Abstract
Measurement of circulating insulin‐like growth factors (IGFs), in particular IGF‐binding protein (IGFBP)‐2, at the time of diagnosis, is independently prognostic in many cancers, but its clinical performance against other routinely determined prognosticators has not been examined. We measured IGF‐I, IGF‐II, pro‐IGF‐II, IGF bioactivity, IGFBP‐2, ‐3, and pregnancy‐associated plasma protein A (PAPP‐A), an IGFBP regulator, in baseline samples of 301 women with breast cancer treated on four protocols (Odense, Denmark: 1993–1998). We evaluated performance characteristics (expressed as area under the curve, AUC) using Cox regression models to derive hazard ratios (HR) with 95% confidence intervals (CIs) for 10‐year recurrence‐free survival (RFS) and overall survival (OS), and compared those against the clinically used Nottingham Prognostic Index (NPI). We measured the same biomarkers in 531 noncancer individuals to assess multidimensional relationships (MDR), and evaluated additional prognostic models using survival artificial neural network (SANN) and survival support vector machines (SSVM), as these enhance capture of MDRs. For RFS, increasing concentrations of circulating IGFBP‐2 and PAPP‐A were independently prognostic [HR biomarker doubling: 1.474 (95% CIs: 1.160, 1.875, P = 0.002) and 1.952 (95% CIs: 1.364, 2.792, P < 0.001), respectively]. The AUCRFS for NPI was 0.626 (Cox model), improving to 0.694 (P = 0.012) with the addition of IGFBP‐2 plus PAPP‐A. Derived AUCRFS using SANN and SSVM did not perform superiorly. Similar patterns were observed for OS. These findings illustrate an important principle in biomarker qualification—measured circulating biomarkers may demonstrate independent prognostication, but this does not necessarily translate into substantial improvement in clinical performance.
Highlights
The insulin-like growth factors, IGF-I and IGF-II, circulate in high concentrations, but at the cellular level, only a small fraction is able to stimulate the IGF-I receptor (IGF-IR), the primary target of the IGFs. This is due to the presence of six high-a ffinity binding proteins (IGFBPs), which are present in molar excess of the IGFs and bind the IGFs with an affinity that exceeds that of the IGF-IR [1]
In addition to Cox regression models, we evaluated prognostic models using survival artificial neural networks (ANN) (SANNs) and survival support vector machines (SSVMs), as these enhance capture of multidimensional relationships
The baseline patient, tumor, and treatment characteristics according to tertiles of IGF-binding protein (IGFBP)-2 and PAPP-A are shown in Table 1 for the 301 women with early-breast cancer treated by surgery at the Odense University Hospital, from 1993 to 1998
Summary
The insulin-like growth factors, IGF-I and IGF-II, circulate in high concentrations, but at the cellular level, only a small fraction is able to stimulate the IGF-I receptor (IGF-IR), the primary target of the IGFs. To activate the IGF-IR, the IGFs need to dissociate from the IGFBPs. To activate the IGF-IR, the IGFs need to dissociate from the IGFBPs This process is enhanced by IGFBP proteases, which cleave the IGFBPs and thereby reduce their ligand affinity markedly. PAPP-A is a metalloproteinase that cleaves a subset of IGFBPs, and functions as a growth-promoting enzyme, releasing bioactive IGF in close proximity to the IGF-IR [2]. PAPP-A is overexpressed in several tumor types, including breast cancer [3], but the prognostic significance of circulating PAPP-A remains uncertain
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