# Multiplex matrix ELISA for T cell protein-interaction networks in cancer

> **NIH NIH R33** · UNIVERSITY OF MISSOURI-COLUMBIA · 2020 · $358,081

## Abstract

Project Summary/Abstract
Cells transmit biological signals by connecting many different proteins together in various conditionally
regulated combinations and quantities, culminating in protein-protein interaction (PPI) signatures. Referring
to these protein combinations as `signatures' is a metaphor that can be extended, wherein PPI constitute a
biochemical `language', in which proteins are members of an `alphabet' that in joining together form `words'
instructing the cell to perform specific functions. Indeed, a central hypothesis of the Interactomics field is that
PPI activity is distinct in healthy versus diseased states: in this model, distinct PPI signatures provide the
signals that cause these opposite outcomes, and if we knew how to define those PPI signatures, we could design
better drugs to halt pathologic signals, while preserving or enhancing healthy ones. Viewed from this
perspective, cancer may be considered a disease of dysregulated PPI, where pathologic PPI signatures originate
from mutation, unhealthy growth factor pathways, and the shutting down of the body's naturally protective
immune system. To better understand these signals in cancer, the field needs technologies that expand our
capability to observe PPI networks, ideally from samples as small as those routinely obtained in the clinic. Our
group has recently mounted a new multiplex microsphere-based platform to address this need, termed
`PiSCES'. The PiSCES platform currently focuses on T cell antigen receptor (TCR) pathway as a prototype PPI
network, due to its importance in T cell-mediated eradication of tumor cells, and its possible suppression
associated with the universally lethal cancer, glioblastoma multiforme (GBM). Our preliminary data already
show that PiSCES can reveal distinct PPI signatures associated with functionally divergent signals, with assay
sensitivity that is compatible with tiny samples originating from experimental mice or human patient biopsies.
The current project is dedicated to advanced development and validation of PiSCES, as it is applied to T cells in
the context of cancer immunotherapy and tumor-induced immune suppression. By showing that the PiSCES
approach works for T cell signaling in cancer, we expect that this will launch the new platform in both mouse
modeling and patient research communities, where it can potentially be applied to any PPI networks in any cell
type of interest in cancer. Visualizing the different activities of physiologic PPI networks in cancer will be a
major step toward understanding them better, and toward designing drugs to combat malignant signals or
enhance the body's immune defenses against tumors.

## Key facts

- **NIH application ID:** 9999951
- **Project number:** 5R33CA228979-03
- **Recipient organization:** UNIVERSITY OF MISSOURI-COLUMBIA
- **Principal Investigator:** Adam G. Schrum
- **Activity code:** R33 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $358,081
- **Award type:** 5
- **Project period:** 2018-09-11 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9999951

## Citation

> US National Institutes of Health, RePORTER application 9999951, Multiplex matrix ELISA for T cell protein-interaction networks in cancer (5R33CA228979-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9999951. Licensed CC0.

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