Biomarkers that can predict response to anti–programmed cell death 1 (PD-1) therapy across multiple tumor types include a T-cell–inflamed gene-expression profile (GEP), programmed death ligand 1 (PD-L1) expression, and tumor mutational burden (TMB). Associations between these biomarkers and the clinical efficacy of pembrolizumab were evaluated in a clinical trial that encompassed 20 cohorts of patients with advanced solid tumors.

KEYNOTE-028 (ClinicalTrials.gov identifier: NCT02054806) is a nonrandomized, phase Ib trial that enrolled 475 patients with PD-L1–positive advanced solid tumors who were treated with pembrolizumab 10 mg/kg every 2 weeks for 2 years or until confirmed disease progression or unacceptable toxicity occurred. The primary end point was objective response rate (ORR; by RECIST v1.1, investigator review). Secondary end points included safety, progression-free survival (PFS), and overall survival (OS). Relationships between T-cell–inflamed GEP, PD-L1 expression, and TMB and antitumor activity were exploratory end points.

ORRs (with 95% CIs) ranged from 0% (0.0% to 14.2%) in pancreatic cancer to 33% (15.6% to 55.3%) in small-cell lung cancer. Across cohorts, median (95% CI) PFS ranged from 1.7 months (1.5 to 2.9 months) to 6.8 months (1.9 to 14.1 months) in pancreatic and thyroid cancers, respectively, and median OS from 3.9 months (2.8 to 5.5 months) to 21.1 months (9.1 to 22.4 months) in vulvar and carcinoid tumors, respectively. Higher response rates and longer PFS were demonstrated in tumors with higher T-cell–inflamed GEP, PD-L1 expression, and/or TMB. Correlations of TMB with GEP and PD-L1 were low. Response patterns indicate that patients with tumors that had high levels of both TMB and inflammatory markers (GEP or PD-L1) represent a population with the highest likelihood of response. Safety was similar and consistent with prior pembrolizumab reports.

A T-cell–-inflamed GEP, PD-L1 expression, and TMB predicted response to pembrolizumab in multiple tumor types. These biomarkers (alone/in combination) may help identify patients who have a higher likelihood of response to anti–PD-1 therapies across a broad spectrum of cancers.

Pembrolizumab, an anti–programmed cell death 1 (PD-1) monoclonal antibody, has demonstrated durable antitumor activity and favorable safety across a spectrum of solid and hematologic malignancies.1,2 Clinical response to anti–PD-1 therapies can vary across tumor types. Biomarkers that may increase the accuracy of predictions of response/resistance to anti–PD-1 therapy in different tumor types are those indicative of an inflamed tumor microenvironment (TME), including programmed death ligand 1 (PD-L1) expression and gene-expression signatures of activated T cells, and those related to tumor antigenicity, such as tumor mutational burden (TMB). Expression of PD-L1 is predictive of response to PD-1/PD-L1 inhibitors in several cancers3-8 and is an approved companion diagnostic assay for the treatment of some cancers with pembrolizumab.2,7,8 High microsatellite instability (MSI-H) is strongly associated with response to anti–PD-1 therapy regardless of tumor type, and pembrolizumab received the first tumor-agnostic approval for a biomarker-defined cancer.2,9,10 Somatic mutations in tumor cells lead to exquisitely tumor-specific and potentially highly immunogenic neoantigens, which can be recognized by T cells; both TMB and neoantigen load are associated with response to anti–PD-1 therapy in several tumor types.11-13 A pan-cancer, T-cell–inflamed gene-expression profile (GEP) comprised of 18 genes indicative of a T-cell–activated TME was associated with response to pembrolizumab in multiple tumor types.14

KEYNOTE-028 is a basket trial of 20 different cohorts of patients with PD-L1–positive, advanced solid tumors designed to assess the antitumor effects of pembrolizumab in tumor types beyond those in which clinical efficacy had been previously demonstrated.1,2 The clinical efficacy and safety for several of the individual tumor-type cohorts in this trial have been described previously.15-29 Here, we report the relationships between biomarkers with clinical efficacy across the total 20-cohort data set.

Study Design

KEYNOTE-028 is a nonrandomized, multicenter, multicohort phase Ib trial of pembrolizumab in patients (N = 474) with one of 20 different PD-L1–positive advanced solid tumors. The trial was conducted in accordance with the International Conference on Good Clinical Practice Standards and the Declaration of Helsinki. The protocol was approved by institutional and ethics committees of participating sites. All patients provided informed consent.

Eligible patients were 18 years of age or older with unresectable and/or metastatic advanced solid tumors by Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 by investigator review (IR), an Eastern Cooperative Oncology Group (ECOG) performance status of 0 or 1, and PD-L1–positive tumors. Key exclusion criteria included prior anticancer monoclonal antibody therapy within 4 weeks of study treatment; prior chemotherapy, small-molecule targeted therapy, or radiation therapy within 2 weeks of study treatment initiation; known active CNS metastases; and diagnosis of immunodeficiency, autoimmune disease, interstitial-lung disease or active infection that required systemic therapy. Patients received pembrolizumab 10 mg/kg every 2 weeks for up to 2 years or until confirmed disease progression or unacceptable toxicity, death, investigator decision, or withdrawal of patient consent occurred.

Clinical End Points

The primary end point was objective response rate (ORR) by IR, defined as confirmed complete response (CR) or partial response (PR) assessed per RECIST version 1.1. Secondary end points included safety, progression-free survival (PFS; time from enrollment to first documented disease progression per RECIST version 1.1 by IR or death as a result of any cause), and overall survival (OS). Exploratory end points included antitumor activity assessed in patients with measurable disease at baseline by independent central radiology review and relationships between biomarkers (T-cell–inflamed GEP, PD-L1 expression, TMB) and antitumor activity. Responses were assessed every 8 weeks for the first 6 months and then every 12 weeks. Adverse events (AEs) were graded using National Cancer Institute Common Terminology Criteria for Adverse Events version 4.0.

Biomarkers

The pan-tumor, 18-gene, T-cell–inflamed GEP was previously derived across a wide variety of solid tumors.14 For T-cell–inflamed GEP analysis, tumor RNA extracted from pretreatment formalin-fixed paraffin-embedded slides was analyzed on the NanoString nCounter system (Seattle, WA), and GEP score was calculated as a weighted sum of normalized expression values for the 18 genes, as described previously.14 The 18 genes in the T-cell–inflamed GEP are identical to the 18 genes in the tumor inflammation signature.30,31

PD-L1 positivity required for enrollment was determined in archival tumor samples or newly obtained excisional biopsies using a prototype assay (≥ 1% modified proportion score or interface pattern, QualTek [Goleta, CA] immunohistochemistry [IHC]).32 Expression of PD-L1 was assessed in pretreatment samples using an investigational version of the PD-L1 IHC 22C3 pharmDx kit (Agilent Technologies, Carpinteria, CA), which differs from the prototype assay used for enrollment.33 Expression levels were reported as combined positive score (CPS; the number of PD-L1–positive cells [tumor cells, lymphocytes, macrophages] divided by the total number of viable tumor cells × 100). The CPS was previously reported as a percentage and is currently reported as a unitless measure equivalent to the former CPS percentage.34 A substantial number of tumors that were scored PD-L1 positive by the enrollment prototype assay had a CPS of 0. Differences in scoring and staining characteristics, assay imprecision, and microscopic tumor heterogeneity contribute to these discrepancies.33 In addition, measurements made on different slides of the biopsies and scored by different pathologists may have also contributed to these differences.

Nonsynonymous tumor mutations (ie, TMB) were assessed by whole-exome sequencing using DNA isolated from formalin-fixed paraffin-embedded slides of normal and tumor samples. Whole-exome sequences were aligned to reference human genome GRCh37 (Burrows-Wheeler Aligner, SourceForge Media, La Jolla, CA), variants were detected using GATK (Genome analysis tool kit, v. 2, The Broad Institute, Cambridge, MA), and somatic single-nucleotide variants were called using MuTect (The Broad Institute, Cambridge, MA).

Statistical Analysis

The data cutoff date was February 20, 2017. Objective response, PFS, and OS were assessed in the full-analysis-set population (all patients who received at least one dose of pembrolizumab and had baseline measurable disease). Safety was assessed in all patients who received at least one dose of pembrolizumab. Patient disposition, baseline characteristics, and AEs were summarized by descriptive statistics. For ORR, 95% CIs and P values were evaluated by the binomial exact method. Median PFS and OS were estimated by the Kaplan-Meier method.

Biomarker IHC and genomic scores were analyzed in a blinded manner without access to clinical outcome information. Relationships between biomarkers and ORR and PFS were assessed in the full-analysis-set population by one-sided testing in logistic (ORR) or Cox (PFS) regression models, adjusted for tumor type and ECOG performance, and reported using square-root (PD-L1 CPS) and log10 (TMB) scales. Only tumor types with at least one responder were used for hypothesis testing of biomarker relationships, because at least one responder per cancer type was required in logistic regression models for convergence; thus, pancreatic cancer was excluded. Five of the tumor types (anal, biliary, colorectal, esophageal, ovarian) from this study were part of the training for the T-cell–inflamed GEP analysis and were excluded from independent hypothesis testing of the relationship with ORR and PFS. Biomarker correlations were estimated using Spearman correlation coefficients and one-sided P values. Effect sizes were not reported, because changes in the odds and hazard ratios per one-unit change of the biomarkers on the native scale are not comparable between biomarkers.

Baseline Characteristics

Between February 18, 2014, and August 26, 2015, 2,354 patients were screened for tumor PD-L1 expression;33 1,286 patients had PD-L1–negative tumors, 764 had PD-L1–positive tumors, and PD-L1 status was not determined in 304 patients. Of the 764 patients with PD-L1–positive tumors, 477 were allocated to treatment. Among these patients, 475 received at least one dose of pembrolizumab, and 471 had measurable disease at baseline (Fig 1). As of the data cutoff date, 28 of the 475 patients who received at least one pembrolizumab dose had completed the protocol-specified 2-year treatment. Three patients remained on treatment, and 444 (93.5%) discontinued treatment because of progressive disease (63%), physician decision (17%), AEs (7%), or patient withdrawal (6%). The median age was 59 years (range, 18 to 87 years), 59% were women, and 64% had an ECOG performance status of 1 (Table 1). Seventy-two percent had stage-M1 disease, and 62% had received two or more prior therapies for recurrent or metastatic disease.

Table

TABLE 1. Demographics and Patient Characteristics in the Overall Study Cohort

Clinical Efficacy and Safety

Among 471 patients with measurable disease by IR, 66 were deemed responders, which resulted in an ORR of 14% (95% CI, 11.0% to 17.5%) across all 20 study cohorts (Fig 2); ORRs ranged from 0% in pancreatic carcinoma to 33% in small-cell lung cancer (SCLC). Of the 66 responders, three patients achieved CR (one patient each in biliary, ovarian, and SCLC cohorts), and 63 patients achieved PR across the cohorts (Appendix Table A1, online only). One hundred sixty patients (34%) had stable disease, and 213 (45%) experienced disease progression. An ORR of greater than 10% was observed in 13 of 20 tumor types. After SCLC, the highest ORRs occurred in the esophageal, nasopharyngeal, mesothelioma, biliary duct, cervical, and prostate cohorts. Decreases from baseline in tumor lesion size occurred in 187 patients (43%) who had both baseline and postbaseline target lesion measurements (n = 437; Appendix Fig A1, online only).

The median PFS by IR across tumor types was 2.2 months (95% CI, 1.9 to 3.4 months) and ranged from 1.7 months in pancreatic cancer to 6.8 months in thyroid cancers (Fig 2). The longest median PFS occurred in the thyroid, nasopharyngeal, carcinoid, mesothelioma, and neuroendocrine cohorts. Six- and 12-month PFS rates by IR were 29% and 17%, respectively, across the study cohorts (Appendix Table A1). The median OS in patients with measurable disease at baseline by IR was 11.3 months (95% CI, 9.8 to 13.7 months) for all 20 cohorts and ranged from 3.8 months in vulvar cancer to 21.1 months in carcinoid tumors; OS was not reached in thyroid cancer (Fig 2). The longest median OS durations (16.5 to 21.1 months) occurred in the carcinoid, neuroendocrine, mesothelioma, endometrial, and nasopharyngeal cohorts. Across the 20 cohorts, 6- and 12-month OS rates by IR were 70% and 49%, respectively (Appendix Table A1). Kaplan-Meier curves of PFS and OS for the majority (n = 16) of the 20 cohorts have been previously published15-29; data for the four cohorts not yet reported are shown in Appendix Fig A2 (online only).

In the prespecified exploratory analysis of patients with measurable disease at baseline by independent central radiology review (n = 426), responses were similar (ORR, 10%; 95% CI, 7.6% to 13.6%) across the 20 study cohorts (Appendix Fig A3, online only). The medians of PFS and OS in all cohorts were also comparable (PFS, 1.9 months [95% CI, 1.9 to 2.1 months]; OS, 11.5 [95% CI, 10.2 to 14.4 months]; Appendix Fig A3).

The safety profile across the cohorts (n = 475) was similar and consistent with those previously reported for pembrolizumab in patients with advanced cancers.2 AEs occurred in 456 patients (96%); those most commonly experienced in 10% or more of patients included fatigue, nausea, decreased appetite, diarrhea, constipation, anemia, pyrexia, and vomiting (grades 1 to 3) as well as cough and pruritus (grades 1 and 2; Appendix Table A2, online only). Treatment-related AEs occurred in 311 patients (66%), and 67 patients (14%) experienced grade 3 or worse AEs. In 43 patients (9%), treatment-related AEs were deemed serious; in 17 (4%), treatment was discontinued because of AEs. The most frequent treatment-related AEs that occurred in greater than 10% of patients were grades 1 to 3 and included fatigue (n = 76; 24%), diarrhea (n = 55; 18%), pruritus (n = 49; 16%), nausea (n = 44; 14%), rash (n = 38; 12%), arthralgia (n = 33; 11%), and asthenia and hypothyroidism (n = 32; 10% each; Appendix Table A3, online only). Treatment-related grade 4 AEs were increased blood bilirubin, blood creatine phosphokinase, and lipase levels; hepatitis; septic shock; and type 2 diabetes—observed in one patient (< 1%) each. Grade 5 events were colitis, interstitial lung disease, intestinal ischemia, and sepsis—also experienced by one patient (< 1%) each. Three deaths (< 1%) attributed to interstitial lung disease, sepsis, and both intestinal ischemia and colitis in one patient were considered treatment related.

Biomarker Analyses

The numbers of patients with available biomarker data per tumor type who were evaluable for clinical efficacy varied, because T-cell–inflamed GEP and TMB were assessed in available tissue samples and only selected cohorts were tested for PD-L1 (Agilent IHC) to inform other ongoing clinical programs using the assay (Table 2). None of the patients with pancreatic cancer experienced a response to pembrolizumab; thus, this cohort was excluded from biomarker analyses.

Table

TABLE 2. Patients With Available Biomarker Data per Tumor Type

Across all tumors evaluated (N = 313), T-cell–inflamed GEP scores were higher in patients who achieved ORR and had longer PFS (Figs 3A and 3B). Independent statistical testing by regression meta-analysis across the 14 cohorts not included in the development of the T-cell–inflamed GEP14 confirmed that GEP score was significantly associated with ORR (P = .012, n = 203) and PFS (P = .017, n = 203), which supports the general relationship observed across the 19 tumor cohorts in Figure 3.

Heat map analysis of the T-cell–inflamed GEP demonstrated a coordinated expression pattern of the 18 genes and an association with improved ORR, as observed by the increased frequency of responses at higher expression levels of the individual genes (Fig 3C), including PD-L1 (CD274). All genes were positively associated with ORR except B7-H3 (CD276), selected as a negatively associated gene when the T-cell–inflamed GEP was developed.14

Importantly, the heat map showed that PD-L1 expression was closely linked to a broader pattern of coregulated gene expression that involves T-cell activation markers, cytokine recruitment of T cells, and antigen presentation, which in general comprise an interferon-driven transcriptional program of gene expression across multiple cell types. Evaluation of PD-L1 expression by IHC CPS33 was available for 198 patients in 13 cohorts. Statistical testing in a regression meta-analysis confirmed significant associations between PD-L1 CPS and both ORR (P = .018) and PFS (P = .005). The T-cell–inflamed GEP and PD-L1 CPS demonstrated a moderate, but highly statistically significant, association (r = 0.40; P < .001) across 151 patients (Appendix Fig A4A, online only), as may be expected given the patterns shown in Figure 3C.

TMB data were evaluable in 77 patients from 16 of 20 cohorts. Higher TMB was significantly associated with patients who achieved ORR and had longer PFS (P = .018 and P = .051, respectively; Figs 4A and 4B). Among the 77 patients, one had an identified MSI-H status. Given the low overall prevalence of MSI-H status for the majority of tumor types in the study, MSI was not included in the analysis; however, inclusion of that patient did not qualitatively change these results. TMB and the T-cell–inflamed GEP had a low, but significant, association (r = 0.29; P = .007; n = 72 for both assays; Fig 4C). Similarly, a low association between PD-L1 CPS and TMB was observed (r = 0.23; P = .082; n = 39 for both; Appendix Fig A4B). Notably, patients with both higher TMB and higher GEP score or PD-L1 CPS were more likely to be deemed responders. These trends indicate that the highest likelihood of clinical efficacy with pembrolizumab may be expected in tumors with both high TMB and high levels of inflammation as measured by GEP or PD-L1, and efficacy may not be expected in tumors with low TMB and low T-cell inflammation.

Pembrolizumab immunotherapy has demonstrated durable antitumor activity and a favorable safety profile in multiple tumor types,1,2 which led to its approval for several cancer types and a tumor-agnostic indication in any solid tumor with MSI-H.2 Conversely, anti–PD-1 therapies, including pembrolizumab, have shown more limited activity in some cancers.9,35,36 The results of KEYNOTE-028 extend these observations and demonstrate clinical responses to pembrolizumab in additional tumor types and a safety profile consistent with that previously reported for pembrolizumab.2

Expression of PD-L1 by IHC, predictive of response to pembrolizumab in several cancers, and MSI-H, predictive of response regardless of tumor type,2,7-9 could be considered specific indicators of tumor inflammatory and mutational states, respectively. To better understand the clinical activity of pembrolizumab in various cancer types and the potential interactions between tumor cell mutational and TME inflammatory states, we investigated associations of a T-cell–inflamed GEP, PD-L1 expression, and TMB with clinical outcome for pembrolizumab. Each of the three biomarkers was associated with clinical activity across the tumor types. The joint associations of inflammation (T-cell–inflamed GEP or PD-L1 CPS) and TMB with ORR and PFS suggest that the highest likelihood of clinical efficacy occurs in tumors with high levels of both inflammatory and mutational biomarkers. Of note, expression of PD-L1 IHC by CPS was associated with clinical efficacy, which demonstrates that, even in a PD-L1–selected population (via prototype assay), the level of PD-L1 expression by CPS in the tumor provided additional information about expected clinical outcome across the evaluated cohorts. Expression of PD-L1 via IHC was moderately correlated with the T-cell–inflamed GEP, as expected for a gene coexpressed within the profile and consistent with the known PD-L1 upregulation in a T-cell–activated TME.37-39 Similarly, the moderate correlation of both PD-L1 CPS and T-cell–inflamed GEP with TMB, in which each shows predictive value individually, suggests that multiple clinical-grade assays to inform patient treatment may be important for the best selection of patients to receive anti–PD-1 monotherapy.

The results of this analysis suggest that all three biomarkers, when assessed separately or in combination, predict clinical efficacy with pembrolizumab across multiple tumor types, consistent with previous data that suggest that these biomarkers function as complementary predictors of response to pembrolizumab.12-14 The use of joint information from these biomarkers may help identify small prevalence populations within cancer types that have low ORRs for which anti–PD-1 monotherapy may still be beneficial. Comparisons of the clinical utility of the biomarkers was not deemed appropriate for this data set; larger studies are needed to obtain more precision about the nature of these relationships within individual cancer types and to assess the potential clinical utility of developing cutoffs for such biomarkers in patient selection for anti–PD-1 therapy. In addition, assessment of these biomarkers in a randomized, comparative setting is required to provide a better understanding of the predictive versus prognostic elements of these relationships and how different components of the TME and mutational status may be used to predict outcome with anti–PD-1 monotherapy and combination therapies relative to standard-of-care treatment.

Although this is a fairly large study that shows significant relationships among the biomarkers and clinical outcome across multiple tumor types, there are limitations. The requirement of PD-L1 positivity for trial participation may have skewed the distribution of the biomarkers evaluated in this data set compared with those in an all-comers (any level of PD-L1 expression) setting. Because of the PD-L1 preselection and the small representation from each cancer type, estimation of response rates at biomarker cut points and detailed analysis of potential clinical utility are beyond the scope of this study. Nonetheless, the significant relationships demonstrated between the biomarkers and clinical outcome are consistent with previous observations.12-14 Despite differences in scoring and staining characteristics shown for various PD-L1 assay methods and associated reproducibility concerns, such as the prototype assay of positivity for enrichment and CPS for full-score evaluation testing involved here, relationships between higher PD-L1 IHC expression and response have been shown to be reproducible.2,34,40 Full evaluation of the clinical utility of these biomarkers either within or across cohorts requires larger studies of all-comer populations.

In conclusion, T-cell–inflamed GEP, PD-L1 expression, and TMB were shown to predict clinical efficacy with pembrolizumab therapy across a diverse set of 20 solid tumors and together may serve as potential biomarkers to identify patients with cancer who likely would benefit from PD-1–directed immunotherapy. These biomarkers may provide a framework in future studies toward unraveling mechanisms of resistance to immunotherapy. Additional clinical trials are ongoing to better understand the relationships between these biomarkers and clinical outcome with immunotherapy in multiple tumor types.

© 2018 by American Society of Clinical Oncology

Presented in part at the European Society for Medical Oncology Congress, Madrid, Spain, September 8-12, 2017.

Supported by Merck Sharp & Dohme, a subsidiary of Merck & Co., Inc. (Kenilworth, NJ).

Clinical trial information: NCT02054806.

Conception and design: Patrick A. Ott, Yung-Jue Bang, Hope S. Rugo, Toshihiko Doi, Jared K. Lunceford

Collection and assembly of data: Patrick A. Ott, Yung-Jue Bang, Albiruni R. Abdul Razak, Hope S. Rugo, Roger B. Cohen, Janice M. Mehnert, Juanita Lopez, Toshihiko Doi, Emilie M.J. van Brummelen, Razvan Cristescu, Kenneth Emancipator, Karen Stein, Andrew K. Joe

Provision of study materials or patients: Albiruni R. Abdul Razak, Bert H. O’Neil

Data analysis and interpretation: Patrick A. Ott, Yung-Jue Bang, Sarina A. Piha-Paul, Albiruni R. Abdul Razak, Jaafar Bennouna, Jean-Charles Soria, Hope S. Rugo, Bert H. O’Neil, Janice M. Mehnert, Juanita Lopez, Toshihiko Doi, Emilie M.J. van Brummelen, Razvan Cristescu, Ping Yang, Karen Stein, Mark Ayers, Andrew K. Joe, Jared K. Lunceford

Manuscript writing: All authors

Final approval of manuscript: All authors

Accountable for all aspects of the work: All authors

T-Cell–Inflamed Gene-Expression Profile, Programmed Death Ligand 1 Expression, and Tumor Mutational Burden Predict Efficacy in Patients Treated with Pembrolizumab Across 20 Cancers: KEYNOTE-028

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 www.asco.org/rwc or ascopubs.org/jco/site/ifc.

Patrick A. Ott

Consulting or Advisory Role: Bristol-Myers Squibb, CytomX Therapeutics, Celldex, Genentech, Neon Therapeutics, Novartis, Pfizer, Merck

Research Funding: Bristol-Myers Squibb (Inst), Merck (Inst), AstraZeneca (Inst), MedImmune (Inst), Celldex (Inst), ARMO BioSciences (Inst), CytomX Therapeutics (Inst), Roche (Inst), Genentech (Inst), Pfizer (Inst), Neon Therapeutics (Inst)

Yung-Jue Bang

Consulting or Advisory Role: AstraZeneca, MedImmune, Novartis, Genentech, Roche, MSD, Pfizer, Bayer, Eli Lilly, Merck Serono, Five Prime Therapeutics, Taiho Pharmaceutical, Ono Pharmaceutical, ADC Therapeutics, BeiGene, Samyang, Green Cross

Research Funding: AstraZeneca (Inst), MedImmune (Inst), Novartis (Inst), Genentech (Inst), Roche (Inst), MSD (Inst), Merck Serono (Inst), Bayer (Inst), GlaxoSmithKline (Inst), Bristol-Myers Squibb (Inst), Pfizer (Inst), Lilly (Inst), Boehringer Ingelheim (Inst), Macrogenics (Inst), Boston Biomedical (Inst), Five Prime Therapeutics (Inst), CKD (Inst), Ono Pharmaceutical (Inst), Taiho Pharmaceutical (Inst), Takeda (Inst), BeiGene (Inst), Curis (Inst), Green Cross (Inst)

Sarina A. Piha-Paul

Consulting or Advisory Role: Genentech

Research Funding: GlaxoSmithKline (Inst), XuanZhu (Inst), Puma Biotechnology (Inst), Novartis (Inst), MSD (Inst), Curis (Inst), Principa Biopharma (Inst), Helix BioPharma (Inst), Bayer (Inst), AbbVie (Inst), Incyte (Inst), Five Prime Therapeutics Therapeutics (Inst), MedImmune (Inst), Medivation (Inst), BlueLink (Inst), Pfizer (Inst), Tesaro (Inst), Pieris Pharmaceuticals (Inst), Genmab (Inst), Taiho Pharmaceutical (Inst)

Albiruni R. Abdul Razak

Honoraria: Boehringer Ingelheim

Consulting or Advisory Role: Eli Lilly, Merck, Boehringer Ingelheim, Bayer

Research Funding: Entremed, Casi Pharmaceuticals, Boehringer Ingelheim, Eli Lilly, Novartis, Deciphera, Karyopharm, Pfizer, Roche, Genentech, Boston Biomedical, Bristol-Myers Squibb, MedImmune, Amgen, Merck, Deciphera, Blueprint Medicines, Beta Cat Pharmaceuticals

Jaafar Bennouna

Honoraria: Roche, Boehringer Ingelheim, Novartis, Pierre Fabre

Consulting or Advisory Role: Roche, Boehringer Ingelheim, Novartis, Pierre Fabre

Jean-Charles Soria

Employment: MedImmune

Consulting or Advisory Role: AstraZeneca, Astex, Clovis, GSK, GamaMabs, Lilly, MSD, Mission Therapeutics, Merus, Pfizer, PharmaMar, Pierre Fabre, Roche/Genentech, Sanofi, Servier, Symphogen, Takeda

Stock or Other Ownership: AstraZeneca, Gritstone

Hope S. Rugo

Honoraria: Celltrion

Research Funding: Plexxikon (Inst), Macrogenics (Inst), OBI Pharma (Inst), Eisai (Inst), Pfizer (Inst), Novartis (Inst), Eli Lilly (Inst), Genentech (Inst), Merck (Inst), Immunomedics (Inst), Daichi (Inst)

Travel, Accommodations, Expenses: Novartis, Roche, Genentech, Pfizer, Puma Biotechnology, Mylan, Amgen

Roger B. Cohen

Honoraria: Bristol-Myers Squibb

Consulting or Advisory Role: Heat Biologics, Takeda, Kyntherapeutics, Tmunity Therapeutics, Innate Pharma, Genocea Bioscences, Cantargia

Research Funding: Heat Biologics (Inst), Macrogenics (Inst), Merck (Inst), Innate Pharma (Inst), Celldex (Inst)

Travel, Accommodations, Expenses: Heat Biologics, Takeda, Innate Pharma

Bert H. O'Neil

Honoraria: Merck

Consulting or Advisory Role: Amgen, Genentech, Roche, Bayer

Janice M. Mehnert

Honoraria: Genentech, EMD Serono, Pfizer

Consulting or Advisory Role: MSD, Amgen, Boehringer Ingelheim, Pfizer

Research Funding: Merck (Inst), Sanofi (Inst), Novartis (Inst), Polynoma (Inst), Immunocore (Inst), Amgen (Inst), AstraZeneca (Inst), Incyte (Inst), Macrogenics (Inst), EMD Serono (Inst), Bristol-Myers Squibb (Inst)

Travel, Accommodations, Expenses: EMD Serono, MDS, Boehringer Ingelheim

Toshihiko Doi

Consulting or Advisory Role: Lilly Japan, Chugai Pharma, Kyowa Hakko Kirin, MSD, Daiichi Sankyo, Amgen, Sumitomo Dainippon, Taiho Pharmaceutical

Research Funding: Taiho Pharmaceutical (Inst), Novartis (Inst), Merck Serono (Inst), Astellas Pharma (Inst), MSD (Inst), Janssen (Inst), Boehringer Ingelheim (Inst), Takeda (Inst), Pfizer (Inst), Eli Lilly Japan (Inst), Sumitomo Group (Inst), Chugai Pharma (Inst), Kyowa Hakko Kirin (Inst), Daiichi Sankyo (Inst), Celgene (Inst), Bristol-Myers Squibb (Inst), AbbVie (Inst), Quintiles (Inst)

Razvan Cristescu

Employment: Merck Sharp & Dohme

Stock or Other Ownership: Merck Sharp & Dohme

Ping Yang

Employment: MSD

Stock or Other Ownership: MSD

Kenneth Emancipator

Employment: Merck Sharp & Dohme, Celgene (I)

Stock or Other Ownership: Merck Sharp & Dohme, Bayer, Johnson & Johnson, Celgene (I)

Karen Stein

Employment: Merck Sharp & Dohme

Stock or Other Ownership: Merck Sharp & Dohme, Novartis, Eisai, Pfizer

Mark Ayers

Employment: Merck Sharp & Dohme

Stock or Other Ownership: Merck Sharp & Dohme

Patents, Royalties, Other Intellectual Property: Patent for the gene signature, ID WO2016094377 (Inst)

Andrew K. Joe

Employment: Merck Sharp & Dohme

Stock or Other Ownership: Merck Sharp & Dohme

Jared K. Lunceford

Employment: Merck Sharp & Dohme

Stock or Other Ownership: Merck Sharp & Dohme

Patents, Royalties, Other Intellectual Property: Patent for the gene signature, ID WO2016094377 (Inst)

No other potential conflicts of interest were reported.

Table

TABLE A1. Additional Clinical Efficacy Parameters Across Cohorts

Table

TABLE A2. Adverse Events in 10% or More of Patients by Maximum Toxicity Grades

Table

TABLE A3. Treatment-Related Adverse Events by Maximum Toxicity Grade

ACKNOWLEDGMENT

We thank the patients and their families as well as all of the investigators and site personnel. We also thank Jon Cheng and Anne Morosky of the study team; Andrew Albright, Xiao Qiao Liu, Xinwei Sher, Michael Nebozhyn, and Terrill McClanahan, who contributed to the biomarker analysis; Joanne Tomassini for medical writing support; and Sheila Erespe and Jennifer Pawlowski for editorial assistance (all employed by Merck & Co., Inc., Kenilworth, NJ). We thank Guoqing Zhao, a former employee of Merck, for statistical support.

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DOI: 10.1200/JCO.2018.78.2276 Journal of Clinical Oncology 37, no. 4 (February 01, 2019) 318-327.

Published online December 13, 2018.

PMID: 30557521

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