# Quantifying heterocellular communication and spatial intratumoral heterogeneity from high dimensional spatial proteomics data > **NIH NIH F31** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2020 · $45,520 ## Abstract The tumor microenvironment (TME) is composed of malignant and non-malignant cells, each contributing to spatial intratumoral heterogeneity (ITH) and heterocellular communication altering the composition and architecture of the TME. A high degree of ITH is correlated to metastatic progression and therapeutic response. Previous studies investigating spatial ITH have been limited due to a steep trade-off between cellular resolution, spatial context, and dimensionality of biomarkers. A recent explosion of multi to hyperplexed imaging modalities (e.g., fluorescence imaging, mass-spec imaging) enable the quantification of greater than 7 and up to > 100 biomarkers through sequentially multiplexed imaging of 2 to 3 biomarkers using iterative cycles of label-image-dye inactivation. The generation of this new type of data poses both unique opportunities and challenges. There are no state-of-the-art methods for harnessing the complexity of spatial data to infer tumor biology with a high dimensionality of biomarkers. In this project, we will probe the spatial complexity of a TME in hyperplexed immunofluorescence (HxIF) based spatial proteomics colorectal carcinoma (CRC) data (51 biomarkers + DAPI, 356 patient samples) to elucidate the heterocellular communication networks promoting spatial ITH through cellular phenotyping, microdomain extraction, and network biology inference algorithms. We will demonstrate the applicability of our algorithms to cancer types beyond CRC with multiplexed immunofluorescence breast cancer tissue samples In Aim 1, we will continue to develop unsupervised learning algorithm for cellular phenotypic heterogeneity (LEAPH) to identify specialized, rare, and transitional cell populations. Initial results applying LEAPH on the HxIF CRC data have revealed cellular heterogeneity patterns consistent with CRC literature (STEM cell differentiation, immune evasion, macrophage evolution). We will incorporate machine learning- based methods into LEAPH to measure spatial distribution patterns of each phenotype and correlate them with CRC progression (e.g., recurrence). In Aim 2, we will quantify spatial ITH in greater detail by identifying differentially expressed pair- or group-wise spatial relationships based on outcome data (e.g., recurrence vs no-recurrence within 5 years) to reveal phenotypic domains, microdomains, with prognostic potential. We expect improvement of prognostic power with pair- or group-wise spatial interactions in comparison to the single-phenotype based spatial ITH characterization of Aim 1. In Aim 3, we will dissect the microdomain- specific heterocellular communication dynamics with causal inference network models. We expect to identify emergent signaling networks conferring malignant phenotypes, such as known features from CRC consensus molecular subtypes. The algorithms constructed in this project will be implemented and disseminated through the Tumor Heterogeneity Research Interactive Visualization Environment (THRIVE... ## Key facts - **NIH application ID:** 10067758 - **Project number:** 1F31CA254332-01 - **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH - **Principal Investigator:** Samantha A. Furman - **Activity code:** F31 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $45,520 - **Award type:** 1 - **Project period:** 2020-09-01 → 2022-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10067758 ## Citation > US National Institutes of Health, RePORTER application 10067758, Quantifying heterocellular communication and spatial intratumoral heterogeneity from high dimensional spatial proteomics data (1F31CA254332-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10067758. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*