Anti-tumour immune activation by checkpoint inhibitors leads to durable responses in a variety of cancers, but combination approaches are required to extend this benefit beyond a subset of patients. ...In preclinical models tumour-derived VEGF limits immune cell activity while anti-VEGF augments intra-tumoral T-cell infiltration, potentially through vascular normalization and endothelial cell activation. This study investigates how VEGF blockade with bevacizumab could potentiate PD-L1 checkpoint inhibition with atezolizumab in mRCC. Tissue collections are before treatment, after bevacizumab and after the addition of atezolizumab. We discover that intra-tumoral CD8(+) T cells increase following combination treatment. A related increase is found in intra-tumoral MHC-I, Th1 and T-effector markers, and chemokines, most notably CX3CL1 (fractalkine). We also discover that the fractalkine receptor increases on peripheral CD8(+) T cells with treatment. Furthermore, trafficking lymphocyte increases are observed in tumors following bevacizumab and combination treatment. These data suggest that the anti-VEGF and anti-PD-L1 combination improves antigen-specific T-cell migration.
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.
Cancer immunotherapy has led to significant therapeutic progress in the treatment of metastatic and formerly untreatable tumors. However, drug response rates are variable and often only a subgroup of ...patients will show durable response to a treatment. Biomarkers that help to select those patients that will benefit the most from immunotherapy are thus of crucial importance. Here, we aim to identify such biomarkers by investigating the tumor microenvironment, i.e., the interplay between different cell types like immune cells, stromal cells and malignant cells within the tumor and developed a computational method that determines spatial tumor infiltration phenotypes. Our method is based on spatial point pattern analysis of immunohistochemically stained colorectal cancer tumor tissue and accounts for the intra-tumor heterogeneity of immune infiltration. We show that, compared to base-line models, tumor infiltration phenotypes provide significant additional support for the prediction of established biomarkers in a colorectal cancer patient cohort (
= 80). Integration of tumor infiltration phenotypes with genetic and genomic data from the same patients furthermore revealed significant associations between spatial infiltration patterns and common mutations in colorectal cancer and gene expression signatures. Based on these associations, we computed novel gene signatures that allow one to predict spatial tumor infiltration patterns from gene expression data only and validated this approach in a separate dataset from the Cancer Genome Atlas.
Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice ...necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities. We evaluated sources of variability in sTIL assessment by pathologists in three previous sTIL ring studies. We identify common challenges and evaluate impact of discrepancies on outcome estimates in early TNBC using a newly-developed prognostic tool. Discordant sTIL assessment is driven by heterogeneity in lymphocyte distribution. Additional factors include: technical slide-related issues; scoring outside the tumor boundary; tumors with minimal assessable stroma; including lymphocytes associated with other structures; and including other inflammatory cells. Small variations in sTIL assessment modestly alter risk estimation in early TNBC but have the potential to affect treatment selection if cutpoints are employed. Scoring and averaging multiple areas, as well as use of reference images, improve consistency of sTIL evaluation. Moreover, to assist in avoiding the pitfalls identified in this analysis, we developed an educational resource available at www.tilsinbreastcancer.org/pitfalls.
In early stage clinical trials, changes to levels of tumor infiltrating lymphocytes (TILs) in the tumor microenvironment (TME) are critical biomarkers of the mechanism of action of novel ...immunotherapies. However, baseline heterogeneity of tumor samples, both between and within patients, and the resultant impact on the validity of clinical trial data is not well defined. Here we identify and quantify the impact of baseline variables on the heterogeneity of FoxP3+ and proliferating CD8+ T-cells levels (MKi67+CD8A+) in the TME both between and within patients for the purpose of informing clinical trial design and analysis.
We compared levels of FoxP3+ and MKi67+CD8+ cell densities (counts/mm
) from >1000 baseline tumor samples from clinical trials and commercially available sources. Using multivariate hierarchical regression techniques, we investigated whether inter-person heterogeneity of activated or regulatory T-cells could be attributed to baseline characteristics including demographics, indication, lesion type, tissue of excision, biopsy method, prior cancer treatment, and tissue type i.e., "fresh" or "archival" status. We also sought to characterize within-patient heterogeneity by lesion type and tissue type.
Prior cancer treatment with hormone therapy or chemotherapy that induces immunogenic cell death may alter the TME. Archival tissue is an unreliable substitute for fresh tissue for determining baseline TIL levels. Baseline and on treatment biopsies should be matched by lesion type to avoid bias.
The development and progression of solid tumors such as colorectal cancer (CRC) are known to be affected by the immune system and cell types such as T cells, natural killer (NK) cells, and natural ...killer T (NKT) cells are emerging as interesting targets for immunotherapy and clinical biomarker research. In addition, CD3
and CD8
T cell distribution in tumors has shown positive prognostic value in stage I-III CRC. Recent developments in digital computational pathology support not only classical cell density based tumor characterization, but also a more comprehensive analysis of the spatial cell organization in the tumor immune microenvironment (TiME). Leveraging that methodology in the current study, we tried to address the question of how the distribution of myeloid derived suppressor cells in TiME of primary CRC affects the function and location of cytotoxic T cells. We applied multicolored immunohistochemistry to identify monocytic (CD11b
CD14
) and granulocytic (CD11b
CD15
) myeloid cell populations together with proliferating and non-proliferating cytotoxic T cells (CD8
Ki67
). Through automated object detection and image registration using HALO software (IndicaLabs), we applied dedicated spatial statistics to measure the extent of overlap between the areas occupied by myeloid and T cells. With this approach, we observed distinct spatial organizational patterns of immune cells in tumors obtained from 74 treatment-naive CRC patients. Detailed analysis of inter-cell distances and myeloid-T cell spatial overlap combined with integrated gene expression data allowed to stratify patients irrespective of their mismatch repair (MMR) status or consensus molecular subgroups (CMS) classification. In addition, generation of cell distance-derived gene signatures and their mapping to the TCGA data set revealed associations between spatial immune cell distribution in TiME and certain subsets of CD8
and CD4
T cells. The presented study sheds a new light on myeloid and T cell interactions in TiME in CRC patients. Our results show that CRC tumors present distinct distribution patterns of not only T effector cells but also tumor resident myeloid cells, thus stressing the necessity of more comprehensive characterization of TiME in order to better predict cancer prognosis. This research emphasizes the importance of a multimodal approach by combining computational pathology with its detailed spatial statistics and gene expression profiling. Finally, our study presents a novel approach to cancer patients' characterization that can potentially be used to develop new immunotherapy strategies, not based on classical biomarkers related to CRC biology.
BackgroundThis phase Ib study evaluated the safety, clinical activity, pharmacokinetics, and pharmacodynamics (PD) of emactuzumab (anti-colony stimulating factor 1 receptor monoclonal antibody (mAb)) ...in combination with selicrelumab (agonistic cluster of differentiation 40 mAb) in patients with advanced solid tumors.MethodsBoth emactuzumab and selicrelumab were administered intravenously every 3 weeks and doses were concomitantly escalated (emactuzumab: 500 to 1000 mg flat; selicrelumab: 2 to 16 mg flat). Dose escalation was conducted using the product of independent beta probabilities dose-escalation design. PD analyzes were performed on peripheral blood samples and tumor/skin biopsies at baseline and on treatment. Clinical activity was evaluated using investigator-based and Response Evaluation Criteria In Solid Tumors V.1.1-based tumor assessments.ResultsThree dose-limiting toxicities (all infusion-related reactions (IRRs)) were observed at 8, 12 and 16 mg of selicrelumab together with 1000 mg of emactuzumab. The maximum tolerated dose was not reached at the predefined top doses of emactuzumab (1000 mg) and selicrelumab (16 mg). The most common adverse events were IRRs (75.7%), fatigue (54.1%), facial edema (37.8%), and increase in aspartate aminotransferase and creatinine phosphokinase (35.1% both). PD analyzes demonstrated an increase of Ki67+-activated CD8+ T cells accompanied by a decrease of B cells and the reduction of CD14Dim CD16bright monocytes in peripheral blood. The best objective clinical response was stable disease in 40.5% of patients.ConclusionEmactuzumab in combination with selicrelumab demonstrated a manageable safety profile and evidence of PD activity but did not translate into objective clinical responses.Trialregistration numberNCT02760797.
Features characterizing the immune contexture (IC) in the tumor microenvironment can be prognostic and predictive biomarkers. Identifying novel biomarkers can be challenging due to complex ...interactions between immune and tumor cells and the abundance of possible features.
We describe an approach for the data-driven identification of IC biomarkers. For this purpose, we provide mathematical definitions of different feature classes, based on cell densities, cell-to-cell distances, and spatial heterogeneity thereof. Candidate biomarkers are ranked according to their potential for the predictive stratification of patients.
We evaluated the approach on a dataset of colorectal cancer patients with variable amounts of microsatellite instability. The most promising features that can be explored as biomarkers were based on cell-to-cell distances and spatial heterogeneity. Both the tumor and non-tumor compartments yielded features that were potentially predictive for therapy response and point in direction of further exploration.
The data-driven approach simplifies the identification of promising IC biomarker candidates. Researchers can take guidance from the described approach to accelerate their biomarker research.
BackgroundThe immune status of a patient’s tumor microenvironment (TME) may guide therapeutic interventions with cancer immunotherapy and help identify potential resistance mechanisms. Currently, ...patients’ immune status is mostly classified based on CD8+tumor-infiltrating lymphocytes. An unmet need exists for comparable and reliable precision immunophenotyping tools that would facilitate clinical treatment-relevant decision-making and the understanding of how to overcome resistance mechanisms.MethodsWe systematically analyzed the CD8 immunophenotype of 2023 patients from 14 phase I–III clinical trials using immunohistochemistry (IHC) and additionally profiled gene expression by RNA-sequencing (RNA-seq). CD8 immunophenotypes were classified by pathologists into CD8-desert, CD8-excluded or CD8-inflamed tumors using CD8 IHC staining in epithelial and stromal areas of the tumor. Using regularized logistic regression, we developed an RNA-seq-based classifier as a surrogate to the IHC-based spatial classification of CD8+tumor-infiltrating lymphocytes in the TME.ResultsThe CD8 immunophenotype and associated gene expression patterns varied across indications as well as across primary and metastatic lesions. Melanoma and kidney cancers were among the strongest inflamed indications, while CD8-desert phenotypes were most abundant in liver metastases across all tumor types. A good correspondence between the transcriptome and the IHC-based evaluation enabled us to develop a 92-gene classifier that accurately predicted the IHC-based CD8 immunophenotype in primary and metastatic samples (area under the curve inflamed=0.846; excluded=0.712; desert=0.855). The newly developed classifier was prognostic in The Cancer Genome Atlas (TCGA) data and predictive in lung cancer: patients with predicted CD8-inflamed tumors showed prolonged overall survival (OS) versus patients with CD8-desert tumors (HR 0.88; 95% CI 0.80 to 0.97) across TCGA, and longer OS on immune checkpoint inhibitor administration (phase III OAK study) in non-small-cell lung cancer (HR 0.75; 95% CI 0.58 to 0.97).ConclusionsWe provide a new precision immunophenotyping tool based on gene expression that reflects the spatial infiltration patterns of CD8+ lymphocytes in tumors. The classifier enables multiplex analyses and is easy to apply for retrospective, reverse translation approaches as well as for prospective patient enrichment to optimize the response to cancer immunotherapy.
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