This white paper presents a detailed analysis using Phenoplex to study a highly multiplexed 30-biomarker assay of colorectal cancer (CRC) tissue, focusing on the spatial distribution of cytotoxic GZMB+ cells within CD8+ T cells and CD56+ NK cells. The study identifies these immune cells and examines their proximity to cytokeratin-expressing epithelial cancer cells. By segmenting the tissue into eight specific regions based on HLA-DR expression, the analysis reveals distinct patterns in immune cell infiltration and proximity, highlighting higher densities of cytotoxic T cells in HLA-DR- regions compared to HLA-DR+ regions. These findings suggest that the immune landscape and spatial relationships in the tumor microenvironment (TME) are crucial for understanding CRC pathology and potentially guiding therapeutic strategies.

The spatial information allows scientists to investigate neighborhoods and distances of specific cell populations to each other to decode the complexity of tissue microenvironments in normal and disease states. Especially in immune oncology the neighborhood composition of tumor and immune cells is important to fully understand cell interactions to develop novel precision diagnostics.

The Phenoplex workflow now includes hypothesis-driven spatial analysis options for cellular proximity counts of neighboring cells and nearest neighbor distance calculations. Using our intuitive workflow, users can define the target cells and interrogate their neighbors within a defined radius.

Solid tumors’ complexity extends beyond the genetic and functional diversity of cancer cells to include the tumor microenvironment (TME). Understanding the TME’s complexity requires a comprehensive analysis of the composition, functional states, and spatial distribution of its cellular components.

In this session, we will explore the application of basic research discoveries through the Harmonic Output of Stromal Traits (HOST) for the evaluation of the TME. HOST is designed to identify TME cells, while the HOST-Factor quantifies their functional states. The HOST-Factor is a numerical value reflecting the relative contribution of cancer-associated fibroblasts (CAFs) to tumor-suppressive or tumor-promoting functions.

Our workflow combines automated high-plex immunofluorescent microscopy with AI-guided image analysis to generate single-cell HOST-Factor values. This approach, applicable to the entire TME or specific regions of interest, provides spatial distribution data and identifies potential tumor-promoting or suppressive neighborhoods.

This talk will highlight the application of Visiopharm’s Phenoplex workflow and its Neighbor Counts module to identify and evaluate HOST+ CAF populations and their spatial distribution. Developed specifically for a pancreatic cancer clinical trial at Fox Chase Cancer Center, this user-friendly pipeline is flexible and useful for assessing diverse stromal compartments in distinct solid tumor cases.

Janusz Franco-Barraza. MD, PhD

Dr. Franco-Barraza obtained his Ph.D. in Molecular Biomedicine in 2010 from CINVESTAV-IPN (Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico). During his postdoctoral work at Fox Chase Cancer Center, he studied integrin-based activation mechanisms used by cancer-associated fibroblasts (CAF) within their self-secreted extracellular matrix (ECM), acting as complex entities called CAF units. He focuses on understanding the functional traits of these CAF units and their roles in tumor promotion or suppression.

Utilizing approaches including multiplexed immunofluorescence and artificial intelligence (AI)-driven digital image mining, he is dedicated to understanding the prevalence and interplay of CAF units with other resident cells within the tumor microenvironment. Currently, his research aims to profile the stroma of cancer patients to validate and implement fibroblastic biomarker signatures that could predict treatment responses and disease outcomes.

Multiplex users deserve analysis software designed for multiplex images. Phenoplex is the only complete workflow software for multiplex tissue images, built on Visiopharm’s best-in-class AI, with interactive verification steps throughout. Make new discoveries, compare between cohorts, or reproduce previous results. Phenoplex’s comprehensive workflow lets you rapidly confirm observations, no matter the ‘plex level, so you can move on to the next multiplex experiment.

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The future of spatial biology promises to reveal the intricate functional interplay between cells, a crucial aspect for understanding disease progression and therapeutic targeting. To decode these cellular conversations, a multiplex multi-omics approach is essential, enabling comprehensive identification and comprehension of diverse analytes. Presently, our capability is limited to combining a few analyte classes on a single slide, such as proteins and mRNA, often at the expense of one class. We can be creative in our assays to go beyond, which reveals the challenge in integrating informatics to not only unify these diverse data types but also to cross-validate and interpret the interactions and their effects on multiple cells in real-time. As cells continually move and interact with different or novel groups, understanding how one cell influences another, and how this influence propagates, is critical. Advancing these techniques will be pivotal in elucidating the dynamic cellular dialogues that underpin disease mechanisms and therapeutic responses.

Jared Burks, PhD Professor, M.D. Anderson
 
Jared started his carrier at Texas A&M University learning about patterns in genes and proteins, allowing and facilitating subcellular protein trafficking. As he has progressed to MD Anderson Cancer Center, he has scaled to cellular trafficking attempting to understand the spatial distribution of cells in organ systems during disease. As in many parts of life, form equals functions. How our cells organize speaks to how they function and respond to their local environment. Bringing together multi-omics approaches allows for greater clarity in these imaging snapshots that are collected.

Delivering high-quality, quantitative data from digital pathology images is one of OracleBio’s core values. Tools such as Visiopharm enable us to achieve this by building robust, AI-powered image analysis workflows.

In this masterclass, we explore the QC strategies used to ensure accuracy and reproducibility in the following case studies:

• The creation of a pathologist-validated and scalable image analysis workflow for a retrospective clinical study involving over 2,200 IHC-stained colorectal polyp whole slide images.
• The use of Deep Learning apps to segment morphologically distinct cell subpopulations and the Phenoplex workflow to streamline the thresholding process for spatial phenotyping in multiplex IF-stained pre-clinical tissue.

Karen McClymont, Image Analysis Project Manager, OracleBio

With a PhD in Biochemistry and her active involvement and support in the analysis of some of OracleBio’s most complex studies, Karen plays a key role leading OracleBio’s image analysis projects and overseeing the team’s continued development.

Gabriel Reines March, R&D Project Manager, OracleBio

With a PhD in Biomedical Image Processing and a background in Electrical Engineering, Gabriel is the OracleBio R&D team lead. He oversees and manages the group’s project pipeline and ensures that the company stays at the bleeding edge of the industry.

Gabriel also manages OracleBio’s involvement in the INCISE project – a collaborative effort between industry, academia and the UK’s National Health Service.