Aptamer-based analysis of plasma proteome of growing tumors
In most cases, detecting malignancies at an early-stage increases treatment options, minimizes the risk of metastases and dramatically improves long-term survival. Nevertheless, tumors are typically discovered at more advanced stages, either with onset of symptoms or as part of unrelated procedures. Non-invasive means of detecting tumors in systemic circulation has long held considerable appeal, but also enormous challenges. A major difficulty is related to the need to detect a small volume of a malignancy remotely, after massive dilution of tumor-associated analytes in the total circulating volume of blood. To date, such “liquid biopsies” have mainly focused on the identification of genetic material unique to transformed cells, typically derived from circulating tumor cells, or cell-free DNA. With proteins, the presence of a tumor is more often accompanied with changes in the levels of endogenous, unmutated proteins in circulation. In this context, knowing which proteins represent the earliest markers or tumor presence would be enormously useful. So what are the first tumor-associated proteins that can be detected in blood as the initial transformed cells establish residence in their tissues of origin?
To answer this question, we used aptamer-based SomaScan® assay to monitor the time course of changes in the plasma proteome of mice carrying transplanted human tumors.
Our study shows that tumor cells representing human lung, ovarian, breast and colon cancer produce both unique and common sentinel proteomic signatures in plasma that could be used to identify both the early presence as well as the identity of tumors growing remotely.
- Mouse models of human tumor growth reveal the emergence of early protein markers of tumor presence in plasma
- Unique and common markers exist for lung, colon, ovarian and breast cancer cell lines
- Tests based on multiple protein analytes reveal tumors at an early stage
- Protein-based tests can enhance the performance of liquid biopsies
Deborah G. Ayers1
Daniel J. Schneider1
Lawrence E. Hunter2
Daniel W. Drolet1
1. SomaLogic Operating Co., Inc., Boulder, CO, USA
2. University of Colorado School of Medicine, Aurora, CO, USA
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Prognostic models for assessing future health outcomes can be developed using time-to-event (also known as “survival”) data. This methodology is ubiquitous in statistical literature and in the analysis of cancer outcomes, but its use in high-dimensional analyses tends to be limited as the methods are difficult to implement in a machine learning environment. Additionally, development of certified prognostic clinical tests using proteomic biomarkers for detecting future cancer risk can be time-consuming, prone to overfitting issues, and difficult to navigate. We demonstrate the utility of combining SomaScan® proteomic data with pipeline machine learning tools and survival analysis methodology to identify powerful and robust LDT-certifiable prognostic tests for assessing future risk of cancer.
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