Stromal vapors for real-time molecular guidance of breast-conserving surgery

Achieving radical tumor resection while preserving disease-free tissue during breast-conserving surgery (BCS) remains a challenge. Here, mass spectrometry technologies were used to discriminate stromal tissues reported to be altered surrounding breast tumors, and build tissue classifiers ex vivo. Additionally, we employed the approach for in vivo and real-time classification of breast pathology based on electrosurgical vapors. Breast-resected samples were obtained from patients undergoing surgery at MUMC+. The specimens were subsequently sampled ex vivo to generate electrosurgical vapors analyzed by rapid evaporative ionization mass spectrometry (REIMS). Tissues were processed for histopathology to assign tissue components to the mass spectral profiles. We collected a total of 689 ex vivo REIMS profiles from 72 patients which were analyzed using multivariate statistical analysis (principal component analysis-linear discriminant analysis). These profiles were classified as adipose, stromal and tumor tissues with 92.3% accuracy with a leave-one patient-out cross-validation. Tissue recognition using this ex vivo-built REIMS classification model was subsequently tested in vivo on electrosurgical vapors. Stromal and adipose tissues were classified during one BCS. Complementary ex vivo analyses were performed by REIMS and by desorption electrospray ionization mass spectrometry (DESI-MS) to study the potential of breast stroma to guide BCS. Tumor border stroma (TBS) and remote tumor stroma (RTS) were classified by REIMS and DESI-MS with 86.4% and 87.8% accuracy, respectively. We demonstrate the potential of stromal molecular alterations surrounding breast tumors to guide BCS in real-time using REIMS analysis of electrosurgical vapors.


Scientific Reports
| (2020) 10:20109 | https://doi.org/10.1038/s41598-020-77102-1 www.nature.com/scientificreports/ feedback about tissue pathology based on the chemical information present in the vapors produced in vivo without changing the operation procedure. This is a crucial point to facilitate clinical implementation of the technology for in vivo metabolic profiling. DESI has determined specific metabolic profiles for adipose, stromal, glandular and tumor of breast tissues 3,4 but REIMS has discriminated normal breast and breast tumor tissues only 6 . The variety of healthy breast tissues reported with DESI-MSI suggests that specific metabolic profiles for different healthy breast tissues could be expected with REIMS, which remains unexploited. Moreover, alterations of stromal tissues surrounding breast tumors have been associated to clinical observations 10,11 . If detectable in real-time, these stromal changes could mark a safe area of resection to complement the assessment of margin of resection by histopathology. They would constitute a classification of healthy and tumor surrounding tissues for precise surgical guidance.
Herein, we investigate the potential of breast stroma to guide BCS by mass spectrometry analysis. First, we built a classification model based on REIMS analysis of ex vivo electrosurgical vapors to recognize breast stroma during BCS. Then, we use REIMS and DESI-MS to investigate further the potential of breast stroma to guide BCS.

Materials and methods
Study population and tissue procurement. This study included 85 female patients (Table S1) who underwent surgery for breast tumor at Maastricht University Medical Centre (MUMC+) between September 2017 and September 2019. The study patient inclusion followed a protocol approved by the Medical Ethics Committee (Medisch-Ethische ToetsingsCommissie) azM/UM of MUMC+ (approval number . The study was conducted with highest practice standards according to the revised version of the Declaration of Helsinki. Written informed consent was obtained from each patient prior to study participation. A pathologist selected samples on the resected-tissue for the present study; samples were taken of the tumor and benign tissue at least 2 cm away from the tumor. Tissue slices were used for REIMS ex vivo analysis, or frozen in liquid nitrogen and stored at − 80 °C until DESI-MSI analysis. REIMS ex vivo analysis. Tissues from 72 patients were cauterized ex vivo using a monopolar hand-piece (iKnife disposable device, Waters, Hungary) equipped with a 1.7 cm-diameter blade electrode, connected to an electrosurgical heat-generator (Force FX, Covidien), operated in cut modality. The generated vapors were aspirated into a mobile REIMS Xevo G2-XS Q-ToF mass analyzer (Waters Corporation, Wilmslow, UK). Isopropanol (Biosolv, France or Honeywell, Germany) containing Leucine-Encephalin (Sigma-Aldrich, The Netherlands) was infused at 150 μL/min 12 . REIMS acquisitions were performed in negative ionization mode over the mass range m/z 100 to 1500. After REIMS analysis, the remaining tissue was formalin fixed and paraffin embedded. Tissue blocks were sectioned at 5 μm-thickness. Tissue sections were stained with hematoxylin and eosin (H&E). A breast pathologist attributed tissue components to the surroundings of the sampling spots without knowledge of the MS profiles. Percentages of tissue components were assigned to each MS profile based on the histology surrounding of each sampling spot. For the classification of tumor border stroma (TBS) and remote tumor stroma (RTS), only the stroma profiles correctly classified in the adipose/tumor/stroma REIMS model were included. Stromal profiles generated on tissue sampled at least 2 cm from the tumor and presenting no tumor were considered RTS profiles. Stroma profiles generated on tissues containing sampling spots containing a tumor component in their pathology examination were considered TBS profiles.

REIMS in vivo analysis.
In vivo measurement was performed on the same instrument during the BCS of one patient. Surgery was performed using commercial hand-piece (Erbe) and heat generator (Valleylab FT10, Covidien). Three GoPros (Hero4) were set up to record surgical site, heat generator and screen of the computer of the mass spectrometer to coordinate in time, site of electrosurgical vapors production, the diathermia parameters and MS profile generation. Synchronization was performed using timestamps associated to sound generated in the operation room during the surgery. Orientation of the resection margins on the specimen was obtained during and after surgery using video recorded and comments made by surgeons at that time and while watching the video post-surgery. Macroscopic and microscopic analyses of the resection margins were inspected by a pathologist according to the surgeons' markings. Conditions for experimental measurements were set up the same as for ex vivo analysis with air gas. Electrosurgical vapors were directed towards the mass spectrometer and the REIMS source and partially discarded as usual.
DESI-MS analysis. Two groups of specimens were selected for DESI-MSI analysis; samples of the tumor site or samples of fibro-glandular tissues at least 2 cm away from the tumor site from 22 patients. Frozen tissues were sectioned using a cryotome (Microm) at 10 μm-thickness, thaw mounted on histological slides (Superfrost) and stored at − 80 °C prior analysis. Experiments were performed on a Xevo G2-XS Q-ToF MS (Waters Corporation, Wilmslow, UK). 98%methanol/water (Biosolv, France) at 1.5 μl/min was used as a solvent. Measurements used for the tissue classification were performed in negative ionization mode, over the mass range m/z 100-1000 at 40 × 40 μm 2 pixel size, acquired at scan rate of 150 μm/s. Tissue sections were stained by H&E and scanned on a slide scanner (Ventana) after DESI-MSI analysis. Stromal areas surrounding remote normal glands and surrounding tumors were selected by a breast pathologist using QuPath (v0.1.2). One data point corresponded to the sum of the signal of 6 adjacent pixels selected in TBS or RTS areas in HD Imaging (v1.5, Waters, UK).

Statement.
We report the in vivo recognition of breast stroma and its potential for real-time molecular guidance of breast-conserving surgery using rapid evaporative ionization mass spectrometry analysis of electrosurgical vapors.

Ex vivo built database enables in vivo tissue recognition.
In total, 689 REIMS ex vivo profiles were generated from 72 patients and were attributed as 209 stroma, 256 adipose and 224 tumor profiles by histopathology (Tables S2-3). REIMS profiles were classified with 92.6% accuracy (Fig. 1A) using PCA-LDA and a leave-one patient-out cross-validation. A PCA score plot (Fig. 1B) showed a separation of adipose and tumor profiles along the PC1 axis (which describes 56.2% of the variance of the data) and a separation of stroma and tumor profiles along the PC2 axis (25.6%). Main discriminators were m/z 893.75 and m/z 919.75 (Fig. 1C) previously assigned as triglycerides 6 for adipose, and m/z 255.25 and m/z 281.25 (Fig. 1D) assigned as fatty acids, palmitic acid and oleic acid respectively, for stroma (Table S5). Single MS profiles for each tissue type and pseudo-LDA score plot are shown in Figure S1.
Next, in vivo REIMS data were collected in an operating room during one BCS performed using a diathermia knife. We detected intense MS signals throughout the operation (Fig. S2A). At around 14 min of recording, scan 845 and scan 851 (Fig. 2B, D, respectively), both displayed MS signals generated in cut modality. The pictures taken during surgery indicate a change in the transected tissue ( Fig. 2A, C). While the most intense MS peaks in scan 845 were detected only in the mass range m/z 200-400, intense MS peaks were displayed in the mass range m/z 800-1000 for scan 851.  (Table S3; Fig. 3A). PCA score plot did not enable separation of TBS and RTS profiles, therefore no straightforward screening and molecular identification of the main MS peaks discriminant of TBS and RTS was possible (Fig. S3A). However, LDA score plot enabled a good separation (Fig. S3B) and MS profiles were classified as TBS or RTS with 86.5% accuracy (Fig. 3B) using PCA-LDA and a leave-one patient-out cross-validation.
We complemented the REIMS experiments with DESI-MSI experiments for more precise analysis of the stromal changes surrounding tumor borders. In total, 196 DESI-MS profiles, including 98 remote tumor stroma profiles (RTS) and 98 tumor border stroma (TBS) profiles, were extracted from pixels of 22 tissue sections from 22 patients (Table S4). DESI-MS profiles were classified as TBS or RTS with 87.8% accuracy (Fig. 4A) using PCA-LDA and leave-one patient-out cross-validation. PCA score plot displayed a separation RTS and TBS mainly along the PC1 axis (Fig. S4A). Main discriminator of TBS along PC1 mass loading plot was a mass feature m/z 201.05 (Fig. S4C) corresponding to mass value m/z 201.04 previously identified (15) and confirmed (Table S6) as lactate dimer. Its distribution delineates the tumor borders with an intense MS signal coming from the TBS areas (Fig. 4B), illustrating how in situ molecular distributions may provide relevant information beyond histopathology.  www.nature.com/scientificreports/

Discussion
We confirm the changes of metabolic profiles between remote and tumor stromas on intraoperative breast biopsies by DESI-MSI 4 . We report similar classification of stromal tissues with REIMS analysis of electrosurgical vapors. Our ex vivo built library of histopathology validated molecular profiles enabled in vivo stroma recognition and therefore illustrates a exciting step towards intraoperative molecular guidance during BCS. Metabolic alterations of the tumor microenvironment lay the bases of how insight into molecular changes can improve surgical precision. Technological improvements of speed and intraoperative sampling [14][15][16] recently contributed to emerging mass spectrometric tissue classifiers for clinical applications 17 . Paradoxically, the molecular margin of resection 18 beyond histopathology, characterized in earlier studies to understand tumor recurrence 19,20 , was set aside as a factor to be considered in the most recent investigations. The benefit of mass spectrometric technologies for clinical applications has been mainly considered only by histopathological validation which remains the gold standard for disease-free patient survival. Nevertheless, the detection of more subtle molecular changes, not detectable by histopathology, could be even more valuable, as previously reported by REIMS on primary liver tumors 8 . REIMS has been mainly reported in the perspective to correct the execution of unsafe resection margins by near real-time classification of tumor and normal tissues during surgery. Our report suggests that surgeons could benefit from a critical information, the distance to the tumor, with recognition of breast stroma metabolic profiles during surgery. Beyond a correction, this would constitute a real-time molecular guidance to improve surgical precision of BCS. This is expected to be a major leap towards achieving the smallest possible resections with safe margins. Our classification of breast pathology from electrosurgical vapors generated ex vivo enabled direct stroma recognition in vivo. This augments the value of REIMS for in vivo tissue recognition 6 and to utilize breast stromal molecular information to guide more precise BCS.

Data availability
The process datasets generated during the current study are available with this article in the supporting material, accessible from Scientific reports website.