Mutational Landscape and Sensitivity to Immune Checkpoint Blockers Roman M

Published OnlineFirst July 7, 2016; DOI: 10.1158/1078-0432.CCR-16-0903

Review Clinical Cancer Research Mutational Landscape and Sensitivity to Immune Checkpoint Blockers Roman M. Chabanon1,2, Marion Pedrero2,Celine Lefebvre1,2, Aurelien Marabelle3,4, Jean-Charles Soria1,2,3, and Sophie Postel-Vinay1,2,3

Abstract

Immunotherapy is currently transforming cancer treatment. from ICB. Thus, closely reflecting the DNA damage repair capacity Notably, immune checkpoint blockers (ICB) have shown unprec- of tumor cells and their intrinsic genomic instability, the muta- edented therapeutic successes in numerous tumor types, includ- tional load and its associated tumor-specific neoantigens appear ing cancers that were traditionally considered as nonimmuno- as key predictive paths to anticipate potential clinical benefits of genic. However, a significant proportion of patients do not ICB. In the era of next-generation sequencing, while more and respond to these therapies. Thus, early selection of the most more patients are getting the full molecular portrait of their sensitive patients is key, and the development of predictive tumor, it is crucial to optimally exploit sequencing data for the companion biomarkers constitutes one of the biggest challenges benefit of patients. Therefore, sequencing technologies, analytic of ICB development. Recent publications have suggested that the tools, and relevant criteria for mutational load and neoantigens tumor genomic landscape, mutational load, and tumor-specific prediction should be homogenized and combined in more inte- neoantigens are potential determinants of the response to ICB and grative pipelines to fully optimize the measurement of such can influence patients' outcomes upon immunotherapy. Further- parameters, so that these biomarkers can ultimately reach the more, defects in the DNA repair machinery have consistently been analytic validity and reproducibility required for a clinical imple- associated with improved survival and durable clinical benefit mentation. Clin Cancer Res; 22(17); 1–13. 2016 AACR.

Introduction among all tumor types included, still do not respond to these drugs, highlighting the urge for developing robust predictive Since their first introduction into the clinic and first approval in biomarkers that would guide appropriate selection of patients. 2011 (1), immune checkpoint blockers (ICB) have transformed Recently, the tumor cell mutational burden has been correlated cancer treatment and allowed unprecedented improvements in with clinical benefits of anti–PD-1 and anti-CTLA-4 therapy in overall survival (OS), progression-free survival (PFS), or overall various tumor types, including malignant myeloma (4, 5), response rates (ORR) in many aggressive diseases (2, 3). Most NSCLC (6), and several DNA repair–deficient tumors (7–9). importantly, benefits of ICB have not been limited to the "tradi- Predicted neoantigen load has also emerged as an interesting tional" immunogenic cancers, malignant melanoma and renal selection biomarker for predicting clinical benefit of these agents. cell carcinoma (RCC), but have also been extended to other Overall, a direct link between DNA repair deficiency, mutational histologies classically described as "nonimmunogenic," such as landscape, predicted neoantigen load, and clinical activity of ICB non–small cell lung cancer (NSCLC) or mismatch-repair–defi- is suggested. cient colorectal cancer (MMR-deficient colorectal cancer; ref. 3). In this review, we discuss the significance and the relevance of Despite these clear clinical advances, the biological mechanisms this correlation in solid tumors. We also provide critical insight that underlie antitumor immunity and determine sensitivity to into the methods and techniques that have been used for per- these agents, notably anti-programmed death receptor-1/-ligand forming analyses of tumor mutational burden, predicted neoan- 1 [anti–PD-(L)1] are still poorly understood. Moreover, a statis- tigen load, and neopeptide formation. We further propose a tically significant proportion of patients, approximately 80%, comprehensive approach that would allow encompassing other potential predictive biomarkers for response to anti–PD-(L)1 inhibitors. 1FacultedeM edicine, Universite Paris Saclay, Universite Paris-Sud, Le Kremlin Bicetre,^ France. 2Inserm Unit U981, Gustave Roussy, Villejuif, France. 3DITEP (Departement d'Innovations Therapeutiques et Essais Immune Escape and Carcinogenesis Precoces), Gustave Roussy, Villejuif, France. 4Inserm Unit U1015, Gus- tave Roussy, Villejuif, France. The original concept of immune surveillance, hypothesized in 1957 (10), and formally established in 1970 (11), postulated that Note: Supplementary data for this article are available at Clinical Cancer the immune system alone could eliminate tumor cells in the early Research Online (http://clincancerres.aacrjournals.org/). stages of carcinogenesis. Since then, this theory has been further Corresponding Author: Sophie Postel-Vinay, Gustave Roussy, 114 Rue Edouard enriched by the "immunoediting" notion (12), which describes Vaillant, Villejuif 98105, France. Phone: 3301-4211-43 43; Fax: 3301-4211-6444; how both innate and adaptive immunity contribute to carcino- E-mail: [email protected] genesis, notably by exerting a Darwinian selection pressure. doi: 10.1158/1078-0432.CCR-16-0903 Immunoediting classically consists of three distinct steps: (i) 2016 American Association for Cancer Research. elimination: the innate and adaptive compartments coordinately

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IL10 TGFb Arginase 1 MDSC Treg TAN TAM 3 – ++ ++

CXCL15 CCL2 IL4 GM-CSF CCL5 NK MHC CSF-1 CXCL1 CXCL2 CCL22 class I CXCL12 CXCL5 IL1 TNFa 1 – TCR PD-L1 Tumor cell – 5 CTL PD-1 PD-L2 iDC DR4/5 FAS PGE2 TRAIL – COX-2 4 FAS-L

mDC

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 presentation and consequently reduced detection by cytotoxic CD8þ T lymphocytes. (2) Cancer cells can activate immunosuppressive mechanisms by inducing immune cells' 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 of cytotoxic CD8þ T cells and NK cells is inhibited. (4) This cytokine imbalance, combined with the secretion of TGFb, COX-2, and PGE2, inhibits dendritic cell differentiation and maturation, thereby affecting antigen presentation and recognition by T cells. The release of additional immune modulators or metabolic regulators, such as IDO and arginase, also favors the establishment of an immunosuppressive tumor microenvironment. (5) Disrupted expression of immune checkpoint ligands by cancer cells provides coinhibitory signals to CD4þ and CD8þ T lymphocytes, preventing them from building a speciﬁc antitumor immune response. CCL, chemokine ligand; COX-2, cyclooxygenase-2; CXCL, chemokine (C-X-C motif) ligand; FAS-L, FAS-ligand; GM-CSF, granulocyte macrophage colony-stimulating factor; iDC, immature dentritic cell; IDO, indoleamine-2,3-deoxygenase; mDC, mature dentritic cell; MDSC, myeloid-derived suppressor cell; PD-1, programmed cell death 1; PD-L, programmed cell death ligand; PGE2, prostaglandin E2; TAN, tumor-associated neutrophil; TCR, T-cell receptor; Treg, regulatory T cells.

drive immune rejection; (ii) equilibrium: through a clonal selec- CD86, and conversely inhibits T cells when CTLA-4 is engaged; tion process, the dynamic balance between tumor and immune and (ii) the PD-1 axis, which provides a strong inhibitory signal cells results in the emergence of specific tumor cell variants with following binding of PD-L1 or PD-L2 to the PD-1 receptor (22). increased resistance, which take advantage of acquired mutations; Contrary to CTLA-4, PD-1 is thought to act predominantly in the (iii) escape: the immune-resistant clones freely expand, circum- tumor microenvironment, where PD-L1 is overexpressed by mul- venting both innate and adaptive immune responses. tiple cell types, including dendritic cells, M2 macrophages, and A variety of mechanisms can facilitate tumor immune escape tumor-associated fibroblasts (23). (Fig. 1). Among them, deregulation of immune checkpoint sig- As opposed to historical immune-based approaches that were naling has been observed in multiple malignancies (13–21). developed in traditionally immunogenic cancers, ICBs have Immune checkpoints involve the interaction between a receptor allowed significant therapeutic successes in many solid tumors expressed on T cells and its ligand located at the surface of antigen- and hematologic malignancies. The anti-CTLA-4 ipilimumab presenting cells. This generates a costimulatory signal, which (Yervoy, Bristol-Myers Squibb) was the first ICB to improve OS triggers either the activation or inhibition of T cells. Two major in malignant melanoma patients (1). In 2012, anti–PD-(L)1 checkpoints regulate T-cell activation: (i) the CD28/CTLA-4 axis, therapies including the anti–PD-1 pembrolizumab (Keytruda, which activates T cells upon engagement of CD28 with CD80 and Merck), and the anti-PD-L1 atezolizumab (MPDL-3280A,

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Genentech/Roche), durvalumab (MEDI-4736, Astra Zeneca/ (4, 5). Subsequently, Rizvi and colleagues correlated high muta- MedImmune), and avelumab (MSB0010718C, Pfizer) entered tional load (defined as >178 nsSNVs per exome) and durable clinical development. Very promising ORR in relapsing/refractory clinical benefit in two partially independent cohorts of NSCLC malignant melanoma, RCC, and NSCLC (3), associated with patients receiving pembrolizumab (6). Of note, the study prolonged PFS and OS, led to their accelerated approval in reported a significantly increased ORR in tumors exhibiting a 2014–2015, and the outstanding activity observed in several smoking molecular signature. Moreover, in responders showing histologies (Supplementary Table S1) awarded them "drugs of the highest mutational burden, specific mutations were identified the year" in 2013 (24). Since then, an exponential number of in DNA repair genes, including POLD1, POLE, MSH2, BRCA2, monotherapy or combination trials have been launched in RAD51C, and RAD17, thus supporting that DNA repair defects multiple cancer types. can increase tumor immunogenicity by favoring somatic mutations. Consistently, later findings showed higher response rates DNA Repair Deficiencies in Cancer to anti–PD-1 therapy in MMR-deficient tumors (9, 36), and in BRCA2-mutated melanoma (37). Interestingly, in the latter study, Contrary to immune escape, DNA repair deficiency has been mutational load did not correlate with tumor response but was successfully exploited as a therapeutic opportunity for more than associated with improved patient survival only, highlighting the 50 years with the use of traditional cytotoxic chemotherapies. If role of additional factors influencing early tumor response and these DNA-damaging agents have initially been developed in a long-term OS. "one-size-fits-all" approach, DNA repair deficiencies are now Now, the major challenges that remain to be addressed to being exploited in a much more targeted fashion, notably using improve robustness of mutational burden include the definition targeted mechanism-based approaches, such as synthetic lethality of optimal tumor purity and sequencing depth, as well as the (25–28). threshold for defining "high" and "low" mutational burden. DNA repair deficiency is one of the main drivers of genomic Indeed, there is a significant overlap in mutation range between instability, a key hallmark of cancer (ref. 29; Table 1). It favors the responders and nonresponders (4, 5): some patients still benefit accumulation of DNA lesions that can arise from two distinct from ICB despite very low mutation rates, and conversely, high processes: (i) exogenous lesions, resulting from exposure to mutational load does not always correlate with response. This is mutagenic agents and carcinogens and (ii) endogenous defects, best illustrated by Hodgkin lymphoma, which is highly sensitive which arise as a consequence of cell metabolism and the inherent to PD-1 blockade (38) despite carrying virtually no mutation. instability of DNA (30). Interestingly, some peculiar types of Mutational signatures, that are functional readouts of the past and exogenous DNA damage are associated with specific patterns of current disease biology in terms of DNA damage and DNA repair, mutations, also called mutational signatures. For example, the could represent an additional genomic determinant of response predominance of C-to-A transitions, due to the effect of the to ICB (35, 39). Their use, combined with evaluation of muta- polycyclic hydrocarbons of tobacco smoke, is characteristically tional load and detection of mutations in DNA repair genes, may found in NSCLC (31). In melanoma, UV radiation creates therefore allow better stratification of patients and identify ICB- pyrimidine dimers, which result in a high prevalence of C- sensitive tumors. to-T transitions on the untranscribed strand (32). Specific Importantly, the above-described analyses of the mutational mutational signatures have also been reported in cancers with landscape only provide an "instantaneous and descriptive" pic- endogenous DNA damage repair defects, for example, BRCA1- ture of a tumor genome. Even mutational signatures, in some or BRCA2-deficient high-grade serous ovarian and triple-nega- cases, might exclusively reflect previous DNA repair deficiencies tive breast cancers, which harbor frequent loss of heterozygosity and may not be relevant markers of the actual DNA repair status of (28, 33); MMR-deficient colorectal cancer (34), associated with the tumor. It is therefore crucial to assess the potential for these a microsatellite instable phenotype and high mutational bur- mutations to functionally enhance antitumor immune responses den; and POLE-deficient endometrial cancers, which exhibit an by creating immunogenic neoantigens. ultra-mutated phenotype (7). It is somehow intuitive that the presence of high tumor mutational burden can increase the likelihood of neoantigens Predicted Neoantigen Load and Response formation, and that the most mutated tumors may also be the to ICB most immunogenic ones (35). However, if high mutational Two main classes of tumor antigens are classically described: burden has repeatedly been associated with response and (i) tumor-associated antigens (TAA), which are nonmutated self- improved outcome on ICB therapy, it would be na€ve to con- antigens that are aberrantly expressed by cancer cells following clude on that basis that there is a general correlation between genetic and epigenetic alterations, and (ii) tumor-specific anti- DNA repair deficiency and sensitivity to anti–PD-(L)1 (Fig. 2), gens, which are neoantigens that form as a result of nonsynon- the reality being much more complex. ymous mutations and are generally unique to a tumor. Among these, the latter only have been consistently associated with Mutational Burden and Response to ICB antitumor T-cell reactivity and clinical efficacy of ICB (40). The description of a correlation between mutational load and Although we can anticipate that highly mutated tumors are response to ICB was allowed by recent advances in next-genera- more prone to form neoantigens, the stochastic nature of neoanti- tion sequencing (NGS) technologies, notably whole-exome gen generation calls for a functional validation, as all formed sequencing (WES) and RNA-sequencing (RNA-seq). High muta- neoantigens may not be immunologically relevant. If it is obvious tional load, defined as >100 nonsynonymous single-nucleotide that nsSNVs represent a mine of immunogenic mutations, frame- variants (nsSNV) per exome, was first associated with clinical shift, splice site mutations, and intragenic fusions are also prone benefit in melanoma patients treated with anti–CTLA-4 therapy to generate neoepitopes when nonfunctional proteins are directed

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

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Table 1. Type and frequency of DNA repair alterations in solid tumors (Cont'd ) Alterations Cancer 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 withan asterisk 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. to the proteasome (41). The correlation between mutational patient was associated with the T-cell response against a single burden and predicted neoantigen load (as defined by the number neoantigen resulting from a nsSNV in HERC1 (6). In another of neoantigens potentially presented by the MHC class I) has been study evaluating response to anti-CTLA-4 in malignant myeloma, achieved by creating bioinformatics analysis pipelines that model Snyder and colleagues identified a set of consensus tetrapeptide the key steps of the antigen presentation process (Fig. 3 and Table sequences exclusively shared by patients exhibiting long-term 2): (i) expression of mutated proteins that are processed by the clinical benefit (4) and being necessary and sufficient for the proteasome, and produce neopeptides; (ii) translocation of the activation of an antitumor T-cell response; these results were neopeptides through the endoplasmic reticulum and binding to unfortunately not confirmed in two later studies (5, 37). the MHC class I molecule with a sufficient affinity to enable T-cell Mutational burden and predicted neoantigen load also shape presentation; and (iii) recognition of the presented neoantigen by the nature and functional properties of antitumor immune infil- a T-cell clone able to detect it. trates. The presence of tumor-infiltrating cytotoxic T-lymphocytes This modeling pipeline has been overall successful in correlat- (CTL) has been correlated to higher immunogenic mutation rate, ing mutational load with predicted neoantigen load. First, muta- using RNA-seq data (45). Rooney and colleagues further tional and predicted neoantigen loads were significantly corre- described that predicted neoantigen load correlated with the lated with clinical benefit in melanoma patients treated with cytolytic activity of intratumoral CTLs and natural killer (NK) ipilimumab (5). Consistently, it was suggested that tumors dis- cells (46), but that a given mutation rate was associated with playing >10 nsSNVs/Mb may produce sufficient neoantigens to distinct cytolytic activities across different histologies. For generate ant-tumor immunogenicity, whereas tumors with <1 instance, cervical cancers exhibit higher cytolytic activity than nsSNV/Mb may not (41). Further consistent observations were melanoma, although this cancer type is not as sensitive to ICB. made in DNA repair–deficient tumors, including MSI-high This suggests that both tissue-specific and tumor-specific factors tumors (7, 36), BRCA-mutated ovarian cancer (8), and melanoma contribute to immune escape regulation. Interestingly, this work (37). However, genomic instability and accumulation of muta- also proposed a model for correlating the subclonal evolution of tions is a double-edged sword process, which both favors the tumor genetics with the cytolytic activity of surrounding CTLs and generation of immunogenic neopeptides, but also allows emer- NK cells, thereby reinforcing the link between continuous tumor gence of less immunogenic new clones that escape immune genetics drift and immune escape. surveillance, thereby favoring primary or acquired resistance. Overall, the data presented above support that high mutational High intratumor heterogeneity (ITH) has indeed been correlated burden associates with increased neoantigens formation and with poorer outcome, whereas sensitivity to ICB is associated with tumor immunogenicity. However, the very high attrition rate, low ITH and high clonal neoantigens (42). This underlines the from a high mutational burden to the very few neoepitopes that paradoxical role of DNA repair defects in dictating response to will eventually produce an antitumor immune response, illus- ICB. Although DNA repair–deficient tumors exhibit high genomic trates well the complexity of predicting tumor immunogenicity instability and high mutational/neoantigen burdens, they are also using genomic data alone. Furthermore, other mechanisms, the most likely to display high ITH due to their propensity to including oncogenic stress (47–49), secretion of immunosup- provoke random mutations (43). If this observation represents a pressive cytokines (e.g., IL10; ref. 50), or downregulation of MHC strong biological argument for treating patients with ICB early in class I (51), also modulate tumor immunogenicity, and muta- the course of the disease, when genomic instability is high, and tional burden is only one component of the determinants of ICB ITH low, we can hypothesize that each clone within the tumor will sensitivity. retain some degree of intrinsic genomic instability, and participate in the generation of immunogenic neoantigens. Other Biomarkers of Response to Therefore, a key issue is the determination of which epitopes – will actually prime T-cell responses, among the bulk of released Anti PD-(L)1 epitopes. The study of a melanoma patient who experienced "Tumor-related" biomarkers complete response after 3 months of ipilimumab treatment Beyond tumor "antigenome," several biomarkers are being revealed that, out of 1,657 nsSNVs, the tumor only displayed developed to predict response to anti–PD-(L)1 therapies 448 immunologically relevant epitopes, and no more than two of (52, 53). The most promising and best validated one is probably them were identified as able to trigger a patient-specific antitumor PD-L1 expression assessment by immunohistochemical staining T-cell response (44). In a similar analysis, Rizvi and colleagues on tumor and/or tumor-infiltrating immune cells (3, 54–57). demonstrated that response to pembrolizumab in a NSCLC However, this biomarker currently lacks sensitivity—some

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A Exogenous stress: tobacco Exogenous stress: tobacco and HPV infection

0% TP53: 75% 24% 24% DSBR: 20% 20% CCNE1: 10% TP53: 70% MYC: 20% 76% SCLC 76% PTEN: 2% 80% CDKN2A: 20% Head and neck cancer Exogenous stress: tobacco DSBR: 45% MMR: 25% 20% 20% 15% 55% 45% NER: 30% DSBR: 15% PTEN: 25% 80% TP53: 80% 85%

80% TNBC

NSCLC RRM1: 65% ATM: 6% Exogenous stress: asbestos

15% 0% 22% 24% DSBR: 10% 85% MMR: 20% 78% 76% TP53: 60% TP53: 50% MDM2: 50% carcinoma Hepatocellular Mesothelioma

5% 19% 19% 15% MMR: 5% MMR: 15% 95% TP53: 2% 82% TP53: 35% 81% PTEN: 10% 85% cancer

PTEN: 15% Gastric CDKN2A: 30% CDKN2A: 35% Clear-cell RCC Clear-cell

15% 17% 10% DSBR: 2% MMR: 15% N.R. MMR: 10% 85% 83% 90% cancer cancer Pancreatic Biliary tract

17% DSBR: 50% 35% MMR: 50% 40% MMR: 6% 50% 50% 65% MRE11: 75% 60% CRC 83% TP53: 67%

POLD3: 37% cancer Ovarian ATR: 44% MMR-deﬁcient Exogenous stress: tobacco DSBR: 20% MMR: 3% 20% 20% 30% ERCC2: 10% N.R. MMR: 30% 80% TP53: 60% 80% POLE: 10% 70%

PTEN: 15% cancer

Urothelial CDKN2A: 50% carcinoma Endometrial Exogenous stress: UV rays

0% BRAF: 50% 32% 35% TP53: 20% 68% N.R. DSBR: 35% 65% cancer Prostate Melanoma

DNA repair defects Anti–PD-(L)1 frequency (%) response rate (%) Positive Responders Negative Refractory

B 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% Anti–PD-(L)1 response rate (%) rate Anti–PD-(L)1 response 0% 0% 10% 20% 30% 40% 50% 60% 70% DNA repair defects frequency (%)

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PD-L1-negative patients consistently experience clinical benefit colleagues in melanoma (37) identified a transcriptional signa- (58, 59), and specificity— not all PD-L1–positive tumors benefit ture associated with resistance to anti–PD-1 therapy. Exclusively from anti–PD-(L)1 therapy (2, 60). Furthermore, the parameters found in the pretreatment tumors of nonresponding patients, this of PD-L1 staining scoring are highly variable, notably the anti-PD- "innate anti–PD-1 resistance" (IPRES) is characterized by the L1 antibody (clone SP142 and clone SP2063, Ventana; and clone upregulation of genes involved in the regulation of epithelial– 28-8 and clone 22C3, Dako), the platform (PD-L1 IHC pharDx, mesenchymal transition (EMT), cell adhesion, extracellular Dako; OptiView DAB IHC Detection Kit, Ventana), the cells of matrix remodeling, angiogenesis, and wound healing. Very inter- interest (cancer cells, stromal cells, immune tumor-infiltrating estingly, this signature was not predictive of resistance to anti– cells), the positivity threshold (1%, 5%, 10%, or 50%), as well as CTLA-4 therapy, but found at variable frequencies across most the tumor material used for analysis (fresh versus archived common cancers, suggesting that some mechanisms of ICB resis- material, and primary versus metastatic tumor; ref. 61). Moreover, tance might be shared by different histologies. PD-L1 expression can be constitutive or inducible (e.g., INFg- In the aggregate, these data highlight that a comprehensive mediated induction; ref. 62). Together, these elements represent and integrated approach, which would encompass tumor significant hurdles for reaching the reproducibility and analytic genetics, immune checkpoint expression, microenvironmental, validity that is required for any companion biomarker develop- and immune-monitoring data, is highly needed to best select ment and clinical implementation. patients.

"Immune-related" biomarkers Beyond tumor-related biomarkers, the exploration of immune Conclusions and Future Challenges infiltrate characteristics may also provide interesting biomarkers. How could we improve and expand the use of DNA repair Analysis of pretreatment samples from melanoma and NSCLC deficiency, mutational burden, and predicted neoantigen load þ patients responding to pembrolizumab revealed higher CD8 T- for selecting patients that are the most likely to benefitfrom cell levels at the tumor-invasive margin, as compared with non- anti–PD-(L)1therapy?Targetedsequencingofhotspotmuta- responders (63, 64). Some more complex immune signatures tions in DNA repair gene panels provides useful but limited have also been explored: for example, Ribas and colleagues information, as it misses nongenetic forms of DNA repair described an immune gene expression signature associated with defects (e.g., secondary to epigenetic alterations), and, most gain in both ORR and PFS in melanoma patients treated with importantly, does not functionally evaluate the tumor DNA pembrolizumab (65), which is being explored in other histolo- repair capacity. The decreasing costs and expanding availability gies. More recently, an eight-gene signature reflecting preexisting of NGS technologies open interesting perspectives for their immunity, the "T-effector/IFNg signature," was explored in the broader use in clinical routine, and mutational load is a simple phase II POPLAR trial. High signature expression levels appeared parameter that is easily calculated and technically reproducible, to predict OS (but not PFS or ORR) benefit in atezolizumab- allowing the comparison and/or merging of various patient treated patients (66). series. We can therefore reasonably hope that, with increasing Immunomonitoring strategies, that is, repeated assessment numbers and open data sharing, relevant mutational thresh- of dynamic circulating biomarkers involved in immune olds for predicting sensitivity to anti–PD-(L)1 therapy, as well response, have also been proposed. These dynamic biomarkers, as tumor purity and sequencing depth that are required, will which include notably cytokines and inflammatory mediators be soon better defined in a histotype-specific fashion. The (Supplementary Table S3; ref. 67), can be monitored at several pipeline optimization and establishment of reference guide- timepoints on trial using a simple blood test. If these circulating lines for predicting neoantigen load will also accelerate the biomarkers have not been robust enough so far to predict clinical implementation of the latter work. Together, especially responders to ICB (52), they clearly represent a powerful and if integrated with PD-L1 IHC scoring and signatures of primary practical tool for monitoring patient response, and deserve as resistance, these data might rapidly become robust enough to such active investigation. be clinically implemented. However, several challenges will still need to be addressed: Anticipating primary treatment resistance (i) tumor material is not always available, and efforts should Finally, as is the case for any targeted therapy, and especially be made to develop equivalent assays on circulating biomar- considering the cost of ICB and their associated biomarkers, early kers, such as cell-free tumor DNA; (ii) tumor heterogeneity prediction of resistance is key. The very recent work by Hugo and needs 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 repair deficiency (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 was calculated on the basis of literature data (see Table 1 for raw data). When DNA repair defects in distinct pathways were mutually exclusive, the sum of 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 also evaluated 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. ORR reported in phase I, II, or III trials performed in the corresponding histologies were taken for estimating the efficacy of anti–PD-(L)1 inhibitors (see Supplementary 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 of statistically significant correlation between DNA repair mutation frequency and response to anti–PD-(L)1 therapies, highlighting the need to take into account 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.

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Tumor cell

Golgi apparatus

DNA

CTL

ER RNA MHC/peptide complex Peptides

TAP protein Proteasome

Transcription Proteasomal TAP-mediated Binding of T-cell recognition Somatic into mutated processing of the peptide transport peptides to MHC of cell surface 1 mutation 2 34 56 mRNA mutated protein into the ER lumen class I complex neoantigens cellular events cellular

HLA-binding Neoantigen synthesis Mutational Expression Proteasomal TAP transport HLA and T-cell reactivity proﬁling proﬁling processing prediction prediction typing prediction analysis

WGS RNA-seq NetCHOP 20S PredTAP Athlates ARB GenScript

WES PCM SVMTAP Polysolver SMM MHC multimer

CGH FragPredict OptiType NetMHC

MSI proﬁling PCleavage NetMHCpan Data NetCHOPCterm generation Analysis

Techniques and tools Techniques Techniques Software

Figure 3. Pipeline for the identification of immune-relevant neoantigens. The typical pipeline consists of six main steps: (1) Tumor mutational load and specific mutations are identified using WES or WGS. Additional techniques such as CGH or MSI-profiling might be of interest to evaluate genomic instability but have not been validated yet in this indication. Moreover, WES is always a required starting point as DNA sequence information is required for subsequent prediction tools. (2) Using RNA-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 dedicated algorithms. (5) To predict binding of peptides on MHC class I molecules, the previously selected peptides are implemented in a dedicated software that infers binding affinity to HLA molecules according to the HLA type of the patient. (6) Eventually, the predicted peptides may be synthesized to test for T-cell reactivity in vitro using the MHC multimer technology. Key technologies most often used in the literature are highlighted in bold. Techniques exclusively used to measure genomic instability are presented in dotted rectangles. ARB, average relative binding; CGH, comparative genomic hybridization; SMM, stabilized matrix method; WGS, whole-genome sequencing.

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Table 2. Advantages and drawbacks of the available techniques to identify immunogenic mutations/neoantigens Relevance for antigenome Technique or software Platform Strengths Weaknesses prediction References WGS Mutational profiling Both coding and noncoding DNA Sequencing depth is usually low, þþ (97) sequences are analysed. which prevents detection of some subclonal mutations. WES Mutational profiling Provides high sequence coverage (i) Only covers the 1% coding þþþ (97) across exome, increasing reliability regions of the genome. and ability to detect subclonal (ii) Some mutations may be missed mutations. due to uneven capture efficiency across exons. MSI profiling Microsatellite Several methods all well-validated. Only provides information on the þ (9) instability microsatellite instability. CGH Genomic instability Global picture of the overall genomic (i) Only provides copy-number þ instability. variations and translocations of large portions of the genome. (ii) No access to the DNA sequence. RNA-seq Expression profiling (i) Focuses on translated mutations (i) Access to matched normal is key þþþ (97) and coding only, that are the most likely to but cannot be achieved in many mutation analysis have functional consequences. cases: hard to distinguish tumor- (ii) Analysis not restricted to known specific mutations from genes: potential for discovering polymorphisms. novel transcripts, splice variants or (ii) Limited calling of mutations fusions. within RNA species due to their (iii) Possibility to correlate mutational low levels, either because of low data with gene expression. level gene expression or because of mRNA stability. NetCHOP 20S Proteasomal N.R. Predictions from in vitro data do not þ (98) PCM (WAPP package) processing capture the full complexity of FragPredict (MAPPP prediction trained proteasomal processing. package) on in vitro data PCleavage NetCHOPCterm Proteasomal (i) In vivo data provide accurate N.R. þþþ (98, 99) processing prediction as predictions are made prediction trained on the entire processing on in vivo data machinery (action of several proteasomes, cytosolic proteases...) (ii) May also capture transport efficiency. PredTAP TAP transport No comparative study available. No comparative study available. N.R. (98, 100) SVMTAP (WAPP package) prediction SMM (stabilized matrix Allele-specific HLA N.R. (ii) Does not account for non- þ (101–103) method) binding affinity linearities and interdependencies ARB average relative prediction between amino acids. binding (matrix-based methods) NetMHC [artificial neural Allele-specific HLA Nonlinear model. Does not allow prediction for all þþ (103, 104) networks (ANN)-based binding affinity known HLA alleles. method] prediction NetMHCpan [Pan-specific Pan-specificHLA (i) Allows predictions to be made for N.R. þþþ (105) artificial neural binding affinity all known HLA Class I alleles, networks (ANN)-based prediction including alleles for which no method] prediction is available with NetMHC. (ii) NetMHCpan is the best-performing method for allele-specific HLA binding affinity prediction. Athlates HLA typing N.R. (i) Early tool with lower accuracy þ (98) than that of up-to-date tools. Restricted to the use of WES data Polysolver HLA typing (i) Provides improved retrieval and (ii) Restricted to the use of WES þþþ (106) alignment of HLA reads. data Polysolver infers HLA-type information with 97% sensitivity and 98% precision from exome- capture sequencing data. (ii) Allows identification of patient- specific mutations in HLA alleles. (Continued on the following page)

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Table 2. Advantages and drawbacks of the available techniques to identify immunogenic mutations/neoantigens (Cont'd ) Relevance for antigenome Technique or software Platform Strengths Weaknesses prediction References OptiType HLA typing (i) Performs fully automated HLA (i) Zygosity detection occasionally þþþ (107) typing with four-digit resolution on fails in cases where alleles with NGS data from RNA-Seq, WES and high sequence similarity WGS technologies. constitute a heterozygous locus. (ii) OptiType showed an accuracy of (ii) Not able to resolve all ambigui- 99.3% on two-digit-level and of ties for every genotype. 97.1% on four-digit-level typing using datasets of RNA-Seq, WES and WGS technologies. MHC multimer technology T-cell reactivity (i) Gold-standard assay to identify N.R. þþþ (44, 97) analysis immunogenic peptides. Can be used to detect even low frequencies of antigen-specificT cells on small amounts of clinical material. (ii) "Peptide exchange technology" allows the production of large collections containing a lot of different peptide–MHC complexes for T-cell staining. NOTE: Multiple NGS technologies, bioinformatics tools, and pipelines are available to analyze tumor samples and predict immunogenic mutations/potential neoantigens in patients (see corresponding steps in Fig. 3). Primarily, genomic data are generated using various NGS technologies, most frequently including WES and RNA-seq to integrate both nsSNVs and expressed nsSNVs. These data are then analyzed using dedicated prediction algorithms corresponding to each step of the neoantigen generation biological process. These filtering tools guide the selection of immunogenic neoantigens among the bulk of candidate neoantigens. Although official guidelines are currently lacking on which tool should preferably be used, most often used algorithms include NetCHOPCterm for proteasomal processing prediction and NetMHC/NetMHCpan for HLA binding prediction. Eventually, a functional validation may be performed using an in vitro T-cell reactivity assay to validate the immunogenicity of the predicted neoantigens. Abbreviations: CGH, comparative genomic hybridization; MSI, microsatellite instability; N.R., not reported; WGS, whole-genome sequencing.

of mutations other than nsSNVs (including fusion transcripts Disclosure of Potential Conflicts of Interest and aberrantly expressed splice variants) requires further A. Marabelle is a consultant/advisory board member for Amgen, Biothera exploration; (iv) developing an integrated approach, that Pharmaceuticals, GlaxoSmithKline, Lytix Biopharma, Nektar, Novartis, Pfi- fi wouldalsoencompasstumormicroenvironment and immune zer, Roche/Genentech, and Seattle Genetics. J.-C. Soria is a scienti c cofounder of Gritstone Oncology and is a consultant/advisory board mem- infiltrates characteristics, as well as immunomonitoring data, ber for AstraZeneca, MSD, Pfizer, and Roche. No potential conflicts of warrants further investigation; and (v) last but not least, cost- interest were disclosed by the other authors. efficacy and health economics studies will be needed to deter- minewhichapproachwilleventually be the most relevant and Grant Support sustainable. R.M. Chabanon was supported by the Fondation Philanthropia. Together, these challenges open very stimulating perspectives and one can be certain that several exciting revolutions are still Received April 8, 2016; revised May 31, 2016; accepted May 31, 2016; to come soon in immuno-oncology. published OnlineFirst July 7, 2016.

References 1. Hodi FS, O'Day SJ, McDermott DF, Weber RW, Sosman JA, Haanen JB, metrial cancers with neoantigen load, number of tumor-infiltrating et al. Improved survival with ipilimumab in patients with metastatic lymphocytes, and expression of PD-1 and PD-L1. JAMA Oncol 2015; melanoma. N Engl J Med 2010;363:711–23. 1:1319–23. 2. Robert C, Schachter J, Long GV, Arance A, Grob JJ, Mortier L, et al. 8. Strickland KC, Howitt BE, Shukla SA, Rodig S, Ritterhouse L, Liu JF, et al. Pembrolizumab versus ipilimumab in advanced melanoma. N Engl J Association and prognostic significance of BRCA1/2-mutation status with Med 2015;372:2521–32. neoantigen load, number of tumor-infiltrating lymphocytes and expres- 3. Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, McDermott sion of PD-1/PD-L1 in high grade serous ovarian cancer. Oncotarget DF, et al. Safety, activity, and immune correlates of anti-PD-1 antibody in 2016;7:13587–98. cancer. N Engl J Med 2012;366:2443–54. 9. Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD, et al. PD-1 4. Snyder A, Makarov V, Merghoub T, Yuan J, Zaretsky JM, Desrichard A, et al. blockade in tumors with mismatch-repair deficiency. N Engl J Med Genetic basis for clinical response to CTLA-4 blockade in melanoma. N 2015;372:2509–20. Engl J Med 2014;371:2189–99. 10. Burnet M. Cancer; a biological approach. I. The processes of control. Br 5. Van Allen EM, Miao D, Schilling B, Shukla SA, Blank C, Zimmer L, et al. Med J 1957;1:779–86. Genomic correlates of response to CTLA-4 blockade in metastatic mel- 11. Burnet FM. The concept of immunological surveillance. Prog Exp Tumor anoma. Science 2015;350:207–11. Res 1970;13:1–27. 6. Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ, et al. 12. Dunn GP, Old LJ, Schreiber RD. The three Es of cancer immunoediting. Mutational landscape determines sensitivity to PD-1 blockade in non- Annu Rev Immunol 2004;22:329–60. small cell lung cancer. Science 2015;348:124–8. 13. Hino R, Kabashima K, Kato Y, Yagi H, Nakamura M, Honjo T, et al. Tumor 7. Howitt BE, Shukla SA, Sholl LM, Ritterhouse LL, Watkins JC, Rodig S, et al. cell expression of programmed cell death-1 ligand 1 is a prognostic factor Association of polymerase e-Mutated and microsatellite-instable endo- for malignant melanoma. Cancer 2010;116:1757–66.

OF10 Clin Cancer Res; 22(17) September 1, 2016 Clinical Cancer Research

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14. Taube JM, Anders RA, Young GD, Xu H, Sharma R, McMiller TL, et al. 36. Dudley JC, Lin M-T, Le DT, Eshleman JR. Microsatellite instability Colocalization of inflammatory response with B7-h1 expression in as a biomarker for PD-1 blockade. Clin Cancer Res 2016;22: human melanocytic lesions supports an adaptive resistance mechanism 813–20. of immune escape. Sci Transl Med 2012;4:127ra37. 37. Hugo W, Zaretsky JM, Sun L, Song C, Moreno Homet B, Hu-Lieskovan S, 15. Shi S-J, Wang L-J, Wang G-D, Guo Z-Y, Wei M, Meng Y-L, et al. B7-H1 et al. Genomic and transcriptomic features of response to anti-PD-1 expression is associated with poor prognosis in colorectal carcinoma and therapy in metastatic melanoma. Cell 2016;165:35–44. regulates the proliferation and invasion of HCT116 colorectal cancer cells. 38. Ansell SM, Lesokhin AM, Borrello I, Halwani A, Scott EC, Gutierrez M, PLoS One 2013;8:e76012. et al. PD-1 Blockade with nivolumab in relapsed or refractory Hodgkin's 16. Mu CY, Huang JA, Chen Y, Chen C, Zhang XG. High expression of PD-L1 lymphoma. N Engl J Med 2015;372:311–9. in lung cancer may contribute to poor prognosis and tumor cells immune 39. Helleday T, Eshtad S, Nik-Zainal S. Mechanisms underlying mutational escape through suppressing tumor infiltrating dendritic cells maturation. signatures in human cancers. Nat Rev Genet 2014;15:585–98. Med Oncol 2011;28:682–8. 40. Lennerz V, Fatho M, Gentilini C, Frye RA, Lifke A, Ferel D, et al. The 17. Yang CY, Lin MW, Chang YL, Wu CT, Yang PC. Programmed cell death- response of autologous T cells to a human melanoma is dominat- ligand 1 expression in surgically resected stage i pulmonary adenocarci- ed by mutated neoantigens. Proc Natl Acad Sci U S A 2005;102: noma and its correlation with driver mutations and clinical outcomes. Eur 16013–8. J Cancer 2014;50:1361–9. 41. Schumacher TN, Schreiber RD. Neoantigens in cancer immunotherapy. 18. Gao Q, Wang XY, Qiu SJ, Yamato I, Sho M, Nakajima Y, et al. Over- Science 2015;348:69–74. expression of PD-L1 significantly associates with tumor aggressiveness 42. McGranahan N, Furness AJ, Rosenthal R, Ramskov S, Lyngaa R, Saini SK, and postoperative recurrence in human hepatocellular carcinoma. Clin et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to Cancer Res 2009;15:971–9. immune checkpoint blockade. Science 2016;351:1463–9. 19. Kuang D-M, Zhao Q, Peng C, Xu J, Zhang J-P, Wu C, et al. Activated 43. Burrell RA, McGranahan N, Bartek J, Swanton C. The causes and con- monocytes in peritumoral stroma of hepatocellular carcinoma foster sequences of genetic heterogeneity in cancer evolution. Nature 2013; immune privilege and disease progression through PD-L1. J Exp Med 501:338–45. 2009;206:1327–37. 44. Van Rooij N, Van Buuren MM, Philips D, Velds A, Toebes M, Heems- 20. Ke W, Kryczek I, Chen L, Zou W, Welling TH. Kupffer cell suppression kerk B, et al. Tumor exome analysis reveals neoantigen-specificT-cell of CD8þ T cells in human hepatocellular carcinoma is mediated by reactivity in an ipilimumab-responsive melanoma. J Clin Oncol B7-H1/programmed death-1 interactions. Cancer Res 2009;69: 2013;31:439–42. 8067–75. 45. Brown SD, Warren RL, Gibb EA, Martin SD, Spinelli JJ, Nelson BH, et al. 21. Konishi J, Yamazaki K, Azuma M, Kinoshita I, Dosaka-Akita H, Nishimura Neo-antigens predicted by tumor genome meta-analysis correlate with M. B7-H1 expression on non-small cell lung cancer cells and its relation- increased patient survival. Genome Res 2014;24:743–50. ship with tumor-infiltrating lymphocytes and their PD-1 expression. Clin 46. Rooney MS, Shukla SA, Wu CJ, Getz G, Hacohen N. Molecular and genetic Cancer Res 2004;10:5094–100. properties of tumors associated with local immune cytolytic activity. Cell 22. Topalian SL, Drake CG, Pardoll DM. Immune checkpoint blockade: a 2015;160:48–61. common denominator approach to cancer therapy. Cancer Cell 2015; 47. Akbay EA, Koyama S, Carretero J, Altabef A, Tchaicha JH, Chris- 27:450–61. tensen CL, et al. Activation of the PD-1 pathway contributes to 23. Chen DS, Irving BA, Hodi FS. Molecular pathways: next-generation immune escape in EGFR-driven lung tumors. Cancer Discov immunotherapy-inhibiting programmed death-ligand 1 and pro- 2013;3:1355–63. grammed death-1. Clin Cancer Res 2012;18:6580–7. 48. Azuma K, Ota K, Kawahara A, Hattori S, Iwama E, Harada T, et al. 24. Robert C, Soria JC, Eggermont AMM. Drug of the year: programmed Association of PD-L1 overexpression with activating EGFR mutations Death-1 receptor/Programmed Death-1 Ligand-1 receptor monoclonal in surgically resected nonsmall-cell lung cancer. Ann Oncol 2014;25: antibodies. Eur J Cancer 2013;49:2968–71. 1935–40. 25. Farmer H, McCabe N, Lord CJ, Tutt ANJ, Johnson DA, Richardson TB, et al. 49. Green MR, Monti S, Rodig SJ, Juszczynski P, Currie T, O'Donnell E, et al. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic Integrative analysis reveals selective 9p24.1 amplification, increased PD-1 strategy. Nature 2005;434:917–21. ligand expression, and further induction via JAK2 in nodular sclerosing 26. Fong PC, Boss DS, Yap TA, Tutt A, Wu P, Mergui-Roelvink M, et al. Hodgkin lymphoma and primary mediastinal large B-cell lymphoma. Inhibition of poly(ADP-ribose) polymerase in tumors from BRCA muta- Blood 2010;116:3268–77. tion carriers. N Engl J Med 2009;361:123–34. 50. Berger S, Siegert A, Denkert C, Kobel€ M, Hauptmann S. Interleukin-10 in 27. Ledermann J, Harter P, Gourley C, Friedlander M, Vergote I, Rustin G, et al. serous ovarian carcinoma cell lines. Cancer Immunol Immunother Olaparib maintenance therapy in platinum-sensitive relapsed ovarian 2001;50:328–33. cancer. N Engl J Med 2012;366:1382–92. 51. Reichel J, Chadburn A, Rubinstein PG, Giulino-Roth L, Tam W, Liu Y, 28. Lord CJ, Ashworth A. The DNA damage response and cancer therapy. et al. Flow-sorting and exome sequencing reveals the oncogenome Nature 2012;481:287–94. of primary Hodgkin and Reed-Sternberg cells. Blood 2015;12:1061– 29. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 72. 2011;144:646–74. 52. Herbst RS, Soria J-C, Kowanetz M, Fine GD, Hamid O, Gordon MS, et al. 30. Lindahl T. Instability and decay of the primary structure of DNA. Nature Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A 1993;362:709–15. in cancer patients. Nature 2014;515:563–7. 31. Pleasance ED, Stephens PJ, O'Meara S, McBride DJ, Meynert A, Jones D, 53. Yearley JH, Gibson C, Yu N. PD-L2 expression in human tumors: rele- et al. A small-cell lung cancer genome with complex signatures of tobacco vance to anti-PD1 therapy in cancer [abstract]. In: Proceedings of the exposure. Nature 2010;463:184–90. European Cancer Congress 2015; 2015 Sep 25–29; Vienna, Austria. 32. Pfeifer GP, You Y-H, Besaratinia A. Mutations induced by ultraviolet light. Brussels (Belgium): European Cancer Organisation; 2015. Abstract nr Mutat Res 2005;571:19–31. 18LBA. 33. Timms KM, Abkevich V, Hughes E, Neff C, Reid J, Morris B, et al. 54. Garon EB, Rizvi NA, Hui R, Leighl N, Balmanoukian AS, Eder JP, et al. Association of BRCA1/2 defects with genomic scores predictive of DNA Pembrolizumab for the treatment of non-small-cell lung cancer. N Engl J damage repair deficiency among breast cancer subtypes. Breast Cancer Res Med 2015;372:2018–28. 2014;16:1–9. 55.BrahmerJR,RizviNA,LutzkyJ,KhleifS,Blake-HaskinsA,LiX,etal. 34. Hewish M, Lord CJ, Martin SA, Cunningham D, Ashworth A. Mismatch Clinical activity and biomarkers of MEDI4736, an anti-PD-L1 anti- repair deficient colorectal cancer in the era of personalized treatment. Nat body, in patients with NSCLC. J Clin Oncol 32:5s, 2014 (suppl; abstr Rev Clin Oncol 2010;7:197–208. 8021^). 35. Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SAJr, Behjati S, Biankin 56. Soria JC, Cruz C, Bahleda R, Delord JP, Horn L, Herbst RS, et al. Clinical A V, et al. Signatures of mutational processes in human cancer. Nature activity, safety, and biomarkers of PD-L1 blockade in non-small cell lung 2013;500:415–21. cancer (NSCLC): additional analyses from a clinical study of the

www.aacrjournals.org Clin Cancer Res; 22(17) September 1, 2016 OF11

Chabanon et al.

engineered antibody MPDL3280A (anti-PDL1) [abstract]. In: Proceedings overall survival in platinum-treated advanced urothelial carcinoma. Clin of the European Cancer Congress 2013; 2013 Sep 27–Oct 1; Amsterdam, Genitourin Cancer 2016;14:352–9. the Netherlands. Brussels (Belgium): European Cancer Organisation; 77. Bai S, Nunez AL, Wei S, Ziober A, Yao Y, Tomaszewski JE, et al. Micro- 2013. Abstract nr 3408. satellite instability and TARBP2 mutation study in upper urinary tract 57. Taube JM, Klein A, Brahmer JR, Xu H, Pan X, Kim JH, et al. Association of urothelial carcinoma. Am J Clin Pathol 2013;139:765–70. PD-1, PD-1 ligands, and other features of the tumor immune microen- 78. Weinstein JN, Akbani R, Broom BM, Wang W, Verhaak RGW, McConkey vironment with response to anti-PD-1 therapy. Clin Cancer Res D, et al. Comprehensive molecular characterization of urothelial bladder 2014;20:5064–74. carcinoma. Nature 2014;507:315–22. 58. Patel SP, Kurzrock R. PD-L1 expression as a predictive biomarker in cancer 79. Stoepker C, Ameziane N, van der Lelij P, Kooi IE, Oostra AB, Rooimans immunotherapy. Mol Cancer Ther 2015;14:847–56. MA, et al. Defects in the Fanconi anemia pathway and chromatid cohesion 59. Kerr KM, Tsao M-S, Nicholson AG, Yatabe Y, Wistuba II, Hirsch FR. in head-and-neck cancer. Cancer Res 2015;75:3543–5. Programmed death-ligand 1 immunohistochemistry in lung cancer: in 80. Lee JM, Ledermann JA, Kohn EC. PARP inhibitors for BRCA1/2 muta- what state is this art? J Thorac Oncol 2015;10:985–9. tion-associated and BRCA-like malignancies. Ann Oncol 2014;25: 60. Robert C, Long GV, Brady B, Dutriaux C, Maio M, Mortier L, et al. 32–40. Nivolumab in previously untreated melanoma without BRAF mutation. 81. Burgess M, Puhalla S. BRCA 1/2-mutation related and sporadic breast N Engl J Med 2015;372:320–30. and ovarian cancers: more alike than different. Front Oncol 2014; 61. Madore J, Vilain RE, Menzies AM, Kakavand H, Wilmott JS, Hyman J, et al. 4:19. PD-L1 expression in melanoma shows marked heterogeneity within and 82. Walsh T, Casadei S, Lee MK, Pennil CC, Nord AS, Thornton AM, et al. between patients: Implications for anti-PD-1/PD-L1 clinical trials. Pig- Mutations in 12 genes for inherited ovarian, fallopian tube, and perito- ment Cell Melanoma Res 2015;28:245–53. neal carcinoma identified by massively parallel sequencing. Proc Natl 62. MacMicking JD. Interferon-inducible effector mechanisms in cell-auton- Acad Sci U S A 2011;108:18032–7. omous immunity. Nat Rev Immunol 2012;12:367–82. 83. Bryant C, Rawlinson R, Massey AJ. Chk1 inhibition as a novel therapeutic 63. Tumeh PC, Harview CL, Yearley JH, Shintaku IP, Taylor EJM, Robert L, strategy for treating triple-negative breast and ovarian cancers. BMC et al. PD-1 blockade induces responses by inhibiting adaptive immune Cancer 2014;14:570. resistance. Nature 2015;515:568–71. 84. Bass AJ, Thorsson V, Shmulevich I, Reynolds SM, Miller M, Bernard B, et al. 64. Hu-Lieskovan S, Goldman J, Han M, Zaretsky J, Shintaku I, Wolf B, Comprehensive molecular characterization of gastric adenocarcinoma. et al. High intratumoral T-cell infiltration correlated with mutational Nature 2014;513:202–9. load and response to pembrolizumab in non-small cell lung cancer 85. Miquel C, Jacob S, Grandjouan S, Aime A, Viguier J, Sabourin JC, et al. [abstract]. In: Proceedings of the 16th World Conference on Lung Frequent alteration of DNA damage signalling and repair pathways in Cancer; 2015 Sep 6–9; Denver, CO. Aurora (CO): International human colorectal cancers with microsatellite instability. Oncogene Association for the Study of Lung Cancer; 2015. Abstract nr 2007;26:5919–26. ORAL3105. 86. The Cancer Genome Atlas Network. Comprehensive molecular char- 65. Ribas A, Robert C, Hodi FS, Wolchok JD, Joshua AM, Hwu W-J, et al. acterization of human colon and rectal cancer. Nature 2012;487: Association of response to programmed death receptor 1 (PD-1) 330–7. blockade with pembrolizumab (MK-3475) with an interferon-inflam- 87. Wang Y, Hong Y, Li M, Long J, Zhao YP, Zhang JX, et al. Mutation matory immune gene signature. J Clin Oncol 33, 2015 (suppl; abstr inactivation of Nijmegen breakage syndrome gene (NBS1) in hepatocel- 3001). lular carcinoma and intrahepatic cholangiocarcinoma. PLoS One 2013;8: 66. Fehrenbacher L, Spira A, Ballinger M, Kowanetz M, Vansteenkiste J, e82426. Mazieres J, et al. Atezolizumab versus docetaxel for patients with 88. Hinrichsen I, Kemp M, Peveling-Oberhag J, Passmann S, Plotz G, Zeuzem previously treated non-small-cell lung cancer (POPLAR): a multicen- S, et al. Promoter methylation of MLH1, PMS2, MSH2 and p16 is a tre, open-label, phase 2 randomised controlled trial. Lancet 2016;387: phenomenon of advanced-stage HCCs. PLoS One 2014;9:e84453. 1837–46. 89. Wani Y, Notohara K, Tsukayama C, Okada S. Reduced expression of 67. Manson G, Norwood J, Marabelle A, Kohrt H, Houot R. Biomarkers hMLH1 and hMSH2 gene products in high-grade hepatocellular carci- associated with checkpoint inhibitors. Ann Oncol 2016;27:1199–206. noma. Acta Med Okayama 2001;55:65–71. 68. O'SullivanCC,MoonDH,KohnEC,LeeJ,SullivanCCO,MoonDH, 90. Moy AP, Shahid M, Ferrone CR, Borger DR, Zhu AX, Ting D, et al. et al. Beyond breast and ovarian cancers: PARP inhibitors for BRCA Microsatellite instability in gallbladder carcinoma. Virchows Arch mutation-associated and BRCA-like solid tumors. Front Oncol 2014; 2015;466:393–402. 4:42. 91. Abraham SC, Lee JH, Boitnott JK, Argani P, Furth EE, Wu TT. Microsatellite 69. Postel-Vinay S, Vanhecke E, Olaussen KA, Lord CJ, Ashworth A, Soria J-C. instability in intraductal papillary neoplasms of the biliary tract. Mod The potential of exploiting DNA-repair defects for optimizing lung cancer Pathol 2002;15:1309–17. treatment. Nat Rev Clin Oncol 2012;9:144–55. 92. Mateo J, Carreira S, Sandhu S, Miranda S, Mossop H, Perez-Lopez R, et al. 70. Huang QM, Tomida S, Masuda Y, Arima C, Cao K, Kasahara TA, et al. DNA-repair defects and olaparib in metastatic prostate cancer. N Engl J Regulation of DNA polymerase POLD4 influences genomic instability in Med 2015;373:1697–708. lung cancer. Cancer Res 2010;70:8407–16. 93.AbeshouseA,AhnJ,AkbaniR,AllyA,AminS,AndryCD,etal.The 71. The Cancer Genome Atlas Network. Comprehensive molecular charac- molecular taxonomy of primary prostate cancer. Cell 2015;163: terization of clear cell renal cell carcinoma. Nature 2013;499:43–9. 1011–25. 72. Feng C, Ding G, Jiang H, Ding Q, Wen H. Loss of MLH1 confers resistance 94. The Cancer Genome Atlas Research NetworkIntegrated genomic charac- to PI3Kb inhibitors in renal clear cell carcinoma with SETD2 mutation. terization of endometrial carcinoma. Nature 2013;497:67–73. Tumor Biol 2015;36:3457–64. 95. Rustgi AK. Familial pancreatic cancer: genetic advances. Genes Dev 73. Yoo KH, Won KY, Lim S-J, Park Y-K, Chang S-G. Deficiency of MSH2 2014;28:1–7. expression is associated with clear cell renal cell carcinoma. Oncol Lett 96. Riazy M, Kalloger SE, Sheffield BS, Peixoto RD, Li-Chang HH, Scudamore 2014;8:2135–9. CH, et al. Mismatch repair status may predict response to adjuvant 74. Nickerson ML, Dancik GM, Im KM, Edwards MG, Turan S, Brown J, et al. chemotherapy in resectable pancreatic ductal adenocarcinoma. Mod Concurrent alterations in TERT, KDM6A, and the BRCA pathway in Pathol 2015;28:1383–9. bladder cancer. Clin Cancer Res 2014;20:4935–48. 97. Heemskerk B, Kvistborg P, Schumacher TNM. The cancer antigenome. 75. Plimack ER, Dunbrack RL, Brennan TA, Andrake MD, Zhou Y, Serebriiskii EMBO J 2013;32:194–203. IG, et al. Defects in DNA repair genes predict response to neoadjuvant 98. Backert L, Kohlbacher O. Immunoinformatics and epitope prediction in cisplatin-based chemotherapy in muscle-invasive bladder cancer. Eur the age of genomic medicine. Genome Med 2015;7:119. Urol 2015;68:959–67. 99. Peters B, Bulik S, Tampe R, Van Endert PM, Holzhutter€ H-G. Identifying 76. Mullane SA, Werner L, Guancial EA, Lis RT, Stack EC, Loda M, et al. MHC class I epitopes by predicting the TAP transport efficiency of epitope Expression levels of DNA damage repair proteins are associated with precursors. J Immunol 2003;171:1741–9.

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100. Zhang GL, Petrovsky N, Kwoh CK, August JT, Brusic V. PRED(TAP): a 104. Lundegaard C, Lund O, Nielsen M. Accurate approximation method system for prediction of peptide binding to the human transporter for prediction of class I MHC affinities for peptides of length 8, 10 and associated with antigen processing. Immunome Res 2006;2:3. 11 using prediction tools trained on 9mers. Bioinformatics 2008;24: 101. Peters B, Sette A. Generating quantitative models describing the sequence 1397–8. specificity of biological processes with the stabilized matrix method. BMC 105. Hoof I, Peters B, Sidney J, Pedersen LE, Sette A, Lund O, et al. NetMHCpan, Bioinform 2005;6:132. a method for MHC class i binding prediction beyond humans. Immu- 102. Bui HH, Sidney J, Peters B, Sathiamurthy M, Sinichi A, Purton KA, et al. nogenetics 2009;61:1–13. Automated generation and evaluation of specific MHC binding predictive 106. Shukla SA, Rooney MS, Rajasagi M, Tiao G, Dixon PM, Lawrence MS, et al. tools: ARB matrix applications. Immunogenetics 2005;57:304–14. Comprehensive analysis of cancer-associated somatic mutations in class I 103. Trolle T, Metushi IG, Greenbaum JA, Kim Y, Sidney J, Lund O, et al. HLA genes. Nat Biotechnol 2015;33:1152–8. Automated benchmarking of peptide-MHC class I binding predictions. 107. Kohlbacher O. OptiType: precision HLA typing from next-generation Bioinformatics 2015;31:2174–81. sequencing data. Bioinformatics 2014;30:3310–6.

www.aacrjournals.org Clin Cancer Res; 22(17) September 1, 2016 OF13

Mutational Landscape and Sensitivity to Immune Checkpoint Blockers

Roman M. Chabanon, Marion Pedrero, Céline Lefebvre, et al.

Clin Cancer Res Published OnlineFirst July 7, 2016.

Updated version Access the most recent version of this article at: doi:10.1158/1078-0432.CCR-16-0903

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