PROJECT

Tumor Cell Heterogeneity & Immune Response

Tumor Cell Heterogeneity & Immune Response

Cancer cell biology is highly complex, comprising multiple cell types in different metabolic states, and is spatially heterogeneous. Over the last 10 years, our GE Research multi-disciplinary team has developed a multiplexed imaging and cell level analytics technology that allows analysis of up to 60 protein biomarkers in every cell in a 5 um FFPE tissue section (Cell DIVE™). Using the biology information and spatial location of every cell, we can begin to ask questions about cell-to-cell interactions, relationships with immune cells, and why certain tumors progress or respond to therapy and others do not.

Using this platform, we are currently funded by the NIH, in collaboration with Indiana University, Singapore General Hospital and Oxford University to measure immune response to DCIS lesions in Caucasian, African American and Asian women (R01 CA194600). This will help shed light on correlations between lesion heterogeneity, immune cell characterization and differences by ethnicity. Using this cellular data we will build prediction models of disease recurrence in DCIS patients to delineate the characteristics of relapsing patients. We are also funded through the US, Ireland, Northern Ireland R&D Partnership program, which is in part funded by the NIH (R01 CA208179), to investigate tumor heterogeneity in colorectal cancer. This 5 year program involves systems biology modeling of apoptosis, and correlation with immune response and recurrence risk. This is in collaboration with Royal College of Surgeons Ireland, Queen’s University Belfast and MSKCC, NY. The success of these projects has relied upon a highly multidisciplinary team that has evolved with the technology and has pulled on our expertise in imaging, systems design, physics, chemistry, computer science, biology, statistics, and engineering.

Project Impact

Cancer research has evolved hugely over the last 10-15 years and many different types of tools and imaging approaches are being used to understand and predict progression and therapy response. While the last decade or so has focused on the genome and its contribution to disease, spatial cell analysis is fast becoming an essential tool to understand the characteristics of cancer cells, immune cells, microenvironment and their interactions. Our work has been commercialized as a service through Neogenomics (MultiOmyx™) and as a research platform with GEHC (Cell DIVE™). The broader availability of this technology provides a new research platform to visualize disease with a new and sharper lens, understand the some of the root causes and begin to understand which therapies patients might respond to. In the longer term, it may lead to new diagnostic approaches for classification of cancer and predicting probability of disease progression and/or therapy response.

Acknowledgment/Disclaimer: Research reported in this website was supported by National Cancer Institute of the National Institutes of Health under award numbers R01 CA194600 and R01 CA208179. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Project Roadmap

Our multidisciplinary program organically evolved over multiple phases, ranging from method development, engineering and system integration, external collaboration, technology transfer and commercialization.

  1. Feb 2008

    Patent on multiplexed tissue analysis

  2. Single cell analytics

    Oct 2010

  3. Oct 2014

    Commercialization as a service

  4. Multi-scale data integration

    Oct 2017

  5. May 2018

    Publication DCIS immune response

  • Our Expertise

    Capabilities utilized for Tumor Cell Heterogeneity & Immune Response project

  • Business & Risk Analytics

    Developing advanced business analytics to model and control financial risks, improve performance, and gain key business insights

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  • Biosciences

    Working from the molecular scale through human health and disease, building novel technology solutions for genomic, protein, cell analysis and imaging applications

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  • MEMS & Microsystems

    Heterogenous integration of microscale sub-systems creating differentiation in our customer’s products that operate in highly constrained environments

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  • Machine Learning

    Developing and scaling machine learning solutions for industrial applications to allow for continuous learning, adapting and improving in dynamic operating environments

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  • Computer Vision

    Performing fundamental and applied research to mimic human visualization and interpretation

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Publications

  1. Gerdes, M.J., Sevinsky, C.J., Sood, A., Adak, S., Bello, M.O., Bordwell, A., Can, A., Corwin, A., Dinn, S., Filkins, R.J. and Hollman, D., 2013. Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue. Proceedings of the National Academy of Sciences, 110(29), pp.11982-11987.
  2. Spagnolo, D.M., Al-Kofahi, Y., Zhu, P., Lezon, T.R., Gough, A., Stern, A.M., Lee, A.V., Ginty, F., Sarachan, B., Taylor, D.L. and Chennubhotla, S.C., 2017. Platform for quantitative evaluation of spatial intratumoral heterogeneity in multiplexed fluorescence images. Cancer research, 77(21), pp.e71-e74.
  3. Santamaria-Pang, A., Padmanabhan, R.K., Sood, A., Gerdes, M.J., Sevinsky, C., Li, Q., LaPlante, N. and Ginty, F., 2017, February. Robust single cell quantification of immune cell subtypes in histological samples. In Biomedical & Health Informatics (BHI), 2017 IEEE EMBS International Conference on (pp. 121-124). IEEE.

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