Abstract
Tumor development is accompanied by a complex host systemic response, which includes inflammatory and angiogenic reactions. Both tumor-derived and systemic response proteins are detected in plasma from cancer patients. However, given their non-specific nature, systemic response proteins can confound the detection or diagnosis of neoplasia. Here, we have applied an in-depth quantitative proteomic approach to analyze plasma protein changes in mouse models of subacute irritant-driven inflammation, autoreactive inflammation, and matrix associated angiogenesis and compared results to previously described findings from mouse models of polyoma middle T-driven breast cancer and Pdx1-Cre KrasG12D Ink4a/Arf lox/lox -induced pancreatic cancer. Among the confounding models, approximately 1/3 of all quantified plasma proteins exhibited a significant change in abundance compared to control mice. Of the proteins that changed in abundance, the majority were unique to each model. Altered proteins included those involved in acute phase response, inflammation, extracellular matrix remodeling, angiogenesis, and TGFβ signaling. Comparison of changes in plasma proteins between the confounder models and the two cancer models revealed proteins that were restricted to the cancer-bearing mice, reflecting the known biology of these tumors. This approach provides a basis for distinguishing between protein changes in plasma that are cancer-related and those that are part of a non-specific host response.
Highlights
The effective treatment of cancer relies on an accurate diagnosis at the earliest stage of the disease and prognosis is significantly improved if cancer is detected early [1]
Plasmas obtained from mice with subacute inflammation, chronic inflammation, and angiogenesis, along with aged-matched control mice were subjected to in-depth proteomic analysis
When we consider only proteins quantified in all three mouse models, comparisons of plasma profiles between the models revealed a 35% overlap in altered proteins between subacute and chronic inflammation models, compared to only a 15% overlap between the inflammation models and the angiogenesis model (Table 1)
Summary
The effective treatment of cancer relies on an accurate diagnosis at the earliest stage of the disease and prognosis is significantly improved if cancer is detected early [1]. While the concept of a blood-based cancer test is simple, its application has been challenging, to the point that very few new cancer biomarkers have been FDA approved in recent years [2]. Those currently in use, such as CA125 and PSA for ovarian and prostate cancer respectively, are substantially limited by high false positive rates and over-diagnosis [3,4]. Levels of candidate biomarkers from cancer patients are frequently compared to healthy individuals In these studies, it is difficult to control for genetic or environmental variables, as well as non-cancerous ‘‘confounding’’ conditions. The fact that many candidate cancer biomarkers lack sufficient specificity to be useful argues for a new approach [1,6]
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