Explore the vast range of titles available.

The level of PD-L1 expression in immunohistochemistry (IHC) assays is a key biomarker for the identification of Non-Small-Cell-Lung-Cancer (NSCLC) patients that may respond to anti PD-1/PD-L1 treatments. The quantification of PD-L1 expression currently includes the visual estimation by a pathologist of the percentage (tumor proportional scoring or TPS) of tumor cells showing PD-L1 staining. Known challenges like differences in positivity estimation around clinically relevant cut-offs and sub-optimal quality of samples makes visual scoring tedious and subjective, yielding a scoring variability between pathologists. In this work, we propose a novel deep learning solution that enables the first automated and objective scoring of PD-L1 expression in late stage NSCLC needle biopsies. To account for the low amount of tissue available in biopsy images and to restrict the amount of manual annotations necessary for training, we explore the use of semi-supervised approaches against standard fully supervised methods. We consolidate the manual annotations used for training as well the visual TPS scores used for quantitative evaluation with multiple pathologists. Concordance measures computed on a set of slides unseen during training provide evidence that our automatic scoring method matches visual scoring on the considered dataset while ensuring repeatability and objectivity.

Tumor programmed cell death ligand-1 (PD-L1) expression is a key biomarker to identify patients with non-small cell lung cancer who may have an enhanced response to anti-programmed cell death-1 (PD-1)/PD-L1 treatment. Such treatments are used in conjunction with PD-L1 diagnostic immunohistochemistry assays. We developed a computer-aided automated image analysis with customized PD-L1 scoring algorithm that was evaluated via correlation with manual pathologist scores and used to determine comparability across PD-L1 immunohistochemistry assays. The image analysis scoring algorithm was developed to quantify the percentage of PD-L1 positive tumor cells on scans of whole-slide images of archival tumor samples from commercially available non-small cell lung cancer cases, stained with four immunohistochemistry PD-L1 assays (Ventana SP263 and SP142 and Dako 22C3 and 28-8). The scans were co-registered and tumor and exclusion annotations aligned to ensure that analysis of each case was restricted to comparable tissue areas. Reference pathologist scores were available from previous studies. F1, a statistical measure of precision and recall, and overall percentage agreement scores were used to assess concordance between pathologist and image analysis scores and between immunohistochemistry assays. In total, 471 PD-L1-evalulable samples were amenable to image analysis scoring. Image analysis and pathologist scores were highly concordant, with F1 scores ranging from 0.8 to 0.9 across varying matched PD-L1 cutoffs. Based on F1 and overall percentage agreement scores (both manual and image analysis scoring), the Ventana SP263 and Dako 28-8 and 22C3 assays were concordant across a broad range of cutoffs; however, the Ventana SP142 assay showed very different characteristics. In summary, a novel automated image analysis scoring algorithm was developed that was highly correlated with pathologist scores. The algorithm permitted quantitative comparison of existing PD-L1 diagnostic assays, confirming previous findings that indicate a high concordance between the Ventana SP263 and Dako 22C3 and 28-8 PD-L1 immunohistochemistry assays.

Background A high-quality programmed cell-death ligand 1 (PD-L1) diagnostic assay may help predict which patients are more likely to respond to anti-programmed cell death-1 (PD-1)/PD-L1 antibody-based cancer therapy. Here we describe a PD-L1 immunohistochemical (IHC) staining protocol developed by Ventana Medical Systems Inc. and key analytical parameters of its use in formalin-fixed, paraffin-embedded (FFPE) samples of non-small cell lung cancer (NSCLC) and head and neck squamous cell carcinoma (HNSCC). Methods An anti-human PD-L1 rabbit monoclonal antibody (SP263) was optimized for use with the VENTANA OptiView DAB IHC Detection Kit on the automated VENTANA BenchMark ULTRA platform. The VENTANA PD-L1 (SP263) Assay was validated for use with FFPE NSCLC and HNSCC tissue samples in a series of studies addressing sensitivity, specificity, robustness, and precision. Samples from a subset of 181 patients from a Phase 1/2 study of durvalumab (NCT01693562) were analyzed to determine the optimal PD-L1 staining cut-off for enriching the probability of responses to treatment. The scoring algorithm was defined using statistical analysis of clinical response data from this clinical trial and PD-L1 staining parameters in HNSCC and NSCLC tissue. Inter-reader agreement was established by three pathologists who evaluated 81 NSCLC and 100 HNSCC samples across the range of PD-L1 expression levels. Results The VENTANA PD-L1 (SP263) Assay met all pre-defined acceptance criteria. For both cancer types, a cut-off of 25 % of tumor cells with PD-L1 membrane staining of any intensity best discriminated responders from nonresponders. Samples with staining above this value were deemed to have high PD-L1 expression, and those with staining below it were deemed to have low or no PD-L1 expression. Inter-reader agreement on PD-L1 status was 97 and 92 % for NSCLC and HNSCC, respectively. Conclusions These results highlight the robustness and reproducibility of the VENTANA PD-L1 (SP263) Assay and support its suitability for use in the evaluation of NSCLC and HNSCC FFPE tumor samples using the devised ≥25 % tumor cell staining cut-off in a clinical setting. The clinical utility of the PD-L1 diagnostic assay as a predictive biomarker will be further validated in ongoing durvalumab studies. Trial registration ClinicalTrials.gov: NCT01693562

ObjectivesThe interaction between the immune system and tumor cells is an important feature for the prognosis and treatment of cancer. Multiplex immunohistochemistry (mIHC) and multiplex immunofluorescence (mIF) analyses are emerging technologies that can be used to help quantify immune cell subsets, their functional state, and their spatial arrangement within the tumor microenvironment.MethodsThe Society for Immunotherapy of Cancer (SITC) convened a task force of pathologists and laboratory leaders from academic centers as well as experts from pharmaceutical and diagnostic companies to develop best practice guidelines for the optimization and validation of mIHC/mIF assays across platforms.ResultsRepresentative outputs and the advantages and disadvantages of mIHC/mIF approaches, such as multiplexed chromogenic IHC, multiplexed immunohistochemical consecutive staining on single slide, mIF (including multispectral approaches), tissue-based mass spectrometry, and digital spatial profiling are discussed.ConclusionsmIHC/mIF technologies are becoming standard tools for biomarker studies and are likely to enter routine clinical practice in the near future. Careful assay optimization and validation will help ensure outputs are robust and comparable across laboratories as well as potentially across mIHC/mIF platforms. Quantitative image analysis of mIHC/mIF output and data management considerations will be addressed in a complementary manuscript from this task force.

Background Antibodies targeting the programmed cell death-1 (PD-1)/PD-ligand 1 (PD-1/PD-L1) checkpoint have shown promising clinical activity in patients with advanced urothelial carcinoma (UC). Expression of PD-L1 in UC tumors has been investigated using different antibody clones, staining protocols, and scoring algorithms. The aim was to establish the extent of concordance among PD-L1 immunohistochemistry (IHC) assays. Methods Tumor biopsy samples ( N  = 335) were assessed using four commercially available PD-L1 assays: VENTANA SP263, VENTANA SP142, PD-L1 IHC 28–8 pharmDx, and PD-L1 IHC 22C3 pharmDx. PD-L1 analytical staining and classification concordance, including agreement between clinically relevant scoring algorithms, were investigated using overall/positive/negative percentage agreement (OPA/PPA/NPA). Results Good analytical correlation was observed among the VENTANA SP263, PD-L1 IHC 22C3 pharmDx, and PD-L1 IHC 28–8 pharmDx assays for tumor cell (TC) and immune cell (IC) PD-L1 staining with Spearman rank coefficients of 0.92–0.93 for TCs and 0.88–0.91 for ICs. However, concordance (preset criterion: ≥85%) between patient PD-L1 status when applying the TC or IC ICArea  ≥ 25% (VENTANA SP263) cutoff was only achieved for PD-L1 IHC 22C3 pharmDx versus VENTANA SP263 (OPA 92.2%, PPA 86.4%, NPA 95.4%). Differences were observed between patient populations with UC tumors classified as PD-L1 high versus PD-L1 low/negative using combined positive score (CPS) ≥1, CPS ≥10, IC ≥5%, and TC/IC ≥25%. Conclusions The VENTANA SP263 and PD-L1 IHC 22C3 pharmDx assays are analytically similar in UC. When the different PD-L1 assays were combined with their specified clinical scoring algorithms, differences were seen in patient classification driven by substantial differences in scoring approaches.

Studies have indicated that programmed death ligand 1 (PD-L1) expression may have utility as a predictive biomarker in patients with advanced/metastatic urothelial carcinoma (UC). Different immunohistochemical (IHC) assays are in development to assess PD-L1 expression on tumor cells (TCs) and tumor-infiltrating immune cells (ICs). In this post hoc analysis of the single-arm, phase 1/2 Study 1108 (NCT01693562), PD-L1 expression was evaluated from tumor samples obtained prior to second-line treatment with durvalumab in patients with advanced/metastatic UC using the VENTANA (SP263) IHC Assay. The primary objective was to determine whether the TC ≥ 25%/IC ≥ 25% algorithm (i.e., cutoff of ≥ 25% TC or ≥ 25% IC with PD-L1 staining at any intensity above background) was optimal for predicting response to durvalumab. PD-L1 expression data were available from 188 patients. After a median follow-up of 15.8 and 14.6 months, higher PD-L1 expression was associated with longer overall survival (OS) and progression-free survival (PFS), respectively, with significant separation in survival curves for PD-L1-high and-low expressing patients for the TC ≥ 25%/IC ≥ 25% cutoff (median OS: 19.8 vs 4.8 months; hazard ratio: 0.46; 90% confidence interval: 0.33, 0.639). OS was also prolonged for PD-L1-high compared with-low patients when samples were categorized using TC/IC combined positive score ≥ 10 and IC≥ 5% cutoffs. In multivariate analysis, IC but not TC PD-L1 expression was significantly associated with OS, PFS, and objective response rate (P < 0.001 for each), although interaction analysis showed similar directionality of benefit for ICs and TCs. These findings support the utility of a combined TC/IC algorithm for predicting response to durvalumab in patients with UC, with the TC≥ 25%/IC≥ 25% cutoff optimal when used with the VENTANA (SP263) IHC Assay.

Background Immune checkpoint therapies (ICTs) targeting the programmed cell death-1 (PD1)/programmed cell death ligand-1 (PD-L1) pathway have improved outcomes for patients with non-small cell lung cancer (NSCLC), particularly those with high PD-L1 expression. However, the predictive value of manual PD-L1 scoring is imperfect and alternative measures are needed. We report an automated image analysis solution to determine the predictive and prognostic values of the product of PD-L1+ cell and CD8+ tumor infiltrating lymphocyte (TIL) densities (CD8xPD-L1 signature) in baseline tumor biopsies. Methods Archival or fresh tumor biopsies were analyzed for PD-L1 and CD8 expression by immunohistochemistry. Samples were collected from 163 patients in Study 1108/NCT01693562, a Phase 1/2 trial to evaluate durvalumab across multiple tumor types, including NSCLC, and a separate cohort of 199 non-ICT- patients. Digital images were automatically scored for PD-L1+ and CD8+ cell densities using customized algorithms applied with Developer XD™ 2.7 software. Results For patients who received durvalumab, median overall survival (OS) was 21.0 months for CD8xPD-L1 signature-positive patients and 7.8 months for signature-negative patients ( p  = 0.00002). The CD8xPD-L1 signature provided greater stratification of OS than high densities of CD8+ cells, high densities of PD-L1+ cells, or manually assessed tumor cell PD-L1 expression ≥25%. The CD8xPD-L1 signature did not stratify OS in non-ICT patients, although a high density of CD8+ cells was associated with higher median OS (high: 67 months; low: 39.5 months, p  = 0.0009) in this group. Conclusions An automated CD8xPD-L1 signature may help to identify NSCLC patients with improved response to durvalumab therapy. Our data also support the prognostic value of CD8+ TILS in NSCLC patients who do not receive ICT. 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).

Background Several anti-programmed cell death-1 (PD-1) and anti-programmed cell death ligand-1 (PD-L1) therapies have shown encouraging safety and clinical activity in a variety of tumor types. A potential role for PD-L1 testing in identifying patients that are more likely to respond to treatment is emerging. PD-L1 expression in clinical practice is determined by testing one tumor section per patient. Therefore, it is critical to understand the impact of tissue sampling variability on patients’ PD-L1 classification. Methods Resected non-small cell lung cancer (NSCLC), head and neck squamous cell carcinoma (HNSCC) and urothelial carcinoma (UC) tissue samples (five samples per tumor type) were obtained from commercial sources and two tumor blocks were taken from each. Three sections from each block (~ 100 μm apart) were stained using the VENTANA PD-L1 (SP263) assay, and scored based on the percentage of PD-L1-staining tumor cells (TCs) or tumor-infiltrating immune cells (ICs) present. Each section was categorized as PD-L1 high or low/negative using a variety of cut-off values, and intra-block and intra-case (between blocks of the same tumor) concordance (overall percentage agreement [OPA]) were evaluated. An additional 200 commercial NSCLC samples were also analyzed, and intra-block concordance determined by scoring two sections per sample (≥70 μm apart). Results Concordance in TC PD-L1 classification was high at all applied cut-offs. Intra-block and intra-case OPA for the 15 NSCLC, HNSCC or UC samples were 100% and 80–100%, respectively, across all cut-offs; intra-block OPA for the 200 NSCLC samples was 91.0–98.5% across all cut-offs. IC PD-L1 classification was less consistent; intra-block and intra-case OPA for the 15 NSCLC, HNSCC or UC samples ranged between 70 and 100% and between 60 and 100%, respectively, with similar observations in the intra-block analysis of the 200 NSCLC samples. Conclusions These results show the reproducibility of TC PD-L1 classification across the depth of the tumor using the VENTANA PD-L1 (SP263) assay. Practically, this means that treatment decisions based on TC PD-L1 classification can be made confidently, following analysis of one tumor section. Although more variable than TC staining, consistent IC PD-L1 classification was also observed within and between blocks and across cut-offs.

ObjectivesMultiplex immunohistochemistry and immunofluorescence (mIHC/IF) are emerging technologies that can be used to help define complex immunophenotypes in tissue, quantify immune cell subsets, and assess the spatial arrangement of marker expression. mIHC/IF assays require concerted efforts to optimize and validate the multiplex staining protocols prior to their application on slides. The best practice guidelines for staining and validation of mIHC/IF assays across platforms were previously published by this task force. The current effort represents a complementary manuscript for mIHC/IF analysis focused on the associated image analysis and data management.MethodsThe Society for Immunotherapy of Cancer convened a task force of pathologists and laboratory leaders from academic centers as well as experts from pharmaceutical and diagnostic companies to develop best practice guidelines for the quantitative image analysis of mIHC/IF output and data management considerations.ResultsBest-practice approaches for image acquisition, color deconvolution and spectral unmixing, tissue and cell segmentation, phenotyping, and algorithm verification are reviewed. Additional quality control (QC) measures such as batch-to-batch correction and QC for assembled images are also discussed. Recommendations for sharing raw outputs, processed results, key analysis programs and source code, and representative photomicrographs from mIHC/IF assays are included. Lastly, multi-institutional harmonization efforts are described.ConclusionsmIHC/IF technologies are maturing and are routinely included in research studies and moving towards clinical use. Guidelines for how to perform and standardize image analysis on mIHC/IF-stained slides will likely contribute to more comparable results across laboratories and pave the way for clinical implementation. A checklist encompassing these two-part guidelines for the generation of robust data from quantitative mIHC/IF assays will be provided in a third publication from this task force. While the current effort is mainly focused on best practices for characterizing the tumor microenvironment, these principles are broadly applicable to any mIHC/IF assay and associated image analysis.

Clinical responses to anti-programmed death receptor-1 and anti-programmed death ligand-1 (PD-L1) agents are generally improved in patients with high PD-L1 expression compared with those with low/negative expression across several tumor types, including urothelial carcinoma. To validate a PD-L1 immunohistochemical diagnostic test in urothelial carcinoma patients treated with the anti-PD-L1 monoclonal antibody durvalumab. The Ventana PD-L1 (SP263) assay was validated for intended use in urothelial carcinoma formalin-fixed, paraffin-embedded samples in studies addressing sensitivity, specificity, robustness, and precision, and implemented in study CD-ON-MEDI4736-1108 (NCT01693562). Efficacy was analyzed in patients classified according to prespecified PD-L1 expression cutoffs: PD-L1 high (if >1% of the tumor area contained tumor-associated immune cells, ≥25% of tumor cells or ≥25% of immune cells stained for PD-L1; if ≤1% of the tumor area contained immune cells, ≥25% of tumor cells or 100% of immune cells stained for PD-L1) and PD-L1 low/negative (did not meet criteria for PD-L1 high). The assay met all predefined acceptance criteria for sensitivity, specificity, and precision. Interreader and intrareader precision overall agreement were 93.0% and 92.4%, respectively. For intraday reproducibility and interday precision, overall agreement was 99.2% and 100%, respectively. Interlaboratory overall agreement was 92.6%. In study CD-ON-MEDI4736-1108, durvalumab demonstrated clinical activity and durable responses in both PD-L1-high and PD-L1-low/negative subgroups, although objective response rates tended to be higher in the PD-L1-high subgroup than in the PD-L1-low/negative subgroup. Determination of PD-L1 expression in urothelial carcinoma patients using the Ventana PD-L1 (SP263) assay was precise, highly reproducible, and clinically relevant.