Association of PD-1/PD-L axis expression with cytolytic PNAS PLUS activity, mutational load, and prognosis in melanoma and other solid tumors

Ludmila Danilovaa,b,c, Hao Wanga, Joel Sunshined, Genevieve J. Kaunitzd, Tricia R. Cottrelle, Haiying Xud, Jessica Esandriod, Robert A. Andersa,c,e, Leslie Copea,c, Drew M. Pardolla,c, Charles G. Drakea,c,f, and Janis M. Taubea,c,d,e,1

aDepartment of Oncology, The Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD 21287; bVavilov Institute of General Genetics, Russian Academy of Science, Moscow, Russia, 119333; cThe Bloomberg-Kimmel Institute, The Johns Hopkins University School of Medicine, Baltimore, MD 21287; dDepartment of Dermatology, The Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD 21287; eDepartment of Pathology, The Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD 21287; and fThe Brady Urological Institute, The Johns Hopkins University School of Medicine, Baltimore, MD 21287

Edited by Antoni Ribas, Ronald Reagan University of California, Los Angeles Medical Center, Los Angeles, CA, and accepted by Editorial Board Member Tak W. Mak October 8, 2016 (received for review June 3, 2016) Programmed cell death -1 (PD-1)/programmed death ligand-1 infiltration and improved overall patient survival (14). Mutational (PD-L1) checkpoint blockade has led to remarkable and durable burden has also recently been demonstrated to predict response to objective responses in a number of different tumor types. A better anti–PD-1 therapy in patients with nonsmall cell lung carcinoma understanding of factors associated with the PD-1/PD-L axis expres- (NSCLC) (15). sion is desirable, as it informs their potential role as prognostic and PD-L1 expression in the pretreatment tumor microenviron- predictive biomarkers and may suggest rational treatment combi- ment has also been shown to predict response to PD-1/PD-L1 nations. In the current study, we analyzed PD-L1, PD-L2, PD-1,and blockade in multiple tumor types (8, 16–19). However, in con- cytolytic activity (CYT) expression, as well as mutational density trast to CD8, PD-L1 expression has been ascribed ambivalent from melanoma and eight other solid tumor types using The Cancer prognostic significance. It has been correlated with decreased Genome Atlas database. We found that in some tumor types, PD-L2 survival in esophageal, gastric, ovarian, pancreatic, and renal cell expression is more closely linked to Th1/IFNG expression and PD-1 carcinomas (20–25), and has been associated with an improved and CD8 signaling than PD-L1. In contrast, mutational load was not survival in patients with Merkel cell (26), breast (27), and cer- Th1/IFNG PD-L1 correlated with a signature in any tumor type. , vical carcinomas (28). Conflicting reports also exist regarding its PD-L2 PD-1 CYT , , expression, and mutational density are all positive significance within the same tumor type (e.g., melanoma and prognostic features in melanoma, and conditional inference model- NSCLC) (6, 29–31). These differences may be attributed to the ing revealed PD-1/CYT expression (i.e., an inflamed tumor microen- vironment) as the most impactful feature, followed by mutational Significance density. This study elucidates the highly interdependent nature of these parameters, and also indicates that future biomarkers for anti– PD-1/PD-L1 will benefit from tumor-type–specific, integrated, mRNA, The Cancer Genome Atlas datasets were used in the current study PD-L1 protein, and genomic approaches. to explore the relationship of programmed death ligand-1 ( ) expression, a cytotoxic T-cell gene signature, and mutational load PD-1 | PD-L1 | PD-L2 | melanoma | mutational load to each other, to immunoactive factors, such as programmed cell death protein-1 (PD-1), PD-L2, and other checkpoint molecules, – and to survival across multiple solid tumor types. We found that he host tumor interaction represents a complex, layered in- PD-L2 expression is more closely related to an ongoing host im- Tterplay of competing factors, the balance of which ultimately mune response in certain tumor types than PD-L1.Notably,mu- impacts patient survival. Genomic instability gives rise to muta- tational load was not immediately related to inflammation in any tions, facilitating tumor progression (1). At the same time, such tumor type studied, and was inferior to an inflamed tumor mi- mutations generate neoepitopes that may be recognized by the + croenvironment for predicting survival in patients with metastatic adaptive immune system, eliciting a protective, CD8 T-cell– – melanoma. Our findings also indicate the need for biomarker mediated antitumor response (2 5). In the next tier of interaction, assays that are tumor-type–specific and include both expression the tumor may demonstrate programmed death ligand-1 (PD- studies and genomic profiling. L1)–mediated adaptive immune resistance, effectively turning off

the surveilling T-cells via the programmed cell death protein-1 Author contributions: L.D., H.W., L.C., D.M.P., C.G.D., and J.M.T. designed research; L.D., (PD-1)/PD-L1 axis (6). Agents blocking either PD-1 or PD-L1 H.W., H.X., J.E., and J.M.T. performed research; L.D., H.W., J.S., G.J.K., T.R.C., R.A.A., L.C., prevent this last interaction, allowing an unbridled host response C.G.D., and J.M.T. analyzed data; and L.D., H.W., J.S., G.J.K., T.R.C., H.X., J.E., R.A.A., L.C., to tumor and potentially tipping the balance in favor of improved D.M.P., C.G.D., and J.M.T. wrote the paper. patient survival (7–9). Conflict of interest statement: R.A.A. is a compensated consultant for Adaptive Biotech. Factors at each of these levels have been highlighted as both D.M.P. has received research grants from Bristol-Myers Squibb and Potenza Therapeutics; + consults for Amgen, Five Prime Therapeutics, GlaxoSmithKline, Jounce Therapeutics, prognostic and predictive. The impact of CD8 T-cell infiltration on MedImmune, Merck, Pfizer, Potenza Therapeutics, and Sanofi; owns stock options in

survival has been the most well-studied. One meta-analysis sum- Jounce and Potenza; and receives patent royalties through his institution, from Bristol- INFLAMMATION + Myers Squibb, and Potenza. C.G.D. is a self-compensated consultant for Bristol-Myers IMMUNOLOGY AND marized that in 58 of 60 articles published, CD8 immune cell in- Squibb, AstraZeneca/Medimmune, Roche/Genentech, Potenza Therapeutics, and Tizona filtrates were a good prognostic feature in a wide variety of solid Therapeutics; and has patents licensed by Bristol-Myers Squibb, AstraZeneca/Medi- + tumor types (10). CD8 T-cell infiltration has also been associated mmune, and Potenza Therapeutics. J.M.T. is a compensated consultant for Bristol-Myers Squibb, AstraZeneca/Medimmune, and Merck. with an improved response to chemotherapy (11, 12), neoadjuvant – This article is a PNAS Direct Submission. A.R. is a Guest Editor invited by the Editorial therapy (13), and most recently, to anti PD-1 immunotherapy (8). Board. Similarly, immunogenic mutations in six solid tumor types (lung, 1To whom correspondence should be addressed. Email: [email protected]. ovary, breast, colorectal, brain, and kidney cancer in combined + This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. analysis) were recently shown to correlate with both CD8 T-cell 1073/pnas.1607836113/-/DCSupplemental.

www.pnas.org/cgi/doi/10.1073/pnas.1607836113 PNAS | Published online November 11, 2016 | E7769–E7777 Downloaded by guest on October 1, 2021 different detection and scoring systems used to quantify PD-L1 kidney renal clear cell carcinoma; LUSC, squamous cell carci- expression, variations in sample size, and variable follow-up du- noma of the lung; LUAD, lung adenocarcinoma; COAD, colonic ration. The mechanisms underlying PD-L1 expression—for exam- adenocarcinoma; HNSC, head and neck squamous cell carci- ple, an adaptive (IFN-γ–mediated) vs. an intrinsic pattern (because noma; PAAD, pancreatic adenocarcinoma; BRCA, breast car- of oncogenic driver mutations, loss of tumor suppressor , or cinoma; BLCA, bladder carcinoma), we determined the relative PD-L gene amplification)—may also influence the impact of PD- expression levels of PD-L1 (CD274), PD-L2 (PDCD1LG2), PD-1 L1 expression on survival as well as its utility as a biomarker for (PDCD1), and CYT (combined GZMA_PRF1 transcript levels, response to anti–PD-1/L1 therapy. The second ligand for PD-1, indicating cytolytic effector activity) (5). All cohorts were pri- PD-L2, is not as well-explored as PD-L1; that is, possible adaptive mary tumors, except for SKCM, where only metastases were vs. innate mechanisms of PD-L2 expression, its relationship to studied because of available sample numbers. Importantly, me- other markers of the PD-1/PD-L axis, and the prognostic signifi- dian levels of PD-Ls, PD-1, and CYT expression did not vary cance of PD-L2 have yet to be broadly and systematically significantly between primary or metastases, or by metastatic site addressed(21,23,25,28,32,33).Studiesataproteinlevelhave for SKCM (Fig. S1). Innate mechanisms of PD-L1 expression likely been limited because of the lack of a reliable, commercially have also been described, including amplifications of the PD-L available anti–PD-L2 antibody. Studies focused on the prognostic genes and mutations in tumor suppressor genes (36–39). We and predictive value of PD-1 in the tumor microenvironment are found that genetic alterations in the PD-Ls themselves occur on also more limited (8, 18, 25, 34, 35). average in <5% of tumor types studied, and that these alter- The purpose of this study was to use The Cancer Genome Atlas ations did not lead to statistically different levels of gene ex- (TCGA) dataset to determine the relationship between cytotoxic pression or altered survival compared with patients lacking such T-cells, PD-1/PD-L axis molecules, and mutational load, and their alterations (Fig. S2 A–D). We also found no impact of muta- relationship to each other and to survival in melanoma and eight tional subtype or phosphatase and tensin homolog deleted on other solid tumor types. The TCGA cohort benefits from a uni- 10 (PTEN) alteration (36) on PD-L expression form approach to specimen collection, methodology, and data levels in patients with melanoma (Fig. S2 E–H). As such, all analysis, allowing for standardized, pan-cancer comparisons. We patients were included in the subsequent analyses. found differences between tumor types with respect to the adap- The highest levels of PD-L1 and -L2 expression were seen in tive mechanism of PD-L/PD-1 expression, the impact of pathway the tumor types with squamous differentiation, whereas colonic member expression on survival, and how additional immuno- adenocarcinoma demonstrated the lowest levels of both PD-Ls modulatory factors associate with the PD-1/PD-L axis. We also of the tumor types studied (Fig. 1A). Like PD-L1 and -L2, mean demonstrate that the PD-L1 or -L2–mediated adaptive immune PD-1 and CYT expression levels trended in a paired fashion resistance phenotype is not a direct function of mutational load in across the tumor types studied (Fig. S3A). KIRC demonstrated any tumor type studied. These findings broaden our understanding the greatest differential expression of CYT relative to PD-1, of adaptive immune resistance by tumor and may help inform whereas SKCM demonstrated the smallest relative difference. rational biomarker and treatment combinations. For all of the studied markers, greater variances in expression were observed within a given tumor type than across tumor types. Results A similar finding was previously reported for mutational fre- PD-1/PD-L Expression Levels and Mutational Load Vary by Tumor quency (40), and was also observed here (Fig. S3B). We gener- Type. Comparisons of PD-L and PD-1 expression levels among ated 3D plots to show the median expression levels for each of different tumor types have been challenging because of the di- the PD-axis molecules, CYT, and mutational load, to determine verse assays used across studies. Using the TCGA public datasets if the relative relationship between these factors was constant for melanoma (SKCM) and eight other solid tumor types (KIRC, across tumor types. We found that BRCA consistently showed

Fig. 1. Relative relationships between PD-L1 and -L2 expression levels, as well as relative relationships between PD-L1, CYT, and mutational load across multiple solid tumor types from ∼3,500 patients. (A) Median expression levels of PD-L2 and PD-L1 differ in some tumor types, with KIRC, SKCM, lung, pancreatic, and breast adenocarcinomas demonstrating the largest statistical difference in levels of expression between the two markers (*P < 0.05, **P < 0.001). Overall, each marker demonstrates greater variation of expression within a single tumor type than across tumor types. CYT and PD-1 expression levels and mutational load by tumor type are shown in Fig. S3 A and B, respectively. (B) Three-dimensional plot demonstrating the relative relationships between median PD-L1, CYT expression, and mutational load across tumor types. Three-dimensional plots demonstrating the relative relationships between PD-L2 and PD-1, CYT expression, and mutational load across tumor types are shown in Fig. S3 C and D.

E7770 | www.pnas.org/cgi/doi/10.1073/pnas.1607836113 Danilova et al. Downloaded by guest on October 1, 2021 PNAS PLUS A PD-L1 PD-L2 PD-1 Mutaonal load

Correlaon Coefficient KIRC KIRC LUSC BLCA KIRC LUSC BRCA BLCA LUAD HNSC PAAD KIRC LUSC BRCA BLCA SKCM COAD LUAD HNSC PAAD BRCA SKCM LUSC COAD LUAD HNSC BLCA PAAD SKCM COAD BRCA HNSC LUAD PAAD SKCM B COAD

SKCM KIRC LUSC LUAD COAD HNSC PAAD BRCA BLCA PD-L1 3.06E-24 NA NA 3.50E-06 4.10E-16 9.00E-07 0.056 5.75E-09 1.08E-05 IFNG signaling pathway PD-L2 1.12E-22 4.17E-12 5.15E-10 3.29E-08 1.33E-06 3.55E-07 0.002 1.13E-07 3.27E-15

PD-L1 1.60E-15 NA NA 0.0029 3.87E-08 0.00078 0.27 2.40E-06 0.00039 CD8 TCR pathway PD-L2 2.24E-16 3.65E-08 6.53E-17 3.23E-10 0.00015 0.00011 0.0029 1.92E-07 3.68E-07

PD-L1 1.03E-15 NA 0.011 0.19 1.77E-12 0.012 0.32 0.0059 0.22 PD-1 signaling pathway PD-L2 1.05E-16 8.62E-12 1.06E-12 1.00E-12 5.72E-09 0.044 0.00097 0.001 5.72E-14

p < 0.00001 0.05 > p > 0.00001 p > 0.05

Fig. 2. Strong correlations are observed between all PD-axis molecules and the presence of a host immune response, but not mutational load. (A) Heat maps of correlation coefficients between PD-molecules and mutational density and a Th1/IFNG gene signature. When viewed from a pan-cancer perspective, PD-L2 is more strongly associated with an antitumor host immune response than PD-L1. Perhaps most notably, mutational load is not associated with a Th1/IFNG gene signature in any tumor type studied. (B) Every other gene in the TCGA dataset was then studied to expand the analysis of factors that correlated and anticorrelated with PD-L1 and -L2. Cut-offs of 0.6 and −0.6 were used for the correlation coefficients for factors that correlated and anticorrelated with PD-L1, and pathway analysis was performed on these factors. PD-L1 and -L2 expression and the strength of their associations with IFNG, CD8 TCR, and PD-1 signaling vary by tumor type, with a notable lack of an association between PD-L1 expression and IFNG and CD8 TCR signaling in KIRC and LUSC. PD-L2 expression is more closely associated with the PD-1 signaling pathway in many of the tumor types studied. Additional pathways of interest are highlighted in Table S1.NA means a significant number of pathway genes did not have a correlation coefficient >0.6 or <−0.6.

relative low median values for each of these parameters, except mutational load are shown in Fig. 2A. Surprisingly, we found that for PD-L2 (Fig. 1B and Fig. S3 B and C), whereas both lung although PD-axis molecule expression is strongly associated with the cancer histologies consistently showed relatively high median Th1/IFNG gene signature, the mutational load of tumors is not. values for each. In general, across the tumor types studied, the The Th1/IFNG gene signature heat maps also highlighted relative PD-molecule and CYT expression levels were more differences in the strength of the associations across tumor types closely related to each other than to mutational load. between PD-molecule expression and the host immune response. We further resolved these findings by performing pairwise cor- PD-1/PD-L Expression but Not Mutational Density Is Highly Associated relations between CYT, PD-1, PD-L, and IFNG expression by INFLAMMATION

with an Antitumor Host Immune Response. The expression of tumor tumor type, and demonstrated that PD-L1 expression is not as- IMMUNOLOGY AND neoantigens and subsequent recognition by host T-cells is hypoth- sociated with PD-1, CYT,orIFNG expression in KIRC or esized to be a major factor underlying the generation of an inflamed squamous cell carcinoma (SCC) of the lung (Dataset S2). We tumor phenotype. The antitumor host immune response may then also performed pathway analysis for IFNG signaling, CD8 T-cell eventuate in PD-L1 and -L2 display in the tumor microenvironment receptor (TCR) pathway and PD-1 signaling on genes correlated as a means of adaptive (IFN-γ-mediated) immune resistance. The with PD-L1 and -L2 expression and found that PD-L2 is more TCGA public datasets were used to test the strength of these as- closely related to IFNG, CD8 TCR, and PD-1 signaling pathway sociations. Heat maps for a combined Th1/IFNG gene signature expression than PD-L1 in renal cell, lung, pancreatic, and blad- (Dataset S1) and the association with PD-molecule expression and der carcinomas (Fig. 2B). Taken together, these findings indicate

Danilova et al. PNAS | Published online November 11, 2016 | E7771 Downloaded by guest on October 1, 2021 that in some tumor types, PD-L2 may be a stronger single PD-L1 coupled with agents targeting these latter molecules may measure of the presence of an IFNG-mediated antitumor re- thus be considered a more “orthogonal” approach to combination. sponse than PD-L1, and that PD-L–mediated adaptive immune resistance is not a direct function of the tumor’s mutational load. Differential Impact of PD-Ls, PD-1, and CYT Expression and Mutational Load on Survival Across Multiple Solid Tumor Types. The potential PD-1/PD-L Axis Expression Associates with Multiple Other Costimulatory impact of PD-molecule and CYT expression on survival were and Immunoactive Factors That Vary by Tumor Type. To expand the assessed for each tumor type. The strongest association for each study of potential factors associated with PD-axis molecule expres- of the factors was seen in SKCM, where each of these factors was sion in each tumor type, correlations were calculated between PD-1, shown to be of positive prognostic significance [PD-L1 hazard PD-L1,andPD-L2 and every other gene in the TCGA dataset to ratio (HR) = 0.807; PD-L2 HR = 0.761; PD-1 HR = 0.808; CYT identify genes either correlated or anticorrelated with expression HR = 0.798] (Fig. 3A). KIRC also showed an association of PD- (correlation coefficient >0.6 or <−0.6.). When pathway analyses L1 with improved survival (HR = 0.816, P value = 0.006), a were conducted on the identified genes, the resultant pathways were finding that contradicts prior reports studying both protein and almost exclusively immune or inflammatory related, and included mRNA expression (22, 25), likely because our data are log- many known costimulatory molecules and molecules associated transformed, curtailing the potential impact of outliers. Across with the adaptive immune response (Table S1). Pancreatic adeno- most of the tumor types studied, CYT expression was associated carcinoma was the only tumor type that had factors that notably with improved survival, achieving statistical significance in some. anticorrelated, and when pathway analysis was performed on these The impact of PD-1 expression on survival tended to track that of factors, metabolic pathways were highlighted, including the TCA CYT, consistent with the high correlations seen between these cycle, oxidative phosphorylation, and respiratory electron transport. markers. Notably, the positive association between high PD-1 When we conducted pairwise analysis between PD-L1, PD-1, expression and survival was statistically significant in the case and CYT expression and other known costimulatory and immu- of HNSC, confirming an earlier report (41). Other members of noactive elements (Datasets S3–S5), respectively, we identified the extended -family (i.e., B7-H3 and -H4) were assessed, and factors that could potentially be combined with anti–PD-1/PD-L1 B7-H3 showed a consistent trend toward worse survival across therapies. Strong associations of PD-1 and PD-L1 were observed nearly all tumor types studied, whereas the impact of B7-H4 with indoleamine 2,3-dioxygenase (IDO), inducible costimulator varied by tumor type (Fig. S4B). (ICOS), CD27, and T-cell immunoreceptor with Ig and ITIM We also tested the impact of mutational load on survival. We domains (TIGIT), among others, in multiple different tumor found that like PD-molecule expression, the impact of muta- types. Notably, the other B7-family members, B7-H3 (CD276)and tional load on survival was also dependent on tumor type (Fig. B7-H4 (VTCN1), did not associate with PD-axis molecules, CYT, 3B). Mutational load was a positive prognostic feature in BLCA or a Th1/IFNG gene signature (Fig. S4A). Similarly, B7-H2 and in SKCM, and on the opposite end of the spectrum, a high (ICOSL) showed only a weak association with PD-L1, depending mutational density was an adverse prognostic feature in pan- on the tumor type (Dataset S3). Regimens including anti–PD-1/ creatic carcinoma, and a similar trend was observed in KIRC.

Fig. 3. The impact of PD-L1, -L2, PD-1, CYT, and mutational load on survival varies by tumor type. (A) Forest plots showing the impact of PD-molecule and CYT expression on HRs from univariate Cox regression. The strongest associations were seen in SKCM, where all factors studied contributed to an improved prognosis. (B) Mutational load is a positive prognostic feature in SKCM and BLCA. *The number of samples varies between the mRNA expression-based assays and the DNA-based (mutation) analysis. **There was no difference in survival between HPV(+) and HPV(−) variants of HNSCC.

E7772 | www.pnas.org/cgi/doi/10.1073/pnas.1607836113 Danilova et al. Downloaded by guest on October 1, 2021 PNAS PLUS A high mutational load has recently been associated with the PC1 PC2 desmoplastic variant of SKCM (42). Additionally, desmoplastic A PD-L1 0.47 -0.15 melanoma is known to have a better survival than other melanoma subtypes (43). Because the TCGA dataset we studied does not PD-L2 0.51 -0.04 include the desmoplastic variant, the association between muta- PD-1 0.51 0.11 tional burden in SKCM and improved prognosis is a feature that extends beyond this rare subtype. The observed relationship be- CYT 0.51 0.11 tween mutational load and survival in SKCM is particularly Mutaonal load 0.02 -0.98 noteworthy, given the lack of an association between mutational Proporon of variance 0.682 0.206 load and a Th1/IFNG gene signature or PD-molecule or CYT expression and mutational burden. We hypothesize that although Cumulave proporon 0.682 0.888 the total mutational burden may not immediately correlate with an antitumor immune response, an increase in mutational load B increases the likelihood that one of the nonsynonymous mutations will ultimately be uniquely immunogenic (44).

PD-Axis Expression, CYT Expression, and Mutational Load Are Interdependent Factors That Associate with Survival in Melanoma. PD-axis molecules, CYT, and mutational load were all signifi- cantly associated with overall survival in SKCM by univariate analysis. When we plotted the expression of CYT, PD-molecule, and mutational load for each individual case in the SKCM TCGA cohort on a hive plot, the long-term survivors tended to demonstrate higher mutational rates, high CYT, and high PD- molecule expression, whereas the opposite was true for short- term survivors (Fig. S5). The interdependence of these factors was assessed using principal component analysis (PCA). By PCA, the first component explains 68% of the observed variance, with PD-1, PD-L1, PD-L2, and CD8 contributing nearly equally (Fig. 4A). The second component is comprised predominantly of mutational load and accounts for 21% of the variance. When we annotated the PCA by long- and short-term survivors, a degree of segregation was observed (Fig. 4B). To better assess the relative contribution of PD-axis molecules, CYT, and mutational load to survival, we used a conditional in- ference tree method to gauge their proportional impact. In this Fig. 4. PCA for PD-1, PD-Ls, CYT expression, and mutational load in SKCM. five-factor model, PD-1 (ostensibly expressed predominantly by (A) PD-1, PD-L1, PD-L2, and CYT expression all contribute nearly equally to + CD8 cytotoxic T cells) was the factor demonstrating the strongest the first principle component (PC1). Mutational load almost solely defines association with survival (P < 0.001) (Fig. 5). In fact, PD-1 was the the second component (PC2). (B) When the annotation for short- vs. long- sole factor predicting survival for patients with markedly elevated term SKCM survivors is superimposed on the PCA plot, a degree of separation PD-1 is evident between the two populations along the PC1 axis. The percentage of (top 15%) PD-1 expression. For patients with expression the total variance captured by each axis is shown in parenthesis. levels below this threshold, mutational load also contributed to outcome (P < 0.001). Finally, for the 25% of patients with both relatively low CYT and a low mutational burden, PD-L2 played a tumor type. We also demonstrated the differential impact of PD- contributing role in survival, with patients demonstrating im- 1/PD-L pathway member expression and mutational density on P = proved survival with increasing PD-L2 expression ( 0.049). survival. In doing so, we found that there is not a simple pan-cancer PD-1 When we removed from the model and created a four-factor “PD-L/PD-1 immunotype,” and provide information that could be CYT model, demonstrated the strongest association with survival, used for rational combinatorial treatment and biomarker strategies. followed by mutational load, affirming the importance of an We previously described PD-L1–mediated adaptive immune inflamed phenotype in SKCM for patient outcome (Fig. S6A). PD-1 PD-L1 PD-L2 CYT resistance in SKCM, and the associated finding that PD-L1 When five-factor models ( , , , ,andmuta- protein expression correlated with improved survival (6). We tional load) were created for the eight other tumor types studied, later showed an association between PD-L1 mRNA and protein only KIRC revealed that a combination of factors—in this case expression (45). Here, we confirm those findings and make the PD-1 and PD-L1 expression levels—was better able to predict observation that median PD-L2 expression levels in the tumor survival than when only individual factors were assessed (Fig. S6B). microenvironment are similar to those of PD-L1 and describe Discussion features consistent with PD-L2–mediated adaptive immune re- Previous studies addressing the prognostic and predictive fea- sistance across numerous solid tumor types. PD-L2 is the second ligand of PD-1, and engagement of this receptor–ligand pair can

tures of the PD-1/PD-L axis have either focused on a single tu- INFLAMMATION – mor type, grouped multiple solid tumor types together, or have also result in T-cell suppression. Anti PD-1 therapies block both IMMUNOLOGY AND – been limited in their ability to compare between tumor types the interaction between PD-1 and PD-L1 and -L2, but anti PD-L1 because of patient sample numbers (16–18). In this study, we therapies do not block the latter interaction. Despite the perils of analyzed ∼3,500 tumors from patients with SKCM and eight cross-trial comparisons, response rates from anti–PD-1 and anti– other solid tumor types available in the TCGA dataset. We PD-L1 agents appear similar (19). This finding suggests that the highlight the differences between tumor types with respect to the PD-1/PD-L1 interaction is dominant over the PD-1/PD-L2 in- adaptive (IFN-γ–mediated) mechanism of PD-L/PD-1 expres- teraction in the hierarchy of intratumoral immunosup- sion, as well as identify additional immunomodulatory factors pression and the potential involvement of additional tolerance and how they associate with PD-L/PD-1 to different degrees by mechanisms. Notwithstanding, our results indicate that PD-L2

Danilova et al. PNAS | Published online November 11, 2016 | E7773 Downloaded by guest on October 1, 2021 Our results also highlight numerous targetable immune genes that accompany PD-1/PD-L expression, many of which may co- ordinately contribute to immunosuppression. Combining agents that target LAG-3, IDO, ICOS, TIM-3,4-1BB,OX40,andCD27 with anti–PD-1/PD-L has been supported by preclinical models (53– 60), and many of these elements (e.g., LAG-3, TIM-3, IDO, and so forth) are currently being tested in clinical trials (https://clinicaltrials. gov; NCT01968109, NCT02608268, NCT02559492). The strength of the associations between PD-Ls and other immunomodulatory factorsdependedonthetumortype.IDO was associated with PD- L1 expression in SKCM, HNSC, COAD, and BRCA, but only weakly associated in LUAD, and showed no association in KIRC or squamous cell LUSC. Immunotherapeutic combinations may achieve synergy by mediating different mechanisms: for example, increasing lymphocyte infiltration into tumors, attenuation of im- munosuppression in the tumor microenvironment, or enhancing the performance of antigen-presenting cells (61). Our results in- dicate that the design of combinatorial regimens should not only take into account the potential complementary mechanisms of ac- tion of the agents being considered, but also the specific tumor type being treated. In contrast to PD-L1 and -L2, the other B7-family members studied here, B7-H3 and -H4, did not demonstrate an association with a Th1/IFNG–type response. Of these family members, B7-H3 may be of particular interest as a druggable target because it is highly expressed in solid tumors (62) and was shown here to be a Fig. 5. Conditional inference tree analysis demonstrating the relative con- tribution of CYT, PD-axis molecules, and mutational load to survival in SKCM negative prognostic feature in nearly every tumor type studied. patients. When the five studied factors are all entered into a multivariate The latter finding is consistent with previous reports showing model, PD-1 expression is the dominant factor influencing patient outcome. In that B7-H3 expression is associated with worse outcome in renal patients demonstrating the highest levels of PD-1, it is the only significantly cell carcinoma (RCC), prostate cancer, pancreatic carcinoma, contributing factor out of those studied. For patients with PD-1 levels below NSCLC, and SKCM (63–67). B7-H3 is thought to down-modu- this threshold, mutational load is the second most important factor influencing late T-cell responses (68), and may also impede NK-mediated survival. In the ∼25% of cases with relatively low PD-1 expression and muta- cell lysis (62). Initial phase I results with an anti–B7-H3 mono- tional load, PD-L2 expression also influences outcome. A four-factor model, clonal antibody showed antitumor activity in several solid including PD-L1, PD-L2, CYT, and mutational load, is shown in Fig. S6A. tumor types and a favorable safety profile (NCT02628535) (69), and studies combining B7-H3 with anti–CTLA-4 are also expression may serve as an even better surrogate marker of an underway (NCT02381314). antitumor immune response than PD-L1 in certain solid tumor It has been challenging to compare the impact of PD-L and PD-1 expression on survival across different tumor types, be- types. Thus, even if agents blocking the PD-1/PD-L2 interaction cause of the different cohort sizes, specimen size, and tissue do not demonstrate improved clinical efficacy over anti–PD-L1 PD-L2 analyzed (tissue microarray vs. biopsy vs. excision), assays, and monotherapy, may demonstrate utility as a biomarker of scoring systems. The strength of this study is that we used the an ongoing host response to tumor that could be potentiated by TCGA dataset, where large numbers of excisional specimens PD-1/L1 checkpoint blockade. from each tumor type were analyzed in a uniform fashion, fa- – We also made several additional observations regarding PD-L cilitating cross-cancer comparisons using meaningful sample mediated adaptive immune resistance that have biomarker impli- sizes. We found that PD-1 expression tracked CYT and thus cations. First, we showed that both PD-L1 and -L2 expression in trended toward a positive prognostic feature in nearly every tu- SKCM are independent of v-Raf murine sarcoma viral oncogene mor type, except in KIRC. These findings are consistent with the homolog B (BRAF), neuroblastoma RAS viral oncogene homolog prior body of literature on CYT (10), as well as prior reports for (NRAS), and PTEN mutations. Thus, BRAF, PTEN, and NRAS RCC (70–72). This seemingly paradoxical finding in RCC may be + mutational status should be used as separate biomarkers from because CD8 T-cells in the local tumor microenvironment re- PD-L1 or -L2 expression when potentially combining anti–PD-1/L1 quire local education by dendritic cells to exert an antitumor therapies with V600E antagonists, selective AKT pathway inhibitors effect; if this process does not take place, the T-cells exhibit an (46), or NRAS inhibitors, respectively (47). Second, we showed that exhaustion phenotype, which includes high PD-1 expression (25). PD-L1 -L2 KIRC and squamous cell NSCLC differ from SKCM in that PD-L1 We also found that and expression are positive CYT prognostic features in SKCM and RCC, and further identified a expression in these tumor types is not associated with the or PD-L1 -L2 Th1/IFNG gene signature. This latter finding provides a potential positive trend for and expression in BLCA and BRCA, respectively. PD-L1 has previously been reported to be a explanation for why PD-L1 expression has not shown clear utility as positive prognostic feature in both NSCLC and BRCA when a predictive biomarker in either of these tumor types (48–51). It also using an in situ approach to mRNA or protein detection and a provides a rationale for why a combined assay for PD-L1 protein dichotomous approach to scoring (30, 73, 74). We were not able expression plus an IFNG gene signature in patients with NSCLC is to demonstrate similar prognostic significance here for PD-L1 in a much better predictor of response than either assay used sepa- NSCLC. This discrepancy highlights one of the weaknesses of rately, because it enriches for the adaptive mechanism of PD-L1 this study, which is that the TCGA results provide a global expression (52). Additionally, we showed that PD-L gene amplifi- measure of marker expression that is not spatially resolved. cation at the 9p24.1 locus in the tumor does not significantly in- Another closely related weakness is that we did not characterize fluence global PD-L1 or L2 expression levels, most likely because of a broad infiltrating immune cell repertoire, and different cellular the presence of infiltrating immune cells lacking the alteration. subsets may exert either potentiating or opposing effects,

E7774 | www.pnas.org/cgi/doi/10.1073/pnas.1607836113 Danilova et al. Downloaded by guest on October 1, 2021 depending on their moiety, density, and the tumor type. In our tant implications, including the notion that CD8 density, PD-L1 PNAS PLUS study, this limitation is likely most relevant to both kidney and expression, or mutational density as solitary biomarkers are in- colon cancer, where it is recognized that parameters, such as the sufficient to characterize the tumor microenvironment. Our data ratio of effector-T cells to regulatory T-cells, levels of Th2 cells, thus support the pursuit of multiplexed biomarker assays that are presence of mature dendritic cells in tertiary lymphoid structures, tumor-type specific and include both expression studies and and the association of B-cells with T-follicular helper cells may genomic profiling. strongly influence patient outcome (25, 51, 75, 76). Specifically, the import of geographic context, cell-type–specific expression Methods patterns, and limited cell types studied may have contributed to Experimental Design. We studied nine cancer types from The Cancer Genome our finding that PD-1 and PD-L expression patterns have opposite Atlas project (TCGA) (https://gdc-portal.nci.nih.gov/): BLCA, BRCA, LUSC, effects on prognosis in KIRC when assessed as global metrics. LUAD, KIRC, COAD, SKCM, PAAD, and HNSC (82–89). All tumors analyzed Finally, because of sample size, we focused on metastatic SKCM were untreated primary lesions, except for melanoma, which were un- in the TCGA dataset, in contrast to the other tumor types studied. treated metastases. RNA sequencing (RNAseq) data level 3 RSEM normalized It is possible that the studied factors may exert different influences data were downloaded from TCGA Data Portal (https://gdc-portal.nci.nih. gov/) and log2-transformed. Microsatellite stability of a colon adenocarci- at various stages in other tumor types. noma sample was derived using MLH1 promoter methylation status (Illu- A number of recent reports showed that mutational burden mina HumanMethylation 450k probe cg00893636) and threshold 0.2. is predictive of response to checkpoint blockade (15, 77–79). Unmethylated samples with β-value less than 0.2 were considered Colorectal tumors with microsatellite instability are also recognized microsatellite stable. to have a better prognosis than their microsatellite-stable counter- Mutation annotation files (MutSig 2.0) were downloaded from the Broad parts. Here, we analyzed the possible prognostic impact of muta- Institute TCGA GDAC Firehose’s analysis_2015_04_02, (gdac.broadinstitute. tional load across multiple tumor types and show that increased org/). Mutational counts were log10-transformed for the further analysis. mutational load is also a positive prognostic factor in cutaneous Additionally, GISTIC2 level 4 data were downloaded the Broad Institute melanoma (nondesmoplastic) and in BLCA. It is notable that we TCGA GDAC Firehose’s analysis_2015_04_02, and all_thresholded.by_genes. did not observe a direct association between mutational load and txt files were used to access copy number calls. IFN-γ or a Th1/IFNG gene signature in any tumor type; the reason TCGA clinical datasets were also downloaded. For SKCM, we used the for this is not immediately clear, but it may be because of the timing curated clinical data from the published marker paper, including post- of when neoantigens in the tumor are recognized by the immune accession survival (86). For KIRC and HNSC, we also used clinical data from the published marker papers (82, 87). For all other cancer types, we down- system. The TCGA represents a snapshot in time, and tumors PD-L1 IFNG loaded clinical files, including survival from the initial pathologic time of exhibiting expression associated with atthetimeof diagnosis, from Genomic Data Commons Data Portal (https://gdc-portal.nci. harvest may be considered as demonstrating evidence of the host nih.gov/). The number of samples varied across the different types of anal- immune system’s ability to exert some degree of control over tumor yses depending on required data availability. growth. Our findings suggest that a separate, subsequent interaction may then occur between mutation-associated neoantigens and Cytolytic Activity and the Combined Th1/IFNG Gene Signature. CYT was calcu- therapy. This therapy, especially when it is immunomodulatory, may lated as the geometric mean of expression levels of GZMA and PRF1 genes, as tip the balance toward tumor rejection. It is possible that if the previously described (5). For the Th1/IFNG gene signature, we combined genes posttherapeutic time point were assayed, a Th1/IFNG signature from a published Th1 signature (76) and genes from IFNG signaling pathway associated with neoantigen recognition would be detected. from Reactome database (www.reactome.org)(Dataset S1). SKCM was distinguished from the other tumor types studied ’ here by the fact that PD-Ls, PD-1, CYT, and mutational load are all Correlation Analysis. The Pearson s correlation coefficients were reported for expression of PD-L1 (CD274), PD-L2 (PDCD1LG2), PD-1 (PDCD1), or CYT,and positive prognostic features. This finding provided us with the op- the rest of the genes and the Spearman’s correlation coefficients were portunity to further explore the relationship between these markers reported for the mutational load. as they relate to survival in this disease. We found that an “inflamed” SKCM phenotype (80), as represented here by CYT or Pathway Analysis. To study potential factors associated with PD-molecule ex- PD-1, is the most dominant feature in predicting survival. The sec- pression in each tumor type, we applied the Fisher’s exact test to estimate the ond most important factor is mutational load. The fact that PD-1/ intersection of genes in a pathway and genes with correlation coefficients >0.6 CYT and mutational load are differential contributors to outcome or <−0.6 in every cancer type. We used gene sets provided by the Molecular supports the earlier finding that the influence of mutational density Signatures Databases (software.broadinstitute.org/gsea/msigdb/index.jsp). The on prognosis may be temporally distinct from the immediately de- pathway gene sets are a collection of annotated gene sets from several online tectable host immune response to tumor. Stratification of this databases, such as the Kyoto Encyclopedia of Genes and Genomes (www.genome. complex interaction also provides a new perspective on how to as- jp/kegg), Reactome (www.reactome.org), and BioCarta (cgap.nci.nih.gov/Pathways/ BioCarta_Pathways), among others. The P values from the Fisher’s exact test were similate previously published studies where CD8 density, mutational adjusted by the Benjamini–Hochberg procedure (false-discovery rate) (90). load, and PD-L1 were tested as solitary biomarkers. It is possible PD-L1 that could emerge as more important to this hierarchy if it is Survival Analyses. Cox regression (the coxph function of the survival package) spatially resolved, rather than studied as a global value; however, a (91, 92) was used with continuous expression values or mutational load to previous study on melanoma brain metastases that assessed geo- estimate the hazard ratio, P value, and concordance for each studied pa- graphic relationships demonstrated that PD-L1 was not an in- rameter. Kaplan–Meier curves were made using the survfit function from dependent biomarker when CD8 infiltration was also assayed (81). the survival package (93, 94). To study the proportional impact of three Taken together, our data portray a comprehensive picture of (PD-L1, CYT, and mutational load) and five factors (PD-L1, PD-L2, PD-1, CYT, PD-L1 PD-L2 PD-1 CYT and mutational load) on survival in SKCM patients, we used a conditional

the interactions between , , , , and muta- INFLAMMATION

tional burden across several tumor types. We found that PD-L2 inference tree method as it implemented in the ctree function of partykit IMMUNOLOGY AND is more highly expressed and often more strongly associated with package (95, 96). Fivefold internal cross-validation was performed on the IFNG than PD-L1. We also found that the coexpression of ad- models. The default settings of the ctree function were used. ditional checkpoint molecules with PD-L1 and -L2 expression Statistical Software and Plots. Data analysis was performed using varies by tumor type. We determined that the parameters under R/Bioconductor software with build-in packages and custom routines (91, 97). study interact in a complex way to impact outcome, most notably Heat maps, box-and-whisker plots, and scatter plots were created using the in SKCM, where we identified mutational burden as a key gplots package and build-in R graphic functions. The HiveR package was used prognostic indicator that is subordinate to PD-1/CYT expression to create the hive plots (98). The forest plots were created with the help of the in its contribution to prognosis. These data have several impor- forestplot function of the rmeta package (99). The built-in R function was used

Danilova et al. PNAS | Published online November 11, 2016 | E7775 Downloaded by guest on October 1, 2021 for principal component analysis. Cross-cancer alterations were obtained from and the W.W. Smith Foundation (J.M.T.); the Melanoma Research Alliance (J.M.T. the MSKCC cBio portal (100, 101). and D.M.P.); Bristol-Myers Squibb (J.M.T., R.A.A., and D.M.P.); 5′ Diagnostics (R.A.A.); Sidney Kimmel Cancer Center Core Grant P30 CA006973 (to J.M.T., Immunohistochemistry. Immunohistochemistry for PD-L1 was performed and D.M.P., C.G.D., H.W., and L.D.); the National Cancer Institute NIH Grant R01 CA142779 (to J.M.T. and D.M.P.); NIH Grant T32 CA193145 (to T.R.C.); the Com- scored as previously described (6). Immunohistochemistry for PTEN was monwealth Foundation (D.M.P.); and Moving for Melanoma of Delaware performed according to standard automated methods using antibody clone 6H2.1 − (J.M.T. and D.M.P.). The authors were also supported by the Bloomberg- (BioCare Medical). A case was considered PTEN if it demonstrated a complete Kimmel Institute for Cancer Immunotherapy and a Stand Up To Cancer– absence of staining in both the nuclear and cytoplasmic compartments. Cancer Research Institute Cancer Immunology Translational Cancer Research Grant (SU2C-AACR-DT1012). Stand Up To Cancer is a program of the Enter- ACKNOWLEDGMENTS. We thank Drs. Jeffrey Sosman and Matthew Hellmann tainment Industry Foundation administered by the American Association for for helpful discussions. This work was supported by the Dermatology Foundation Cancer Research.

1. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: The next generation. Cell 29. Hino R, et al. (2010) Tumor cell expression of programmed cell death-1 ligand 1 is a 144(5):646–674. prognostic factor for malignant melanoma. Cancer 116(7):1757–1766. 2. Lennerz V, et al. (2005) The response of autologous T cells to a human melanoma is 30. Velcheti V, et al. (2014) Programmed death ligand-1 expression in non-small cell lung dominated by mutated neoantigens. Proc Natl Acad Sci USA 102(44):16013–16018. cancer. Lab Invest 94(1):107–116. 3. Matsushita H, et al. (2012) Cancer exome analysis reveals a T-cell-dependent mech- 31. Wang A, et al. (2015) The prognostic value of PD-L1 expression for non-small cell anism of cancer immunoediting. Nature 482(7385):400–404. lung cancer patients: A meta-analysis. Eur J Surg Oncol 41(4):450–456. 4. Robbins PF, et al. (2013) Mining exomic sequencing data to identify mutated anti- 32. Zhang Y, et al. (2014) Protein expression of programmed death 1 ligand 1 and ligand gens recognized by adoptively transferred tumor-reactive T cells. Nat Med 19(6): 2 independently predict poor prognosis in surgically resected lung adenocarcinoma. 747–752. Onco Targets Ther 7:567–573. 5. Rooney MS, Shukla SA, Wu CJ, Getz G, Hacohen N (2015) Molecular and genetic 33. Shi M, et al. (2014) Expression of programmed cell death 1 ligand 2 (PD-L2) is a properties of tumors associated with local immune cytolytic activity. Cell 160(1-2): distinguishing feature of primary mediastinal (thymic) large B-cell lymphoma and 48–61. associated with PDCD1LG2 copy gain. Am J Surg Pathol 38(12):1715–1723. 6. Taube JM, et al. (2012) Colocalization of inflammatory response with B7-h1 ex- 34. Thompson RH, et al. (2007) PD-1 is expressed by tumor-infiltrating immune cells and pression in human melanocytic lesions supports an adaptive resistance mechanism of is associated with poor outcome for patients with renal cell carcinoma. Clin Cancer immune escape. Sci Transl Med 4(127):127ra37. Res 13(6):1757–1761. 7. Robert C, et al. (2015) Nivolumab in previously untreated melanoma without BRAF 35. Ngiow SF, et al. (2015) A threshold level of intratumor CD8+ T-cell PD1 expression – mutation. N Engl J Med 372(4):320 330. dictates therapeutic response to anti-PD1. Cancer Res 75(18):3800–3811. 8. Tumeh PC, et al. (2014) PD-1 blockade induces responses by inhibiting adaptive 36. Parsa AT, et al. (2007) Loss of tumor suppressor PTEN function increases B7-H1 ex- – immune resistance. Nature 515(7528):568 571. pression and immunoresistance in glioma. Nat Med 13(1):84–88. 9. Brahmer J, et al. (2014) Nivolumab (anti-PD-1, BMS-936558, ONO-4538) in patients 37. Green MR, et al. (2010) Integrative analysis reveals selective 9p24.1 amplification, (pts) with advanced non-small-cell lung cancer (NSCLC): Survival and clinical activity increased PD-1 ligand expression, and further induction via JAK2 in nodular scle- by subgroup analysis. J Clin Oncol 32:8112 (abstr). rosing Hodgkin lymphoma and primary mediastinal large B-cell lymphoma. Blood 10. Fridman WH, Pagès F, Sautès-Fridman C, Galon J (2012) The immune contexture in 116(17):3268–3277. – human tumours: impact on clinical outcome. Nat Rev Cancer 12(4):298 306. 38. Pardoll DM (2012) The blockade of immune checkpoints in cancer immunotherapy. 11. Sotiriou C, Pusztai L (2009) Gene-expression signatures in breast cancer. N Engl J Med Nat Rev Cancer 12(4):252–264. – 360(8):790 800. 39. Skoulidis F, et al. (2015) Co-occurring genomic alterations define major subsets of 12. Halama N, et al. (2011) Localization and density of immune cells in the invasive KRAS-mutant lung adenocarcinoma with distinct biology, immune profiles, and margin of human colorectal cancer liver metastases are prognostic for response to therapeutic vulnerabilities. Cancer Discov 5(8):860–877. chemotherapy. Cancer Res 71(17):5670–5677. 40. Lawrence MS, et al. (2013) Mutational heterogeneity in cancer and the search for 13. Denkert C, et al. (2010) Tumor-associated lymphocytes as an independent predictor new cancer-associated genes. Nature 499(7457):214–218. of response to neoadjuvant chemotherapy in breast cancer. J Clin Oncol 28(1): 41. Badoual C, et al. (2013) PD-1-expressing tumor-infiltrating T cells are a favorable 105–113. prognostic biomarker in HPV-associated head and neck cancer. Cancer Res 73(1): 14. Brown SD, et al. (2014) Neo-antigens predicted by tumor genome meta-analysis 128–138. correlate with increased patient survival. Genome Res 24(5):743–750. 42. Shain AH, et al. (2015) Exome sequencing of desmoplastic melanoma identifies re- 15. Rizvi NA, et al. (2015) Cancer immunology. Mutational landscape determines sen- current NFKBIE promoter mutations and diverse activating mutations in the MAPK sitivity to PD-1 blockade in non-small cell lung cancer. Science 348(6230):124–128. pathway. Nat Genet 47(10):1194–1199. 16. Topalian SL, et al. (2012) Safety, activity, and immune correlates of anti-PD-1 anti- 43. Busam KJ, et al. (2004) Cutaneous desmoplastic melanoma: Reappraisal of mor- body in cancer. N Engl J Med 366(26):2443–2454. phologic heterogeneity and prognostic factors. Am J Surg Pathol 28(11):1518–1525. 17. Taube JM, et al. (2014) Association of PD-1, PD-1 ligands, and other features of the 44. Gubin MM, Schreiber RD (2015) CANCER. The odds of immunotherapy success. tumor immune microenvironment with response to anti-PD-1 therapy. Clin Cancer Science 350(6257):158–159. Res 20(19):5064–5074. 45. Taube JM, et al. (2015) Differential expression of immune-regulatory genes associ- 18. Herbst RS, et al. (2014) Predictive correlates of response to the anti-PD-L1 antibody ated with PD-L1 display in melanoma: Implications for PD-1 pathway blockade. Clin MPDL3280A in cancer patients. Nature 515(7528):563–567. – 19. Sunshine J, Taube JM (2015) PD-1/PD-L1 inhibitors. Curr Opin Pharmacol 23:32–38. Cancer Res 21(17):3969 3976. 20. Wu C, et al. (2006) Immunohistochemical localization of programmed death-1 li- 46. Peng W, et al. (2016) Loss of PTEN promotes resistance to T cell-mediated immu- – gand-1 (PD-L1) in gastric carcinoma and its clinical significance. Acta Histochem notherapy. Cancer Discov 6(2):202 216. 108(1):19–24. 47. Ascierto PA, et al. (2013) MEK162 for patients with advanced melanoma harbouring 21. Ohigashi Y, et al. (2005) Clinical significance of programmed death-1 ligand-1 and NRAS or Val600 BRAF mutations: a non-randomised, open-label phase 2 study. – programmed death-1 ligand-2 expression in human esophageal cancer. Clin Cancer Lancet Oncol 14(3):249 256. Res 11(8):2947–2953. 48. Brahmer J, et al. (2015) Nivolumab versus docetaxel in advanced squamous-cell non- – 22. Thompson RH, et al. (2006) Tumor B7-H1 is associated with poor prognosis in renal small-cell lung cancer. N Engl J Med 373(2):123 135. cell carcinoma patients with long-term follow-up. Cancer Res 66(7):3381–3385. 49. Motzer RJ, et al. (2015) Nivolumab for metastatic renal cell carcinoma: Results of a – 23. Hamanishi J, et al. (2007) Programmed cell death 1 ligand 1 and tumor-infiltrating randomized phase II trial. J Clin Oncol 33(13):1430 1437. CD8+ T lymphocytes are prognostic factors of human ovarian cancer. Proc Natl Acad 50. Motzer RJ, et al.; CheckMate 025 Investigators (2015) Nivolumab versus everolimus – Sci USA 104(9):3360–3365. in advanced renal-cell carcinoma. N Engl J Med 373(19):1803 1813. 24. Nomi T, et al. (2007) Clinical significance and therapeutic potential of the pro- 51. McDermott DF, et al. (2016) Atezolizumab, an anti-programmed death-ligand 1 grammed death-1 ligand/programmed death-1 pathway in human pancreatic can- antibody, in metastatic renal cell carcinoma: Long-term safety, clinical activity, and cer. Clin Cancer Res 13(7):2151–2157. immune correlates from a phase Ia study. J Clin Oncol 34(8):833–842. 25. Giraldo NA, et al. (2015) Orchestration and prognostic significance of immune 52. Higgs BW, et al. (2015) High tumoral IFNg mRNA, PD-L1 protein, and combined IFNg checkpoints in the microenvironment of primary and metastatic renal cell cancer. mRNA/PD-L1 protein expression associates with response to durvalumab (anti-PD-L1) Clin Cancer Res 21(13):3031–3040. monotherapy in NSCLC patients. European Cancer Conference 2015 (European 26. Lipson EJ, et al. (2013) PD-L1 expression in the Merkel cell carcinoma microenvi- Cancer Congress, Vienna), abstr 15LBA. ronment: Association with inflammation, Merkel cell polyomavirus and overall sur- 53. Woo SR, et al. (2012) Immune inhibitory molecules LAG-3 and PD-1 synergistically vival. Cancer Immunol Res 1(1):54–63. regulate T-cell function to promote tumoral immune escape. Cancer Res 72(4): 27. Wimberly H, et al. (2015) PD-L1 expression correlates with tumor-infiltrating lym- 917–927. phocytes and response to neoadjuvant chemotherapy in breast cancer. Cancer 54. Chauvin JM, et al. (2015) TIGIT and PD-1 impair tumor antigen-specific CD8⁺ T cells in Immunol Res 3(4):326–332. melanoma patients. J Clin Invest 125(5):2046–2058. 28. Karim R, et al. (2009) Tumor-expressed B7-H1 and B7-DC in relation to PD-1+ T-cell 55. Spranger S, et al. (2014) Mechanism of tumor rejection with doublets of CTLA-4, PD-1/ infiltration and survival of patients with cervical carcinoma. Clin Cancer Res 15(20): PD-L1, or IDO blockade involves restored IL-2 production and proliferation of CD8(+) 6341–6347. T cells directly within the tumor microenvironment. J Immunother Cancer 2:3.

E7776 | www.pnas.org/cgi/doi/10.1073/pnas.1607836113 Danilova et al. Downloaded by guest on October 1, 2021 56. Ding H, et al. (2006) Delivering PD-1 inhibitory signal concomitant with blocking 77. Le DT, et al. (2015) PD-1 blockade in tumors with mismatch-repair deficiency. N Engl PNAS PLUS ICOS co-stimulation suppresses lupus-like syndrome in autoimmune BXSB mice. Clin J Med 372(26):2509–2520. Immunol 118(2-3):258–267. 78. Van Allen EM, et al. (2015) Genomic correlates of response to CTLA-4 blockade in 57. Sakuishi K, et al. (2010) Targeting Tim-3 and PD-1 pathways to reverse T cell ex- metastatic melanoma. Science 350(6257):207–211. haustion and restore anti-tumor immunity. J Exp Med 207(10):2187–2194. 79. Snyder A, et al. (2014) Genetic basis for clinical response to CTLA-4 blockade in 58. Melero I, Grimaldi AM, Perez-Gracia JL, Ascierto PA (2013) Clinical development of melanoma. N Engl J Med 371(23):2189–2199. immunostimulatory monoclonal antibodies and opportunities for combination. Clin 80. Gajewski TF, Schreiber H, Fu YX (2013) Innate and adaptive immune cells in the Cancer Res 19(5):997–1008. tumor microenvironment. Nat Immunol 14(10):1014–1022. 59. Koyama S, et al. (2016) Adaptive resistance to therapeutic PD-1 blockade is associ- 81. Kluger HM, et al. (2015) Characterization of PD-L1 expression and associated T-cell ated with upregulation of alternative immune checkpoints. Nat Commun 7:10501. infiltrates in metastatic melanoma samples from variable anatomic sites. Clin Cancer 60. Buchan SL, et al. (2015) OX40- and CD27-mediated costimulation synergizes with Res 21(13):3052–3060. anti-PD-L1 blockade by forcing exhausted CD8+ T cells to exit quiescence. J Immunol 82. Cancer Genome Atlas Research Network (2013) Comprehensive molecular charac- – 194(1):125–133. terization of clear cell renal cell carcinoma. Nature 499(7456):43 49. 61. Melero I, et al. (2015) Evolving synergistic combinations of targeted immunother- 83. Cancer Genome Atlas Research Network (2014) Comprehensive molecular charac- – apies to combat cancer. Nat Rev Cancer 15(8):457–472. terization of urothelial bladder carcinoma. Nature 507(7492):315 322. 62. Yi KH, Chen L (2009) Fine tuning the immune response through B7-H3 and B7-H4. 84. Cancer Genome Atlas Research Network (2014) Comprehensive molecular profiling – Immunol Rev 229(1):145–151. of lung adenocarcinoma. Nature 511(7511):543 550. 63. Crispen PL, et al. (2008) Tumor cell and tumor vasculature expression of B7-H3 85. Cancer Genome Atlas Research Network (2012) Comprehensive genomic character- – predict survival in clear cell renal cell carcinoma. Clin Cancer Res 14(16):5150–5157. ization of squamous cell lung cancers. Nature 489(7417):519 525. 64. Roth TJ, et al. (2007) B7-H3 ligand expression by prostate cancer: A novel marker of 86. Cancer Genome Atlas Network (2015) Genomic classification of cutaneous mela- noma. Cell 161(7):1681–1696. prognosis and potential target for therapy. Cancer Res 67(16):7893–7900. 87. Cancer Genome Atlas Network (2015) Comprehensive genomic characterization of 65. Wang J, et al. (2013) B7-H3 associated with tumor progression and epigenetic reg- head and neck squamous cell carcinomas. Nature 517(7536):576–582. ulatory activity in cutaneous melanoma. J Invest Dermatol 133(8):2050–2058. 88. Cancer Genome Atlas Network (2012) Comprehensive molecular portraits of human 66. Mao Y, et al. (2015) B7-H1 and B7-H3 are independent predictors of poor prognosis breast tumours. Nature 490(7418):61–70. in patients with non-small cell lung cancer. Oncotarget 6(5):3452–3461. 89. Cancer Genome Atlas Network (2012) Comprehensive molecular characterization of 67. Xu H, et al. (2016) B7-H3 and B7-H4 are independent predictors of a poor prognosis human colon and rectal cancer. Nature 487(7407):330–337. in patients with pancreatic cancer. Oncol Lett 11(3):1841–1846. 90. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: A practical and 68. Leitner J, et al. (2009) B7-H3 is a potent inhibitor of human T-cell activation: No powerful approach to multiple testing. J Roy Stat Soc B Met 57(1):289–300. evidence for B7-H3 and TREML2 interaction. Eur J Immunol 39(7):1754–1764. 91. R Core Team (2004) R: A Language and Environment for Statistical Computing. 69. Powderly J, et al. (2015) Interim results of an ongoing Phase I, dose escalation study (R Foundation for Statistical Computing, Vienna, Austria). Available at https://www. of MGA271 (Fc-optimized humanized anti-B7-H3 monoclonal antibody) in patients r-project.org/. Accessed May 6, 2015. with refractory B7-H3-expressing neoplasms or neoplasms whose vasculature ex- 92. Smyth G (2005) Limma: Linear models for microarray data. Bioinformatics and presses B7-H3. J Immunother Cancer 3(Suppl 2):O8. Computational Biology Solutions using R and Bioconductor, eds Gentleman R, 70. Nakano O, et al. (2001) Proliferative activity of intratumoral CD8(+) T-lymphocytes as Carey VJ, Dudoit S, Irizarry R, Huber W (Springer, New York), pp 397–420. a prognostic factor in human renal cell carcinoma: clinicopathologic demonstration 93. Andersen PK, Gill RD (1982) Cox’s regression model for counting processes: A large – of antitumor immunity. Cancer Res 61(13):5132 5136. sample study. Ann Stat 10(4):1100–1120. 71. Beuselinck B, et al. (2015) Molecular subtypes of clear cell renal cell carcinoma are 94. Therneau TM, Grambsch PM (2000) Modeling Survival Data: Extending the Cox associated with sunitinib response in the metastatic setting. Clin Cancer Res 21(6): Model (Springer, New York), 1st Ed. – 1329 1339. 95. Hothorn T, Hornik K, Zeileis A (2006) Unbiased recursive partitioning: A conditional 72. Remark R, et al. (2013) Characteristics and clinical impacts of the immune environ- inference framework. J Comput Graph Stat 15(3):651–674. ments in colorectal and renal cell carcinoma lung metastases: influence of tumor 96. Hothorn T, Zeileis A (2015) partykit: A modular toolkit for recursive partytitioning in origin. Clin Cancer Res 19(15):4079–4091. R. J Mach Learn Res 16:3905–3909. 73. Schalper KA, et al. (2014) In situ tumor PD-L1 mRNA expression is associated with 97. Gentleman RC, et al. (2004) Bioconductor: Open software development for com- increased TILs and better outcome in breast carcinomas. Clin Cancer Res 20(10): putational biology and bioinformatics. Genome Biol 5(10):R80. 2773–2782. 98. Hanson BA (2014) The HiveR Package.1-23. Available at academic.depauw.edu/ 74. Cooper WA, et al. (2015) PD-L1 expression is a favorable prognostic factor in early ∼hanson/HiveR/HiveR.html. Accessed May 6, 2015. stage non-small cell carcinoma. Lung Cancer 89(2):181–188. 99. Lumley T (2012) rmeta: Meta-analysis. R package version. Available at CRAN.R- 75. Senbabaoglu Y, et al. (September 1, 2015) The landscape of T cell infiltration in project.org/package=rmeta. Accessed May 6, 2015. human cancer and its association with antigen presenting . bioRxiv, 100. Gao J, et al. (2013) Integrative analysis of complex cancer genomics and clinical 10.1101/025908. profiles using the cBioPortal. Sci Signal 6(269):pl1. 76. Bindea G, et al. (2013) Spatiotemporal dynamics of intratumoral immune cells reveal 101. Cerami E, et al. (2012) The cBio cancer genomics portal: An open platform for ex- the immune landscape in human cancer. Immunity 39(4):782–795. ploring multidimensional cancer genomics data. Cancer Discov 2(5):401–404. INFLAMMATION IMMUNOLOGY AND

Danilova et al. PNAS | Published online November 11, 2016 | E7777 Downloaded by guest on October 1, 2021