Understanding intratumoral heterogeneity-the molecular variation among cells within a tumor-promises to address outstanding questions in cancer biology and improve the diagnosis and treatment of specific cancer subtypes. Single-cell analyses, especially RNA sequencing and other genomics modalities, have been transformative in revealing novel biomarkers and molecular regulators associated with tumor growth, metastasis and drug resistance. However, these approaches fail to provide a complete picture of tumor biology, as information on cellular location within the tumor microenvironment is lost. New technologies leveraging multiplexed fluorescence, DNA, RNA and isotope labeling enable the detection of tens to thousands of cancer subclones or molecular biomarkers within their native spatial context. The expeditious growth in these techniques, along with methods for multiomics data integration, promises to yield a more comprehensive understanding of cell-to-cell variation within and between individual tumors. Here we provide the current state and future perspectives on the spatial technologies expected to drive the next generation of research and diagnostic and therapeutic strategies for cancer.

Spatial omics and multiplexed imaging to explore cancer biology

The tumor microenvironment is an integral player in cancer initiation, tumor progression, response and resistance to anti-cancer therapy. Understanding the complex interactions of tumor immune architecture (referred to as 'immune contexture') has therefore become increasingly desirable to guide our approach to patient selection, clinical trial design, combination therapies, and patient management. Quantitative image analysis based on multiplexed fluorescence immunohistochemistry and deep learning technologies are rapidly developing to enable researchers to interrogate complex information from the tumor microenvironment and find predictive insights into treatment response. Herein, we discuss current developments in multiplexed fluorescence immunohistochemistry for immune contexture analysis, and their application in immuno-oncology, and discuss challenges to effectively use this technology in clinical settings. We also present a multiplexed image analysis workflow to analyse fluorescence multiplexed stained tumor sections using the Vectra Automated Digital Pathology System together with FCS express flow cytometry software. The benefit of this strategy is that the spectral unmixing accurately generates and analyses complex arrays of multiple biomarkers, which can be helpful for diagnosis, risk stratification, and guiding clinical management of oncology patients.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cti2.1183

Immune contexture analysis in immuno‐oncology: applications and challenges of multiplex fluorescent immunohistochemistry

Tumor microenvironments (TMEs) contain vast amounts of information on patient’s cancer through their cellular composition and the spatial distribution of tumor cells and immune cell populations. Exploring variations in TMEs between patient groups, as well as determining the extent to which this information can predict outcomes such as patient survival or treatment success with emerging immunotherapies, is of great interest. Moreover, in the face of a large number of cell interactions to consider, we often wish to identify specific interactions that are useful in making such predictions. We present an approach to achieve these goals based on summarizing spatial relationships in the TME using spatial K functions, and then applying functional data analysis and random forest models to both predict outcomes of interest and identify important spatial relationships. This approach is shown to be effective in simulation experiments at both identifying important spatial interactions while also controlling the false discovery rate. We further used the proposed approach to interrogate two real data sets of Multiplexed Ion Beam Images of TMEs in triple negative breast cancer and lung cancer patients. The methods proposed are publicly available in a companion R package funkycells . 

pdf/using-random-forests-to-uncover-the-predictive-power-of-qb8ffevtct.pdf

Using random forests to uncover the predictive power of distance-varying cell interactions in tumor microenvironments

Here we report the use of the MACSima™ imaging platform to perform high-content imaging for the pre-clinical validation of tumor target expression on tumor and healthy human tissues indicative for potential on-target/off-tumor toxicity in vivo. Major advances have been achieved in cancer therapy in the past decade. In particular, targeted-immunotherapy has progressed and clinical benefits for patients have been achieved. However, on-target/off-tumor toxicity is a potential threat which has been shown to be more pronounced in solid than in liquid tumors. These findings highlight the need for better pre-clinical assays to improve the safety profile of immunotherapies. On-target/off-tumor toxicity is mainly based on the expression of tumor-associated antigens (TAA) in healthy tissues under physiological conditions. Currently, most prediction methods for on-target/off-tumor expression are based on bulk mRNA expression data of healthy tissue. These prediction models, however, have limitations, mainly poor predictable relation between RNA and protein level. Moreover, it is frequently unclear which cell types are affected by on-target/off-tumor effects. To overcome these limitations we employ multi-parameter imaging to analyze the expression of TAAs directly at the protein level. Additionally, we gain spatial information about tissue and cellular distribution of TAAs. Notably, our novel high-content imaging technology potentially allows for the analysis of hundreds of markers in a single experiment, paving the way for high-dimensional characterization of cells within complex solid tissues. We performed high-content imaging with the MACSima™ platform to validate the expression of known TAAs across tumor and healthy tissue samples. Subsequently, we employed unbiased cluster analysis revealing correlation patterns within the datasets to identify cell types at risk. In detail, we analyzed several high-grade serous ovarian carcinoma and pancreatic ductal adenocarcinoma for the expression of known TAAs, described tumor markers, and tissue lineage markers. Additionally, we evaluated the expression of these TAAs across several healthy human tissues. Next we performed pixel- and object-based data analysis for unbiased cluster analysis. Thereby we identified cell clusters that express TAAs in ovarian and pancreatic cancers, as well as in healthy tissues. We found that primarily epithelial cells express the analyzed TAAs in different tissues and that TAA expression shows inter- and intra-tumor heterogeneity. Taken together, we established a novel workflow for multi-parameter characterization of tissues employing the MACSima™ imaging platform. Our work enables efficient identification of potential tissues for on-target/off-tumor toxicity, paving the way for improved pre-clinical evaluation of TAAs as new targets. Citation Format: Christoph Herbel, Vera Dittmer, Manuel Martinez-Osuna, Laura Nadine Kuester, Daniel Schaefer, Jan Drewes, Jutta Kollet, Werner Mueller, Michael Mallmann, Peter Mallmann, Philipp Stroebel, Olaf Hardt, Dominik Eckardt, Andreas Bosio. Evaluation of tumor-associated antigen expression with the MACSimaTM high-content imaging platform [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 4694.

Abstract 4694: Evaluation of tumor-associated antigen expression with the MACSimaTMhigh-content imaging platform

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Abstract 4694: Evaluation of tumor-associated antigen expression with the MACSimaTMhigh-content imaging platform