Precis Oncol by American Society of Clinical Oncology

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1 original report Programmed Cell Death 1 (PD-1) Ligand (PD-L1) Expression in Solid Tumors As a Predictive Biomarker of Benefit From PD-1/PD-L1 Axis Inhibitors: A Systematic Review and Meta-Analysis Monica Khunger Adrian V. Hernandez Vinay Pasupuleti Sagar Rakshit Nathan A. Pennell James Stevenson Sanjay Mukhopadhyay Kurt Schalper Vamsidhar Velcheti Monica Khunger, Sagar Rakshit, Sanjay Mukhopadhyay, Cleveland Clinic; Adrian V. Hernandez, University of Connecticut/Hartford Hospital, Universidad Peruana de Ciencias Aplicades; Vinay Pasupuleti, Case Western Reserve University School of Medicine; Nathan A. Pennell, James Stevenson and Vamsidhar Velcheti, Taussig Cancer Center, Cleveland, OH; and Kurt Schalper, Yale University School of Medicine, New Haven, CT. Corresponding author: Vamsidhar Velcheti, MD, FACP, Center for Immuno-Oncology Research, Cleveland Clinic, Cleveland, OH 4419; velchev@ ccf.org. abstract Purpose Drugs targeting the programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) pathway show significant clinical activity across several tumor types. However, a majority of patients do not respond to these agents. Use of biomarker assays to predict response to these agents is an active area of research; however, the predictive value of PD-L1 immunohistochemistry (IHC) assays is largely inconsistent across clinical s. In this meta-analysis of clinical s of PD-1/PD-L1 targeted agents, we evaluate the predictive value of a tumor and tumor-infiltrating immune cell PD-L1 IHC assay as a biomarker for objective response to PD-1/ PD-L1 inhibitors. Methods We searched databases (PubMed, Medline, ASCO abstracts, European Society for Medical Oncology abstracts, and Scopus) up until December 2016 for clinical s using PD-1/ PD-L1 inhibitors with reported PD-L1 biomarker data. Objective response rates (primary end point) from all phase I to III s investigating nivolumab, pembrolizumab, atezolizumab, durvalumab, and avelumab in advanced solid tumors were collected. Odds ratios (ORs) for response in PD-L1 positive patients compared with PD-L1 negative patients were calculated using the DerSimonian-Laird random effects model to combine s. We performed metaanalysis as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Results Forty-one distinct s with 6,664 patients were identified. PD-L1 expression was predictive of favorable response across all tumor types (OR, 2.26; 9% CI, 1.8 to 2.7; P <.001), with the significantly largest effect observed in non small-cell lung cancer (OR, 2.1; 9% CI, 1.99 to 3.17; P <.001). A subgroup analysis across all non small-cell lung cancer s using nivolumab and Dako clone 28-8 (Dako, Carpinteria, CA) IHC antibody assay yielded a significantly higher objective response rate in patients with tumor PD-L1 expression even at the minimum cutoff value of 1% (OR, 2.17; 9% CI, 1.03 to 4.7). Conclusion Our meta-analysis shows that tumor and tumor-infiltrating immune cell PD-L1 overexpression based on IHC is associated with significantly higher response rates to PD-1/ PD-L1 axis inhibitors across a range of malignant solid tumors. Precis Oncol by American Society of Clinical Oncology INTRODUCTION Tumors often evade the immune system by either ineffective antigen presentation or by using mechanisms to thwart an effector response. 1-3 Programmed cell death 1 (PD-1) is a negative regulator or checkpoint receptor on T cells that is a key determinant of induction of immune tolerance in the tumor microenvironment. The 1 ascopubs.org/journal/po JCO Precision Oncology 2017 by American Society of Clinical Oncology

2 ligands for PD-1 are PD-L1 (B7-1 H) and PD-L2 (B7-DC). PD-L1 and PD-L2 expression in the normal tissues is a major mechanism of physiologic peripheral immune tolerance to dampen tissue autoimmune responses after a sustained inflammatory response to tissue damage. PD- L1 is upregulated in various tumor types including melanoma, non small-cell lung cancer (NSCLC), and squamous cell head and neck carcinomas and is a major mechanism of immune evasion. Monoclonal antibodies (MoAbs) against both PD-1 and PD-L1 show clinical activity in various tumors. However, many patients do not benefit from these drugs. In early s with these agents, PD-L1 expression on tumor cells (TCs), measured by immunohistochemistry (IHC) assay, was thought to be an important biomarker for predicting response to these agents. 4 However, subsequent s, particularly atezolizumab s, incorporated both TC and tumorinfiltrating immune cell (IC) PD-L1 expression, and this combined PD-L1 expression was also associated with significantly higher objective response rates (ORRs) in clinical s. Pharmaceutical companies have developed different PD-L1 IHC assays, and their comparability and equivalence are the subject of active research.,6 However, most such studies have included relatively small numbers of patients and have not used response or outcome as an end point. The aim of this meta-analysis was to evaluate thepredictiveroleofpd-l1expressionasabiomarker; this was achieved by combining and analyzing simultaneously all the studies in NSCLC, melanoma, renal cell carcinoma (RCC), bladder cancer, gastroesophageal cancer, Merkel cell cancer, head and neck cancer, and small-cell lung cancer reporting the ORR of all patients receiving anti PD- 1/PD-L1 MoAbs. METHODS We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement in conducting this systematic review and metaanalysis. 7 We searched for all published studies and abstracts that report the ORR of patients with NSCLC, melanoma, RCC, bladder cancer, gastroesophageal cancer, Merkel cell cancer, head and neck cancer, and small-cell lung cancer treated with anti PD-1/PD-L1 MoAbs, stratified by TC or tumor-infiltrating IC PD-L1 expression status. Data Sources and Searches A comprehensive literature search was conducted to identify all relevant articles. The databases searched included PubMed, Medline, Embase, ASCO abstracts, European Society for Medical Oncology abstracts, Google Scholar, and Scopus. The dates searched were from the inception of each database to December 4, Two investigators (M.K. and S.R.) independently screened all available studies. The search terms included the following keywords: PD-1, PD-L1, CD274, programmed cell death receptor 1, programmed cell death receptor ligand, immune checkpoint inhibitor, nivolumab, BMS9368, pembrolizumab, MK-347, MPDL3280A, atezolizumab, avelumab, MSB C, durvalumab, MEDI-4736, predictive biomarker, and cancer immunotherapy. The search was extended by review of references of articles included in the final selection. Selection and Data Extraction Clinical s that fulfilled all of the following predetermined inclusion criteria were included: only patients with metastatic NSCLC, melanoma, RCC, bladder cancer, gastroesophageal cancer, Merkel cell cancer, head and neck cancer, and small-cell lung cancer were included; studies that report the ORR of patients treated with anti PD- 1/PD-L1 inhibitors as a single agent were included; and studies that report the ORR by the Response Evaluation Criteria in Solid Tumors (RECIST; version 1.1) stratified according to the TC, IC, and combined TC and IC PD-L1 expression were included. selection was restricted to English-language publications. Two investigators (M.K. and S.R.) independently reviewed all the retrieved articles to choose potentially relevant articles, and disagreements about studies were discussed and resolved with consensus. Two reviewers (M.K. and S.R.) independently extracted data from studies using standardized data extraction sheets, and all discrepancies were resolved with consensus. The following information was extracted: study design, study population and setting, mean patient age, type of cancer, type of treatment, PD-L1 expression cutoff values, and follow-up time. The primary outcome of interest was the ORR. Evaluation of Quality and Publication Bias We assessed the risk of bias of the included randomized controlled s only according to the Cochrane Collaboration s tool. 8 The following seven items were considered: random sequence generation (selection bias); allocation concealment (selection bias); blinding of participants and personnel (performance bias); blinding of outcome assessment (detection bias); incomplete 2 ascopubs.org/journal/po JCO Precision Oncology

3 outcome data (attrition bias); selective reporting (reporting bias); and other bias. For each randomized controlled, each item was described as low risk of bias, high risk of bias, or unclear risk of bias by two independent investigators (M.K. and S.R.). To examine publication bias in the results of the meta-analysis, Egger s test was used to evaluate asymmetry of the funnel plot. 9 Data Synthesis and Analysis Some degree of heterogeneity was expected, and random-effects meta-analyses were performed using DerSimonian-Laird random effects models. 10 To consider the sources of heterogeneity, the following two subgroup meta-analyses were prespecified: type of cancer and type of PD-L1 IHC assay. We used the inverse variance method to calculate pooled odds ratios (ORs) and their 9% CIs. Statistical heterogeneity was evaluated using the Cochran x 2 test and the I 2 statistic. P,.1 for the x 2 test was defined as indicating the presence of heterogeneity. I 2 values of 30% to 60% represented a moderate level of heterogeneity. We used Review Manager (RevMan.3; Cochrane Collaboration, Copenhagen, Denmark) for our statistical analyses. RESULTS Eligible Studies Our search retrieved 3,42 publications. After excluding duplicates and screening titles of the studies, 122 articles were selected based on relevance to the study topic. After screening the abstracts of these potentially relevant articles, 86 articles remained for full-text review based on the relevance to the study topic. The flowchart for study selection is represented in Figure 1. Each clinical was identified with National Clinical Trial (ClinicalTrials.gov) identifier number to prevent duplication of studies. Forty-one randomized s (Table 1) 11-0 involving 6,664 patients who reported TC, IC, or TC or IC PD-L1 IHC data and overall response rates with PD-1/ PD-L1 inhibitor therapy in solid tumors were included in this systematic review and metaanalysis. Characteristics of Studies Included in Meta-Analysis Table 1 lists the main characteristics of the included studies Forty-one s were included (14 studies in NSCLC, eight in melanoma, four in RCC, seven in bladder cancer, three in gastroesophageal cancer, two in head and neck cancer, two in Merkel cell cancer, and one in small-cell lung cancer). PD-L1 clones used for the evaluation of PD-L1 expression varied across different s; 16 studies used 28-8, seven studies used 22C3, 10 studies used SP142, three studies used SP263, and five studies used Dako clone (Dako, Carpinteria, CA). Among all of the included studies, 10 were randomized clinical s, whereas the rest were single-arm s. Quality Analysis Risk of bias for all included randomized clinical s was assessed by the Cochrane risk of bias tool (Appendix Fig A1, online only). All or a majority of the included studies had low risk of bias on items, attrition bias, reporting bias and other bias. The majority of the studies had high risk of bias on performance bias and detection bias. Meta-Analyses of the Association Between PD-L1 Expression and ORR No evidence of publication bias was observed in the funnel plot (Egger s test P =.4; Appendix Fig A2, online only). A meta-analysis of all 41 studies (N = 6,664) showed a significantly higher objective response rate (ORR) with PD-1/PD-L1 inhibitors in PD-L1 positive patients compared with PD-L1 negative patients (OR, 2.26; 9% CI, 1.8 to 2.7; P,.001). There was low heterogeneity of effects across studies (I 2 = 0%; Fig 2). Subgroup Analyses When studies were subgrouped by type of malignancy, significantly higher ORRs were observed for PD-L1 positive tumors versus PD-L1 negative tumors in NSCLC (14 s; n = 3,279; OR, 2.1; 9% CI, 1.99 to 3.17; P,.001), melanoma (eight s; n = 1,987; OR, 2.04; 9% CI, 1.19 to 3.49; P,.001), and bladder cancer (seven s; n = 67; OR, 2.20; 9% CI, 1.33 to 3.64; P =.002). There was low heterogeneity of effects across studies in all categories (Fig 2). We performed a subgroup analysis across all NSCLC s simultaneously assessing cutoff values of 1%, %, and 10% for PD-L1 positive TCs to identify the optimal IHC cutoff threshold. This analysis included the following four s: Brahmer et al, 14 Borghaei et al, 1 Rizvi et al, 17 and Gettinger et al. 20 All of these s included patients with NSCLC (both squamous and nonsquamous histology) treated with nivolumab and used the Dako 28-8 antibody for PD-L1 IHC. Significantly higher ORRs were observed for PD-L1 positive tumors versus PD-L1 negative tumors across all cutoff values, including 1% (four s; 3 ascopubs.org/journal/po JCO Precision Oncology

4 Identification Screening Eligibility Included Fig 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram: study selection process. NSCLC, non small-cell lung cancer; ORR, objective response rate; PD-L1, programmed cell death ligand 1; RCC, renal cell cancer. Total studies (N = 3,42) PubMed studies (n = 1,312) Embase studies (n = 912) ASCO abstracts (n = 22) Scopus studies identified for title review (n = 1,093) Studies remained for abstract review (n = 122) Studies remained for full article/presentation review (n = 86) Studies with PD-L1 association data (n = 42) NSCLC Melanoma RCC Bladder Gastroesophageal Merkel cell cancer Head and neck cancer Small-cell lung cancer Excluded studies Duplicates Liquid tumors Irrelevant Articles excluded Combination Review Wrong design Total studies excluded Insufficient follow-up ORR data not provided Nonindependent PD-L1 association not reported Ovarian cancer excluded because the percentage of PD-L1 expression was not provided (n = 1) (n = 14) (n = 8) (n = 4) (n = 7) (n = 3) (n = 2) (n = 2) (n = 1) (n = 3,420) (n = 132) (n = 28) (n = 3,260) (n = 36) (n = 12) (n = 14) (n = 10) (n = 44) (n = 1) (n = 1) (n = 4) (n = 24) n = 470; OR, 2.17; 9% CI, 1.03 to 4.7), % (four s; n = 470; OR, 2.80; 9% CI, 1.6 to.02), and 10% (four s; n = 470; OR, 2.84; 9% CI, 1.40 to.77). There was limited heterogeneity of effects across studies in all subgroups (Figs 3A-C). An additional subgroup analysis was performed across different PD-L1 IHC assays (Fig 4). Significantly higher ORRs were observed in s using Dako clone 28-8 (16 s; n = 1,764; OR, 2.3; 9% CI, 1.86 to 2.97; P,.001). Similarly, a significantly higher ORR was observed in PD- L1 positive patients compared with patients with low PD-L1 expression with clone SP142 (10 s; n = 2,317; OR, 2.02; 9% CI, 1.62 to 2.1; P,.001), clone 22C3 (seven s; n = 2,200; OR, 2.27; 9% CI, 1.0 to 4.91; P =.04),and clone (five s; n = 224; OR, 4.42; 9% CI, 1.84 to 10.62; P,.001). DISCUSSION Several studies have demonstrated that PD-L1 overexpression by IHC is predictive of response to PD-1 andpd-l1 inhibitors. 1-3 Thefindings ofour metaanalysis indicate that, across a range of solid tumors, PD-L1 positivity in TCs and ICs is associated with significantly higher OR, supporting the role of TC and IC PD-L1 expression as a predictive biomarker of clinical benefit with PD-1/PD-L1 inhibitors. Many of the initial studies with PD-1/PD-L1 inhibitors, especially nivolumab studies with clone 28-8, stained TCs. 11,14,1 Subsequently, numerous s in NSCLC, bladder cancer, and melanoma, studying pembrolizumab and atezolizumab using clones 22C3 and SP142, respectively, stained both TCs and ICs. 18,27,36-38,48-0 However, similar to TCs, IC PD-L1 expression was also associated with significantly higher ORRs in PD-L1 positive patients compared with patients with low PD-L1 expression. The OAK (Phase III Trial of Atezolizumab Versus Docetaxel in NSCLC) compared overall survival (OS) in nonoverlapping patients with positive PD-L1 expression on TCs (TC 2/3 and IC 0; defined as tumor PD-L1 expression. %) and ICs (IC 2/3 and TC 0; defined as IC PD-L1 expression. %) and concluded that there were no statistically significant differences in OS between the two groups and that across both of the groups higher TC or IC PD-L1 expression (TC 3 or IC 3; defined as TC or IC PD-L1 expression. 0%) was associated with longest OS. 49 Similarly, another in bladder cancer that assessed durvalumab in patients with advanced urothelial bladder cancer and defined PD-L1 status based on its expression on either TCs or ICs demonstrated a significantly higher ORR (46.4%) in the PD-L1 positive subgroup compared with the PD-L1 negative subgroup (0%), with no significant differences between the two groups. 36 The findings from these s are consistent with an emerging body of evidence supporting the hypothesis that PD-L1 expression on either TCs or ICs may be associated with improved response to PD-1/PD-L1 antibodies. As a result, to allow inclusion of a maximum number of patients, we included all s defining TC or IC PD-L1 4 ascopubs.org/journal/po JCO Precision Oncology

5 Table 1. Main Characteristics of All Included Studies NCT No. Phase Population Sample Size: No. of PD-L1 Positive/ PD-L1 Negative Patients Age (years), Mean (range) Male (%) PD-1/PD-L1 Inhibitor Comparator Group Type of Follow-Up Time (weeks) PD-L1 Staining Cell Type PD-L1 Cutoff (%) NCT Gettinger Phase I NSCLC 33/3 63 (29-8) 66 Nivolumab None Single-arm et al Tumor cells NCT Garon et al 12 Phase I NSCLC 176/28 64 (28-93) 2.7 Pembrolizumab None Single-arm 48 Tumor cells 1 73/131 0 NCT Herbst et al 13 Phase I NSCLC 2/13 60 (24-84) 6 Atezolizumab None Single-arm 18 Tumor cells 1 1/7 /6 10 NCT Brahmer et al 14 Phase III NSCLC (squamous) 63/4 63 (39-8) 76 Nivolumab Docetaxel RCT 48 Tumor cells 1 42/7 36/81 10 NCT Phase III NSCLC (nonsquamous) 123/ (21-8) Nivolumab Docetaxel RCT 6 Tumor cells 1 Borghaei et al 1 9/136 86/14 10 NCT Rizvi et al 17 Phase II NSCLC 4/31 6 (7-71) 73 Nivolumab None Single-arm 47 Tumor cells 1 2/1 2/1 10 NCT Gettinger Phase I NSCLC (first-line et al 20 monotherapy) 32/14 67 (43-8) 0 Nivolumab None Single-arm 62 Tumor cells 1 26/20 20/ / /34 0 NCT Herbst et al l 19 Phase III NSCLC 86/ (6-69) 62 Pembrolizumab Docetaxel RCT 6 Tumor cells 0 NCT Wakelee et al 48 Phase II NSCLC 86/302 NA NA Atezolizumab None Single-arm 60 Tumor and immune cells 0 NCT Rittmeyer Phase III NSCLC 43/ (33-82) NA Atezolizumab Docetaxel RCT 91 Tumor and immune et al 49 29/129 cells 1 22/72 0 NCT Fehrenbacher Phase II NSCLC 17/93 62 (42-82) NA Atezolizumab Docetaxel RCT 6 Tumor and immune et al 0 11/0 cells 1 9/24 0 NCT Spigel 0a Phase II NSCLC 14/43 NA NA Atezolizumab None Single-arm 26 Tumor cells 0 (Continued on following page) ascopubs.org/journal/po JCO Precision Oncology

6 Table 1. Main Characteristics of All Included Studies (Continued) NCT No. Phase Population Sample Size: No. of PD-L1 Positive/ PD-L1 Negative Patients Age (years), Mean (range) Male (%) PD-1/PD-L1 Inhibitor Comparator Group Type of Follow-Up Time (weeks) PD-L1 Staining Cell Type PD-L1 Cutoff (%) NCT Verschraegen Phase I NSCLC 7/ Avelumab None Single-arm 13 Tumor cells 1 et al 18 6/28 Tumor cells 2/13 Tumor cells 2 2/4 Immune cells 10 NCT Antonia et al 16 Phase I NSCLC 14/ Durvalumab None Single-arm 49 Tumor cells 2 NCT Weber et al 21 Phase III Melanoma 77/87 9 (23-88) 6 Nivolumab Ipilimumab RCT 24 Tumor cells NCT Robert et al 22 Phase III Melanoma 74/ (18-86) 7.6 Nivolumab Dacarbazine RCT 20 Tumor cells NCT Larkin et al 23 Phase III Melanoma 80/208 0 (2-90) 63.9 Nivolumab Ipilimumab RCT 2 Tumor cells NCT Robert et al 28 Phase III Melanoma 14/ (18-89) 8 Pembrolizumab Ipilimumab RCT 2 Tumor cells 1 NCT Hodi et al 2 Phase I Melanoma 26/1 61 (29-89) 67 Nivolumab None Single-arm 168 Tumor cells 1 18/23 NCT Hamid et al 24 Phase I Melanoma 1/1 63 (21-82) 68 Atezolizumab None Single-arm 46 Tumor cells NCT Ribas et al 26 Phase II Melanoma 1/ (1-87) 8 Pembrolizumab Chemotherapy RCT 41 Tumor cells 1 NCT Duad et al 27 Phase I Melanoma 118/ (18-94) 62 Pembrolizumab None Single-arm 78 Tumor and immune cells 90/ / /7 66 NCT Topalian Phase I RCC 4/1 8 (3-74) 76 Nivolumab None Single-arm et al Tumor cells NCT Motzer et al 30 Phase II RCC 29/ Nivolumab None Single-arm 104 Tumor cells NCT Chouieri et al 31 Phase I RCC 18/ Nivolumab None Single-arm 24 Tumor cells NCT McDermott Phase I RCC 6/33 62 (33-79) NA Atezolizumab None Single-arm et al Tumor cells 1 NCT Plimack et al 33 Phase I Bladder cancer 18/11 70 (44-8) 69.7 Pembrolizumab None Single-arm 64 Tumor cells 1 NCT Powles et al 34 Phase I Bladder cancer 21/ (42-86) 83 Atezolizumab None Single-arm 14 Tumor cells NCT Massard et al 36 Phase II Bladder cancer 7/1 67 (34-79) 7 Durvalumab None Single-arm 28 Tumor cells 2 10/18 Immune cells 2 13/28 Tumor and immune cells 2 (Continued on following page) 6 ascopubs.org/journal/po JCO Precision Oncology

7 Table 1. Main Characteristics of All Included Studies (Continued) NCT No. Phase Population Sample Size: No. of PD-L1 Positive/ PD-L1 Negative Patients Age (years), Mean (range) Male (%) PD-1/PD-L1 Inhibitor Comparator Group Type of Follow-Up Time (weeks) PD-L1 Staining Cell Type PD-L1 Cutoff (%) NCT Rosenberg Phase II Bladder cancer 37/ (41-84) 78 Atezolizumab None Single-arm 1 Immune cells 1 et al 37 26/100 NCT Balar et al 38 Phase II Bladder cancer 19/80 73 (1-92) 81 Atezolizumab None Single-arm 73 Immune cells 1 6/32 NCT Sharma et al 39 Phase I/ II Bladder cancer 6/2 6. (31-8) 69 Nivolumab None Single-arm 66 Tumor cells 1 NCT Ferris et al 41 Phase III Head and neck cancer 1/88 9 (29-83) 82 Nivolumab Methotrexate, RCT 22 Tumor cells 1 12/4 docetaxel, or cetuximab 12/43 10 NCT Segal et al 40 Phase I/ II Head and neck cancer 22/37 8 (24-96) 3 Durvalumab None Single-arm 24 Tumor cells 2 NCT Le et al 42 Phase I/ II Gastroesophageal cancer 23/36 60 (29-80) NA Nivolumab None Single-arm 2 Tumor cells 1 NCT Nishina et al 43 Phase I Gastroesophageal cancer /14 69 (37-76) 70 Avelumab None Single-arm 24 Tumor cells 1 NCT Apolo et al 3 Phase I Bladder cancer 10/22 68 (30-84) 68.2 Avelumab None Single-arm 14 Tumor cells NCT Chung et al 44 Phase I Gastroesophageal cancer 20/3 7 (30-8) 76.4 Avelumab None Single-arm 14 Tumor cells 1 9/46 2/3 2 NCT Nghiem et al 46 Phase II Merkel cell cancer 8/12 68 (7-91) 62 Pembrolizumab None Single-arm 33 Tumor cells 1 NCT Kaufman Phase II Merkel cell cancer 20/8 72. (64.-77) 74 Avelumab None Single-arm et al 4 44 Tumor cells 1 NCT Antonia et al 47 Phase I Small-cell lung cancer 3/10 63 (7-78) 62 Nivolumab None Single-arm 12 Tumor cells 1 Abbreviations: NA, not available; NSCLC, non small-cell lung cancer; PD-1, programmed cell death 1; PD-L1, programmed cell death ligand 1; RCC, renal cell carcinoma; RCT, randomized controlled. 7 ascopubs.org/journal/po JCO Precision Oncology

8 Fig 2. Forest plots representing odds ratios of objective response in programmed cell death ligand 1 (PD-L1) positive patients compared with PD-L1 negative patients across different cancer types. NSCLC, non smallcell lung cancer; RCC, renal cell cancer; SCLC, small-cell lung cancer. and Cancer Type NSCLC PD-L1 Positive PD-L1 Negative Odds Ratio No. of Total No. No. of Total No. Weight IV, Random (9% CI) Events of Patients Events of Patients (%) Antonia et al Borghaei et al Brahmer et al Fehrenbacher et al Garon et al Gettinger et al Gettinger et al Herbst et al Herbst et al l Rittmeyer et al Rizvi et al Spigel Verschraegen et al Wakelee et al Subtotal 1,29 1, Heterogeneity: 2 = 0.03; 2 = 1.46, df = 13 (P =.28); l 2 = 16% Test for overall effect: Z = 7.72 (P <.001) 3.20 (0.37 to 28.01) 4.86 (2.43 to 9.72) 1.9 (0.60 to 4.21) 2.37 (0.93 to 6.0) 3.31 (0.96 to11.4) 1.90 (0.42 to 8.70) 2.2 (0.7 to11.10) 0.7 (0.08 to 7.44) 3.79 (2.1 to.73) 2.73 (1. to 4.83) 1.98 (0.9 to 6.70) 1.86 (0.87 to 3.99) 4.09 (0.4 to 37.3) 1.7 (1.28 to 2.41) 2.1 (1.99 to 3.17) Odds Ratio IV, Random (9% CI) Melanoma Duad et al 27 Hamid et al 24 Hodi et al 2 Larkin et al 23 Ribas et al 26 Robert et al 22 Robert et al 28 Weber et al 21 Subtotal , Heterogeneity: 2 = 0.42; 2 = 31.39, df = 7 (P <.001); l 2 = 78% Test for overall effect: Z = 2.9 (P =.010) 4.08 (1.70 to 9.77) 1.4 (0.26 to 8.01).33 (1.16 to 24.60) 1.92 (1.14 to 3.24) 2.03 (1.06 to 3.89) 2.2 (1.26 to 4.02) 0.61 (0.39 to 0.94) 3.03 (1.3 to 6.02) 2.04 (1.19 to 3.49) Bladder cancer Apolo et al (0.97 to 4.79) Balar et al (0.9 to 3.80) Massard et al (0.79 to 11.94) Plimack et al (0.1 to 48.7) Powles et al (0.37 to 4.17) Rosenberg et al (1.84 to 6.76) Sharma et al (0.28 to 2.80) Subtotal (1.33 to 3.64) Heterogeneity: 2 = 0.11; 2 = 7.90, df = 6 (P =.2); l 2 = 24% Test for overall effect: Z = 3.07 (P =.002) RCC Chouieri et al McDermott et al Motzer et al Topalian et al Subtotal Heterogeneity: 2 = 0.00; 2 = 0.27, df = 3 (P =.97); l 2 = 0% Test for overall effect: Z = 2.27 (P =.02) Gastro esophageal cancer Chung et al Le et al Nishina et al Subtotal Heterogeneity: 2 = 0.28; 2 = 2.44, df = 2 (P =.29); l 2 = 18% Test for overall effect: Z = 1.96 (P =.0) Head and neck cancer Ferris et al Segal et al Subtotal Heterogeneity: 2 = 0.00; 2 = 0.01, df = 1 (P =.91); l 2 = 0% Test for overall effect: Z = 2.14 (P =.03) Merkel cell carcinoma Kaufman et al Nghiem et al Subtotal Heterogeneity: 2 = 0.00; 2 = 0.08, df = 1 (P =.78); l 2 = 0% Test for overall effect: Z = 1.29 (P =.20) SCLC Antonia et al Subtotal Heterogeneity: Not applicable Test for overall effect: Z = 1.11 (P =.27) Total 3,060 3, Heterogeneity: 2 = 0.13; 2 = 6.80, df = 40 (P =.006); I 2 = 39% Test for overall effect: Z = 8.12 (P <.001) Test for subgroup differences: 2 = 1.20, df = 7 (P =.99), I 2 = 0% 3.33 (0.66 to 16.8) 2.22 (0.41 to 12.18) 2.06 (0.77 to.46) 3.00 (0.08 to 11.34) 2.34 (1.12 to 4.88) (1.03 to ) 1.63 (0.36 to 7.30) 8.67 (0.8 to ) 3.8 (1.00 to 14.7) 2.26 (0.94 to.4) 2.2 (0.1 to12.0) 2.32 (1.07 to.01) 2.28 (0.8 to 8.9) 1.67 (0.31 to 9.01) 2.01 (0.70 to.83) 2.38 (0.2 to10.97) 2.38 (0.2 to 10.97) 2.26 (1.8 to 2.7) Favors PD-L1 Negative Favors PD-L1 Positive 8 ascopubs.org/journal/po JCO Precision Oncology

9 A Borghaei et al 1 Brahmer et al 14 Gettinger et al 20 Rizvi et al 17 PD-L1 Positive PD-L1 Negative Odds Ratio Odds Ratio No. of Events Total No. of Patients No. of Total No. of Events Patients Weight (%) IV, Random (9% CI) (2.06 to 9.32) (0.40 to 2.78) (0.44 to 12.64) (0.47 to 6.06) IV, Random (9% CI) Total (1.03 to 4.7) 67 2 Heterogeneity: 2 = 0.26; 2 =.49, df = 3 (P =.14); I 2 = 4% Test for overall effect: Z = 2.0 (P =.04) Favors PD-L1 Negative Favors PD-L1 Positive B Borghaei et al 1 Brahmer et al 14 Gettinger et al 20 Rizvi et al 17 PD-L1 Positive PD-L1 Negative Odds Ratio Odds Ratio No. of Events Total No. of Patients No. of Total No. of Events Patients Weight (%) IV, Random (9% CI) (2.43 to 9.72) (0.60 to 4.21) (0.7 to 11.10) (0.9 to 6.70) IV, Random (9% CI) Total (1.6 to.02) 7 3 Heterogeneity: 2 = 0.09; 2 = 3.98, df = 3 (P =.26); I 2 = 2% Test for overall effect: Z = 3.44 (P <.001) Favors PD-L1 Negative Favors PD-L1 Positive C Borghaei et al 1 Brahmer et al 14 Gettinger et al 20 Rizvi et al 17 PD-L1 Positive PD-L1 Negative Odds Ratio Odds Ratio No. of Events Total No. of Patients No. of Total No. of Events Patients Weight (%) IV, Random (9% CI) 4.78 (2.42 to 9.42) 1.26 (0.46 to 3.49).11 (1.14 to 22.89) 1.98 (0.9 to 6.70) IV, Random (9% CI) Total (1.40 to.77) 3 39 Heterogeneity: 2 = 0.23; 2 =.0, df = 3 (P =.14); I 2 = 4% Test for overall effect: Z = 2.88 (P =.004) Favors PD-L1 Negative Favors PD-L1 Positive Fig 3. Forest plots representing odds ratios of objective response in programmed cell death ligand 1 (PD-L1) positive patients compared with PD-L1 negative patients with non small-cell lung cancer across (A) 1%, (B) %, and (C) 10% cutoff values of PD-L1 expression. expression. However, because of the paucity of data and power limitations, we could not compare TC and IC subgroups separately. A major objective of our study was to identify the optimal PD-L1 cutoff value associated with maximum objective response. This was achieved by performing a subgroup analysis of four s simultaneously reporting objective response data for three different cut points (1%, %, and 10%) in NSCLC. 14,1,17,20 We restricted the analysis to these four s to remove heterogeneity associated with tumor type, treatment agents, and PD- L1 antibody used for IHC assay. All four s assessed patients with NSCLC (both squamous and nonsquamous histology) treated with nivolumab and used the 28-8 (Dako) clone for PD-L1 IHC. On the basis of our analysis, ORRs were significantly higher in patients with tumor PD- L1 expression even at the lowest IHC cutoff of 1%. This restrictive subgroup analysis further eliminates the bias associated with differences in inherent immunogenicity across different tumor types. Unfortunately, because of power limitations,wewereunabletoassesstheoptimal IHC cutoff points of the US Food and Drug Administration approved PD-L1 antibodies 22C3 and SP142. We were also unable to analyze cutoff points across major NSCLC subtypes (eg, different histologies or molecular variants of lung adenocarcinoma). These issues may be significant and require additional study. A standardized biomarker such as TC and IC PD- L1 expression can identify patients who will derive maximum benefit from these novel agents, sparing others from potentially harmful immune-related toxicities and elevated costs. However, an important 9 ascopubs.org/journal/po JCO Precision Oncology

10 Fig 4. Forest plots representing odds ratios of objective response programmed cell death ligand 1 (PD-L1) positive patients compared with PD-L1 negative patients across different PD-L1 immunohistochemistry assays. and Assay Subgroup PD-L1 Positive No. of Events Total No. of Patients PD-L1 Negative Total No of Patients No. of Events Weight (%) 28-8 Antonia et al 47 Borghaei et al 1 Brahmer et al 14 Chouieri et al 31 Ferris et al 41 Gettinger et al 11 Gettinger et al 20 Hodi et al 2 Larkin et al 23 Le et al Motzer et al 30 Rizvi et al 17 Robert et al 22 Sharma et al 39 Topalian et al 29 Weber et al 21 Subtotal , Heterogeneity: 2 = 0.00; 2 = 10.47, df = 1 (P =.79); I 2 = 0% Test for overall effect: Z = 7.17 (P <.001) 34 Odds Ratio IV, Random (9% Cl) 2.38 (0.2 to 10.97) 4.86 (2.43 to 9.72) 1.9 (0.60 to 4.21) 3.33 (0.66 to 16.8) 2.26 (0.94 to.4) 1.90 (0.42 to 8.70) 2.2 (0.7 to 11.10).33 (1.16 to 24.60) 1.92 (1.14 to 3.24) 1.63 (0.36 to 7.30) 2.06 (0.77 to.46) 1.98 (0.9 to 6.70) 2.2 (1.26 to 4.02) 0.89 (0.28 to 2.80) 3.00 (0.08 to 11.34) 3.03 (1.3 to 6.02) 2.3 (1.86 to 2.97) Odds Ratio IV, Random (9% Cl) SP142 Balar et al Fehrenbacher et al Hamid et al Herbst et al McDermott et al Powles et al Rittmeyer et al Rosenberg et al Spigel Wakelee et al Subtotal 732 1, Heterogeneity: 2 = 0.00; 2 = 6.74, df = 9 (P =.66); I 2 = 0% Test for overall effect: Z = 6.29 (P <.001) 22C3 Duad et al Garon et al Herbst et al l Nghiem et al Plimack et al Ribas et al Robert et al Subtotal 1, Heterogeneity: 2 = 0.80; 2 = 41.0, df = 6 (P <.001); I 2 = 86% Test for overall effect: Z = 2.08 (P =.04) (0.9 to 3.80) 2.37 (0.93 to 6.0) 1.4 (0.26 to 8.01) 0.7 (0.08 to 7.44) 2.22 (0.41 to 12.18) 1.24 (0.37 to 4.17) 2.73 (1. to 4.83) 3.3 (1.84 to 6.76) 1.86 (0.87 to 3.99) 1.7 (1.28 to 2.41) 2.02 (1.62 to 2.1) 4.08 (1.70 to 9.77) 3.31 (0.96 to 11.4) 3.79 (2.1 to.73) 1.67 (0.31 to 9.01).00 (0.1 to 48.7) 2.03 (1.06 to 3.89) 0.61 (0.39 to 0.94) 2.27 (1.0 to 4.91) Apolo et al (0.97 to 4.79) Chung et al (1.03 to ) Kaufman et al (0.8 to 8.9) Nishina et al (0.8 to ) Verschraegen et al (0.4 to 37.3) Subtotal (1.84 to 10.62) 34 8 Heterogeneity: 2 = 0.00; 2 = 2.00, df = 4 (P =.74); I 2 = 0% Test for overall effect: Z = 3.32 (P <.001) SP263 Antonia et al (0.37 to 28.01) Massard et al (0.79 to 11.94) Segal et al (0.1 to 12.0) Subtotal (1.13 to 7.36) 2 10 Heterogeneity: 2 = 0.00; 2 = 0.04, df = 2 (P =.98); I 2 = 0% Test for overall effect: Z = 2.22 (P =.03) Total 3,060 3, (1.8 to 2. 7) Heterogeneity: 2 = 0.13; 2 = 6.80, df = 40 (P =.006); I 2 = 39% Test for overall effect: Z = 8.12 (P <.001) Test for subgroup differences: 2 = 3.6, df = 4 (P =.46); I 2 = 0% Favors PD-L1 Negative Favors PD-L1 Positive point to note is that tumor PD-L1 expression is not universally associated with higher objective response, as was seen in CheckMate 017. This study did not report any additional benefit in patients with overexpression of tumor PD-L1 treated with nivolumab. 14 In contrast, CheckMate 07 (nivolumab in nonsquamous NSCLC) concluded that patients with tumor PD-L1 positivity had ORRs that were three times greater than those of patients with low tumor PD-L1 expression. 1 These contrary findings suggestthatthepredictive role of tumor PD-L1 expression as a biomarker may depend on tumor histology; additional analysis that accounts for tumor histology is needed. 10 ascopubs.org/journal/po JCO Precision Oncology

11 A vexing issue is the finding that, in some studies, patients respond to PD-1/PD-L1 axis inhibitors despite negative tumor PD-L1 expression. This result is most pronounced in melanoma s. In CheckMate 037, 21 20% of patients determined to be PD-L1 negative based on IHC assay had an objective response. Similar findings were also documented in the CheckMate 066, 22 where one third of the patient population responded despite having low tumor PD-L1 expression levels. In s combining two different checkpoint inhibitors (eg, CheckMate 067), maximal progression-free survival was noted in the combination group in the context of negative PD-L1 tumor expression. 23 More importantly, PD-L1 expression has been linked with poor OS, as reported by a recent meta-analysis that concluded that overexpression of TC and IC PD-L1 is associated with worse 3- and -year OS rates. 4 Various studies have shown that higher mutational load correlates with higher response rate in patients treated with checkpoint inhibitors. -7 The proposed explanation for this phenomenon is that coding somatic mutations create new epitopes or peptides potentially recognized by the immune system favoring higher antitumor immunity. Keeping this in mind, a multifactorial approach combining mutational load with PD-L1 expression may be another potential biomarker for treatment with checkpoint inhibitors. This assumes special importance in the current scenario of synergistic treatment combinations to enhance therapeutic response to anti PD-1 therapy. Some other limitations to using tumor PD-L1 as a standardized biomarker include different collection times of the pathology specimen and the dynamic nature of the tumor microenvironment. PD-L1 staining in most cases is performed on archival formalin-fixed, paraffinembedded tissue, because fresh biopsies just before or close to treatment are often not available. It has been suggested that previous lines of therapy might alter tumor PD-L1 expression and may explain some of the confounding results across different s. 11 However, we believe this may not have been a significant confounder in our meta-analysis because, more recently, PD-1/ PD-L1 inhibitor s have included fresh biopsy specimens (within 6 months of starting treatment), especially many of the recent s with PD-1/PD-L1 inhibitors in the front-line setting. 8 The success of KEYNOTE-24, which included treatment-naïve patients with NSCLC with tumor PD-L1 expression. 0% scored by the 22C3 IHC assay further highlights the importance of PD-L1 expression as an important patient selection criterion. The response rate and progressionfree survival (primary end point of the study) were higher in the pembrolizumab group than in the chemotherapy group (ORR, 44.8% in pembrolizumab arm v 27.8% in chemotherapy arm). Subsequently, pembrolizumab was approved by the US Food and Drug Administration for patients with NSCLC in the front-line setting with tumor PD-L1 expression. 0%. This is in stark contrast to the results of the CheckMate-26 (nivolumab v platinum-based chemotherapy in the front-line setting), which included treatment-naïve patients with advanced NSCLC and allowed a more liberal inclusion criterion of. 1% tumor PD-L1 expression. 9 This failed to achieve its primary end point of progression-free survival. These studies further highlight the importance of incorporating PD-L1 expression into future s of PD-1/PD-L1 MoAbs, especially in the front-line setting where patients have multiple treatment options. We used objective response as the primary end point of our analysis to ensure inclusion of maximum patients, because mature survival data were not available for many of the recent s. However, in some s with PD-1/PD-L1 inhibitors especially in NSCLC, discordance between ORR and OS has been observed. For instance, in the CheckMate 17, CheckMate 7, POPLAR, and OAK s, objective response did not improve as much as the OS in the PD-1/PD-L1 arm, suggesting postprogression survival advantage. 14,1,49,0 However, this should not significantly affect our aim of establishing the validity of PD-L1 expression as a biomarker because ORR is commonly used as a surrogate biomarker for clinical benefit in oncology. Future efforts will be pursued to collect mature progression-free survival and OS data to establish the survival impact of PD-L1 as a biomarker with PD-1/PD-L1 axis inhibitors. In conclusion, our meta-analysis shows that TC and tumor-infiltrating IC PD-L1 overexpression based on IHC is associated with significantly higher response rates to PD-1/PD-L1 axis inhibitors across a range of malignant solid tumors. Despite several limitations, it is safe to conclude that detection of PD-L1 expression in TCs and tumor-infiltrating ICs using IHC is highly accessible and has significant potential as a biomarker predictive of response to PD-1/PD-L1 axis therapies across various tumor types. DOI: Published online on ascopubs.org/journal/po on May 18, ascopubs.org/journal/po JCO Precision Oncology

12 AUTHOR CONTRIBUTIONS Conception and design: Monica Khunger, Sagar Rakshit, Kurt Schalper, Vamsidhar Velcheti Financial support: Vamsidhar Velcheti Administrative support: Vamsidhar Velcheti Provision of study material or patients: Sanjay Mukhopadhyay, Vamsidhar Velcheti Collection and assembly of data: Monica Khunger, Sagar Rakshit, Sanjay Mukhopadhyay, Vamsidhar Velcheti Data analysis and interpretation: Monica Khunger, Adrian V. Hernandez, Vinay Pasupuleti, Nathan A. Pennell, James Stevenson, Kurt Schalper, Vamsidhar Velcheti Manuscript writing: All authors Final approval of manuscript: All authors Accountable for all aspects of the work: All authors AUTHORS DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST Programmed Cell Death 1 (PD-1) Ligand (PD-L1) Expression in Solid Tumors As a Predictive Biomarker of Benefit From PD-1/PD-L1 Axis Inhibitors: A Systematic Review and Meta-Analysis The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO s conflict of interest policy, please refer to or po.ascopubs.org/site/ifc. Monica Khunger No relationship to disclose Adrian V. Hernandez No relationship to disclose Vinay Pasupuleti No relationship to disclose Sagar Rakshit No relationship to disclose Nathan A. Pennell Consulting or Advisory Role: Boehringer Ingelheim, AstraZeneca, Eli Lilly, Regeneron Research Funding: Genentech (Inst), Newlink Genetics (Inst), Clovis Oncology (Inst), Astex Pharmaceuticals (Inst), Celgene (Inst), AstraZeneca (Inst), Pfizer (Inst), Merck James Stevenson Research Funding: Merck Sharp & Dohme (Inst), Bayer (Inst), Bristol-Myers Squibb (Inst) Sanjay Mukhopadhyay Research Funding: Philips Healthcare Other Relationship: Phillips Kurt Schalper Honoraria: Takeda Research Funding: Vasculox, Tesaro, Onkaido Therapeutics, Genoptix, Takeda Vamsidhar Velcheti Honoraria: Novartis, Foundation Medicine, Merck, Bristol- Myers Squibb, Genentech Consulting or Advisory Role: Clovis Oncology, Genentech, Bristol-Myers Squibb, Merck, Celgene, Foundation Medicine, AstraZeneca/MedImmune, Genoptix Research Funding: Genentech (Inst), Trovagene (Inst), Eisai (Inst), OncoPlex Diagnostics (Inst), Alkermes (Inst), NantOmics (Inst), Genoptix (Inst), Altor BioScience (Inst), Merck (Inst), Bristol-Myers Squibb (Inst), Atreca (Inst), Heat Biologics (Inst), Leap Therapeutics (Inst) Travel, Accommodations, Expenses: AstraZeneca/ MedImmune, Eisai REFERENCES 1. Postow MA, Callahan MK, Wolchok JD: Immune checkpoint blockade in cancer therapy. J Clin Oncol 33: , Velcheti V, Schalper K: Basic overview of current immunotherapy approaches in cancer. Am Soc Clin Oncol Educ Book 3: , Velcheti V, Schalper KA, Carvajal DE, et al: Programmed death ligand-1 expression in non small cell lung cancer. Lab Invest 94: , Brahmer JR: Harnessing the immune system for the treatment of non small-cell lung cancer. J Clin Oncol 31: , National Comprehensive Cancer Network: NCCN collaborates with Bristol-Myers Squibb to study PD-L1 expression and test interpretation in lung cancer Hirsch F, McElhinny A, Stanforth D, et al: PD-L1 immunohistochemistry assays for lung cancer: Results from phase 1 of the Blueprint PD-L1 Assay Comparison Project. J Thorac Oncol 12: , Moher D, Liberati A, Tetzlaff J, et al: Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med 6:e , Higgins JP, Altman DG, Gøtzsche PC, et al: The Cochrane Collaboration s tool for assessing risk of bias in randomised s. BMJ 343:d928, Egger M, Davey Smith G, Schneider M, et al: Bias in meta-analysis detected by a simple, graphical test. BMJ 31: , DerSimonian R, Laird N: Meta-analysis in clinical s. Control Clin Trials 7: , ascopubs.org/journal/po JCO Precision Oncology

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