Resolution of tissue signatures of therapy response in patients with recurrent GBM treated with neoadjuvant anti-PD1

The response of patients with recurrent glioblastoma multiforme to neoadjuvant immune checkpoint blockade has been challenging to interpret due to the inter-patient and intra-tumor heterogeneity. We report on a comparative analysis of tumor tissues collected from patients with recurrent glioblastoma and high-risk melanoma, both treated with neoadjuvant checkpoint blockade. We develop a framework that uses multiplex spatial protein profiling, machine learning-based image analysis, and data-driven computational models to investigate the pathophysiological and molecular factors within the tumor microenvironment that influence treatment response. Using melanoma to guide the interpretation of glioblastoma analyses, we interrogate the protein expression in microscopic compartments of tumors, and determine the correlates of cytotoxic CD8+ T cells, tumor growth, treatment response, and immune cell-cell interaction. This work reveals similarities shared between glioblastoma and melanoma, immunosuppressive factors that are unique to the glioblastoma microenvironment, and potential co-targets for enhancing the efficacy of neoadjuvant immune checkpoint blockade.

Advances in tissue-based imaging: impact on oncology research and clinical practice

INTRODUCTION

Tissue-based imaging has emerged as a critical tool in translational cancer research and is rapidly gaining traction within a clinical context. Significant progress has been made in the digital pathology arena, particularly in respect of brightfield and fluorescent imaging. Critically, the cellular context of molecular alterations occurring at DNA, RNA, or protein level within tumor tissue is now being more fully appreciated. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumor microenvironment, including the potential interplay between various cell types.

AREAS COVERED

This review summarizes the recent developments within the field of tissue-based imaging, centering on the application of these approaches in oncology research and clinical practice.

EXPERT OPINION

Significant advances have been made in digital pathology during the last 10 years. These include the use of quantitative image analysis algorithms, predictive artificial intelligence (AI) on large datasets of H&E images, and quantification of fluorescence multiplexed tissue imaging data. We believe that new methodologies that can integrate AI-derived histologic data with omic data, together with other forms of imaging data (such as radiologic image data), will enhance our ability to deliver better diagnostics and treatment decisions to the cancer patient.

New tools for pathology: a user's review of a highly multiplexed method for in situ analysis of protein and RNA expression in tissue

Tumor cell heterogeneity and tumor cell-stromal interactions are being explored as determinants of disease progression and treatment resistance in solid tumor and hematological malignancies. As such, tools simultaneously capable of highly multiplexed profiling of tissues' protein and RNA content, as well as interrogation of rare or single cells, are required to precisely characterize constituent tumor cell populations, infiltrating lymphocytes and stromal elements. Access to spatial relationships will enable more precise characterization of tumors, support patient stratification and may help to identify novel drug targets. Multiple platforms are being developed to address these critical unmet needs. The NanoString digital spatial profiling (DSP) platform enables highly multiplexed, spatial assessment of protein and/or RNA targets in tissues by detecting oligonucleotide barcodes conjugated via a photocleavable linker to primary antibodies or nucleic acid probes. Although this platform enables high-dimensional spatial interrogation of tissue protein and RNA expression, a detailed understanding of its composition, function and chemistry is advisable to guide experimental design and data interpretation. The purpose of this review is to provide an independent, comprehensive description of the DSP technology, including an overview of NanoString's capture and antibody barcode conjugation chemistries, experimental workflow, data output and analysis methods. The DSP technology will be discussed in the context of other highly multiplexed immunohistochemistry methods, including imaging mass cytometry and multiplexed ion beam imaging, to inform potential users of the advantages and limitations of each. Additional issues such as preanalytical variability, sampling and specimen adequacy will be considered with respect to the platforms to inform potential experimental design. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.