1-s2.0-S1074761315004045-main

of SCFAs in the gut so as to drive the dif-ferentiation of naive CD4+T cells into Treg cells and not into Th1and Th17cells.This can be achieved with the use of appro-priate types of dietary?ber that have the ability to support the growth and prolifer-ation of SCFA-producing gut microbes. Prebiotics such as fructo-oligosaccha-rides are known to increase the levels of SCFAs in the gut.Probiotics containing selective species of bacteria known to produce SCFAs as the major fermentation products can also be used to achieve this goal.Oral delivery of SCFAs might be problematic because these fatty acids are metabolized extensively in the upper portions of the intestinal tract with only a minor fraction of the original oral dose entering the ileum and colon where major-ity of mucosal immune cells reside.But, SCFAs can be chemically modi?ed (e.g.,SCFAs esteri?ed to starch)and then administered orally,an approach

that is likely to result in delivery of appre-

ciable amounts of SCFAs to the distal

portions of the intestinal tract.The studies

by Haghikia and colleagues(2015)pro-

vide convincing evidence in support of

therapeutic potential of such approaches

for prevention and/or treatment of autoim-

mune diseases.

ACKNOWLEDGMENTS

This work was supported in part by the National In-

stitutes of Health Grant CA190710and by the

Welch Endowed Chair in Biochemistry,Grant No.

BI-0028,at Texas Tech University Health Sciences

Center.

REFERENCES

Ganapathy,V.,Thangaraju,M.,Prasad,P.D.,and

Singh,N.(2013).Curr.Opin.Pharmacol.13,

869–874.

Gurav, A.,Sivaprakasam,S.,Bhutia,Y.D.,

Boettger,T.,Singh,N.,and Ganapathy,V.(2015).

Biochem.J.469,267–278.

Haghikia,A.,Jorg,S.,Duscha,A.,Berg,J.,Manzel,

A.,Waschbisch,A.,Hammer,A.,Lee,D.-H.,May,

C.,Wilck,N.,et al.(2015).Immunity43,this issue,

817–829.

Kim,C.H.,Park,J.,and Kim,M.(2014).Immune

Netw.14,277–288.

Kuhn,K.A.,and Stappenbeck,T.S.(2013).Semin.

Immunol.25,364–369.

Longman,R.S.,and Littman,D.R.(2015).Curr.

Opin.Rheumatol.27,381–387.

Magee,T.,and Seabra,M.C.(2005).Curr.Opin.

Cell Biol.17,190–196.

Singh,N.,Thangaraju,M.,Prasad,P.D.,Martin,

P.M.,Lambert,N.A.,Boettger,T.,Offermanns,

S.,and Ganapathy,V.(2010).J.Biol.Chem.285,

27601–27608.

Vieira,S.M.,Pagovich,O.E.,and Kriegel,M.A.

(2014).Lupus23,518–526.

Tumor Microenvironment and Immunotherapy:

The Whole Picture Is Better Than a Glimpse

Sarah E.Church1,2,3and Je′ro?me Galon1,2,3,*

1INSERM,UMRS1138,Laboratory of Integrative Cancer Immunology,Paris,75006,France

2Universite′Paris Descartes,Paris75006,France

3Cordeliers Research Centre,Universite′Pierre et Marie Curie Paris6,Paris75006,France

*Correspondence:jerome.galon@crc.jussieu.fr

https://www.wendangku.net/doc/b37281824.html,/10.1016/j.immuni.2015.10.004

Predicting cancer patients’response to therapy is essential for curing disease and improving quality of life. Garraway and colleagues demonstrate that the frequency and number of neoantigens,non-synonymous mutations,and adaptive immune genes,but not the assessment of individual recurrent neoantigens or mu-tations,predicts patient responses to immunotherapy.

The recent success of immunotherapies targeting immune-checkpoint molecules cytotoxic T lymphocyte-associated anti-gen-4(CTLA4),programmed cell death-1(PD-1),and programmed cell death li-gands(PD-L1and PDL-2)in the treatment of cancer has emphasized the essential role of the immune system in the eradica-tion of tumors.Although these immuno-therapies have had stunning results,the percentage of patients who see a clinical bene?t is limited,and the factors that determine whether a patient will have an improved clinical response are not well

understood.The ability to predict whether

a patient will respond or become resistant

to immunotherapy is essential for?nding a

cure for cancer.

One approach to?nding new cancer-

response biomarkers is identifying anti-

gens that are unique to tumors.An

example of these are neoantigens,which

are mutated peptides that arise in tumors

and thus are not present in the normal

genome(Schumacher and Schreiber,

2015).Identifying novel neoantigens has

recently become more feasible with

whole-exome sequencing.Researchers

use RNA sequencing of tumors to identify

mutations in expressed genes.They can

then process these data in silico to iden-

tify biologically likely peptide-MHC com-

binations.It is hypothesized that more

immunogenic tumor types have higher

rates of somatic mutation and therefore

elevated numbers of neoantigens(Schu-

macher and Schreiber,2015).

In a recent issue of Science,Van Allen

et al.describe extensive analysis

of

Immunity43,October20,2015a2015Elsevier Inc.631 Immunity

Previews

whole-exome sequencing and transcrip-tome data from tumor biopsies of40pa-tients with metastatic melanoma before and after CTLA-4-blocking immuno-therapy(Van Allen et al.,2015).They show that patients with a high number and frequency of neoantigens and non-synchronous mutations in their tumor are more likely to respond to anti-CTLA-4 immunotherapy.However,when identi-fying common neoantigens between pa-tients that responded to therapy,the au-thors found that no single antigen or mutation correlated with clinical bene?t. Interestingly,when the authors examined the expression of immune genes associ-ated with cytolytic activity(perforin,gran-zyme A)and checkpoint molecules (CTLA-4,PD-L2),patients who had clin-ical bene?t from therapy had higher expression of these genes in their tumors. These?ndings support accumulating data suggesting that it is not singular mu-tations that predict patients’clinical outcome,but the presence of a high num-ber of mutations and global T cell re-sponses in the tumor microenvironment. Similarly to the?ndings of Van Allen et al.,several studies have previously demonstrated that the mutational load and the frequency of neoantigens corre-lates with the response to anti-CTLA-4 and anti-PD-1and/or anti-PD-L1immu-notherapy in melanoma,lung,and MSI-positive colorectal cancers(Koster et al., 2015).Although there has been consider-able research into single neoantigens that could serve as targets for immunotherapy or as biomarkers for patient survival, relapse,or response to therapy,the biology of peptide presentation is exces-sively complex and has yielded few good candidates.This is partially due to the fact that antigenic peptide processing is a complicated process that involves a multitude of human lymphocyte antigens (HLAs)(Schumacher and Schreiber, 2015).

Multiple types of cancer antigens, including(1)neoantigens,such as muta-tions and viral antigens,(2)self-proteins that are either overexpressed or usually not expressed in most of the adult body (e.g.,cancer testis antigens),and(3)tis-sue-speci?c gene products,which would be of interest if the cancer affects a tissue or cell type not essential for the life of the patient(e.g.,B cells,melanocytes or pros-tate),have been characterized.Further-more,antigenic peptides do not simply

correspond to fragments of conventional

proteins but rather result from aberrant

transcription,incomplete splicing,transla-

tion of alternative or cryptic open-reading

frames,or post-translational modi?ca-

tions.Proteasome peptide splicing repre-

sents another mechanism that increases

the diversity of antigenic peptides pre-

sented to T cells(Vigneron et al.,2004).

Cancer-associated aberrant-protein O-

glycosylation can modify antigen pro-

cessing and immune response(Madsen

et al.,2012).MHC class I–associated

phosphopeptides are the targets of mem-

ory-like immunity,and results point to a

role for phosphopeptide-speci?c immu-

nity as a component of tumor recognition

and control(Cobbold et al.,2013).Thus,

beyond exome sequencing and point mu-

tations,various tumor alterations might

lead to tumor-speci?c immunity,and mul-

tiple immune biomarkers are likely candi-

dates for predicting a patient’s response

to immune-checkpoint therapies.

Interestingly,when focusing on clusters

of mutations that predict patient outcome,

there is growing evidence that immune

gene expression is an attractive candi-

date(Bindea et al.,2013).Similar to the

?ndings of Van Allen et al.,studies in colo-

rectal cancer have shown that there are

many common germline mutations

among tumors but that neoantigen muta-

tions are distinct between patients(Ange-

lova et al.,2015).However,in compari-

sons of highly mutated tumors to less

mutated tumors,tumors with more muta-

tions had an immune signature consisting

of depleted immunosuppressive cells

and upregulated immune-inhibitory mole-

cules.Conversely,less mutated tumors

had ampli?ed immunosuppressive cells,

downregulation of HLA molecules,and

reduced expression of immune-inhibitory

molecules.Additionally,the adaptive im-

mune response is highly accurate at pre-

dicting patient outcome(Bindea et al.,

2013).This is particularly true for genomic

alterations in chemokines and cytokines

related to T cell traf?cking and homeosta-

sis.The adaptive immune response is

shaped by CD8+and CD4+T cells,B cells,

and follicular helper T cells(Tfh)that help

organize lymphoid structures.Inter-

leukin-21(IL-21)and IL-15are part of the

gamma-chain cytokine family and are

crucial for survival and proliferation of

Tfh,cytotoxic,and memory T cells.

Consequently,both IL-21and IL-15are

being used in clinical trials as immuno-

therapies for cancer.

The predictive capability of immuno-

genic mutations in the tumor highlights

the importance of studying the tumor

microenvironment as a whole.The im-

mune contexture of the tumor microenvi-

ronment de?nes the immune parameters

associated with survival of the patients

(Galon et al.,2013;Galon et al.,2006).It

has been extensively reported that pa-

tients with cytotoxic T cells in the primary

tumor have improved survival regardless

of the type of therapy administered(Galon

et al.,2013).Moreover,if the pre-existing

adaptive immune response within the tu-

mor microenvironment is further de?ned

by location,either at the tumor center or

the invasive margin,by a measure termed

Immunoscore,this statistically predicts

patient outcome better than the current

TNM staging strategy(Galon et al.,2013).

The presence of T cells in the tumor as a

predictor of clinical bene?t to immuno-

therapy has also been shown in response

to anti-PD1immunotherapy(Koster et al.,

2015).The density of CD8T cells,as as-

sessed by immunohistochemistry,in the

tumor center and especially at the inva-

sive margin were directly correlated with

response to PD-1-blocking immuno-

therapy.Multiple studies have shown

that PD-L1co-localizes with CD8in?l-

trates in tumors.Thus,in?ltrating T cells

induced adaptive immune resistance

associated with increased expression of

PD-L1.Similar to the?ndings of Van Allen

et al.,both CD8and PD-L1were also

signi?cantly more highly expressed in tu-

mors of patients that responded to anti-

PD-1therapy than in those of patients

that did not respond.

Prognostic markers are useful for as-

sessing the risk for an individual patient

and providing helpful insights into cancer

biology.However,they do not assist in

treatment selection according to the likeli-

hood of therapeutic effectiveness,a role

that pertains to predictive biomarkers.

Determining the mechanisms(mecha-

nistic signatures)associated with treat-

ment success(or failure)at the time

when tumor rejection is occurring is crit-

ical to understanding immune-mediated

tumor regression(Galon et al.,2013).Pa-

rameters associated with the immune

contexture have been associated with

predictive signatures and include T helper

632Immunity43,October20,2015a2015Elsevier Inc.

Immunity Previews

type 1(Th1)factors,CD8T cells and prolif-erating T cells,immune effector or cyto-toxic factors,chemokines,and adhesion molecules (Galon et al.,2013;Galon et al.,2006).Prognostic,predictive,and mechanistic immune signatures are largely overlapping.The similarity be-tween mechanistic signatures associated with tumor rejection among distinct immu-notherapeutic approaches (including anti-CTLA4)provides evidence that tissue destruction triggered by different im-mune-therapy approaches converges,when effective,into a common mecha-nism (Galon et al.,2013).(Figure 1).

Overall,the studies evaluating immune-checkpoint blockade molecules (i.e.,anti-CTLA4and anti-PD1and/or anti-PD-L1mAbs)have provided two important hy-potheses.The ?rst is that in metastatic

cancers,the immune system is not an inert system but rather is an actively sup-pressed one.The second hypothesis is that tumors responsive to treatment display an in?ammatory status accompa-nied by the concomitant counter-activa-tion of immune-suppressive mechanisms,probably re?ecting ongoing immune-escape processes.Thus,pre-exiting adaptive immunity is essential for im-mune-checkpoint immunotherapies.Mutational load and neoantigen fre-quency appear to be among the tumor molecular alterations leading to stronger pre-exiting adaptive immunity and to the expression of cytolytic markers that are robust determinants of response to im-mune-checkpoint inhibitors.

REFERENCES

Angelova,M.,Charoentong,P.,Hackl,H.,Fischer,M.L.,Snajder,R.,Krogsdam,A.M.,Waldner,M.J.,Bindea,G.,Mlecnik,B.,Galon,J.,and Trajanoski,Z.(2015).Genome Biol.16,64.

Bindea,G.,Mlecnik,B.,Tosolini,M.,Kirilovsky,A.,Waldner,M.,Obenauf,A.C.,Angell,H.,Fredriksen,T.,Lafontaine,L.,Berger,A.,et al.(2013).Immunity 39,782–795.

Cobbold,M.,De La Pen

?a,H.,Norris,A.,Polefrone,J.M.,Qian,J.,English, A.M.,Cummings,K.L.,Penny,S.,Turner,J.E.,Cottine,J.,et al.(2013).Sci.Transl.Med.5,203ra125.

Galon,J.,Costes,A.,Sanchez-Cabo,F.,Kirilov-sky,A.,Mlecnik,B.,Lagorce-Page

`s,C.,Tosolini,M.,Camus,M.,Berger,A.,Wind,P.,et al.(2006).Science 313,1960–1964.

Galon,J.,Angell,H.K.,Bedognetti,D.,and Marin-cola,F.M.(2013).Immunity 39,11–26.

Koster,B.D.,de Gruijl,T.D.,and van den Eertwegh,A.J.(2015).Curr.Opin.Oncol.Published online September 8,2015.https://www.wendangku.net/doc/b37281824.html,/10.1097/CCO.0000000000000221.

Madsen,C.B.,Petersen,C.,Lavrsen,K.,Harndahl,M.,Buus,S.,Clausen,H.,Pedersen,A.E.,and Wandall,H.H.(2012).PLoS ONE 7,e50139.Schumacher,T.N.,and Schreiber,R.D.(2015).Sci-ence 348,69–74.

Van Allen,E.M.,Miao,D.,Schilling,B.,Shukla,S.A.,Blank,C.,Zimmer,L.,Sucker,A.,Hillen,U.,Geukes Foppen,M.H.,Goldinger,S.M.,et al.(2015).Science,aad0095.

Vigneron,N.,Stroobant,V.,Chapiro,J.,Ooms,A.,Degiovanni,G.,Morel,S.,van der Bruggen,P.,Boon,T.,and Van den Eynde,B.J.(2004).Science 304,587–590

.

Figure 1.Predictive and Mechanistic Signatures for Cancer Immunotherapy

Predictive biomarkers (blue and green)that are present in the tumor microenvironment prior to treatment and that infer clinical bene?t to immunotherapy include large numbers of immunogenic mutations,anti-tumor T cells (CD3),cytotoxic T cells (granzymes-GZM,perforin-PRF,and granulysin-GNLY),chemo-kines,TCR repertoire diversity,anti-tumor antibodies,decreased circulating myeloid-derived suppressor cells (cMDSCs),and soluble CD25(sCD25)in the serum.Highly mutated tumors can contain a wide variety of immunogenic peptides that help elicit the anti-tumor immune response.Many predictive markers also serve as mechanistic biomarkers (green),which are increased (or decreased)after treatment with immunotherapy and correlate with clinical bene?t.Additionally,there are unique mechanistic signatures (yellow)that one can measure after immunotherapy to determine clinical bene?t.These include increased absolute leukocyte count (ALC),decreased circulating ratio of neutrophils to lymphocytes (cNLR),and decreased lactate dehydrogenase (LDH)in the serum.An asterisk marks previously described immuno-genic peptides,including overexpressed self-proteins (e.g.,Tyrosinase),cancer testis antigens (e.g.,NY-ESO-1),tissue-speci?c gene products (e.g.,MART-1),phosphopeptides,and peptides resulting from aberrant transcription,incomplete splicing,translation of alternative or cryptic open-reading frames,post-translational modi?cations,proteasome peptide splicing,and aberrant protein O-glycosylation.A number sign marks biomarkers described by Van Allen et al .

Immunity 43,October 20,2015a2015Elsevier Inc.633

Immunity

Previews