Mutational Landscape and Sensitivity to Immune Checkpoint ......(iii) escape: the immune-resistant clones freely expand, circum-venting both innate and adaptive immune responses. A

Review

Mutational Landscape and Sensitivity to ImmuneCheckpoint BlockersRoman M. Chabanon1,2, Marion Pedrero2, C�eline Lefebvre1,2, Aur�elien Marabelle3,4,Jean-Charles Soria1,2,3, and Sophie Postel-Vinay1,2,3

Abstract

Immunotherapy is currently transforming cancer treatment.Notably, immune checkpoint blockers (ICB) have shown unprec-edented therapeutic successes in numerous tumor types, includ-ing cancers that were traditionally considered as nonimmuno-genic. However, a significant proportion of patients do notrespond to these therapies. Thus, early selection of the mostsensitive patients is key, and the development of predictivecompanion biomarkers constitutes one of the biggest challengesof ICB development. Recent publications have suggested that thetumor genomic landscape, mutational load, and tumor-specificneoantigens are potential determinants of the response to ICB andcan influence patients' outcomes upon immunotherapy. Further-more, defects in theDNA repairmachinery have consistently beenassociated with improved survival and durable clinical benefit

from ICB. Thus, closely reflecting theDNAdamage repair capacityof tumor cells and their intrinsic genomic instability, the muta-tional load and its associated tumor-specific neoantigens appearas key predictive paths to anticipate potential clinical benefits ofICB. In the era of next-generation sequencing, while more andmore patients are getting the full molecular portrait of theirtumor, it is crucial to optimally exploit sequencing data for thebenefit of patients. Therefore, sequencing technologies, analytictools, and relevant criteria for mutational load and neoantigensprediction should be homogenized and combined in more inte-grative pipelines to fully optimize the measurement of suchparameters, so that these biomarkers can ultimately reach theanalytic validity and reproducibility required for a clinical imple-mentation. Clin Cancer Res; 22(17); 4309–21. �2016 AACR.

IntroductionSince their first introduction into the clinic and first approval in

2011 (1), immune checkpoint blockers (ICB) have transformedcancer treatment and allowed unprecedented improvements inoverall survival (OS), progression-free survival (PFS), or overallresponse rates (ORR) in many aggressive diseases (2, 3). Mostimportantly, benefits of ICB have not been limited to the "tradi-tional" immunogenic cancers, malignant melanoma and renalcell carcinoma (RCC), but have also been extended to otherhistologies classically described as "nonimmunogenic," such asnon–small cell lung cancer (NSCLC) or mismatch-repair–defi-cient colorectal cancer (MMR-deficient colorectal cancer; ref. 3).Despite these clear clinical advances, the biological mechanismsthat underlie antitumor immunity and determine sensitivity tothese agents, notably anti-programmed death receptor-1/-ligand1 [anti–PD-(L)1] are still poorly understood. Moreover, a statis-tically significant proportion of patients, approximately 80%,

among all tumor types included, still do not respond to thesedrugs, highlighting the urge for developing robust predictivebiomarkers that would guide appropriate selection of patients.Recently, the tumor cell mutational burden has been correlatedwith clinical benefits of anti–PD-1 and anti-CTLA-4 therapy invarious tumor types, including malignant myeloma (4, 5),NSCLC (6), and several DNA repair–deficient tumors (7–9).Predicted neoantigen load has also emerged as an interestingselection biomarker for predicting clinical benefit of these agents.Overall, a direct link between DNA repair deficiency, mutationallandscape, predicted neoantigen load, and clinical activity of ICBis suggested.

In this review, we discuss the significance and the relevance ofthis correlation in solid tumors. We also provide critical insightinto the methods and techniques that have been used for per-forming analyses of tumor mutational burden, predicted neoan-tigen load, and neopeptide formation. We further propose acomprehensive approach that would allow encompassing otherpotential predictive biomarkers for response to anti–PD-(L)1inhibitors.

Immune Escape and CarcinogenesisThe original concept of immune surveillance, hypothesized in

1957 (10), and formally established in 1970 (11), postulated thatthe immune system alone could eliminate tumor cells in the earlystages of carcinogenesis. Since then, this theory has been furtherenriched by the "immunoediting" notion (12), which describeshow both innate and adaptive immunity contribute to carcino-genesis, notably by exerting a Darwinian selection pressure.Immunoediting classically consists of three distinct steps: (i)elimination: the innate and adaptive compartments coordinately

1Facult�e de M�edicine, Universit�e Paris Saclay, Universit�e Paris-Sud, LeKremlin Bicetre, France. 2Inserm Unit U981, Gustave Roussy, Villejuif,France. 3DITEP (D�epartement d'Innovations Th�erapeutiques et EssaisPr�ecoces), Gustave Roussy,Villejuif, France. 4Inserm Unit U1015, Gus-tave Roussy, Villejuif, France.

Note: Supplementary data for this article are available at Clinical CancerResearch Online (http://clincancerres.aacrjournals.org/).

Corresponding Author: Sophie Postel-Vinay, Gustave Roussy, 114 Rue EdouardVaillant, Villejuif 98105, France. Phone: 3301-4211-43 43; Fax: 3301-4211-6444;E-mail: [email protected]

doi: 10.1158/1078-0432.CCR-16-0903

�2016 American Association for Cancer Research.

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drive immune rejection; (ii) equilibrium: through a clonal selec-tion process, the dynamic balance between tumor and immunecells results in the emergence of specific tumor cell variants withincreased resistance, which take advantage of acquiredmutations;(iii) escape: the immune-resistant clones freely expand, circum-venting both innate and adaptive immune responses.

A variety of mechanisms can facilitate tumor immune escape(Fig. 1). Among them, deregulation of immune checkpoint sig-naling has been observed in multiple malignancies (13–21).Immune checkpoints involve the interaction between a receptorexpressed on T cells and its ligand located at the surface of antigen-presenting cells. This generates a costimulatory signal, whichtriggers either the activation or inhibition of T cells. Two majorcheckpoints regulate T-cell activation: (i) the CD28/CTLA-4 axis,which activates T cells upon engagement of CD28with CD80 and

CD86, and conversely inhibits T cells when CTLA-4 is engaged;and (ii) the PD-1 axis, which provides a strong inhibitory signalfollowing binding of PD-L1 or PD-L2 to the PD-1 receptor (22).Contrary to CTLA-4, PD-1 is thought to act predominantly in thetumor microenvironment, where PD-L1 is overexpressed by mul-tiple cell types, including dendritic cells, M2 macrophages, andtumor-associated fibroblasts (23).

As opposed to historical immune-based approaches that weredeveloped in traditionally immunogenic cancers, ICBs haveallowed significant therapeutic successes in many solid tumorsand hematologic malignancies. The anti-CTLA-4 ipilimumab(Yervoy, Bristol-Myers Squibb) was the first ICB to improve OSin malignant melanoma patients (1). In 2012, anti–PD-(L)1therapies including the anti–PD-1 pembrolizumab (Keytruda,Merck), and the anti-PD-L1 atezolizumab (MPDL-3280A,

© 2016 American Association for Cancer Research

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Figure 1.

Mechanisms of immune escape in the tumor microenvironment. Several mechanisms, involving multiple immune components, contribute to tumor immune escape.(1) Immune recognition can be impaired following reduced expression of MHC class I molecules in malignant cells, resulting in decreased antigen presentationand consequently reduced detection by cytotoxic CD8þ T lymphocytes. (2) Cancer cells can activate immunosuppressive mechanisms by inducing immunecells' apoptosis through the expression of death signals (including FAS- and TRAIL-ligands). (3) Tumor cells release in the microenvironment a variety of immune-modulatory molecules that inhibit the immune system, such as IL6 and IL10, by inducing immunosuppressive Treg cells and MDSC, whereas the activity ofcytotoxic CD8þ T cells and NK cells is inhibited. (4) This cytokine imbalance, combined with the secretion of TGFb, COX-2, and PGE2, inhibits dendriticcell differentiation and maturation, thereby affecting antigen presentation and recognition by T cells. The release of additional immune modulators or metabolicregulators, such as IDO and arginase, also favors the establishment of an immunosuppressive tumor microenvironment. (5) Disrupted expression of immunecheckpoint ligands by cancer cells provides coinhibitory signals to CD4þ and CD8þ T lymphocytes, preventing them from building a specific antitumorimmune response. CCL, chemokine ligand; COX-2, cyclooxygenase-2; CXCL, chemokine (C-X-C motif) ligand; FAS-L, FAS-ligand; GM-CSF, granulocytemacrophage colony-stimulating factor; iDC, immature dentritic cell; IDO, indoleamine-2,3-deoxygenase; mDC, mature dentritic cell; MDSC, myeloid-derivedsuppressor cell; PD-1, programmed cell death 1; PD-L, programmed cell death ligand; PGE2, prostaglandin E2; TAN, tumor-associated neutrophil; TCR, T-cellreceptor; Treg, regulatory T cells.

Chabanon et al.

Clin Cancer Res; 22(17) September 1, 2016 Clinical Cancer Research4310




Genentech/Roche), durvalumab (MEDI-4736, Astra Zeneca/MedImmune), and avelumab (MSB0010718C, Pfizer) enteredclinical development. Very promisingORR in relapsing/refractorymalignant melanoma, RCC, and NSCLC (3), associated withprolonged PFS and OS, led to their accelerated approval in2014–2015, and the outstanding activity observed in severalhistologies (Supplementary Table S1) awarded them "drugs ofthe year" in 2013 (24). Since then, an exponential number ofmonotherapy or combination trials have been launched inmultiple cancer types.

DNA Repair Deficiencies in CancerContrary to immune escape, DNA repair deficiency has been

successfully exploited as a therapeutic opportunity for more than50 years with the use of traditional cytotoxic chemotherapies. Ifthese DNA-damaging agents have initially been developed in a"one-size-fits-all" approach, DNA repair deficiencies are nowbeing exploited in a much more targeted fashion, notably usingtargetedmechanism-based approaches, such as synthetic lethality(25–28).

DNA repair deficiency is one of the main drivers of genomicinstability, a key hallmark of cancer (ref. 29; Table 1). It favors theaccumulation of DNA lesions that can arise from two distinctprocesses: (i) exogenous lesions, resulting from exposure tomutagenic agents and carcinogens and (ii) endogenous defects,which arise as a consequence of cell metabolism and the inherentinstability of DNA (30). Interestingly, some peculiar types ofexogenous DNA damage are associated with specific patterns ofmutations, also called mutational signatures. For example, thepredominance of C-to-A transitions, due to the effect of thepolycyclic hydrocarbons of tobacco smoke, is characteristicallyfound in NSCLC (31). In melanoma, UV radiation createspyrimidine dimers, which result in a high prevalence of C-to-T transitions on the untranscribed strand (32). Specificmutational signatures have also been reported in cancers withendogenous DNA damage repair defects, for example, BRCA1-or BRCA2-deficient high-grade serous ovarian and triple-nega-tive breast cancers, which harbor frequent loss of heterozygosity(28, 33); MMR-deficient colorectal cancer (34), associated witha microsatellite instable phenotype and high mutational bur-den; and POLE-deficient endometrial cancers, which exhibit anultra-mutated phenotype (7).

It is somehow intuitive that the presence of high tumormutational burden can increase the likelihood of neoantigensformation, and that the most mutated tumors may also be themost immunogenic ones (35). However, if high mutationalburden has repeatedly been associated with response andimproved outcome on ICB therapy, it would be na€�ve to con-clude on that basis that there is a general correlation betweenDNA repair deficiency and sensitivity to anti–PD-(L)1 (Fig. 2),the reality being much more complex.

Mutational Burden and Response to ICBThe description of a correlation between mutational load and

response to ICB was allowed by recent advances in next-genera-tion sequencing (NGS) technologies, notably whole-exomesequencing (WES) and RNA-sequencing (RNA-seq). High muta-tional load, defined as >100 nonsynonymous single-nucleotidevariants (nsSNV) per exome, was first associated with clinicalbenefit in melanoma patients treated with anti–CTLA-4 therapy

(4, 5). Subsequently, Rizvi and colleagues correlated high muta-tional load (defined as >178 nsSNVs per exome) and durableclinical benefit in two partially independent cohorts of NSCLCpatients receiving pembrolizumab (6). Of note, the studyreported a significantly increased ORR in tumors exhibiting asmoking molecular signature. Moreover, in responders showingthe highest mutational burden, specificmutations were identifiedin DNA repair genes, including POLD1, POLE, MSH2, BRCA2,RAD51C, and RAD17, thus supporting that DNA repair defectscan increase tumor immunogenicity by favoring somatic muta-tions. Consistently, later findings showed higher response ratesto anti–PD-1 therapy in MMR-deficient tumors (9, 36), and inBRCA2-mutatedmelanoma (37). Interestingly, in the latter study,mutational load did not correlate with tumor response but wasassociated with improved patient survival only, highlighting therole of additional factors influencing early tumor response andlong-term OS.

Now, the major challenges that remain to be addressed toimprove robustness of mutational burden include the definitionof optimal tumor purity and sequencing depth, as well as thethreshold for defining "high" and "low" mutational burden.Indeed, there is a significant overlap in mutation range betweenresponders and nonresponders (4, 5): some patients still benefitfrom ICB despite very low mutation rates, and conversely, highmutational load does not always correlate with response. This isbest illustrated by Hodgkin lymphoma, which is highly sensitiveto PD-1 blockade (38) despite carrying virtually no mutation.Mutational signatures, that are functional readouts of the past andcurrent disease biology in terms of DNA damage and DNA repair,could represent an additional genomic determinant of responseto ICB (35, 39). Their use, combined with evaluation of muta-tional load and detection of mutations in DNA repair genes, maytherefore allow better stratification of patients and identify ICB-sensitive tumors.

Importantly, the above-described analyses of the mutationallandscape only provide an "instantaneous and descriptive" pic-ture of a tumor genome. Even mutational signatures, in somecases, might exclusively reflect previous DNA repair deficienciesandmaynot be relevantmarkers of the actualDNA repair status ofthe tumor. It is therefore crucial to assess the potential for thesemutations to functionally enhance antitumor immune responsesby creating immunogenic neoantigens.

Predicted Neoantigen Load and Responseto ICB

Two main classes of tumor antigens are classically described:(i) tumor-associated antigens (TAA), which are nonmutated self-antigens that are aberrantly expressed by cancer cells followinggenetic and epigenetic alterations, and (ii) tumor-specific anti-gens, which are neoantigens that form as a result of nonsynon-ymous mutations and are generally unique to a tumor. Amongthese, the latter only have been consistently associated withantitumor T-cell reactivity and clinical efficacy of ICB (40).

Although we can anticipate that highly mutated tumors aremore prone to formneoantigens, the stochastic nature of neoanti-gen generation calls for a functional validation, as all formedneoantigensmay not be immunologically relevant. If it is obviousthat nsSNVs represent a mine of immunogenic mutations, frame-shift, splice site mutations, and intragenic fusions are also proneto generate neoepitopeswhennonfunctional proteins are directed

Mutational Landscape and Immunity in Cancer

www.aacrjournals.org Clin Cancer Res; 22(17) September 1, 2016 4311




Table 1. Type and frequency of DNA repair alterations in solid tumors

AlterationsCancer type Gene Type Frequency References

Non–small cell lung cancer BRCA1 Reduced mRNA and protein expression 44% (68)FANCF Promoter methylation 14% (69)ATM Somatic mutations 6% (69)MSH2 Reduced protein expression 18%–38% (69)ERCC1 Reduced protein expression 22%–66% (69)RRM1 Loss of heterozygosity 65% (69)

Small-cell lung cancer POLD4� Reduced mRNA expression N.R. (70)Clear-cell renal cell carcinoma ATM Somatic mutations 3% (71)

NSB1 Somatic mutations 0.5%MLH1 Homozygous deletion 3%–5% (72)MSH2 Promoter hypermethylation N.R. (73)

Urothelial carcinoma BRCA1 Somatic mutations 14% (74–76)BRCA2 Somatic mutations 14%PALB2 Somatic mutations 14%ATM Somatic mutations 29%MSH2 Loss of protein expression 3% (77)ERCC2 Somatic mutations 12% (78)

Head and neck cancer FANCB� Promoter methylation 31% (79)FANCF� Promoter methylation 15%FANCJ Reduced protein expression (IHC) N.R.FANCM Reduced protein expression (IHC) N.R.BRCA1 Reduced protein expression (IHC) N.R.BRCA2 Reduced protein expression (IHC) N.R.FANCD2 Reduced protein expression (IHC) N.R.

Ovarian cancer BRCA1/BRCA2 Germline mutations 15% (80, 81)Somatic mutations 35%Promoter methylation 11%–35%

FANCF Promoter methylation N.R.FANCD2 Reduced protein expression N.R.BARD1 Germline mutations 6% (82)BRIP1 Germline mutations 6%PALB2 Germline mutations 6%MRE11 Germline mutations 6%RAD50 Germline mutations 6%RAD51C Germline mutations 6%NSB1 Germline mutations 6%MSH6 Inactivating mutations 6% (82)

Triple-negative breast cancer BRCA1 Germline mutations 5%–10% (80, 83)BRCA2 Somatic mutations 10%

Gastric cancer MLH1 Loss of protein expression (IHC) 18% (84)Promoter hypermethylation 15%

MSH2 Loss of protein expression (IHC) 3%MMR-deficient colorectal cancer MRE11 Somatic mutations 75% (34, 85, 86)

RAD50 Somatic mutations 21%–46%BRCA2 Somatic mutations 2%MSH3 Somatic mutations 22%–51% (34, 85, 86)MSH6 Somatic mutations 9%–38%MLH3 Somatic mutations 9%–28%POLD3 Somatic mutations 37% (34, 85, 86)

Hepatocellular carcinoma NSB1 Somatic mutations 10% (87)MSH2 Promoter hypermethylation 25% (88, 89)

Reduced protein expression 18%PMS2 Promoter hypermethylation 15%MLH1 Promoter hypermethylation 8%

Reduced protein expression 38%Biliary tract cancer MSH2 Loss of protein expression (IHC) 7% (90, 91)

MSH6 Loss of protein expression (IHC) 7%MLH1 Loss of protein expression (IHC) 1.5%PMS2 Loss of protein expression (IHC) 1.5%

Prostate cancer BRCA2 Homozygous deletion/heterozygous deletion/frameshift mutation 14% (92, 93)ATM Frameshift mutation 12%PALB2 Frameshift mutation 4%CHK2 Homozygous deletion 4%FANCA Homozygous deletion 6%BRCA1 Homozygous deletion 2%MRE11 Frameshift mutation 2%NSB1 Frameshift mutation 2%MLH3 Frameshift mutation 4% (92, 93)

(Continued on the following page)

Chabanon et al.





to the proteasome (41). The correlation between mutationalburden and predicted neoantigen load (as defined by the numberof neoantigens potentially presented by theMHC class I) has beenachieved by creating bioinformatics analysis pipelines thatmodelthe key steps of the antigen presentation process (Fig. 3 and Table2): (i) expression of mutated proteins that are processed by theproteasome, and produce neopeptides; (ii) translocation of theneopeptides through the endoplasmic reticulum and binding totheMHC class I molecule with a sufficient affinity to enable T-cellpresentation; and (iii) recognition of the presented neoantigen bya T-cell clone able to detect it.

This modeling pipeline has been overall successful in correlat-ing mutational load with predicted neoantigen load. First, muta-tional and predicted neoantigen loads were significantly corre-lated with clinical benefit in melanoma patients treated withipilimumab (5). Consistently, it was suggested that tumors dis-playing >10 nsSNVs/Mb may produce sufficient neoantigens togenerate ant-tumor immunogenicity, whereas tumors with <1nsSNV/Mb may not (41). Further consistent observations weremade in DNA repair–deficient tumors, including MSI-hightumors (7, 36),BRCA-mutated ovarian cancer (8), andmelanoma(37). However, genomic instability and accumulation of muta-tions is a double-edged sword process, which both favors thegeneration of immunogenic neopeptides, but also allows emer-gence of less immunogenic new clones that escape immunesurveillance, thereby favoring primary or acquired resistance.High intratumor heterogeneity (ITH) has indeed been correlatedwith poorer outcome, whereas sensitivity to ICB is associatedwithlow ITH and high clonal neoantigens (42). This underlines theparadoxical role of DNA repair defects in dictating response toICB. AlthoughDNA repair–deficient tumors exhibit high genomicinstability andhighmutational/neoantigen burdens, they are alsothe most likely to display high ITH due to their propensity toprovoke random mutations (43). If this observation represents astrong biological argument for treating patients with ICB early inthe course of the disease, when genomic instability is high, andITH low,we canhypothesize that each clonewithin the tumorwillretain somedegreeof intrinsic genomic instability, andparticipatein the generation of immunogenic neoantigens.

Therefore, a key issue is the determination of which epitopeswill actually prime T-cell responses, among the bulk of releasedepitopes. The study of a melanoma patient who experiencedcomplete response after 3 months of ipilimumab treatmentrevealed that, out of 1,657 nsSNVs, the tumor only displayed448 immunologically relevant epitopes, and nomore than two ofthemwere identified as able to trigger a patient-specific antitumorT-cell response (44). In a similar analysis, Rizvi and colleaguesdemonstrated that response to pembrolizumab in a NSCLC

patient was associated with the T-cell response against a singleneoantigen resulting from a nsSNV in HERC1 (6). In anotherstudy evaluating response to anti-CTLA-4 inmalignant myeloma,Snyder and colleagues identified a set of consensus tetrapeptidesequences exclusively shared by patients exhibiting long-termclinical benefit (4) and being necessary and sufficient for theactivation of an antitumor T-cell response; these results wereunfortunately not confirmed in two later studies (5, 37).

Mutational burden and predicted neoantigen load also shapethe nature and functional properties of antitumor immune infil-trates. The presence of tumor-infiltrating cytotoxic T-lymphocytes(CTL) has been correlated to higher immunogenic mutation rate,using RNA-seq data (45). Rooney and colleagues furtherdescribed that predicted neoantigen load correlated with thecytolytic activity of intratumoral CTLs and natural killer (NK)cells (46), but that a given mutation rate was associated withdistinct cytolytic activities across different histologies. Forinstance, cervical cancers exhibit higher cytolytic activity thanmelanoma, although this cancer type is not as sensitive to ICB.This suggests that both tissue-specific and tumor-specific factorscontribute to immune escape regulation. Interestingly, this workalso proposed a model for correlating the subclonal evolution oftumor genetics with the cytolytic activity of surrounding CTLs andNK cells, thereby reinforcing the link between continuous tumorgenetics drift and immune escape.

Overall, the data presented above support that highmutationalburden associates with increased neoantigens formation andtumor immunogenicity. However, the very high attrition rate,from a high mutational burden to the very few neoepitopes thatwill eventually produce an antitumor immune response, illus-trates well the complexity of predicting tumor immunogenicityusing genomic data alone. Furthermore, other mechanisms,including oncogenic stress (47–49), secretion of immunosup-pressive cytokines (e.g., IL10; ref. 50), or downregulation ofMHCclass I (51), also modulate tumor immunogenicity, and muta-tional burden is only one component of the determinants of ICBsensitivity.

Other Biomarkers of Response toAnti–PD-(L)1"Tumor-related" biomarkers

Beyond tumor "antigenome," several biomarkers are beingdeveloped to predict response to anti–PD-(L)1 therapies(52, 53). The most promising and best validated one is probablyPD-L1 expression assessment by immunohistochemical stainingon tumor and/or tumor-infiltrating immune cells (3, 54–57).However, this biomarker currently lacks sensitivity—some

Table 1. Type and frequency of DNA repair alterations in solid tumors (Cont'd )

AlterationsCancer type Gene Type Frequency References

Endometrial cancer MLH1 Promoter hypermethylation 30% (7, 94)POLE Somatic mutations 10% (7, 94)

Pancreatic cancer BRCA2 Germline mutations 1.5% (68, 95)MSH2 Loss of protein expression (IHC) 15% (96)MSH6 Loss of protein expression (IHC) 15%MLH1 Loss of protein expression (IHC) 15%PMS2 Loss of protein expression (IHC) 15%

NOTE: Genes in blue are related to DSBR, in green to MMR, in red to NER, in orange to nucleotide synthesis, and in gray to DNA replication. Genes marked with anasterisk refer to data reported in cell lines only. Mutations or alterations in genes related to cell cycle are described in Supplementary Table S2.Abbreviations: DSBR, double-strand break repair; NER, nucleotide-excision repair; N.R., not reported.







Exogenous stress: tobacco Exogenous stress: tobacco and HPV infection

Exogenous stress: tobacco

Exogenous stress: tobacco

Exogenous stress: UV rays

DNA repair defectsfrequency (%)

PositiveNegative

Anti–PD-(L)1 response rate (%)RespondersRefractory

DNA repair defects frequency (%)

A

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60%

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19%

82%

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22%

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55%

76%

TP53: 75%CCNE1: 10%

MYC: 20%

DSBR: 45%MMR: 25%NER: 30%

PTEN: 25%RRM1: 65%

ATM: 6%

DSBR: 10%MMR: 20%TP53: 50%

MMR: 5%TP53: 2%

PTEN: 15%CDKN2A: 30%

MMR: 50%MRE11: 75%

POLD3: 37%ATR: 44%

BRAF: 50%TP53: 20%

Exogenous stress: asbestos

70%

30%

17%50% 50%

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15%19%

76%

80%

20%

80%

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0%24%

76%

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81% 85%

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90%

83%

17%

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65%35%

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N.R.

DSBR: 35%

MMR: 30%POLE: 10%

DSBR: 50%MMR: 6%

TP53: 67%

MMR: 10%

MMR: 15%TP53: 35%PTEN: 10%CDKN2A: 35%

TP53: 60%MDM2: 50%

DSBR: 20%TP53: 70%PTEN: 2%CDKN2A: 20%

DSBR: 15%TP53: 80%

DSBR: 20%MMR: 3%

ERCC2: 10%TP53: 60%PTEN: 15%

CDKN2A: 50%

DSBR: 2%MMR: 15%

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PD-L1-negative patients consistently experience clinical benefit(58, 59), and specificity— not all PD-L1–positive tumors benefitfrom anti–PD-(L)1 therapy (2, 60). Furthermore, the parametersof PD-L1 staining scoring are highly variable, notably the anti-PD-L1 antibody (clone SP142 and clone SP2063, Ventana; and clone28-8 and clone 22C3, Dako), the platform (PD-L1 IHC pharDx,Dako; OptiView DAB IHC Detection Kit, Ventana), the cells ofinterest (cancer cells, stromal cells, immune tumor-infiltratingcells), the positivity threshold (1%, 5%, 10%, or 50%), as well asthe tumor material used for analysis (fresh versus archivedmaterial, and primary versusmetastatic tumor; ref. 61).Moreover,PD-L1 expression can be constitutive or inducible (e.g., INFg-mediated induction; ref. 62). Together, these elements representsignificant hurdles for reaching the reproducibility and analyticvalidity that is required for any companion biomarker develop-ment and clinical implementation.

"Immune-related" biomarkersBeyond tumor-related biomarkers, the exploration of immune

infiltrate characteristics may also provide interesting biomarkers.Analysis of pretreatment samples from melanoma and NSCLCpatients responding to pembrolizumab revealed higher CD8þ T-cell levels at the tumor-invasive margin, as compared with non-responders (63, 64). Some more complex immune signatureshave also been explored: for example, Ribas and colleaguesdescribed an immune gene expression signature associated withgain in both ORR and PFS in melanoma patients treated withpembrolizumab (65), which is being explored in other histolo-gies. More recently, an eight-gene signature reflecting preexistingimmunity, the "T-effector/IFNg signature," was explored in thephase II POPLAR trial. High signature expression levels appearedto predict OS (but not PFS or ORR) benefit in atezolizumab-treated patients (66).

Immunomonitoring strategies, that is, repeated assessmentof dynamic circulating biomarkers involved in immuneresponse, have also been proposed. These dynamic biomarkers,which include notably cytokines and inflammatory mediators(Supplementary Table S3; ref. 67), can be monitored at severaltimepoints on trial using a simple blood test. If these circulatingbiomarkers have not been robust enough so far to predictresponders to ICB (52), they clearly represent a powerful andpractical tool for monitoring patient response, and deserve assuch active investigation.

Anticipating primary treatment resistanceFinally, as is the case for any targeted therapy, and especially

considering the cost of ICB and their associated biomarkers, earlyprediction of resistance is key. The very recent work by Hugo and

colleagues in melanoma (37) identified a transcriptional signa-ture associated with resistance to anti–PD-1 therapy. Exclusivelyfound in the pretreatment tumors of nonresponding patients, this"innate anti–PD-1 resistance" (IPRES) is characterized by theupregulation of genes involved in the regulation of epithelial–mesenchymal transition (EMT), cell adhesion, extracellularmatrix remodeling, angiogenesis, and wound healing. Very inter-estingly, this signature was not predictive of resistance to anti–CTLA-4 therapy, but found at variable frequencies across mostcommon cancers, suggesting that some mechanisms of ICB resis-tance might be shared by different histologies.

In the aggregate, these data highlight that a comprehensiveand integrated approach, which would encompass tumorgenetics, immune checkpoint expression, microenvironmental,and immune-monitoring data, is highly needed to best selectpatients.

Conclusions and Future ChallengesHow could we improve and expand the use of DNA repair

deficiency, mutational burden, and predicted neoantigen loadfor selecting patients that are the most likely to benefit fromanti–PD-(L)1 therapy? Targeted sequencing of hotspot muta-tions in DNA repair gene panels provides useful but limitedinformation, as it misses nongenetic forms of DNA repairdefects (e.g., secondary to epigenetic alterations), and, mostimportantly, does not functionally evaluate the tumor DNArepair capacity. The decreasing costs and expanding availabilityof NGS technologies open interesting perspectives for theirbroader use in clinical routine, and mutational load is a simpleparameter that is easily calculated and technically reproducible,allowing the comparison and/or merging of various patientseries. We can therefore reasonably hope that, with increasingnumbers and open data sharing, relevant mutational thresh-olds for predicting sensitivity to anti–PD-(L)1 therapy, as wellas tumor purity and sequencing depth that are required, willbe soon better defined in a histotype-specific fashion. Thepipeline optimization and establishment of reference guide-lines for predicting neoantigen load will also accelerate theclinical implementation of the latter work. Together, especiallyif integrated with PD-L1 IHC scoring and signatures of primaryresistance, these data might rapidly become robust enough tobe clinically implemented.

However, several challenges will still need to be addressed:(i) tumor material is not always available, and efforts shouldbe made to develop equivalent assays on circulating biomar-kers, such as cell-free tumor DNA; (ii) tumor heterogeneityneeds to be anticipated (42); (iii) the immunogenic potential

Figure 2.DNA repair defects and their association with anti–PD-(L)1 efficacy in solid tumors. A, representation, per tumor type, of the median frequency of DNA repairdeficiency (yellow pie charts) and the median efficacy of anti-PD-(L)1 (blue pie charts). For each histology, the median rate of DNA repair defects wascalculated on the basis of literature data (see Table 1 for raw data). When DNA repair defects in distinct pathways were mutually exclusive, the sumof their frequency was taken; when overlaps were observed between several DNA repair defects, the median of all DNA repair defects was chosen.The frequency of additional defects in other genes relevant for DNA repair (i.e., genes involved in cell cycle regulation or DNA replication) were alsoevaluated and are depicted on the side of the pie chart graphs. Tumor types resulting from exposure to a mutagenic agent are highlighted by a skull. ORRreported in phase I, II, or III trials performed in the corresponding histologies were taken for estimating the efficacy of anti–PD-(L)1 inhibitors (seeSupplementary Table S1 for raw data). The data cut-off for collecting anti–PD-(L)1 efficacy was January 2016. B, scatter plot illustrating the lack ofstatistically significant correlation between DNA repair mutation frequency and response to anti–PD-(L)1 therapies, highlighting the need to take intoaccount additional parameters for predicting response to these drugs. DSBR, double-strand break repair; HPV, human papillomavirus; NER, nucleotide-excision repair; N.R., not reported; TNBC, triple-negative breast cancer.







Golgiapparatus

MHC/peptidecomplex

DNA

RNA

TAP protein

Proteasome

cellu

lar e

vent

s

Somaticmutation 3 4 5 621

Mutationalprofiling

MSI profilingData

generationNetCHOPCterm

PCleavage

FragPredict

PCM SVMTAP

Neoantigen loadMutational load and genomic instability

NetMHCpan

NetMHC

SMM

ARB

MHC multimer

GenScript

OptiType

Polysolver

AthlatesPredTAPNetCHOP 20S

TechniquesAnalysis

Software

RNA-seq

CGH

WES

WGS

Expressionprofiling

Proteasomalprocessing prediction

HLAtyping

HLA-binding

prediction

Neoantigen synthesisand T-cell reactivity

analysis

TAP transportprediction

Transcriptioninto mutated

mRNA

Proteasomalprocessing of themutated protein

TAP-mediatedpeptide transportinto the ER lumen

T-cell recognitionof cell surfaceneoantigens

Binding ofpeptides to MHCclass I complex

Tech

niqu

es a

nd to

ols

Cand

idat

esEn

dpoi

nts

Pipe

line

Peptides

ER

Tumor cell

CTL

Figure 3.

Pipeline for the identification of immune-relevant neoantigens. The typical pipeline consists of six main steps: (1) Tumor mutational load and specific mutationsare identified using WES or WGS. Additional techniques such as CGH or MSI-profiling might be of interest to evaluate genomic instability but have not beenvalidated yet in this indication. Moreover,WES is always a required starting point as DNA sequence information is required for subsequent prediction tools. (2) UsingRNA-seq, previously generated sequencing data are filtered for gene expression to restrict neoantigen prediction to the set of translated mutations("expressed nsSNV"). Subsequently, predictions for (3) proteasomal processing and (4) TAP-mediated transport of peptides are completed using dedicatedalgorithms. (5) To predict binding of peptides on MHC class I molecules, the previously selected peptides are implemented in a dedicated software that infersbinding affinity to HLA molecules according to the HLA type of the patient. (6) Eventually, the predicted peptides may be synthesized to test for T-cellreactivity in vitro using the MHC multimer technology. Key technologies most often used in the literature are highlighted in bold. Techniques exclusively usedto measure genomic instability are presented in dotted rectangles. ARB, average relative binding; CGH, comparative genomic hybridization; SMM, stabilizedmatrix method; WGS, whole-genome sequencing.

Chabanon et al.





Table 2. Advantages and drawbacks of the available techniques to identify immunogenic mutations/neoantigens

Technique or software Platform Strengths Weaknesses

Relevance forantigenomeprediction References

WGS Mutational profiling Both coding and noncoding DNAsequences are analysed.

Sequencing depth is usually low,which prevents detection of somesubclonal mutations.

þþ (97)

WES Mutational profiling Provides high sequence coverageacross exome, increasing reliabilityand ability to detect subclonalmutations.

(i) Only covers the �1% codingregions of the genome.

(ii) Some mutations may be misseddue to uneven capture efficiencyacross exons.

þþþ (97)

MSI profiling Microsatelliteinstability

Several methods all well-validated. Only provides information on themicrosatellite instability.

þ (9)

CGH Genomic instability Global picture of the overall genomicinstability.

(i) Only provides copy-numbervariations and translocations oflarge portions of the genome.

(ii) No access to the DNA sequence.

þ

RNA-seq Expression profilingand codingmutation analysis

(i) Focuses on translated mutationsonly, that are the most likely tohave functional consequences.

(ii) Analysis not restricted to knowngenes: potential for discoveringnovel transcripts, splice variants orfusions.

(iii) Possibility to correlate mutationaldata with gene expression.

(i) Access to matched normal is keybut cannot be achieved in manycases: hard to distinguish tumor-specific mutations frompolymorphisms.

(ii) Limited calling of mutationswithin RNA species due to theirlow levels, either because of lowlevel gene expression or becauseof mRNA stability.

þþþ (97)

NetCHOP 20SPCM (WAPP package)FragPredict (MAPPPpackage)

Proteasomalprocessingprediction trainedon in vitro data

N.R. Predictions from in vitro data do notcapture the full complexity ofproteasomal processing.

þ (98)

PCleavageNetCHOPCterm Proteasomal

processingprediction trainedon in vivo data

(i) In vivo data provide accurateprediction as predictions are madeon the entire processingmachinery (action of severalproteasomes, cytosolicproteases. . .)

(ii) May also capture transportefficiency.

N.R. þþþ (98, 99)

PredTAP TAP transportprediction

No comparative study available. No comparative study available. N.R. (98, 100)SVMTAP (WAPPpackage)SMM (stabilized matrixmethod)

Allele-specific HLAbinding affinityprediction

N.R. (ii) Does not account for non-linearities and interdependenciesbetween amino acids.

þ (101–103)

ARB average relativebinding (matrix-basedmethods)

NetMHC [artificial neuralnetworks (ANN)-basedmethod]

Allele-specific HLAbinding affinityprediction

Nonlinear model. Does not allow prediction for allknown HLA alleles.

þþ (103, 104)

NetMHCpan [Pan-specificartificial neuralnetworks (ANN)-basedmethod]

Pan-specific HLAbinding affinityprediction

(i) Allows predictions to be made forall known HLA Class I alleles,including alleles for which noprediction is available withNetMHC.

(ii) NetMHCpan is the best-performingmethod for allele-specific HLAbinding affinity prediction.

N.R. þþþ (105)

Athlates HLA typing N.R. (i) Early tool with lower accuracythan that of up-to-date tools.Restricted to the use ofWES data

þ (98)

Polysolver HLA typing (i) Provides improved retrieval andalignment of HLA reads.Polysolver infers HLA-typeinformation with 97% sensitivityand 98% precision from exome-capture sequencing data.

(ii) Allows identification of patient-specific mutations in HLA alleles.

(ii) Restricted to the use of WESdata

þþþ (106)

(Continued on the following page)






of mutations other than nsSNVs (including fusion transcriptsand aberrantly expressed splice variants) requires furtherexploration; (iv) developing an integrated approach, thatwould also encompass tumor microenvironment and immuneinfiltrates characteristics, as well as immunomonitoring data,warrants further investigation; and (v) last but not least, cost-efficacy and health economics studies will be needed to deter-mine which approach will eventually be the most relevant andsustainable.

Together, these challenges open very stimulating perspectivesand one can be certain that several exciting revolutions are stillto come soon in immuno-oncology.

Disclosure of Potential Conflicts of InterestA. Marabelle is a consultant/advisory board member for Amgen, Biothera

Pharmaceuticals, GlaxoSmithKline, Lytix Biopharma, Nektar, Novartis, Pfi-zer, Roche/Genentech, and Seattle Genetics. J.-C. Soria is a scientificcofounder of Gritstone Oncology and is a consultant/advisory board mem-ber for AstraZeneca, MSD, Pfizer, and Roche. No potential conflicts ofinterest were disclosed by the other authors.

Grant SupportR.M. Chabanon was supported by the Fondation Philanthropia.

Received April 8, 2016; revised May 31, 2016; accepted May 31, 2016;published OnlineFirst July 7, 2016.

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Table 2. Advantages and drawbacks of the available techniques to identify immunogenic mutations/neoantigens (Cont'd )

Technique or software Platform Strengths Weaknesses

Relevance forantigenomeprediction References

OptiType HLA typing (i) Performs fully automated HLAtypingwith four-digit resolution onNGS data from RNA-Seq, WES andWGS technologies.

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N.R. þþþ (44, 97)

NOTE: Multiple NGS technologies, bioinformatics tools, and pipelines are available to analyze tumor samples and predict immunogenic mutations/potentialneoantigens in patients (see corresponding steps in Fig. 3). Primarily, genomic data are generated using various NGS technologies, most frequently including WESandRNA-seq to integrate both nsSNVs andexpressed nsSNVs. These data are then analyzedusingdedicated prediction algorithms corresponding to each step of theneoantigen generation biological process. These filtering tools guide the selection of immunogenic neoantigens among the bulk of candidate neoantigens. Althoughofficial guidelines are currently lacking on which tool should preferably be used, most often used algorithms include NetCHOPCterm for proteasomal processingprediction and NetMHC/NetMHCpan for HLA binding prediction. Eventually, a functional validation may be performed using an in vitro T-cell reactivity assay tovalidate the immunogenicity of the predicted neoantigens.Abbreviations: CGH, comparative genomic hybridization; MSI, microsatellite instability; N.R., not reported; WGS, whole-genome sequencing.

Chabanon et al.





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2016;22:4309-4321. Published OnlineFirst July 7, 2016.Clin Cancer Res Roman M. Chabanon, Marion Pedrero, Céline Lefebvre, et al. BlockersMutational Landscape and Sensitivity to Immune Checkpoint

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Mutational Landscape and Sensitivity to Immune Checkpoint ......(iii) escape: the immune-resistant clones freely expand, circum-venting both innate and adaptive immune responses. A