Objectives Multiplex 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. Methods The 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. Results Best-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. Conclusions mIHC/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.

Background Predictive models applied to digital pathology images show promise for the rapid and objective analysis of patient samples to identify features of the tumor microenvironment (TME) predictive of treatment response. Here we compare two tissue and cell identification approaches —multiplex immunofluorescence (mIF) and deep learning models applied to H&E-stained slides. Methods Adjacent sections from primary or metastatic tumors (n=91) from patients with colorectal, non-small cell lung, ovarian, pancreatic, and breast cancer were stained by mIF and H&E. mIF image analysis was done for tumor-stroma segmentation and to identify necrotic tissue within the pathologist-annotated tumor bed. Cytotoxic T cells, immune cells, and fibroblasts were identified using CD8, CD45, and COL1A1 stain thresholding, respectively. AI-powered TME models developed by PathAI (Boston, MA; commercially available as PathExploreTM) were deployed on the H&E slides for tissue classification (tumor epithelium, stroma, necrosis) and cell identification (cancer cells, lymphocytes, macrophages, plasma cells, fibroblasts). Tissue and cell features were compared between the approaches. Areas of tumor epithelium, stroma, and necrosis were assessed qualitatively with areas of disagreement undergoing independent pathologist review. The density of CD8+ cells from mIF was compared to lymphocytes from H&E, of CD45+ immune cells from mIF to lymphocytes, macrophages, and plasma cells from H&E, and of COL1A1+ cells from mIF to fibroblasts from H&E, recognizing that these cell populations do not overlap completely. Results The mIF and H&E approaches showed good tissue segmentation performance, producing broadly similar annotations, with differences attributable to staining co-occurrence in mIF, lower performance of H&E models on metastatic samples, and disagreement at the tumor bed periphery. Cell identification showed broad agreement between the density of CD8+ by mIF and lymphocytes by H&E (r=0.66, range 0.30–0.93 by indication), CD45+ cells by mIF with immune cells by H&E (r=0.60, range 0.23–0.87), and COL1A1+ cells by mIF with fibroblasts by H&E (r=0.51, range 0.08–0.56) (figure 1, table 1). Conclusions Automated analysis of digital pathology images is a rapidly emerging field with broad potential to analyze pathology tissues accurately and reproducibly across tumor types. PathAI's TME models are a robust tool to distinguish tissue and cell features from H&E slides, comparable to mIF image analysis, but requiring less effort, time, and expense. Indication-specific differences in cell classifications point to more accurate performance by H&E models than mIF. With additional refinement, these technologies could allow efficient evaluation of large pathology datasets for discovery of novel features to inform biology and patient care.

The presence of tertiary lymphoid structures (TLS) in the tumor microenvironment is associated with better clinical outcome in many cancers. In non-small cell lung cancer (NSCLC), we have previously showed that a high density of B cells within TLS (TLS-B cells) is positively correlated with tumor antigen-specific antibody responses and increased intratumor CD4 + T cell clonality. Here, we investigated the relationship between the presence of TLS-B cells and CD4 + T cell profile in NSCLC patients. The expression of immune-related genes and proteins on B cells and CD4 + T cells was analyzed according to their relationship to TLS-B density in a prospective cohort of 56 NSCLC patients. We observed that tumor-infiltrating T cells showed marked differences according to TLS-B cell presence, with higher percentages of naïve, central-memory, and activated CD4 + T cells and lower percentages of both immune checkpoint (ICP)-expressing CD4 + T cells and regulatory T cells (Tregs) in the TLS-B high tumors. A retrospective study of 538 untreated NSCLC patients showed that high TLS-B cell density was even able to counterbalance the deleterious impact of high Treg density on patient survival, and that TLS-B high Treg low patients had the best clinical outcomes. Overall, the correlation between the density of TLS-B high tumors with early differentiated, activated and non-regulatory CD4 + T cell cells suggest that B cells may play a central role in determining protective T cell responses in NSCLC patients.

The overall density of CD8+ tumor-infiltrating lymphocytes (TILs) is important for characterizing the level of immune activity in the tumor microenvironment (TME). Beyond the densities of CD8+ TILs, both their location and distributional patterns may also have relevance to immune activity. We evaluated 645 resected tumors encompassing seven cancer types, and correlate location and spatial patterns of CD8+ TILs to immune pathway activity. We integrated image analysis results from digitized immunohistochemistry (IHC) slides with gene expression data from a targeted Ion Torrent Panel. Overall density of CD8+ TILs and the exact position of individual CD8+ lymphocytes were determined from IHC slides. A dissemination score was defined as ratio of global density and average local density of CD8+ TILs. This score is the inverse of the Ripley’s K statistic and becomes high for disseminated spatial patterns. We used this quotient as a continuous metric to identify tumors with a disseminated TIL pattern and to distinguish them from tumors with focal distribution of CD8+ TILs. Within a subset of tumors, the continuous dissemination metric was correlated with biological pathways using targeted mRNA sequencing and gene set enrichment analysis. In addition, association of the dissemination score with overall survival was tested on a subset of cases. CD8+ TIL distributional patterns differed significantly between tumor types. Breast and pancreatic cancers more frequently showed a focal distribution of CD8+ TILs, while lung tumors comparatively exhibited a disseminated pattern. Transcriptional profiling data revealed differences between both image analysis phenotypes. On average, cases with more disseminated patterns of CD8+ T cells were associated with mRNA expression of genes that fall in pathways related to motility, migration and activation status of tumor infiltrating T cells. We also found a trend to better overall survival in patients whose tumors had a disseminated TIL score compared to those with a focal pattern. This trend was significant in non-small cell adenocarcinoma of the lung (log rank p = 0.018). We demonstrate the value of spatial image analysis to automatically score CD8+ TIL dissemination as a marker of immune activity in the TME. Jointly analyzing transcriptional profiles appears to identify a biologically meaningful activation phenotype in tumors with high dissemination scores. Our data further suggests that this phenotype is associated with improved overall survival in some cancer patients. Citation Format: Stefan Bentink, Andreas Spitzmueller, Tze Heng Tan, Hadassah Sade, Song Wu, Brandon W. Higgs, Keith E. Steele. Dissemination score of CD8+ TILs by automated image analysis is a potential marker of immune activity in human cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2795.

Background The tumor immune microenvironment (TIME) may hold critical information for developing and optimizing immuno-therapeutic approaches, identifying predictive signatures, and selecting the most adequate treatment option for a given patient. Tissue phenomics facilitates the use of the TIME to derive predictive conclusions. The visual information content in histological sections is systematically converted into numerical readouts using artificial intelligence (AI). Resulting quantitative descriptors, phenes, of detected structures are mined to yield local expression profiles; this spatial data aggregation detects categories of local environments, which are correlated to clinical, genomic or other -omics data to identify relevant cohort subpopulations. Methods Exploration of this technology is illustrated by various examples on different cohorts of NSCLC patients: A categorization of n = 45 non-IO-treated patients with respect to local immune profiles learned via AI in a hypothesis-free scenario was examined. A deep learning based PD-L1 scoring was compared to 3 pathologist’s scoring on n = 40 durvalumab-treated patients using the cutoff 25% of tumor cells staining positive for PD-L1 at any intensity. The predictive value of a digital signature combining cell densities of PD-L1 and CD8+ was tested on n = 163 durvalumab-treated and n = 199 non-IO-treated samples. Results A categorization into biologically interpretable classes learned by AI illustrates the exploratory benefits of tissue phenomics. The scoring algorithm could reproduce survival prediction when compared to pathologist’s visual scoring.The digital signature suggests a predictive value for patient stratification into responders and non-responders for durvalumab, while no prognostic value could be found on the non-IO-treated patients. Kaplan-Meier plots for the 2 latter examples will be presented in the poster. Conclusions Tissue phenomics facilitates the quantitative assessment of the tumor geography and may lead to improved tools for biomarker analysis and diagnosis. Analysis on larger and prospective datasets are to be conducted in the future to strengthen the findings. Clinical trial identification All of these results have been generated retrospectively from samples unrelated to a trial or related to the durvalumab-trial NCT01693562. Legal entity responsible for the study The authors. Funding Boehringer Ingelheim, MedImmune, Definiens AG. Disclosure M. Groher: Full / Part-time employment: Definiens AG. J. Zimmermann: Shareholder / Stockholder / Stock options: AstraZeneca; Full / Part-time employment: Definiens AG. H. Musa: Full / Part-time employment: Boehringer Ingelheim. A. Ackermann: Full / Part-time employment: Boehringer Ingelheim. M. Surace: Shareholder / Stockholder / Stock options, Full / Part-time employment: AstraZeneca. J. Rodriguez-Canales: Shareholder / Stockholder / Stock options, Full / Part-time employment: AstraZeneca. M. Rebelatto: Shareholder / Stackeholder / Stock options: AstraZenec LLC; Full / Part-time employment: AstraZeneca LLC. K. Steele: Shareholder / Stockholder / Stock options, Full / Part-time employment: AstraZeneca; Spouse / Financial dependant: Arcellx LLC. A. Kapil: Full / Part-time employment: Definiens AG. N. Brieu: Shareholder / Stockholder / Stock options, Full / Part-time employment: Definiens AG. L. Rognoni: Full / Part-time employment: Definiens AG. F. Segerer: Full / Part-time employment: Definiens AG. A. Spitzmuller: Full / Part-time employment: Definiens AG. T. Tan: Full / Part-time employment: Definiens AG. A. Schape: Full / Part-time employment: Definiens AG. G. Schmidt: Full / Part-time employment: Definiens AG; Shareholder / Stockholder / Stock options: AstraZeneca.

The safety, efficacy, pharmacokinetics, and pharmacodynamics of the anti-programmed cell death-1 antibody MEDI0680 were evaluated in a phase I, multicenter, dose-escalation study in advanced solid malignancies. MEDI0680 was administered intravenously once every 2 weeks (Q2W) or once every 3 weeks at 0.1, 0.5, 2.5, 10 or 20 mg/kg. Two cohorts received 20 mg/kg once a week for 2 or 4 weeks, then 20 mg/kg Q2W. All were treated for 12 months or until progression. The primary endpoint was safety. Secondary endpoints were efficacy and pharmacokinetics. Exploratory endpoints included pharmacodynamics. Fifty-eight patients were treated. Median age was 62.5 years and 81% were male. Most had kidney cancer (n = 36) or melanoma (n = 9). There were no dose-limiting toxicities. Treatment-related adverse events occurred in 83% and were grade ≥ 3 in 21%. Objective clinical responses occurred in 8/58 patients (14%): 5 with kidney cancer, including 1 with a complete response, and 3 with melanoma. The relationship between dose and serum levels was predictable and linear, with apparent receptor saturation at 10 mg/kg Q2W and all 20 mg/kg cohorts. MEDI0680 induced peripheral T-cell proliferation and increased plasma IFNγ and associated chemokines regardless of clinical response. CD8+ T-cell tumor infiltration and tumoral gene expression of IFNG, CD8A, CXCL9, and granzyme K (GZMK) were also increased following MEDI0680 administration. NCT02013804 ; date of registration December 12, 2013.

The analysis of the tumor environment on digital histopathology slides is becoming key for the understanding of the immune response against cancer, supporting the development of novel immuno-therapies. We introduce here a novel deep learning solution to the related problem of tumor epithelium segmentation. While most existing deep learning segmentation approaches are trained on time-consuming and costly manual annotation on single stain domain (PD-L1), we leverage here semi-automatically labeled images from a second stain domain (Cytokeratin-CK). We introduce an end-to-end trainable network that jointly segment tumor epithelium on PD-L1 while leveraging unpaired image-to-image translation between CK and PD-L1, therefore completely bypassing the need for serial sections or re-staining of slides. Extending the method to differentiate between PD-L1 positive and negative tumor epithelium regions enables the automated estimation of the PD-L1 Tumor Cell (TC) score. Quantitative experimental results demonstrate the accuracy of our approach against state-of-the-art segmentation methods.

Introduction: Durvalumab selectively blocks programmed cell death ligand-1 (PD-L1) binding to programmed cell death-1. Encouraging clinical activity and manageable safety were reported in urothelial carcinoma, non-small-cell lung cancer (NSCLC), hepatocellular carcinoma (HC) and small-cell lung cancer (SCLC) in a multicenter phase I/II study. Safety and clinical activity in recurrent/metastatic head and neck squamous cell carcinoma (HNSCC) were evaluated in the expansion phase. Methods: Patients received 10 mg/kg of durvalumab intravenously every 2 weeks for 12 months or until confirmed progressive disease or unacceptable toxicity. The primary objective was safety; clinical activity was a secondary objective. Results: Sixty-two patients were enrolled and evaluable (received first dose >= 24 weeks before data cutoff). Median age was 57 years; 40.3% were human papillomavirus (HPV)-positive; 32.3% had tumour cell PD-L1 expression >= 25%, and 62.9% were current/former smokers. They had a median of 2 prior systemic treatments (range, 1-13). All-causality adverse events (AEs) occurred in 98.4%; drug-related AEs occurred in 59.7% and were grade III-IV in 9.7%. There were no drug-related discontinuations or deaths. Objective response rate (blinded independent central review) was 6.5% (15.0% for PD-L1 >= 25%, 2.6% for <25%). Median time to response was 2.7 months (range, 1.2-5.5); median duration was 12.4 months (range, 3.5 -20.5+). Median progression-free survival was 1.4 months; median overall survival (OS) was 8.4 months. OS rate was 62% at 6 months and 38% at 12 months (42% for PD-L1 >= 25%, 36% for <25%). Conclusions: Durvalumab safety in HNSCC was manageable and consistent with other cohorts of the study. Early, durable responses in these heavily pretreated patients warrant further investigation; phase III monotherapy and combination therapy studies are ongoing. Clinical trial registry: clinicaltrials.gov NCT01693562; MedImmune study 1108. (C) 2019 Elsevier Ltd. All rights reserved.

BackgroundImmune 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.MethodsArchival 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.ResultsFor 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.ConclusionsAn 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 registrationClinicalTrials.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).

Multiplex immunofluorescence (MIF) staining of tumor sections combined with computational pathology quantifies phenotypic variants of tumor and immune cells and assesses their spatial relationships. Here, we discuss a MIF panel composed of cytokeratin, PD-L1, PD1, CD8, CD68, and Ki67 applied to non-small cell lung cancer (NSCLC) to demonstrate key components of the immune response to this cancer. We also describe a method of whole-slide multiplex imaging and digital multispectral image analysis. Key aspects of marker labeling and digital tissue and cellular classification are highlighted. We then illustrate how digital analysis can measure the spatial relationships among important cell types. This approach is presented in the context of a multidisciplinary team of scientists who together can optimize the combined methods to increase the impact of the study findings. Recommendations are provided to assist others to apply similar methods to further understand the immune response to NSCLC.

Continued developments in immuno-oncology require an increased understanding of the mechanisms of cancer immunology. The immunoprofiling analysis of tissue samples from formalin-fixed, paraffin-embedded (FFPE) biopsies has become a key tool for understanding the complexity of tumor immunology and discovering novel predictive biomarkers for cancer immunotherapy. Immunoprofiling analysis of tissues requires the evaluation of combined markers, including inflammatory cell subpopulations and immune checkpoints, in the tumor microenvironment. The advent of novel multiplex immunohistochemical methods allows for a more efficient multiparametric analysis of single tissue sections than does standard monoplex immunohistochemistry (IHC). One commercially available multiplex immunofluorescence (IF) method is based on tyramide-signal amplification and, combined with multispectral microscopic analysis, allows for a better signal separation of diverse markers in tissue. This methodology is compatible with the use of unconjugated primary antibodies that have been optimized for standard IHC on FFPE tissue samples. Herein we describe in detail an automated protocol that allows multiplex IF labeling of carcinoma tissue samples with a six-marker multiplex antibody panel comprising PD-L1, PD-1, CD68, CD8, Ki-67, and AE1/AE3 cytokeratins with 4′,6-diamidino-2-phenylindole as a nuclear cell counterstain. The multiplex panel protocol is optimized in an automated IHC stainer for a staining time that is shorter than that of the manual protocol and can be directly applied and adapted by any laboratory investigator for immuno-oncology studies on human FFPE tissue samples. Also described are several controls and tools, including a drop-control method for fine quality control of a new multiplex IF panel, that are useful for the optimization and validation of the technique.

There is an important need in immuno-oncology to develop reliable immunohistochemistry (IHC) to assess the expression of CTLA-4+ tumor-infiltrating lymphocytes in human cancers and quantify them with image analysis (IA). We used commercial polyclonal and monoclonal antibodies and characterized three chromogenic cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) assays with suitable specificity and sensitivity for use in formalin-fixed, paraffin-embedded (FFPE) tissues. We found variable numbers of CTLA-4+ lymphocytes in multiple types of cancer and secondary lymphoid organs (SLOs) and other normal human tissues. Combining CTLA-4 with CD3, CD4, or CD8 by immunofluorescence showed that CTLA-4+ lymphocytes in SLOs and tumors were typically CD3+ and CD4+, but not CD8+. Individual lymphocytes expressed CTLA-4 either as primarily granular cytoplasmic staining or as excentric globular deposits. The CTLA-4/FoxP3 (forkhead box P3 protein) duplex IHC demonstrated that CTLA-4+/FoxP3− lymphocytes predominated in the germinal centers of SLOs and tumor tertiary lymphoid structures (TLSs), whereas CTLA-4+/FoxP3+ lymphocytes populated the T-cell zone of SLOs and TLSs, plus tumor stroma. IA scoring was highly comparable with pathologist scoring for CTLA-4 and CTLA-4/FoxP3 assays and a FoxP3 single IHC. Our findings show that CTLA-4 IHC can be used to reliably label lymphocytes in FFPE human tissues, making it possible to investigate the role of CTLA-4 in the tumor microenvironment.

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.

Multiplex immunohistochemistry allows the demonstration of multiple protein antigens in individual histological sections of formalin-fixed paraffin-embedded tumors or other types of tissue. Carefully designed and optimized immunohistochemistry (IHC) assays not only maximize the information available from limited tissues, but also enable a higher level interpretation of that information by demonstrating the histo-anatomical relationships among key cell types which express the included biomarkers. Programmable automated IHC instruments support the development and application of complicated multiplex IHC protocols, help save time and effort, and enhance immunostaining quality and reproducibility. Simple data can be extracted from immunostained tissues to include qualitative (descriptive) findings and semiquantitative analysis. The value of multiplex IHC can be increased further by the utilization of image analysis software either to better visualize multiple markers or by applying suitable digital scoring solutions to capture data (automated pathology).Here, we describe a five-marker multiplex based on application of two individual assays to serial sections of non-small cell lung carcinoma (NSCLC). We use this assay to label PD1, PD-L1, CD3, CD68, and cytokeratins in relation to tertiary lymphoid structures (TLS) and other regions of the tumor microenvironment. We illustrate how visualization of the immunostaining results can be used to understand TLS organization and other aspects of the tumor microenvironment, and briefly consider means to further yield additional information.

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/mm 2 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).

### A1 Intratumoral immunotherapy. B16-F10 murine melanoma model #### J. Ženka1, V. Caisova1, O. Uher1, P. Nedbalova1, K. Kvardova1, K. Masakova1, G. Krejcova1, L. Paďoukova1, I. Jochmanova2, K. I. Wolf3, J. Chmelař1, J. Kopecký1 ##### 1Department of Medical Biology, Faculty of