Evasion ofapoptosis

Insensitivity togrowth inhibitors

Self-sufficiency ingrowth signals

An inflammatorymicroenvironment

Sustainedangiogenesis

Evasion of

Tissue invasion& metastasis

Self-sufgrowt

inedenesis

Limitless replicativepotential

particular vascular endothelial growth fac-tor, which is mobilized by enzymes originat-ing from inflammatory white blood cells and which promotes blood-vessel formation dur-ing tumour progression4,5 . Moreover, during the development of cancers caused by human papillomavirus, immune cells called B cells

orchestrate inflammation remotely by producing antibodies that become localized in the extracellular matrix11. What’s more, a macrophage-derived

extracellular-matrix protein called SPARC facilitates tumour-cell motility and meta-

stasis12. So it seems that extracellular-matrix components are much more than a scaffold, or a substrate to be consumed during tumour-cell invasion, but instead represent a central component of cancer-

related inflammation.The present study2 offers unexpected

vistas on the molecular pathways that link inflammation to acquisition of the capacity to metastasize during tumour progression. It will be essential to assess the significance

of versican and other extracellular-matrix proteins in models that reflect the diversity of human cancer, for from such work innovative therapeutic strategies may follow. ■

Alberto Mantovani is at the Istituto Clinico Humanitas IRCCS, and the University of Milan, Rozzano, Milan 20089, Italy. e-mail: alberto.mantovani@humanitas.it

1. Fidler, I. J. Nature Rev. Cancer 3, 453–458 (2003).2. Kim, S. et al. Nature 457, 102–106 (2009).3. Mantovani, A., Allavena, P., Sica, A. & Balkwill, F. Nature

454, 436–444 (2008).4. Coussens, L. M. & Werb, Z. Nature 420, 860–867

(2002).5. Hanahan, D. & Weinberg, R. A. Cell 100, 57–70 (2000).6. Giavazzi, R. et al. Cancer Res. 50, 4771–4775 (1990).7. Luo, J. L. et al. Nature 446, 690–694 (2007).8. Wyckoff, J. B. et al. Cancer Res. 67, 2649–2656 (2007).9. Mantovani, A., Schioppa, T., Porta, C., Allavena, P.

& Sica, A. Cancer Metastasis Rev. 25, 315–322 (2006).10. Yang, L. et al. Cancer Cell 13, 23–35 (2008).11. de Visser, K. E., Korets, L. V. & Coussens, L. M. Cancer Cell 7,

411–423 (2005).12. Sangaletti, S. et al. Cancer Res. 68, 9050–9059 (2008).

to overt inflammatory conditions (such as breast cancer), the activation of oncogenes can orchestrate the pro-duction of inflammatory molecules and the recruitment of inflammatory cells. In the tumour microenviron-ment, inflammatory cells and molecules influence almost every aspect of cancer progress, including the tumour cells’ abil-ity to metastasize3. Thus, whereas there were previously six recognized hallmarks of cancer — unlimited replicative potential, self-suffi-ciency in growth signals, insensi-tivity to growth inhibitors, evasion of programmed cell death, ability to develop blood vessels, and tissue invasion and metastasis5 — cancer-related inflammation now emerges as number seven (Fig. 1).

A group of cytokine proteins, including IL-1, IL-6, TNF and RANKL, activate inflammation and are known to augment tumour cells’ ability to metastasize by affecting several steps in the cells’ dissemina-tion and implantation at secondary sites3,6,7. Inflammatory cytokines lie downstream of the ‘master’ gene transcription factor for promot-ing inflammation — NF-κB — which is itself activated by them3. A major source of inflam-matory cytokines in the tumour microen-vironment are specialized white blood cells called macrophages. Tumour-associated macrophages assist the malignant behav-iour of tumour cells, not just by producing cytokines, but also by secreting growth factors and matrix-degrading enzymes8–10.

Kim et al.2 explored the molecular path-ways linking tumour cells, macrophages and metastasis. By purifying the components of the medium in which the tumour cells (the Lewis lung carcinoma cell line) were grown, they isolated a factor that induced cytokine pro-duction by macrophages. They identified this tumour-derived macrophage activator as ver-sican, a protein of the extracellular matrix that is frequently upregulated in human tumours. The authors found that versican is recognized by TLR2 and TLR6, two receptor proteins that belong to a family of cellular sensors of microbially derived molecules and tissue dam-age. They then went on to silence versican in tumour cells by an RNA interference technique, and to use mice in which TNF and TLR were absent. On the basis of the evidence obtained, the authors propose that, in the Lewis lung car-cinoma model, tumour-derived versican acts on macrophages through TLR2/TLR6, leading to the production of inflammatory cytokines, which enhance metastasis.

Kim and colleagues’ observations highlight the importance of the extracellular matrix in cancer-related inflammation. The matrix acts as a depot of cytokines and growth factors, in

Figure 1 | The hallmarks of cancer. In 2000, Hanahan and Weinberg5 proposed a model to define the six properties that a tumour acquires. These are unlimited replicative potential, ability to develop blood vessels (angiogenesis), evasion of programmed cell death (apoptosis), self-sufficiency in growth signals, insensitivity to inhibitors of growth, and tissue invasion and metastasis. Kim and colleagues’ findings2, together with those of other studies3,4, indicate that this model should be revised to include cancer-related inflammation as an additional hallmark. (Adapted from ref. 5.)

ASTROPHYSICS

Star formation branches outRalph E. Pudritz

Deciphering how stars form within turbulent, dense clouds of molecular gas has been a challenge. An innovative technique that uses a tree diagram provides insight into the process.

An understanding of how stars form has been hampered by the complexity of the clouds of cold molecular gas within which their formation occurs. To elucidate this process, the effects of gravity must be traced across a wide range of scales, particularly at the large distances over which it operates in these gas clouds. On page 63 of this issue, Goodman et al.1 show how a hierarchical tree diagram — a ‘dendrogram’ — can be used to disentangle the gravitational connections that tie the gas together on many different scales.

The movements of gas in molecular clouds are measured by spectral shifts. Depending on whether a parcel of gas is moving towards or

away from the observer, the wavelength of the millimetre emission from a molecule such as carbon monoxide is shifted towards shorter (‘blueshifted’) or longer (‘redshifted’) wave-lengths. By measuring these Doppler shifts at each point on the sky, one can determine the relative velocities of parcels of gas in the cloud along each line of sight. It turns out that the gas motions in such clouds are mainly highly supersonic. Indeed, computer simulations show2 that the network of dense filamentary structures seen in clouds is probably a direct consequence of such supersonic gas flows (Fig. 1).

As with many astronomical observations,

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however, we do not know the distance of any object (gas in this case) from Earth without using further painstaking methods. In observ-ing molecular clouds, one is limited to measur-ing the two-dimensional position of the total gas emission on the sky. This measurement involves two coordinates that are akin to lati-tude and longitude on Earth’s surface, as well as the relative velocity of gas at that position. Thus, a map of such a cloud is a sequence of position–position–velocity (p–p–v) measure-ments of the gas across the whole cloud. But without the ability to observe the full, three-dimensional gas cloud, how can its true struc-ture be deduced, let alone the strength of the various forces that control where and when stars will form?

The approach usually taken to tackle this problem consists of segmenting the clumpy cloud into a collection of structures (clumps) using a computer program called CLUMPFIND. The end result is analogous to a topographical relief map of a mountain range. Such a map typically shows peaks that stand

out from ridges or are isolated, and provide a series of contours that demarcate different elevations. If one now decided to break the range up into discrete ‘mountains’, one would identify the peaks and, using the various contour levels, try to decide whether smaller outcrops ‘belonged’ to a given mountain or were independent structures. In a p–p–v map of a molecular cloud, it is the contours and peaks of gas-column density — the sum of the emission from all gas parcels moving at a given velocity along a given line of sight — that play the role of topological relief in this mountain-range analogy.

The problem with this approach arises as soon as the results are used to try to provide insight into how stars form. For example, the column densities allow one to measure the masses of the clumps. One can then count the number of clumps with a given mass. The resulting distribution of clump masses is used to work out how star formation might occur3,4. With programs similar to CLUMPFIND, the data suggest that the

clump-mass distribution closely resembles the distribution of star masses. A plausible but debated inference is that the origin of stellar masses may derive directly from the turbulent process that produced the clumps.

The fly in this ointment, however, as Good-man et al.1 show (see Fig. 1 of their Supplemen-tary Information), is that if one adopts different threshold levels for contouring the maps, the column-density distribution changes. This is similar to the situation in a topographical relief map of a mountain range: the list of discrete mountains and their properties depends on the choice of threshold level picked to define mountains and smaller outcrops. This is unset-tling — such an approach does not provide a completely objective way of measuring the actual distribution of column densities.

Enter the dendrogram technique advocated by the authors1. Rather than dividing the p–p–v data into a priori distinct structures in a sub-jective way, they use a method that is sensitive to the structures’ intrinsic hierarchy (struc-ture within structure). The data are broken up into ‘leaves’, ‘branches’ and ‘trunks’. Leaves are identified as sufficiently strong maxima in the column-density maps, and connections between them are made by branches (their environs). The collection of physically related branches defines a trunk.

Every point on the dendrogram corresponds to a closed ‘isosurface’ on the map that encloses one or more column-density maxima (see Fig. 2 on page 64). The authors then show how physical properties can be ascribed to regions within these isosurfaces. A critical property is the ‘virial’ parameter — the ratio of the energy of the gas motions (which depends on the gas velocity in the region) to the gravitational energy (which depends on the mass and size of the region). If this ratio is sufficiently small, the gas in that region will be self-gravitating and prone to form stars. The dendrogram thus traces the relative strength of the gravitational force across the gas cloud.

Application of the dendrogram technique to observations of the gas cloud L1448, and to computer simulations in which only turbulence — and not self-gravity — is taken into account, shows inconsistencies. In contrast to the simulations, in which most of the gas is found to be self-gravitating on all spatial scales, the observations show that, although a large frac-tion of the gas is self-gravitating at large scales, at smaller scales that fraction is much lower.

Interestingly, strong local column-density maxima — which correspond to the dense gas ‘cores’ in which stars are observed to form — are sparser in Goodman and colleagues’ dendrogram than those found with the CLUMPFIND algorithm, and turn out to be in larger regions of self-gravitating gas. And here we arrive at what may be the most tanta-lizing point of all. A debate that has enlivened star-formation theory for nearly a decade is how stars acquire their mass5. Do they accrete their gas from relatively isolated cores, or do

Figure 1 | Stellar nursery. The image shows a computer simulation2 of the formation of stars within a turbulent, self-gravitating cloud of gas. The initial mass of this star-cluster-forming cloud, which is modelled as a sphere of uniform density, is 500 solar masses. The sphere’s radius is 83,300 astronomical units (1 au is the mean distance between Earth and the Sun) and its temperature is 10 kelvin. Supersonic turbulence compresses the gas into many filaments and smaller, dense regions. The simulation is viewed after one free-fall time — the time taken for a gas parcel to collapse freely to the cloud centre — has elapsed, which is 1.9×105 years for these simulation parameters. The white dots correspond to small, dense gas ‘cores’ that collapse to form individual stars. These would correspond physically to regions such as those denoted by the billiard balls in Figure 1 of the paper by Goodman et al.1 (page 63).

BLA

CK

WEL

L PU

BLIS

HIN

G

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they accrete material as they move about in a broader gravitational potential, gathering mass through competition with other dense regions in the same gravitationally bound region6 (the ‘competitive accretion’ picture)? Although neither hypothesis is amenable to definitive observational tests, the dendrogram method developed by Goodman et al. has the potential to answer this question and to identify the real conditions in which stars form. ■

Ralph E. Pudritz is in the Department of Physics and Astronomy, McMaster University, 1280 Main

GAME THEORY

How to treat those of ill repute Bettina Rockenbach and Manfred Milinski

A much-needed theoretical analysis deals with whether the principle known as ‘costly punishment’ helps to maintain cooperation in human society. It will prompt a fresh wave of experiments and theory.

Human societies are built on cooperation, especially on reciprocation1 — I help you and you help me, or I help you and someone else helps me. In the first case, help is directly recip-rocated by help. In the second, called indirect reciprocity, I gain a good reputation and so I can expect help when in need.

But how shall I treat someone with a bad reputation? Shall I just refuse help or shall I punish this person at a cost to myself? Costly punishment can enhance cooperation2,3 in experiments with human subjects, but potentially with no net benefit4: the costs of punishment usually, although not always5, neutralize gains from enhanced cooperation. On page 79 of this issue, Ohtsuki et al.6 describe a theoretical test of whether either refusing help to or punishing someone with a bad reputation might lead to a cooperative soci-ety. They conclude that, except under certain rare conditions, punishment does not produce that outcome.

When you meet someone needing help, you can help (cooperate), refuse to help (defect) or not only refuse to help but, in addition, decrease the needy person’s wealth (punish). Both cooperation and punishment are costly for you, but respectively create a larger ben-efit or larger loss for the person needing help. Defection is cost neutral.

How you behave depends on the reputation — good or bad — of the needy person, and depends upon your ‘action rule’. An example is ‘cooperate with someone with a good rep-utation and defect with someone with a bad reputation’ (CD). The reputation you yourself gain by applying your action rule depends on the social norm of your society. Under the norm ‘stern-judging’, for example, you gain a good reputation when cooperating with good or when defecting with bad, and a bad reputation

in all other cases. Thus CD always leads to a good reputation under stern-judging.

Another action rule, CP, prescribes ‘cooperate with good and punish bad’. Under stern-judging, with CP you will achieve a good repu-tation when you interact with someone with a good reputation and a bad reputation when you interact with someone with a bad reputa-tion. But under a different social norm, ‘shun-ning’ (cooperation with good or punishment of bad leads to a good reputation), CP will always provide you with a good reputation (Fig. 1).

In their simplest model, Ohtsuki et al.6 assume that everybody has the same opinion of the reputation of another person or has the same level of fallibility in assigning an

incorrect reputation. For such a society, they test for each of the 64 different social norms (Fig. 1) whether an action rule exists that both generates a cooperative society and is evolu-tionarily stable — meaning one that resists replacement (invasion) by any of the other eight possible action rules (Fig. 1).

Ohtsuki et al. find that the two action rules that induce cooperation and resist invasion are those described above — CD under stern-judging and CP under shunning. Nonetheless, the average pay-off is lower if the action rule uses costly punishment, while the stability conditions are less restrictive.

However, which parameters determine which rule is most efficient in the sense of leading to the highest average pay-off at equilibrium? It turns out that a crucial one is the accuracy of assigning the correct reputation to everybody. If this accuracy is too low then only a DD action rule is efficient, under which nobody co operates. If the accuracy is high enough, then CD can be efficient. For intermediate values of the accuracy, there is a small window in which CP can be efficient, as reflected in the title of the paper6: “Indirect reciprocity provides only a narrow margin of efficiency for costly punishment.”

In a further step in their modelling, Ohtsuki et al.6 dropped the assumption that all good or bad reputations are publicly known, and allowed individual knowledge of reputa-tions. They found that the stability of both CD and CP is lost when there is the smallest error in distinguishing between good and bad. When individuals start to communicate with each other and adjust their assessments of everybody’s reputation, the CP action rule can be stably maintained. Then, when repu-tations become even more publicly agreed upon through more efficient gossip, both CD and CP are stably maintained under their

Figure 1 | Action rules, social norms and the story of Alice and Bob. Bob meets Alice and learns whether Alice has a good or a bad reputation. On the basis of that, Bob may either help (cooperate), refuse help (defect) or refuse help and, in addition, decrease Alice’s wealth (punish). How Bob reacts is specified in his action rule. Bob’s own reputation depends on how society’s social norm evaluates Bob’s re action to Alice’s reputation. The social norm ‘stern-judging’ evaluates coope ration with good as good and punishment of bad as bad; the social norm ‘shunning’ evaluates cooperation with good as good and punishment of bad as good. Each social norm specifies which reputation to assign for each of the 6 possible scenarios (3 actions of Bob for 2 reputations of Alice). This leads to 26 = 64 social norms.

Alice’s reputation

Good Bad

Alice’s reputation

Good Bad

Alice’s reputation

Good Bad

Cooperate Cooperate

Cooperate Defect Social norm Stern-judging Shunning

Bob’saction rule

Cooperate PunishBob’s newreputation

Good Bad Good Good

Defect Cooperate

Defect Defect

Defect Punish

Punish Cooperate

Punish Defect

Punish Punish

Street West, Hamilton, Ontario L8S 4M1, Canada.e-mail: pudritz@physics.mcmaster.ca

1. Goodman, A. A. et al. Nature 457, 63–66 (2009).2. Bate, M. R. Mon. Not. R. Astron. Soc. (in the press);

preprint at http://arxiv.org/abs/0811.0163 (2008). 3. Motte, F., André, P. & Neri, R. Astron. Astrophys. 336,

150–172 (1998).4. Johnstone, D. et al. Astrophys. J. 545, 327–339

(2000).5. McKee, C. F. & Ostriker, E. C. Annu. Rev. Astron. Astrophys.

45, 565–687 (2007).6. Bonnell, I. A. & Bate, M. R. Mon. Not. R. Astron. Soc. 370,

488–494 (2006).

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