A Randomized Ph2 Study of MEDI0680 in Combination With Durvalumab vs. Nivolumab Monotherapy in Patients With Advanced or Metastatic Clear Cell Renal Cell Carcinoma

BACKGROUND: MEDI0680 is a humanized anti-programmed cell death-1 (PD-1) antibody and durvalumab is an anti-PD-L1 antibody. Combining treatment using these antibodies may improve efficacy versus blockade of PD-1 alone. This phase 2 study evaluated antitumor activity and safety of MEDI0680 plus durvalumab versus nivolumab monotherapy in immunotherapy naïve patients with advanced clear cell renal cell carcinoma who received at least one prior line of anti-angiogenic therapy. METHODS: Patients received either MEDI0680 (20 mg/kg) with durvalumab (750 mg) or nivolumab (240 mg), all IV Q2W. The primary endpoint was investigator-assessed objective response rate (ORR). Secondary endpoints included best overall response, progression-free survival (PFS), safety, overall survival (OS), and immunogenicity. Exploratory endpoints included changes in circulating tumor DNA (ctDNA), baseline tumor mutational burden (TMB), and tumor-infiltrated immune cell profiles. RESULTS: Sixty-three patients were randomized (combination, n = 42; nivolumab, n = 21). ORR was 16.7% (7/42; 95% CI, 7.0-31.4) with combination treatment and 23.8% (5/21; 95% CI, 8.2- 47.2) with nivolumab. Median PFS was 3.6 months in both arms; median OS was not reached in either arm. Due to AEs, 23.8% of patients discontinued MEDI0680 and durvalumab and 14.3% of patients discontinued nivolumab. In the combination arm, reduction in ctDNA fraction was associated with longer PFS. ctDNA mutational analysis did not demonstrate an association with response in either arm. Tumor-infiltrated immune profiles showed an association between immune cell activation and response in the combination arm. CONCLUSIONS: MEDI0680 combined with durvalumab was safe and tolerable; however, it did not improve efficacy versus nivolumab monotherapy

Measuring multiple parameters of CD8+ tumor-infiltrating lymphocytes in human cancers by image analysis

Abstract Background Immuno-oncology and cancer immunotherapies are areas of intense research. The numbers and locations of CD8+ tumor-infiltrating lymphocytes (TILs) are important measures of the immune response to cancer with prognostic, pharmacodynamic, and predictive potential. We describe the development, validation, and application of advanced image analysis methods to characterize multiple immunohistochemistry-derived CD8 parameters in clinical and nonclinical tumor tissues. Methods Commercial resection tumors from nine cancer types, and paired screening/on-drug biopsies of non–small-cell lung carcinoma (NSCLC) patients enrolled in a phase 1/2 clinical trial investigating the PD-L1 antibody therapy durvalumab (NCT01693562), were immunostained for CD8. Additional NCT01693562 samples were immunostained with a CD8/PD-L1 dual immunohistochemistry assay. Whole-slide scanning was performed, tumor regions were annotated by a pathologist, and images were analyzed with customized algorithms using Definiens Developer XD software. Validation of image analysis data used cell-by-cell comparison to pathologist scoring across a range of CD8+ TIL densities of all nine cancers, relying primarily on 95% confidence in having at least moderate agreement regarding Lin concordance correlation coefficient (CCC = 0.88–0.99, CCC_lower = 0.65–0.96). Results We found substantial variability in CD8+ TILs between individual patients and across the nine types of human cancer. Diffuse large B-cell lymphoma had several-fold more CD8+ TILs than some other cancers. TIL densities were significantly higher in the invasive margin versus tumor center for carcinomas of head and neck, kidney and pancreas, and NSCLC; the reverse was true only for prostate cancer. In paired patient biopsies, there were significantly increased CD8+ TILs 6 weeks after onset of durvalumab therapy (mean of 365 cells/mm2 over baseline; P = 0.009), consistent with immune activation. Image analysis accurately enumerated CD8+ TILs in PD-L1+ regions of lung tumors using the dual assay and also measured elongate CD8+ lymphocytes which constituted a fraction of overall TILs. Conclusions Validated image analysis accurately enumerates CD8+ TILs, permitting comparisons of CD8 parameters among tumor regions, individual patients, and cancer types. It also enables the more complex digital solutions needed to better understand cancer immunity, like analysis of multiplex immunohistochemistry and spatial evaluation of the various components comprising the tumor microenvironment. Trial registration ClinicalTrials.gov identifier: NCT01693562. Study code: CD-ON-MEDI4736–1108. Interventional study (ongoing but not currently recruiting). Actual study start date: August 29, 2012. Primary completion date: June 23, 2017 (final data collection date for primary outcome measure)

Additional file 2: of Measuring multiple parameters of CD8+ tumor-infiltrating lymphocytes in human cancers by image analysis

CD8/PD-L1 dual IHC, quality control; co-registration; details of the automated classification assessment; details of digital IA scoring solutions; measurement of CD8+ TILs in PD-L1–positive tumor. (DOCX 45 kb

Additional file 1: of Measuring multiple parameters of CD8+ tumor-infiltrating lymphocytes in human cancers by image analysis

Table S1 Relevant image information for nonclinical sample sets. Table S2 Nonclinical tumor samples, key patient data.Table S3 Overview of data used for statistical analysis. Table S4 Validation of CD8 IA using CD8 single stain and CD8/PD-L1 dual stain. (DOCX 91 kb

Additional file 3: of Measuring multiple parameters of CD8+ tumor-infiltrating lymphocytes in human cancers by image analysis

Figure S1 IA classification of CD8+ lymphocytes. Figure S2 IA classification of elongate CD8+ lymphocytes. Figure S3 Elongate CD8+ TILS detected by IA are also CD3+. Figure S4 Tumor landscape of elongate CD8+ TILs. (DOCX 1795 kb

Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community

Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community

Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community