The synergism of spatial metabolomics and morphometry improves machine learning‐based renal tumour subtype classification

Tumours of the kidney are a heterogeneous group of various types of cancer with characteristic histologic or genetic features that require tumour type-specific therapies.1 Chromophobe renal cell carcinomas (chRCC) and renal oncocytomas – two tumour types that can sometimes be difficult to distinguish based on morphology alone – are associated with different prognosis, and the former has the potential to progress and metastasize.2,3 Both immunoncological and targeted therapies are investigated; however, immunotyping and genotyping are laborious, fall short of standardization, and immunohistochemical markers have been shown to be unreliable.4,5 We used matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) because one of its greatest strengths is the ability to combine in situ mass spectrometric data with conventional histology or immunohistochemistry, making it a powerful and very useful tool for multiparametric high-dimensional multi-omics analyses.6–10 This potential has also been successfully applied for biomarker discovery and machine learning-based renal tumour subtyping using unique molecular data.11–15 Our study was performed on a large patient cohort (n = 853, Table 1) and on clinically relevant FFPE tissue samples to distinguish clear cell renal cell carcinomas (ccRCC, n = 552), papillary renal cell carcinomas (pRCC, n = 122), chRCC (n = 108) and renal oncocytomas (RO, n = 71). [...]fewer morphometric features are ranked among the top 50 (40%), but they are still beneficial for tumour subtype classification. [...]we propose to utilize the so far unrecognized potential and synergy of computer-aided image analysis and spatial metabolomics – both types of data available in all MSI experiments – to improve AI-based diagnostics and tumour subtyping in general. Schöne C, Höfler H, Walch A. MALDI imaging mass spectrometry in cancer research: combining proteomic profiling and histological evaluation.