Interim clinical trial analysis of intraoperative mass spectrometry for breast cancer surgery

Abstract

Optimal resection of breast tumors requires removing cancer with a rim of normal tissue while preserving uninvolved regions of the breast. Surgical and pathological techniques that permit rapid molecular characterization of tissue could facilitate such resections. Mass spectrometry (MS) is increasingly used in the research setting to detect and classify tumors and has the potential to detect cancer at surgical margins. Here, we describe the ex vivo intraoperative clinical application of MS using a liquid micro-junction surface sample probe (LMJ-SSP) to assess breast cancer margins. In a midpoint analysis of a registered clinical trial, surgical specimens from 21 women with treatment naïve invasive breast cancer were prospectively collected and analyzed at the time of surgery with subsequent histopathological determination. Normal and tumor breast specimens from the lumpectomy resected by the surgeon were smeared onto glass slides for rapid analysis. Lipidomic profiles were acquired from these specimens using LMJ-SSP MS in negative ionization mode within the operating suite and post-surgery analysis of the data revealed five candidate ions separating tumor from healthy tissue in this limited dataset. More data is required before considering the ions as candidate markers. Here, we present an application of ambient MS within the operating room to analyze breast cancer tissue and surgical margins. Lessons learned from these initial promising studies are being used to further evaluate the five candidate biomarkers and to further refine and optimize intraoperative MS as a tool for surgical guidance in breast cancer.

Fig. 1. Workflow for intraoperative MS of surgical breast tissue.

a Lumpectomy, (b) tissue allocation, (c) tissue smear using squash prep, (d) surface extraction using liquid microjunction surface sample probe (LMJ-SSP), and (e) metabolite profiles generated using electrospray ionization-ion-trap mass spectrometry. *Tumor and normal samples allocated in the frozen section room when adequate tissue is available for diagnosis.

Fig. 2. Mass spectra, histological and stimulated Raman scattering microscopy of normal and tumor tissue (case 13).

a Intraoperative mass spectrum generated from normal (benign) breast tissue. b H&E stain of normal tissue. c SRS image of sister section. d Intraoperative mass spectrum generated from tumor tissue. The scale bar is 250 µm. e H&E stain of tumor tissue. f SRS image of the parallel section. In SRS image, areas of green correlate with lipids, while orange correlates with collagen. The scale bar is 500 µm.

Fig. 3. SRS images and intraoperative mass spectra from the tumor, normal, and lumpectomy margins from Case 13.

Pseudocolor green: SRS imaging of lipids in adipocytes; pseudocolor red: SHG imaging of collagen. Tumor tissue was featured by significantly reduced lipid contents and increased collagen deposition.

Fig. 4. Multivariate analysis.

a Feature extraction using significance analysis of microarrays (SAM) analysis highlighting five molecules (in red) which represent the most distinguishing features in the tumor compared to normal samples, with a false discovery rate of <0.001 and confidence interval of 90%. b The hierarchical map was constructed in an unsupervised and randomized manner, resulting in a heat map demonstrating relative differences in ion intensities of the 5 features between tumor and normal samples.

Fig. 5. t-distributed stochastic neighbor embedding (t-SNE) demonstrating the placement of each specimen as a data point in an unsupervised manner based on the 5 most discriminant features.

Data points are labeled by their case number followed by N (normal) in green or T (tumor) in red, and then by sample number if there was more than one specimen for that designation. The data points were then colored based on the ion intensities of the five significant lipid peaks.