Feasibility of MR metabolomics for immediate analysis of resection margins during breast cancer surgery

Abstract

In this study, the feasibility of high resolution magic angle spinning (HR MAS) magnetic resonance spectroscopy (MRS) of small tissue biopsies to distinguish between tumor and non-involved adjacent tissue was investigated. With the current methods, delineation of the tumor borders during breast cancer surgery is a challenging task for the surgeon, and a significant number of re-surgeries occur. We analyzed 328 tissue samples from 228 breast cancer patients using HR MAS MRS. Partial least squares discriminant analysis (PLS-DA) was applied to discriminate between tumor and non-involved adjacent tissue. Using proper double cross validation, high sensitivity and specificity of 91% and 93%, respectively was achieved. Analysis of the loading profiles from both principal component analysis (PCA) and PLS-DA showed the choline-containing metabolites as main biomarkers for tumor content, with phosphocholine being especially high in tumor tissue. Other indicative metabolites include glycine, taurine and glucose. We conclude that metabolic profiling by HR MAS MRS may be a potential method for on-line analysis of resection margins during breast cancer surgery to reduce the number of re-surgeries and risk of local recurrence.

Figure 1. HR MAS spectra and illustration of typical features observed in the corresponding HES images (200X).

(A) Invasive ductal carcinoma with an estimated tumor content of 80% in the analysed biopsy. (B) Invasive mucinous carcinoma with an estimated tumor content of 60% in the analysed biopsy. (C) Normal breast tissue (adjacent to tumor). No tumor cells were detected, and the HES image shows the typical feature of normal terminal lobular duct units. The poor signal to noise ratio in this spectrum is probably due to the high level of connective tissue (85%).

Figure 2. Variation in tumor cell content as described by PCA.

(A) The score plot of the pre-processed spectra, colored according to the tumor cell content (%) of the corresponding biopsies. (B) The corresponding loading profile of PC1, explaining 40.1% of the total variation of the data. β-Glc, β-glucose; Asc, ascorbate; Lac, lactate; Cr, creatine; Gly, glycine; Tau, taurine; GPC, glycerophosphocholine; PCho, phosphocholine; Cho, free choline.

Figure 3. PLS- DA classification of tumor and non-involved tissue.

(A) The score plot, separating tumor and non-involved tissue, and (B) the corresponding loading profiles of LV1 and LV2. LV1 and LV2 explain 35.8% and 13.2% of the x-variation and 53.3% and 4.9% of the y-variation, respectively. β-Glc, β-glucose; Asc, ascorbate; Lac, lactate; Cr, creatine; Gly, glycine; Tau, taurine; GPC, glycerophosphocholine; PCho, phosphocholine; Cho, free choline.

Figure 4. Variation in tumor cell content as described by PCA of the choline region.

(A) The score plot of the choline-containing metabolite region of the spectra, colored according to the tumor cell content (%) of the corresponding biopsies. (B) The corresponding loading profile of PC1 explaining 71.3% of the total variation of the data.