Mass spectrometry and machine learning for classification and molecular phenotyping of renal cell carcinoma and benign tumors

Pathology classification of cancer tumor subtypes and benign tumors can be difficult due to cellular heterogeneity and similar morphological features. Renal Cell Cancer (RCC) poses a major challenge for uropathologists as advanced RCC is often incidentally diagnosed with complex histological characteristics. We hypothesised that molecular profiling of RCC and benign tumor specimen by mass spectrometry with machine learning methods enable accurate tumor classification and identification of candidate RCC biomarkers. Mass spectrometry imaging and quantitative proteomics provided peptide and protein profiles of RCC neoplasms and benign tumors presented in tissue microarray (TMA) format of 541 tissue cores from 128 patients. Machine learning models classified RCC subtypes: Clear cell RCC (ccRCC), Chromophobe RCC (chRCC), papillary RCC (pRCC), and oncocytoma (OC). Known and novel candidate protein biomarker panels for discrimination of RCC subtypes were identified. RCC peptide profiles and machine learning classified ccRCC, chRCC, pRCC, OC and healthy tissue with 95.6% - 100% accuracy at the patient level. Quantitative proteomics identified over 5000 proteins and machine learning classified ccRCC, chRCC, pRCC, OC and healthy tissue with 98.6% accuracy. We report new candidate protein biomarkers for ccRCC and pRCC and distinguish histologically similar subtypes OC (benign) and chRCC (malignant), as demonstrated by mass spectrometry-based immunohistochemistry. Candidate prognostic biomarkers for ccRCC contribute to risk of relapse (CDK6, CDK18) and risk of metastasis (DDB2, MK03). Digital histo-molecular differentiation of cancer tumor subtypes and benign tumors by mass spectrometry and macbine learning enables retrospective and prospective studies. Identified candidate protein biomarkers complement existing protocols in tumor pathology.