Abstract
Pathology differentiation of renal cancer types is challenging due to tissue similarities or overlapping histological features of various tumor (sub)types. As assessment is often manually conducted outcomes can be prone to human error and therefore require high-level expertise and experience. Mass spectrometry can provide detailed histo-molecular information on tissue and is becoming increasingly popular in clinical settings. Spatially resolving technologies such as mass spectrometry imaging and quantitative microproteomics profiling in combination with machine learning approaches provide promising tools for automated tumor classification of clinical tissue sections.
In this proof of concept study we used MALDI-MS imaging (MSI) and rapid LC-MS/MS-based microproteomics technologies (15 min/sample) to analyze formalin-fixed paraffin embedded (FFPE) tissue sections and classify renal oncocytoma (RO, n=11), clear cell renal cell carcinoma (ccRCC, n=12) and chromophobe renal cell carcinoma (ChRCC, n=5). Both methods were able to distinguish ccRCC, RO and ChRCC in cross-validation experiments. MSI correctly classified 87% of the patients whereas the rapid LC-MS/MS-based microproteomics approach correctly classified 100% of the patients.
This strategy involving MSI and rapid proteome profiling by LC-MS/MS reveals molecular features of tumor sections and enables cancer subtype classification. Mass spectrometry provides a promising complementary approach to current pathological technologies for precise digitized diagnosis of diseases.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
text has been rewritten, redundant infromation has been removed. More details have been added to M&M part. - Former Figure 3 has been put into supplementaries - median scores have been added into Figure 3 (formerly Figure 4). - Sample identified as sarcomatoid has been taken out of the classification model --> Classification has been updated. - Example in Figure 5C was run with k=5 (formerly k=6) - to estimate the influence of imputation the classification has alos been run with 100% valid values. Data has been added - Figure 6 was Supplementaries: figure order has been changed, boxplot and table of median fitted score from MSI classification was added. Supplementaries have been updated accordingly.