism, bet-hedging, cell-cell heterogeneity, cellular plasticity, oncogenesis, stochastic gene expression, transcriptional diversity

Stochastic gene expression plays a leading developmental role through its contribution to cell differentiation. It is also proposed to promote phenotypic diversification in malignant cells. However, it remains unclear if these two forms of cellular bet-hedging are identical or rather display distinct features. Here we argue that bet-hedging phenomena in cancer cells are more similar to those occurring in unicellular organisms than to those of normal metazoan cells. We further propose that the atavistic bethedging strategies in cancer originate from a hijacking of the normal developmental bet-hedging of metazoans. Finally, we discuss the constraints that may shape the atavistic bet-hedging strategies of cancer cells.

INTRODUCTION

Random cell-to-cell phenotypic fluctuations originating from stochastic gene expression (the so-called gene expression noise) are now considered to be major contributors to the differentiation processes in multicellular organisms. [START_REF] Macarthur | Statistical mechanics of pluripotency[END_REF][START_REF] Mojtahedi | Cell fate decision as high-dimensional critical state transition[END_REF][START_REF] Richard | Single-cell-based analysis highlights a surge in cell-to-cell molecular variability preceding irreversible commitment in a differentiation process[END_REF][START_REF] Mohammed | Single-cell landscape of transcriptional heterogeneity and cell fate decisions during mouse early gastrulation[END_REF][START_REF] Stumpf | Stem cell differentiation as a non-Markov stochastic process[END_REF][START_REF] Capp | A Darwinian and physical look at stem cell biology helps understanding the role of stochasticity in development[END_REF] Indeed, high gene expression stochasticity, manifested as strong fluctuations in the abundance of expressed molecules at the single-cell level, and the subsequent highly variable gene expression patterns, are necessary for developmental "choices". [START_REF] Macarthur | Statistical mechanics of pluripotency[END_REF] For example, genome-wide transcriptional variability from cell-to-cell precedes cell fate decisions in hematopoietic progenitor cells. [START_REF] Chang | Transcriptome-wide noise controls lineage choice in mammalian progenitor cells[END_REF] Also, single-cell transcriptional diversity, which is the number of expressed genes per cell and is globally correlated to the level of gene expression stochasticity (both phenomena occur thanks to a more permissive chromatin (Figure 1)), is a hallmark of developmental potential. [START_REF] Gulati | Single-cell transcriptional diversity is a hallmark of developmental potential[END_REF] This explains the mixed-lineage states and patterns of gene expression revealed by single-cell analyses in progenitor cells before commitment, especially in the hematopoietic system. [START_REF] Hu | Multilineage gene expression precedes commitment in the hemopoietic system[END_REF][START_REF] Laslo | Multilineage transcriptional priming and determination of alternate hematopoietic cell fates[END_REF] Interestingly, lineage-specific transcription factors from alternate lin-eages are co-expressed at the protein level in early progenitor cells, as exemplified in human erythropoiesis, [START_REF] Palii | Single-cell proteomics reveal that quantitative changes in co-expressed lineage-specific transcription factors determine cell fate[END_REF] and cell fate decisions occur through gradual rather than abrupt quantitative changes in factor abundance.

Differentiation is then linked to the progressive reduction in chromatin accessibility during lineage commitment, [START_REF] Aughey | CATaDa reveals global remodelling of chromatin accessibility during stem cell differentiation in vivo[END_REF][START_REF] Pękowska | Gain of CTCF-anchored chromatin loops marks the exit from naive pluripotency[END_REF] and to a decrease in transcriptional diversity. [START_REF] Gulati | Single-cell transcriptional diversity is a hallmark of developmental potential[END_REF] Therefore, it can now be proposed that the looser chromatin in less mature cells permits a more stochastic gene expression and a wider sampling of the transcriptome, whereas chromatin accessibility, transcriptional diversity and expression stochasticity are restricted in more differentiated cells as they specialize [START_REF] Macarthur | Statistical mechanics of pluripotency[END_REF] (Figure 1). It is of note that an increase in cell-to-cell gene expression variability is observed in vitro as hematopoietic progenitor cells start differentiating when environmental constraints (medium composition) that maintained the progenitor state are released, whereas variability drops to levels far below the initial level as cells become terminally differentiated. [START_REF] Mojtahedi | Cell fate decision as high-dimensional critical state transition[END_REF][START_REF] Richard | Single-cell-based analysis highlights a surge in cell-to-cell molecular variability preceding irreversible commitment in a differentiation process[END_REF] A similar phenomenon is observed in vivo in mouse embryonic development, in which an increase in transcriptional noise is observed prior to F I G U R E 1 Main non-genetic differences between normal/cancer stem cells and more differentiated normal and cancer cells. (A) The more permissive chromatin in normal/cancer stem cells generates both a more pervasive expression of the genome, enhancing single-cell transcriptional diversity, and a higher gene expression stochasticity (more fluctuations in the abundance of expressed molecules at the single-cell level). These phenomena produce higher gene expression variability from cell-to-cell, higher cellular plasticity which corresponds to a higher cellular responsiveness to internal or external conditions, phenotypic variability, and strong bet-hedging. Nevertheless, this chromatin is not completely open (fully permissive) because if there is no stochasticity for genes that are always transcribed. Stochasticity requires chromatin that is neither perfectly closed, nor perfectly open. The chromatin should occasionally open up to generate stochasticity, so it should only be partially permissive.

(B) In more differentiated normal and cancer cells, the chromatin is globally less permissive (more closed), reducing single-cell trancriptional diversity and gene expression stochasticity. This also reduces gene expression variability from cell-to-cell and cellular plasticity, and allows phenotypic stability and only limited bet-hedging lineage commitment from implantation to early gastrulation, with possible consequences for symmetry breaking and cell fate decisionmaking. [START_REF] Mohammed | Single-cell landscape of transcriptional heterogeneity and cell fate decisions during mouse early gastrulation[END_REF] A role for gene expression noise in early embryonic cell fate commitment was proposed 15 years ago [START_REF] Dietrich | Stochastic patterning in the mouse pre-implantation embryo[END_REF] and is still widely discussed. [START_REF] Simon | Making lineage decisions with biological noise: Lessons from the early mouse embryo[END_REF] There is still controversy over the role of gene expression variability in cell fate decision-making and the fact that the observed changes in variability can drive differentiation [START_REF] Eling | Challenges in measuring and understanding biological noise[END_REF] . However, it is widely accepted that stochastic gene expression can generate random phenotypic fluctuations and cell states that likely induce differentiation. This can be seen as a "developmental bet-hedging strategy" that has similarities with the bet-hedging strategies observed in unicellular organisms, [START_REF] Urban | Buffering and amplifying transcriptional noise during cell fate specification[END_REF] where gene expression variability diversifies functional roles across a population of cells. [START_REF] Balázsi | Cellular decision making and biological noise: From microbes to mammals[END_REF][START_REF] Ackermann | A functional perspective on phenotypic heterogeneity in microorganisms[END_REF] This is also coherent with the observed stochasticity in cell differentiation processes (for instance, see [START_REF] Stumpf | Stem cell differentiation as a non-Markov stochastic process[END_REF] for a detailed description of stem cell differentiation as a stochastic process and [START_REF] Capp | A Darwinian and physical look at stem cell biology helps understanding the role of stochasticity in development[END_REF] for a review).

Here, we explore the differences between these two types of bethedging strategies; we then argue that phenotypic diversification in cancer cells could also rely on bet-hedging phenomena more similar to those observed in unicellular organisms than to those observed in developmental multicellular systems. We propose that cancer cells use "atavistic bet-hedging strategies," which constitutes a perversion of the way random single-cell phenotypic fluctuations are exploited in multicellular organisms.

DIFFERENCES BETWEEN DEVELOPMENTAL AND ATAVISTIC BET-HEDGING

The most cited noise-based model of differentiation relies on the notion of "attractor" in the dynamical-systems theory. [START_REF] Huang | Systems biology of stem cells: Three useful perspectives to help overcome the paradigm of linear pathways[END_REF] Within this theory, the global regulatory network (GRN) generates attractor states that are meta-stable and that can be regarded as distinct cell types (the stem cell state being the original attractor). [START_REF] Huang | Systems biology of stem cells: Three useful perspectives to help overcome the paradigm of linear pathways[END_REF] Noise-induced transition between attractors can occur when random expression fluctuations are sufficient to destabilize the current attractor state and generate a switch towards another attractor. Cell-to-cell communication can be considered a way to exploit and buffer intercellular variability, [START_REF] Simon | Making lineage decisions with biological noise: Lessons from the early mouse embryo[END_REF][START_REF] Furusawa | A dynamical-systems view of stem cell biology[END_REF] but the attractors are fundamentally encoded in the GRN and allow the embryo and the differentiating tissue to self-organize.

In a more Darwinian view of differentiation, we and others have suggested that these varied patterns could act as a substrate for selection by environmental cues through a "chance-selection" process. [START_REF] Capp | A Darwinian and physical look at stem cell biology helps understanding the role of stochasticity in development[END_REF][START_REF] Kupiec | A chance-selection model for cell differentiation[END_REF][START_REF] Moris | Transition states and cell fate decisions in epigenetic landscapes[END_REF] In this case, developmental bet-hedging processes would consist of randomly generated phenotypic fluctuations among which some gene expression patterns would later be stabilized and selected if the generated phenotypes were adequate in the current environment. Thus, this variability would reflect the need to switch to different fates based on specific selective external cues that stabilize specific chromatin structures.

Of note, this type of stabilization occurs outside of development and has been explored in multiple works for non-developmental bethedging, [START_REF] Blake | Phenotypic consequences of promoter-mediated transcriptional noise[END_REF][START_REF] Fraser | A chance at survival: Gene expression noise and phenotypic diversification strategies[END_REF][START_REF] Charlebois | Gene expression noise facilitates adaptation and drug resistance independently of mutation[END_REF] so it is not specific to development. Nevertheless, in the developmental context, this would allow integration of the cells within the developing or renewing tissue through the establishment of direct or distant cell-cell interactions. [START_REF] Capp | A Darwinian and physical look at stem cell biology helps understanding the role of stochasticity in development[END_REF][START_REF] Kupiec | A chance-selection model for cell differentiation[END_REF] In that sense, developmental bet-hedging would be specific and different than other types of bet-hedging: not in its molecular origins but rather in its consequences in term of cellular integration in a stably functioning structure in which differentiation, equivalent to phenotypic stabilization produced by cell signaling from the established cell-cell interactions, and quiescence would be linked to the decreased level of gene expression noise and the progressive chromatin closure originating from environmental cues.

Thus, a state with a chromatin structure more typical of a fully differentiated state would be much too resistant to switching, due to the stable cell-cell interaction network that has progressively taken place during differentiation and canalized cells towards differentiation.

We already mentioned that this progressive stochasticity-based cell differentiation and specialization is associated with formation of the multicellular organism in the developmental context, with the cells being ultimately controlled at the proliferation level and integrated into a cell community functioning for the benefit of the whole organism. [START_REF] Capp | A similar speciation process relying on cellular stochasticity in microbial and cancer cell populations[END_REF] This canalization process leading to cell differentiation can be viewed as a constrained random process consisting of a permanent interplay between the stochastic dynamics of biochemical reactions at the cellular level and the environmental constraints that leads to a stabilized state of equilibrium. [START_REF] Capp | A Darwinian and physical look at stem cell biology helps understanding the role of stochasticity in development[END_REF] Quiescence and integration into the functioning of the multicellular organism result from an evolutionary history in which a hereditary "memory" has been established

based on the fact that these stochastic processes have been exploited

and proved useful in the past to build multicellularity.

On the contrary, bet-hedging processes in unicellular organisms are used to specialize cells with the aim of maximizing cell proliferation, for example, by exploiting alternative carbon sources or adapting to fluctuating environments. [START_REF] Fraser | A chance at survival: Gene expression noise and phenotypic diversification strategies[END_REF][START_REF] Grimbergen | Microbial bet-hedging: The power of being different[END_REF][START_REF] Morawska | Diversity of bet-hedging strategies in microbial communities-Recent cases and insights[END_REF] This microbial bet-hedging has been characterized and is different from developmental bet-hedging in that it does not aim to integrate cells into a cooperating community of quiescent cells working for the whole community (the multicellular organism), but is rather aimed at adaptation through specialization or re-specialization for the cell's own benefit. In this case, the subsequent decrease in cell stochasticity associated with specialization is not associated with quiescence, as it is for differentiated cells in multicellular organisms, but with maximized proliferation when environmental conditions are favorable [START_REF] Capp | A similar speciation process relying on cellular stochasticity in microbial and cancer cell populations[END_REF] (if these conditions become too unfavorable, many bacteria and fungi can also enter a quiescent, or dormant, nonreplicating state, e.g., persistence with/tolerance to antibiotics [START_REF] Fisher | Persistent bacterial infections and persister cells[END_REF] ).

While it remains basically a selfish strategy to benefit the survival and proliferation of the individual cell, this strategy does not exclude the establishment of cooperation with subpopulations having different phenotypic behaviors or even with other organisms. For instance, an example of social behavior originating from phenotypic fluctuations is observed in the budding yeast Saccharomyces cerevisiae, linked to the FLO11 locus. The adhesin Flo11 functions in cell-cell and cell-surface adhesion, and has been hypothesized to function in cell-cell recognition and allows for cell-cell homotypic interactions. [START_REF] Douglas | Expression and characterization of the flocculin Flo11/Muc1, a Saccharomyces cerevisiae mannoprotein with homotypic properties of adhesion[END_REF] FLO11 is heterogeneously expressed in clonal populations, which is likely to be adaptive, because this variegated FLO11 expression leads to increased biomass and space usage in nutrient-limiting environments. [START_REF] Regenberg | Clonal yeast biofilms can reap competitive advantages through cell differentiation without being obligatorily multicellular[END_REF] Indeed, stable proportions of Flo11 + and Flo11 -cells result from differential expression of FLO11 in clonal S. cerevisiae biofilm colonies, and differentiated Flo11 +/-colonies, composed of adhesive and non-adhesive cells, have a strong growth advantage over undifferentiated colonies. [START_REF] Regenberg | Clonal yeast biofilms can reap competitive advantages through cell differentiation without being obligatorily multicellular[END_REF] Moreover, works on natural genetic variations in the FLO11 locus found that particular FLO11 alleles were associated with increased sociality, and strongly suggest selection on heterogeneous FLO11 expression in clonal lineages. [START_REF] Oppler | Variation at an adhesin locus suggests sociality in natural populations of the yeast Saccharomyces cerevisiae[END_REF] Thus this example seems to provide a good illustration of the role of phenotypic fluctuations in giving rise to cooperative behavior between different phenotypes in microbial populations.

Interestingly, cancer cells seem to harbor similar trends to exploit cellular stochasticity through bet-hedging processes. [START_REF] Guinn | Observation and control of gene expression noise: Barrier crossing analogies between drug resistance and metastasis[END_REF][START_REF] Capp | Does cancer biology rely on Parrondo's principles?[END_REF] Even though the strategies of retaining high variability at the phenotypic level, and of being more phenotypically stable may both appear to be losing strategies in the cancerous environment, the combination and fluctuation between them could constitute a Parrondo's paradox by being a winning strategy resulting from two losing strategies. [START_REF] Capp | Does cancer biology rely on Parrondo's principles?[END_REF] In other words, transitions between a state with strongly fluctuating expressions and lack of differentiated phenotypes that can be called cancer stem cell (CSC) in analogy with the properties of the "normal" stem cells, [START_REF] Capp | Cancer stem cells: From historical roots to a new perspective[END_REF] and more differentiated states, would be advantageous in an evolutionary perspective.

In line with this hypothesis, we propose that bet-hedging processes, which can be called "atavistic bet-hedging", are crucial in cancer cell populations because they could provide cells with new gene expression patterns that could allow local adaptation to a given environment.

This strategy would be associated with decreased stochasticity and improved cell proliferation, but with the possibility that cell stochasticity (and plasticity which is the cellular responsiveness to internal or external conditions and can result in higher phenotypic variability) could reincrease and restore atavistic bet-hedging if cells are no longer on an adaptive summit. This is closer to a unicellular bet-hedging strategy than a developmental bet-hedging strategy.

Thus, at any moment of cancer development, a mix of cells with new phenotypic combinations attained through atavistic bet-hedging strategies along with more specialized and proliferative cells would coexist. The spatial repartition of these subpopulations would depend on the tumor's structure, with variably fluctuating and challenging environments that would require variably adaptive capacities. For instance, at the surface of a solid tumor, where cells are confronted with the need to deeply modify well-structured tissue (even if it is already perturbed by paracrine molecules), bet-hedging is probably more important than in more internal zones of the tumor, where most of the cancer cells have reached a more stable state adapted to the cancerous microenvironment and where structuration is optimal for proliferation. In blood cancers, these same atavistic bet-hedging strategies could occur but may be less intensive because tissue barriers are less stringent.

However, increased cell plasticity-even though it is considered the result of an "oncogene-induced reprogramming" and not produced by a microenvironmental disruption-is now considered a key factor in leukemic cell function. [START_REF] Fregona | Oncogene-induced reprogramming in acute lymphoblastic leukemia: Towards targeted therapy of Leukemia-initiating cells[END_REF] The early acquisition of cell plasticity as a consequence of an increase gene expression stochasticity can easily be the best way to exploit an atavistic, stochasticity-based bet-hedging process for cancer development.

To reach tumor structuration from cells adopting atavistic bethedging strategies, the establishment and maintenance of cooperative mechanisms are required. As is the case for developmental processesbut without the reproducible chance-selection processes based on developmental bet-hedging gained and optimized for ontogenesis across evolution-random single-cell phenotypic fluctuations would inevitably lead to cooperative behaviors, as observed in microbial communities. [START_REF] Morawska | Diversity of bet-hedging strategies in microbial communities-Recent cases and insights[END_REF] This can be considered the basis for the structuration of tumors-as well as for multicellular organisms-but through an always-new process that has no heritable "memory" of previous stochastically based cancer development (except in the case of transmissible cancers; see below).

Finally, the ability to switch from higher or lower cellular stochasticity and its exploitation for maximizing cell proliferation through atavistic bet-hedging requires that cells are no longer canalized nor maintained under the control of a healthy microenvironment. We next consider how cancer cells transit from developmental to atavistic bet-hedging.

FROM DEVELOPMENTAL TO ATAVISTIC BET-HEDGING IN CANCER CELLS

Many studies have noted numerous cancer driver mutations in histologically normal human tissues, [START_REF] Martincorena | Constrained positive selection on cancer mutations in normal skin[END_REF][START_REF] Krimmel | Ultradeep sequencing detects ovarian cancer cells in peritoneal fluid and reveals somatic TP53 mutations in noncancerous tissues[END_REF][START_REF] Martincorena | Somatic mutant clones colonize the human esophagus with age[END_REF][START_REF] Yoshida | Tobacco smoking and somatic mutations in human bronchial epithelium[END_REF][START_REF] Colom | Spatial competition shapes the dynamic mutational landscape of normal esophageal epithelium[END_REF][START_REF] Moore | The mutational landscape of normal human endometrial epithelium[END_REF][START_REF] Fowler | Selection of oncogenic mutant clones in normal human skin varies with body site[END_REF] supporting investigations that have recently reconsidered the role of host microenvironment perturbation in tumor development. [START_REF] Kennedy | Cancer-associated mutations but no cancer: Insights into the early steps of carcinogenesis and implications for early cancer detection[END_REF][START_REF] Balmain | The critical roles of somatic mutations and environmental tumor-promoting agents in cancer risk[END_REF][START_REF] Laconi | Cancer as a disease of old age: Changing mutational and microenvironmental landscapes[END_REF][START_REF] Solary | The role of host environment in cancer evolution[END_REF] These studies highlighted the fact that mutations alone are not sufficient for tumor development, and that tissue disruption has a more active role than previously thought in the early steps of oncogenesis. However, the obligatory preexistence of genetic alterations in healthy tissue that develops cancer is rarely questioned. Only a few studies, [START_REF] Castillo | Do microenvironmental changes disrupt multicellular organisation with ageing, enacting and favouring the cancer cell phenotype?[END_REF][START_REF] Soto | The cancer puzzle: Welcome to organicism[END_REF] including ours, [START_REF] Capp | Tissue disruption increases stochastic gene expression thus producing tumors: Cancer initiation without driver mutation[END_REF][START_REF] Capp | Multiple myeloma as a bone disease? The tissue disruption-induced cell stochasticity (TiDiS) theory[END_REF][START_REF] Capp | Tissue-disruption-induced cellular stochasticity and epigenetic drift: Common origins of aging and cancer?[END_REF] have proposed microenvironmental changes and the disruption of multicellular organization as possible initiating events in oncogenesis.

In particular, we suggested that a link between the disrupted microenvironment (perturbation of the environmental constraints acting at the tissue level through cell-cell interactions and communications, and at the organism level through endocrine, immunity, and blood networks) and enhanced cellular stochasticity was responsible for the loss of stable and well-defined phenotypic and differentiated features and/or the loss of canalization of normal stem cells. [START_REF] Capp | Tissue disruption increases stochastic gene expression thus producing tumors: Cancer initiation without driver mutation[END_REF][START_REF] Capp | Multiple myeloma as a bone disease? The tissue disruption-induced cell stochasticity (TiDiS) theory[END_REF][START_REF] Capp | Tissue-disruption-induced cellular stochasticity and epigenetic drift: Common origins of aging and cancer?[END_REF] This microenvironmentally-induced enhanced stochasticity would not only produce cells with stem cell-like properties (called CSC), but also produce a switch from developmental to atavistic bet-hedging in cancer cells. The role of phenotypic plasticity and bet-hedging processes have already been highlighted in the context drug resistance and tumor relapse in prostate cancer by giving rise to drug-tolerant persisters, [START_REF] Jolly | Phenotypic plasticity, bet-hedging, and androgen independence in prostate cancer: Role of non-genetic heterogeneity[END_REF] but we suggest here to adopt a broader perspective and to consider bet-hedging processes as a central phenomenon in tumor growth.

Indeed, as soon as the dynamically evolving environmental constraints no longer exert their canalizing effect, and as soon as the selective developmental process mentioned above can no longer occur because direct and/or indirect cellular interactions are disrupted, the random generation of single-cell phenotypic fluctuations can only serve as a substrate for bet-hedging strategies similar to those observed in microorganisms.

While the normal developmental process is also based on a principle of "chance-selection" where noise plays a major role, in the language of bifurcation theory, bifurcations have been "optimized" so as to be rapid, unique and unidirectional in response to the canalizing environment. On the contrary, in the disrupted cancerous environment, cells are freer to explore the phenotypic space and behave more stochastically in terms a mixed phenotypes, phenotypic reversibility, more in a unicellular mode.

This perspective certainly presents many convergences with the Davies and Lineweaver's 2011 atavism theory of cancer where the authors argued that cancer is due to an accidental reactivation of a highly conserved survival program encrypted in every eukaryotic cell and a reversion to "Metazoa 1.0". [START_REF] Davies | Cancer tumors as Metazoa 1.0: Tapping genes of ancient ancestors[END_REF] However, in the present article and a previous one, [START_REF] Capp | A similar speciation process relying on cellular stochasticity in microbial and cancer cell populations[END_REF] we rather consider a renewed atavism theory of cancer based on cellular stochasticity. Indeed, without the need to invoke an ancestral legacy, tumor adaptations may also result from a somatic evolution with selection of malignant cells that adopt a stochasticity-based microbial cell lifestyle which could be the most adapted given the new environmental constraints. [START_REF] Capp | A similar speciation process relying on cellular stochasticity in microbial and cancer cell populations[END_REF] Of note, this microenvironmental disruption would make the cancer cell phenotypes much more plastic with reversible transitions from more differentiated to more stem-like cancer cells during tumor growth, compared to the essentially irreversible nature of differentiation during orchestrated development. Even if this reversibility could also rely on broken regulatory links arising from mutations that can act as tumor "promotors", [START_REF] Capp | Tissue disruption increases stochastic gene expression thus producing tumors: Cancer initiation without driver mutation[END_REF] it would mainly originate from to the disrupted microenvironment, making mutations not absolutely necessary, although unavoidable in tumor growth.

Therefore, a non-genetic stochasticity-based construction of the tumor architecture would then be possible and take place. Spatial organization and cooperative behaviors between distinct subpopulations would result from a selection process based on random phenotypic fluctuations produced by uncanalized enhanced stochastic gene expression rather than being the result of ontogenesis as for developmenting tissues, but following new dynamics because tumor development cannot be influenced by future environmental conditions.

RELATIONSHIPS WITH THE PHENOTYPIC COMPOSITION OF THE TUMORAL GROUP

It is increasingly argued that tumor growth does not solely rely on the quantitative rate of cell proliferation inside the tumor, but also on the way the produced cells succeed in forming a functional group. Tumors consist of a consortium of cooperating malignant clones organized into functional compartments, with a division of labor among these compartments. [START_REF] Grunewald | Understanding tumor heterogeneity as functional compartmentssuperorganisms revisited[END_REF][START_REF] Ramón Y Cajal | Cancer as an ecomolecular disease and a neoplastic consortium[END_REF][START_REF] Hausser | Tumor diversity and the trade-off between universal cancer tasks[END_REF] The resulting tumoral group phenotypic composition (GPC) is therefore key to understanding the ecological and evolutionary trajectories of malignancies. [START_REF] Capp | Group phenotypic composition in cancer[END_REF] However, because malignant cell proliferations rely on atavistic bet-hedging, it is expected that they will produce different possibilities of tumoral GPCs (e.g., depending on which cells have a selective advantage inside the tumor at a given time). Since evolution is not affected by the future and because there is no encoded program to produce optimal tumor tumorigenesis, atavistic bet-hedging is expected to regularly yield non-optimal, or even detrimental, tumoral GPCs. [START_REF] Arnal | Evolutionary perspective of cancer: Myth, metaphors, and reality[END_REF] This lack of adequacy between optimal and observed GPCs can in theory stop or slow down tumor progression.

In addition, there is not only one optimal GPC that would provide the tumor with adequate combinations of different and complementary cells during tumorigenesis. For instance, tumors of different volumes or in different organs have different needs, and they also have different interactions with their microenvironment. Producing tumoral GPCs via atavistic bet-hedging that adequately vary with tumor stage and the microenvironment is improbable, and this explains why only a tiny proportion of emerging neoplasia succeed at each step and evolve into metastatic cancers. The inability of the atavistic bet-hedging to generate tumors with adequate GPCs at each step would also explain why we accumulate various stages of stable neoplasms in the body through time that can sometimes even regress. [START_REF] Folkman | Cancer without disease[END_REF] A major constraint limiting the possibility that malignant atavistic bet-hedging can evolve over evolutionary time to produce progressively enhanced GPCs is that cancers are usually not transmissible.

Therefore, there is no possibility of selecting bet-hedging options that would progressively yield tumors with optimal levels of functionality. In comparison, a complex organism with different organs is also characterized by its GPC; however, since individuals reproduce, there is the possibility of selecting GPC combinations that are optimal for organismal development, functioning, and reproduction in its environment.

THE CASE OF TRANSMISSIBLE CANCERS

In contrast to most cancers that die with their host, transmissible malignancies are composed of clonal cell lineages that have acquired the ability to pass from host to host. [START_REF] Pearse | Evolution in a transmissible cancer: A study of the chromosomal changes in devil facial tumor (DFT) as it spreads through the wild Tasmanian devil population[END_REF][START_REF] Metzger | Horizontal transmission of clonal cancer cells causes leukemia in soft-shell clams[END_REF][START_REF] Baez-Ortega | Somatic evolution and global expansion of an ancient transmissible cancer lineage[END_REF][START_REF] Dujon | Transmissible Cancers in an Evolutionary Perspective[END_REF][START_REF] Kwon | Evolution and lineage dynamics of a transmissible cancer in Tasmanian devils[END_REF] These cancers truly evolve as novel parasitic life forms and are not reinvented each time they emerge as classical malignant cells. [START_REF] Arnal | Evolutionary perspective of cancer: Myth, metaphors, and reality[END_REF][START_REF] Dujon | Transmissible Cancers in an Evolutionary Perspective[END_REF] Because of this important particularity, we might expect that these transmissible cancers undergo evolution and/or reversion (malignant cells derive from healthy cells), from an atavistic bet-hedging to a more canalized developmental one, allowing optimization of the adaptive traits related to the parasitic lifestyle. To our knowledge, this question has not yet been addressed. Interestingly, the age of transmissible cell lines is highly variable between the few biological models that have been identified, ranging from a few years (e.g., 26 for devil facial tumor disease in Tasmanian devils) to about 10 000 years (for canine transmissible venereal tumor). We cannot exclude that there would be intermediate forms of transmissible cancers ranging between atavistic bet-hedging and developmental one depending on their age. Ultimately, a more reproducible GPC may occur in the transmissible ones, albeit going in the direction of proliferation, and not for the control of proliferation for the benefit of the whole organism. Studies mimicking the transmission of malignant cells in an experimental evolutionary context (e.g., [START_REF] Ibrahim-Hashim | Artificial selection for host resistance to tumour growth and subsequent cancer cell adaptations: An evolutionary arms race[END_REF] ) could help explore this hypothesis. Also, given that we currently underestimate the number of transmissible cancers, [START_REF] Dujon | Transmissible cancers in mammals and bivalves: How many examples are there?: Predictions indicate widespread occurrence[END_REF] it seems likely that the future discovery of novel models will help to address this question.

CONCLUDING REMARKS

Since the late Precambrian, multicellularity evolved independently on multiple occasions, but each time it required that the newly evolved multicellular phenotypes had the ability to control their cellular proliferation. [START_REF] Aktipis | Evolutionary foundations for cancer biology[END_REF][START_REF] Boutry | The evolution and ecology of benign tumors[END_REF] The achievement of this fundamental condition was not incompatible with the presence of high levels of stochasticity in gene expression, and it provided mechanisms that could subsequently constrain gene expression patterns to a certain homogenization during differentiation. As a form of collateral effect, it is increasingly argued that tumorigenesis could be initiated by disrupting constraints that canalize cells towards their stable phenotypic state, yielding a form of cellular speciation relying on increased cellular stochasticity. [START_REF] Capp | A similar speciation process relying on cellular stochasticity in microbial and cancer cell populations[END_REF] Whether this phenomenon is a reversion to prior phylogenetical capabilities or conversely corresponds to newly acquired cellular traits remains an ongoing debate. In any case, increased cellular stochasticity does not mean that there are no longer constraints on this trait.

Inappropriate levels of stochasticity can yield poorly functional GPC within a tumor, even if this phenomenon remains in itself unlikely to trigger selective processes favoring more adapted adjustments (except with transmissible cancers). Another constraint-in this case able to shape the level of stochasticity-is related to the degree of environmental adversity for cancer cells in different organs of a multicellular organism's body. Giraudeau et al. [START_REF] Giraudeau | From developmental to atavistic bet-hedging: how cancer cells pervert the exploitation of random single-cell phenotypic fluctuations[END_REF] argued that for ecological and evolutionary reasons, important variability exists between organs in their capacity to suppress tumors. When a tumor develops in an organ exerting strong selection pressure on malignancies, cancer cells (at least those that survive and are detectable) are expected to display a large range of survival strategies, like hyper-variable phenotypes relying on bet-hedging to persist, or conversely variants with low variabilities that succeed in escaping anticancer defenses. Because these constraints are not observed in organs that are less able to eradicate tumors, selective pressures favoring extreme survival strategies are relaxed, and this is likely to allow a wider range of cellular stochasticity levels. Therefore, even if bet-hedging strategies in cancer cells are similar to those observed in unicellular organisms, it is still important to consider the local specificity of the multicellular body viewed as a habitat for cancer cells.

  Higher gene expression stochasƟcity (more fluctuaƟons in the abundance of expressed molecules at the singlecell level)

  15211878, 2022, 9, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/bies.202200048 by Biu Montpellier, Wiley Online Library on [18/01/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

15211878, 2022, 9, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/bies.202200048 by Biu Montpellier, Wiley Online Library on [18/01/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

ACKNOWLEDGMENTS

This work was supported by the MAVA Foundation and by the ANR TRANSCAN (ANR-18-CE35-0009).

DATA AVAILABILITY STATEMENT

Not applicable as this is a perspective article.

ORCID

Jean-Pascal Capp https://orcid.org/0000-0002-6470-079X

AUTHOR CONTRIBUTIONS

Jean-Pascal Capp and Frédéric Thomas formulated the hypotheses and wrote the manuscript.

CONFLICT OF INTEREST

The authors declare no conflict of interest.


Elie Saikali

Anne Sergent

Christian Tenaud

Gilles Bernard-Michel

Bernard-Michel Gilles

Numerical modelling of an air-helium buoyant jet in a two vented enclosure

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

We present a DNS study of an air-helium buoyant jet rising in a two vented cavity. The geometrical configuration mimics the helium release in an experimental set-up [START_REF] Bernard-Michel | Experimental measurements, CFD simulations and model for a helium release in a two vent enclosure[END_REF] built at CEA Saclay in the framework of security assessment of hydrogen-based systems with an indoor usage. The dimension of the enclosure was chosen to ensure a laminar-turbulent transition occurring at about the middle height of the cavity.

This study aims at determining the relevant computational domain and boundary conditions necessary to accurately model the jet development in a vented cavity where the mass flow rates crossing the vents are not prescribed. We observe that applying constant pressure outlet boundary conditions directly at the vent interfaces underestimates the volumetric flow rate of air entering the enclosure and thus overestimates the helium mass inside the cavity. On the contrary, adding a surrounding volume into the computational domain better predicts the air flow-rate across the vents and leads to a better agreement with the experimental Particle Image Velocimetry measurements along the vents but also inside the cavity.

Once the numerical model being set, helium distribution inside the cavity is compared to the Linden theory [START_REF] Linden | The fluid mechanics of natural ventilation[END_REF]. Then the dominant terms of the Turbulent Kinetic Energy equation expressed in the Reynolds averaged formulation are investigated and in particular the buoyancy production term.

  Sorbonne Université, IFD, 4 place Jussieu, 75252 PARIS cedex 05, France

	6th European Conference on Computational Mechanics (ECCM 6)
	7th European Conference on Computational Fluid Dynamics (ECFD 7)
	11-15 June 2018, Glasgow, UK
	NUMERICAL MODELLING OF AN AIR-HELIUM
	BUOYANT JET IN A TWO VENTED ENCLOSURE
	E. Saikali 1,2,3 , A. Sergent 1,4 , C. Tenaud 1 , G. Bernard-Michel 2
	1 LIMSI, CNRS, Université Paris-Saclay, F-91405 Orsay, France, elie.saikali@limsi.fr,
	anne.sergent@limsi.fr, christian.tenaud@limsi.fr
	2 DEN/DANS/DM2S/STMF/LIEFT, CEA Saclay, 91191 Gif-Sur-Yvette cedex, France,
	elie.saikali@cea.fr, gilles.bernard-michel@cea.fr
	3

4

Sorbonne Université, UFR d'Ingénierie, F-75005 Paris, France Keywords: Buoyant jet, vented enclosure, direct numerical simulations, boundary conditions