MSACL 2018 US Abstract

Topic: Tissue Imaging & Analysis

Towards in situ Tissue Classification to Improve Prostate Cancer Diagnosis Using Mass Spectrometry Imaging

Elizabeth Randall (Presenter)
Brigham and Women's Hospital, Harvard Medical Scho

Bio: I graduated from the University of Birmingham (UK) with an honors degree in Chemistry (MSci) in 2012, and went on to study the Physical Sciences of Imaging in Biomedical Sciences (MSc) also at the University of Birmingham. I graduated with Distinction and continued with research in biomedical imaging with a PhD in partnership with the National Physical Laboratory (NPL), AstraZeneca and the University of Birmingham (all UK). This education provided an excellent interdisciplinary background in chemistry, physics, biomedical and computer sciences, necessary for a researcher in the field of medical imaging. After graduating from my PhD program in December 2016, I was accepted as an R25 Fellow in Image-Guided Therapy at the Brigham and Women’s Hospital, Harvard Medical School, under the mentorship of Nathalie Agar, PhD. My fellowship concerns the development and application of mass spectrometry imaging and analysis for guidance in prostate cancer care. At the Brigham and Women’s Hospital we have the first intra-operative mass spectrometry platform; a platform which we hope to develop for improved diagnosis and surgical outcomes to ultimately provide the best in patient care.

Authorship: Elizabeth C. Randall (1), Giorgia Zadra (2), Massimo Loda (2), Clare M. Tempany (1), Fiona M. Fennessy (1), Nathalie Y. R. Agar (1,2)
(1) Brigham and Women’s Hospital, Harvard medical School, Boston, MA, (2) Dana Farber Cancer Institute, Harvard Medical School, Boston, MA

Short Abstract

We present a workflow for the identification of biomarkers of prostate cancer and Gleason grade. High resolution matrix assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) was performed on prostate tissue from patients with Gleason grade from 6 to 9. Unsupervised data analysis revealed ions that were differentially expressed between tumor and normal tissue, and between different Gleason grades. Ions were assigned based on high mass accuracy data searched using the Human Metabolome Database (HMDB). MALDI results were then correlated with a fast, ambient MS method for validation as a clinical tool to support image-guided prostate biopsy.

Long Abstract


Prostate cancer is the most commonly diagnosed cancer in men in the US.(1,2) The standard of care for diagnosis of suspected prostate cancer is systematic biopsy and pathologist annotation of Gleason grade based on tissue micro-architecture; however, systematic biopsy misses 21-28% of prostate cancers and under-grades 14-17%.(3) Image-guided biopsy methods such as in bore MR guided transperineal (4) or MRI/ultrasound fusion have improved the sensitivity of diagnosis by biopsy,(5) but typically a pathologist will not grade the biopsy until after the procedure is complete, by which time there is little opportunity to re-sample. In situ analysis of robust molecular biomarkers could ensure that the tumor is reached during the procedure and also enable faster, more accurate diagnosis and tumor grading. In this work, we present a mass spectrometry imaging (MSI) workflow to identify and map biomolecules in prostate cancer. Metabolomics studies have previously identified a number of polar metabolites and lipids which appear to be characteristic of tumor (6,7) and we now demonstrate how these map across heterogeneous and multi-focal prostate cancer samples, and suggest a method for their analysis in situ. This method could be implemented into our prostate biopsy program using our intra-operative mass spectrometry platform in the Advanced Multimodal Image-Guided Operating (AMIGO) suite at Brigham and Women’s Hospital.


Matrix assisted laser desorption ionization (MALDI) MSI was performed on 12 µm sections of fresh frozen tissue from 6 patients with prostate cancer (2 x Gleason 6, 2 x Gleason 7, 1 x Gleason 8, 1 x Gleason 9). Tissue was obtained from the Gelb Centre tissue bank repository at the Dana Farber Cancer Institute. Samples were obtained and analyzed under Institutional Review Board-approved research protocol. Sections were coated with α-CHCA matrix using a TM-Sprayer (HTX Imaging, Carrboro, NC). MSI was performed on a 9.4 Tesla SolariX XR FT ICR MS (Bruker Daltonics, Billerica, MA). Images were acquired in positive ion mode (m/z 50 – 1500) with a pixel size of 120 µm. After MALDI imaging samples were washed and H&E stained; histological and ion images were registered. Data were analyzed using SCiLS Lab software (Buker Daltonics, Bremen, Germany). Principal component analysis (PCA) and bisecting k-means clustering were used to segment images. Regions of interest were defined based on the resulting segmentation map and receiver operating characteristic (ROC) analysis was performed to find ions which discriminated between tumor and normal tissue. Ions were preliminarily identified by searching accurate mass against HMDB (assignments accepted based on Δppm <10). A further set of samples was analyzed by liquid extraction surface analysis (LESA) coupled to an AmazonSpeed ion trap mass spectrometer (Bruker Daltonics, Billerica, MA) in parallel with MALDI MSI.


MALDI MS imaging was performed in the low mass range to determine the distribution of metabolites and lipids in prostate cancer. Imaging data were analyzed using two methods to extract spatial and spectral features: PCA and bisecting k-means. Both methods produced segmented images in good agreement with pathologist annotated H&E. Regions corresponding to tumor and normal tissue were selected from segmented images and ions were assessed for how well they discriminated between the different regions. A number of lipid classes were detected with increased intensity in tumor compared to healthy tissue (AUC of ROC plot between 0.75 and 0.99) including: phosphatidylcholines (PC) (>20 species), phosphoethanolamines (PE) (>5 species), triacylglycerides (TG) (>5 species) and sphingomyelins (SM) (>5 species). Additionally, ions were found which associated with different histological features including prostatic intraepithelial neoplasia (PIN). These results were in good agreement with LC-MS metabolomics studies, (6) but with the further advantage of providing spatial information for direct validation against standard histopathology. This allowed better spatial characterization of heterogeneous, multi-focal tumors. Lipid markers identified by MALDI were then correlated with LESA MS which was performed on a further set of tissue samples prior to MALDI. Further analyses on a larger sample set are underway to validate our preliminary findings.

Conclusions & Discussion

The identification of biomolecular pathways implicated in prostate cancer could provide insight into disease onset and progression in addition to providing molecular targets for diagnosis and potentially therapy. These preliminary results provide the groundwork towards developing a high-throughput ambient MS analysis methodology which could be implemented on our intra-operative mass spectrometry platform at BWH, to support image-guided prostate biopsy.

References & Acknowledgements:


ECR is in receipt of an NIH R25 (R25 CA-89017) Fellowship in partnership with the Ferenc Jolesz National Centre for Image Guided Therapy at BWH. CMT receives support from NIH P41-EB-015898, U01 HD 087211 & R25 CA 89017.


1. Litwin, M. S. & Tan, H.-J. The Diagnosis and Treatment of Prostate Cancer. JAMA 317, 2532 (2017).

2. Miller, K. D. et al. Cancer treatment and survivorship statistics, 2016. CA. Cancer J. Clin. 66, 271–89 (2016).

3. Bjurlin, M. A. et al. Optimization of initial prostate biopsy in clinical practice: sampling, labeling and specimen processing. J. Urol. 189, 2039–46 (2013).

4. Penzkofer, T. et al. Transperineal In-Bore 3-T MR Imaging–guided Prostate Biopsy: A Prospective Clinical Observational Study. Radiology 274, 170–180 (2015).

5. Siddiqui, M. M. et al. Comparison of MR/Ultrasound Fusion–Guided Biopsy With Ultrasound-Guided Biopsy for the Diagnosis of Prostate Cancer. JAMA 313, 390 (2015).

6. Cacciatore, S. et al. Metabolic Profiling in Formalin-Fixed and Paraffin-Embedded Prostate Cancer Tissues. Mol. Cancer Res. 15, 439–447 (2017).

7. Kelly, R. S., Vander Heiden, M. G., Giovannucci, E. & Mucci, L. A. Metabolomic Biomarkers of Prostate Cancer: Prediction, Diagnosis, Progression, Prognosis, and Recurrence. Cancer Epidemiol. Biomarkers Prev. 25, 887–906 (2016).

Financial Disclosure

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