2.1 Cancer - Inflammation lights the way to metastasis

The model’s predictive accuracy may be very useful to the network of researchers who determine which strains to include in the seasonal-flu vaccine. Currently, vaccine strains are chosen using assays that quantify antigenic differences between circulating strains4. This approach is highly effective in some years, but antigenic mismatches between the vaccine strain and the strain that ends up dominat-ing the next flu season do occur. ?uksza and L?ssig’s model provides insight into why such mismatches might arise: deleterious muta-tions in non-epitope regions might suppress the most antigenically distinct strains that are prime vaccine candidates. The model also delivers another way of choosing vaccine strains — by including strain cross-immunity estimates as well as the inferred fitness and frequency of current strains.

Although ?uksza and L?ssig’s model pre-sents a new perspective on what contributes to viral fitness and what aspects of flu evolu-tion can feasibly be predicted, it also points to necessary future work. First, the model assumes a simple relationship between the genetic distance between strains and the extent of cross-immunity that they induce in a host. However, antigenic analysis has shown that some amino-acid changes in epitope regions have only a slight antigenic effect, whereas others have a pronounced one5. Incorporat-ing a more empirically informed ‘map’ of the associations between viral genotypes and their antigenic characteristics might increase the model’s predictive power. Second, although the authors have already taken the important step of broadening predictive models to include the effects of non-epitope mutations in the haemagglutinin protein, viral fitness also surely depends on the virus’s seven other gene segments. Incorporating whole-genome analysis into the model may therefore further improve prediction, especially if mutations interact in a non-additive manner (epistati-cally) across gene segments.

Finally, in terms of strain selection for vac-cines, it is worth bearing in mind that the ultimate goal of vaccination might not be to reduce the number of flu infections, but rather to minimize the number of flu deaths or the overall economic cost of infections6. Luckily, modifying the model to incorporate such aims seems relatively straightforward, provided that sufficient data are available to quantify how viral strains differ in virulence or other relevant properties. Thus, although further work is needed, it is clear that ?uksza and L?ssig have significantly advanced the difficult task of flu prediction, especially about the future. ■Katia Koelle and David A. Rasmussen are in the Department of Biology, Duke University, Durham, North Carolina 27708, USA.

e-mails: katia.koelle@https://www.360docs.net/doc/147826808.html,;

david.rasmussen@https://www.360docs.net/doc/147826808.html,

1. ?uksza, M. & L?ssig, M. Nature507, 57–61 (2014).

2. St?hr, K. Lancet Infect. Dis.2, 517 (2002).

3. Carrat, F. & Flahault, A. Vaccine25, 6852–6862 (2007).

4. Russell, C. A. et al.Vaccine26 (Suppl. 4), D31–D34

(2008).

5. Smith, D. J. et al.Science305, 371–376 (2004).

6. Medlock, J. & Galvani, A. P. Science325,

1705–1708 (2009).

This article was published online on 26 February 2014.

susceptible hosts such that even newer strains gain the advantage. This continuous process of evolution — referred to as antigenic drift — results in rapid turnover of the viral population and thus the possibility of an individual becom-ing reinfected with flu within a few years. More-over, it leads to the need to regularly update the composition of seasonal-flu vaccines3. Although flu’s antigenic drift is well charac-terized, predicting exactly what strains carry-ing which antigenic mutations will circulate in the future remains problematic. This is largely because the stochastic nature of the mutational process itself leads to uncertainty over what mutations will arise. Apart from this, even predicting the fate of strains currently resid-ing in the population is a formidable challenge, because multiple strains carrying different combinations of mutations co-circulate and to some extent compete with one another for susceptible hosts. The predictive model that ?uksza and L?ssig present addresses this prob-lem by targeting a somewhat more manage-able question: can one predict changes in the frequencies of groups of viral strains (clades) from one year to the next? The answer seems to be yes, and with considerable accuracy.

At its core, ?uksza and L?ssig’s model predicts viral clade frequencies in a given year using strain frequencies and fitness values from the preceding year (Fig. 1). Its effective-ness therefore relies on how accurately the model assigns fitness values to strains, a dif-ficult task to do well, given that we have little understanding of how any individual mutation affects fitness. To make this task feasible, the authors consider only the fitness effects of two classes of mutation — epitope and non-epitope

mutations — in the haemagglutinin surface protein of the virus.

Although simple, this approach has a clear biological rationale. Mutations at epitopes are likely to be beneficial to the virus, because they alter the structural features targeted by host antibodies. Thus, a strain can have higher fitness than its competitors by being antigeni-cally more distinct from previously circulat-ing strains. By contrast, mutations outside epitope regions are often deleterious because they reduce protein stability or upset evolu-tionarily conserved viral functions. By train-ing the model on the evolutionary dynamics of historical strains, the authors were able to estimate the fitness effects of these two classes of mutation, and thereby to quantify the fitness of currently circulating strains on the basis of the mutations they carried.

Using these estimates, ?uksza and L?ssig projected clade frequencies a year into the future, and determined the accuracy of their model’s predictions by comparing the mag-nitude of predicted clade-frequency changes with that of the changes observed. They found that their model correctly predicted growth in viral clades 93% of the time and correctly predicted decline 76% of the time.

CANCER

Inflammation lights the way to metastasis

Tumour spread is the main cause of death in patients with melanoma. Exposure of melanoma to ultraviolet radiation has now been found to cause an inflammatory response that drives the formation of distant metastases. See Letter p.109

S E T H B.C O F F E LT &K A R IN E. D E VI SS E R

M elanoma is the deadliest form of

skin cancer. The main risk factor

for its development is ultraviolet

(UV) radiation from the sun, which directly

induces alterations in the DNA of melanocytes,

the skin’s pigment-producing cells1. Once a

melanoma has escaped from its primary loca-

tion to distant organs and formed metastases,

there is only a limited chance that the cancer

can be controlled. But what causes melanoma

to spread? In this issue, Bald et al.2 (page 109)

have uncovered another surprising role of UV

radiation in this cancer type — it promotes

metastasis through the activation of an inflam-

matory response.

Besides inducing mutations in melanocytes,

exposure to UV radiation disrupts the epithe-

lial cells (keratinocytes) that form the skin’s

outer layer, and also causes inflammation.

But whether these other damaging effects of

the radiation influence melanoma progres-

sion was unknown until now. Using a trans-

genic mouse model that closely mimics the

human disease, Bald and co-workers observed

48|N A T U R E|V O L507|6M A R C H2014

that repetitive UV irradiation of developing tumours increased the formation of metastases in the lung, without affecting the growth of the primary melanoma. Two observations led the authors to hypoth-esize that non-cancer cells were involved in driving metastasis. First, they noted that irra-diation of melanomas caused a striking influx of neutrophils — white blood cells that are key players in immune defence and inflamma-tory disorders. Second, irradiation induced the migration of melanoma cells towards blood vessels and their subsequent move-ment along the vessels’ surfaces. The authors saw that, near the tumours, the neutrophils were attracted to UV-damaged keratinocytes by a molecule called HMGB1, a protein nor-mally found in the nucleus of healthy cells but which is released by dying or stressed cells. Depletion of neutrophils or inhibition of their HMGB1-dependent recruitment reduced the migration of melanoma cells along blood ves-sels and abrogated the UV-radiation-induced lung metastasis. These data indicate that the neutro p hil-rich inflammatory response caused the spread of melanoma cells to distant organs (Fig. 1). Through a series of in vitro experiments, the authors demonstrated that activated neutro-phils secrete the pro-inflammatory protein TNF, which stimulates new blood vessel formation and the migration of melanoma cells. They also studied tumour tissues from 178 patients with melanoma and found, in line with their experimental data, that ulceration of melanomas (breakdown of the skin layer above the cancer) and neutrophil influx were associated with the presence of melanoma cells adjacent to blood vessels and increased incidence of metastatic disease. Thus, Bald et al. have uncovered a mechanism by which UV radiation triggers the activa-tion of non-cancer cells in the vicinity of a melanoma. These cells initiate a harmful inflammatory cascade that leads to increased interaction between the cancer cells and blood vessels, culminating in the spread of the can-cer cells to distant organs. The data add to the emerging realization that metastasis is not just a process that is intrinsic to cancer cells, but rather is one that depends on complex and reciprocal interactions with non-cancer cells in the tumour microenvironment 3. As such, targeting UV-activated, pro-meta-static neutrophils, either by preventing their acti v ation and accumulation or by inhibit-ing their downstream effects, represents a potential therapeutic opportunity to interfere with metastatic melanoma. Moreover, block-ing neutrophil function may be relevant to other cancer types, because there is evidence that neutrophils can promote metastasis in other tumour models 4,5.At present, it is unclear how Bald and colleagues’ findings could be translated into preventive measures. Many individuals who have precancerous lesions are unaware of them, and continue to expose themselves to the sun. Furthermore, it is still uncertain how much UV-radiation exposure is required to induce the localized cellular changes that lead to metastasis in humans. As an alternative to preventing the harmful effects of UV radiation directly (through minimizing exposure), inhibiting its downstream effector mecha-nisms could also be an approach to curbing metastatic melanoma. Therefore, it will be useful to determine whether TNF is the sole neutrophil-derived mediator driving UV-induced meta s tasis, or whether other factors are important as well. Further characteriza-tion of pro-metastatic neutrophils, for exam-ple by gene expression and functional assays, might provide greater insight into their activity. Similarly, the potential pro-metastatic effects of other cancer-cell mutations that occur through repeated exposure to UV radiation should also be determined. Such analyses will help to better define UV-triggered melanoma metastasis and may identify new targets for this deadly disease.Previous work has shown that UV radiation not only induces melanoma-initiating muta-tions in melanocytes 1, but also causes a distinct mutational signature resulting in the formation of altered cellular proteins (antigens) that are recognized as foreign by the T cells of the immune system 6,7. This abundance of new, UV-radiation-induced antigens largely explains the recent successes seen in patients with advanced melanoma following cancer immunotherapy 8,9, a treatment aimed at har-nessing the patient’s own immune system to attack tumours. Bald and colleagues’ dem-onstration that metastasis can be induced through the activation of keratinocytes and neutro p hils is therefore the third known effect of UV radiation in melanoma. Their findings, together with those of others (reviewed in ref. 10), indicate that, in addition to activating antitumour immunity, inhibiting pro-tumour inflammatory responses is an attractive anticancer approach that might increase the number of patients with melanoma who are successfully treated. ■

Seth B. Coffelt and Karin E. de Visser are in the Division of Immunology, Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands. e-mail: k.d.visser@nki.nl

1. Hodis, E. et al . Cell 150, 251–263 (2012).

2. Bald, T. et al. Nature 507, 109–113 (2014).

3. Quail, D. F. & Joyce, J. A. Nature Med. 19, 1423–1437 (2013).

4. Kowanetz, M. et al . Proc. Natl Acad. Sci. USA 107, 21248–21255 (2010).

5. Huh, S. J., Liang, S., Sharma, A., Dong, C. & Robertson, G. P . Cancer Re s. 70, 6071–6082 (2010).

6. Robbins, P . F . et al. Nature Med. 19, 747–752 (2013).

7. van Rooij, N. et al . J. Clin. Oncol . 31, e439–e442 (2013).

8. Wolchok, J. D. et al . N. Engl. J. Med . 369, 122–133 (2013).

9. Couzin-Frankel, J. Science 342, 1432–1433 (2013).10. C oussens, L. M., Zitvogel, L. & Palucka, A. K. Science 339, 286–291 (2013).

This article was published online on 26 February 2014.UV irradiation Keratinocyte HMGB1TNF Metastasis Neutrophil Melanoma cells Blood vessel Figure 1 | Exposure to ultraviolet radiation drives metastasis. UV radiation directly induces DNA alterations that lead to melanomas. It also initiates other processes in the skin, including inflammation and damage to keratinocytes in the outer skin layer. Bald et al .2 show that keratinocyte secretion of HMGB1 — a protein released by dying or stressed cells — initiates an influx of neutrophils. These cells produce inflammatory molecules, such as TNF, which stimulate the formation of new blood vessels and induce the migration of melanoma cells along these vessels, thereby facilitating the cancer’s spread to distant organs. 6 M A R C H 2014 | V O L 507 | N A T U R E | 49