In the development of a multiplex immunofluorescence (IF) platform and the optimization and validation of new multiplex IF panels using a tyramide signal amplification system, several technical requirements are important for high-quality staining, analysis, and results. The aim of this review is to discuss the basic requirements for performing multiplex IF tyramide signal amplification (TSA) in formalin-fixed, paraffin-embedded cancer tissues to support translational oncology research. Our laboratory has stained approximately 4000 formalin-fixed, paraffin-embedded tumor samples using the multiplex IF TSA system for immune profiling of several labeled biomarkers in a single slide to elucidate cancer biology at a protein level and identify therapeutic targets and biomarkers. By analyzing several proteins in thousands of cells on a single slide, this technique provides a systems-level view of various processes in various tumor tissues. Although this technology shows high flexibility in cancer studies, it presents several challenges when applied to study different histology cancers. Our experience shows that adequate antibody validation, staining optimization, analysis strategies, and data generation are important steps for generating quality results. Tissue management, fixation procedures, storage, and cutting can also affect the results of the assay and must be standardized. Overall, this method is reliable for supporting translational research given a precise, step-by-step approach.
Every day, more evidence is revealed regarding the importance of the relationship between the response to cancer immunotherapy and the cancer immune microenvironment. It is well established that a profound characterization of the immune microenvironment is needed to identify prognostic and predictive immune biomarkers. To this end, we find phenotyping cells by multiplex immunofluorescence (mIF) a powerful and useful tool to identify cell types in biopsy specimens. Here, we describe the use of mIF tyramide signal amplification for labeling up to eight markers on a single slide of formalin-fixed, paraffin-embedded tumor tissue to phenotype immune cells in tumor tissues. Different panels show different markers, and the different panels can be used to characterize immune cells and relevant checkpoint proteins. The panel design depends on the research hypothesis, the cell population of interest, or the treatment under investigation. To phenotype the cells, image analysis software is used to identify individual marker expression or specific co-expression markers, which can differentiate already selected phenotypes. The individual-markers approach identifies a broad number of cell phenotypes, including rare cells, which may be helpful in a tumor microenvironment study. To accurately interpret results, it is important to recognize which receptors are expressed on different cell types and their typical location (i.e., nuclear, membrane, and/or cytoplasm). Furthermore, the amplification system of mIF may allow us to see weak marker signals, such as programmed cell death ligand 1, more easily than they are seen with single-marker immunohistochemistry (IHC) labeling. Finally, mIF technologies are promising resources for discovery of novel cancer immunotherapies and related biomarkers. In contrast with conventional IHC, which permits only the labeling of one single marker per tissue sample, mIF can detect multiple markers from a single tissue sample, and at the same time, deliver extensive information about the cell phenotypes composition and their spatial localization. In this matter, the phenotyping process is critical and must be done accurately by a highly trained personal with knowledge of immune cell protein expression and tumor pathology.
312 Background: Sitra is a spectrum-selective receptor tyrosine kinase inhibitor (TKI) that targets TAM receptors (TYRO3, AXL, MERTK), VEGFR2, c-Kit, and MET. These receptors regulate several immune suppressive cell types in the tumor microenvironment, including M2-polarized macrophages, MDSCs, and T regulatory cells, which are implicated in resistance to checkpoint inhibitors. ccRCC is characterized by upregulation of VEGF and overexpression of MET and AXL. Sitra may combine effectively with immune checkpoint inhibition to augment antitumor activity in ccRCC. About 39% of patients with accRCC who receive surgery with curative intent relapse representing an unmet need in this setting. Together these data support the evaluation of neoadjuvant sitra with nivo in accRCC. Methods: This phase II study (NCT03680521) evaluated sitra and nivo in pts with locally- advanced ccRCC who were candidates for curative nephrectomy. Single-agent sitra (120 mg) was administered daily (QD) for 2 weeks, with nivo (240 mg intravenously Q2W) added to sitra for 4-6 weeks. A plan for potential dose de-escalation was implemented using a modified toxicity probability interval method with a maximum toxicity of 20% at the tolerated dose. Pts underwent pathology/tissue evaluation at 3 timepoints: biopsy prior to treatment, biopsy prior to the addition of nivo, and nephrectomy specimen evaluation at time of nephrectomy. The primary endpoint was objective response (RECIST 1.1); secondary endpoints included safety, PK, and correlative immune effects (selected protein and gene expression and immune cell populations). Results: A total of 20 pts were evaluated for safety (95% had T3 or higher stage tumors, 65% with baseline hypertension). Dose-limiting toxicities (DLTs) led to a dose de-escalation, resulting in 7 pts treated at 120 mg QD sitra and 13 pts treated at 80 mg QD. DLTs included grade 3 (Gr3) hypertension (n=6); deep vein thrombosis and pulmonary embolism (Gr3) were observed in 1 additional pt. Median duration of sitra treatment was 6.3 weeks at the 80 mg dose and 7.1 weeks at the 120 mg dose. With a median follow-up of 9.4 months after initiation of systemic therapy, no pts have relapsed. In 17 pts evaluable for efficacy, the investigator-assessed confirmed ORR was 11.8%, including 2 PRs (33.3% ORR in pts who received 120 mg sitra). No pts experienced progressive disease while on therapy. Median DFS was not reached. There was 1 delayed surgery due to nivo-related thyroiditis that resolved. Reported TRAEs: Gr1/Gr2 in 55% of pts (dysphonia 50%, fatigue 45%, diarrhea 40%, hypertension 30%, increased ALT 30%), Gr3 in 45% of pts (hypertension 30%). There were no Gr4/Gr5 TRAEs. Correlative blood and tissue analyses will be presented. Conclusions: The combination of sitra and nivo is clinically active with a manageable safety profile as a neoadjuvant therapy for accRCC. Clinical trial information: NCT03680521 .
Immune profiling of formalin-fixed, paraffin-embedded tissues using multiplex immunofluorescence (mIF) staining and image analysis methodology allows for the study of several biomarkers on a single slide. The pathology quality control (PQC) for tumor tissue immune profiling using digital image analysis of core needle biopsies is an important step in any laboratory to avoid wasting time and materials. Although there are currently no established inclusion and exclusion criteria for samples used in this type of assay, a PQC is necessary to achieve accurate and reproducible data. We retrospectively reviewed PQC data from hematoxylin and eosin (H&E) slides and from mIF image analysis samples obtained during 2019. We reviewed a total of 931 reports from core needle biopsy samples; 123 (13.21%) were excluded during the mIF PQC. The most common causes of exclusion were the absence of malignant cells or fewer than 100 malignant cells in the entire section (n = 42, 34.15%), tissue size smaller than 4 × 1 mm (n = 16, 13.01%), fibrotic tissue without inflammatory cells (n = 12, 9.76%), and necrotic tissue (n = 11, 8.94%). Baseline excluded samples had more fibrosis (90 vs 10%) and less necrosis (5 vs 90%) compared with post-treatment excluded samples. The most common excluded organ site of the biopsy was the liver (n = 19, 15.45%), followed by soft tissue (n = 17, 13.82%) and the abdominal region (n = 15, 12.20%). We showed that the PQC is an important step for image analysis and that the absence of malignant cells is the most limiting sample characteristic for mIF image analysis. We also discuss other challenges that pathologists need to consider to report reliable and reproducible image analysis data.
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