Sentinel lymph node tumor burden quantified non-invasively and non-radioactively with multi-tracer and multispectral optoacoustic imaging
Final Report Abstract
Many cancer guidelines include sentinel lymph node (SLN) staging to identify microscopic metastatic disease. Current SLN analysis of melanoma patients has the substantial drawback that only a small portion of the node is sampled, while most of the tissue is discarded. This might explain the high false-negative rate of current SLN diagnosis in melanoma leading to treatment failure by withholding adjuvant therapy. Furthermore, the quantitative assessment of metastatic load and microanatomical localization might yield prognosis with higher precision. Thus, methods to analyze entire SLNs with cellular resolution are required. By incorporating “paired-agent” molecular imaging methods into current MSOT assessments of melanoma spread to SLN, we assumed to achieve a 10-cell sensitivity of tumor cells in SLN of mice. Therefore, we established a xenograft mouse model of malignant melanoma which reliably induced axillary and inguinal lymph node metastases. But MSOT revealed to be insufficient to detect fluorescence labelled antibodies in our mouse model. Furthermore, MSOT could not differentiate between specific and unspecific imaging agents (with different wavelengths) in our mouse model. To continue with our project we we developed and used an algorithm-enhanced light sheet fluorescence microscopy (LSFM) approach to threedimensionally reconstruct the entire SLN with single-cell resolution to detect cancer in our xenograft mouse model. Then the COVID-19 pandemic made it difficult to receive mice for over a year, as employees were sent into home office work and mice were not bred during that time. As a consequence, we translated our LSFM approach to human sentinel nodes and designed a first-in-human prospective study. We comprehensively quantified total tumor volume while simultaneously visualizing cellular and anatomical hallmarks of the associated SLN architecture. In 21 human SLN of 11 patients LSFM not only identified all metastases seen histologically, but additionally detected metastases not recognized by routine histology. This led to additional therapeutic options for one patient. Thus, our 3-D digital pathology approach can improve the identification of SLN metastasis as an alternative to current routine histology. Our data support future clinical trials of LSFM to detect cancer and to evaluate if this approach can potentially alleviate the need for conventional histopathological assessment.
Publications
- High-resolution three-dimensional imaging for precise staging in melanoma. Eur J Cancer. 2021 Nov 10;159:182-193
Simon F Merz, Philipp Jansen, Ricarda Ulankiewicz, Lea Bornemann, Tobias Schimming, Klaus Griewank, Zülal Cibir, Andreas Kraus, Ingo Stoffels, Timo Aspelmeier, Sven Brandau, Dirk Schadendorf, Eva Hadaschik, Gernot Ebel, Matthias Gunzer, Joachim Klode
(See online at https://doi.org/10.1016/j.ejca.2021.09.026)
