Cancer tissue classification using supervised machine learning applied to maldi mass spectrometry imaging

Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) can determine the spatial distribution of analytes such as protein distributions in a tissue section according to their mass-to-charge ratio. Here, we explored the clinical potential of machine learning (ML) applied to MALDI MSI data for cancer diagnostic classification using tissue microarrays (TMAs) on 302 colorectal (CRC) and 257 endometrial cancer (EC)) patients. ML based on deep neural networks discriminated colorectal tumour from normal tissue with an overall accuracy of 98% in balanced cross-validation (98.2% sensitivity and 98.6% specificity). Moreover, our machine learning approach predicted the presence of lymph node metastasis (LNM) for primary tumours of EC with an accuracy of 80% (90% sensitivity and 69% specificity). Our results demonstrate the capability of MALDI MSI for complementing classic histopathological examination for cancer diagnostic applications.Paul Mittal, Mark R. Condina, Manuela Klingler-Hoffmann, Gurjeet Kaur, Martin K. Oehler, Oliver M. Siebe

BRAF V600E Mutant Colorectal Cancer Subtypes Based on Gene Expression.

Mutation of BRAF at the valine 600 residue occurs in approximately 10% of colorectal cancers, a group with particularly poor prognosis. The response of BRAF mutant colorectal cancer to recent targeted strategies such as anti-BRAF or combinations with MEK and EGFR inhibitors remains limited and highly heterogeneous within BRAF V600E cohorts. There is clearly an unmet need in understanding the biology of BRAF V600E colorectal cancers and potential subgroups within this population. In the biggest yet reported cohort of 218 BRAF V600E with gene expression data, we performed unsupervised clustering using non-negative matrix factorization to identify gene expression-based subgroups and characterized pathway activation. We found strong support for a split into two distinct groups, called BM1 and BM2. These subtypes are independent of MSI status, PI3K mutation, gender, and sidedness. Pathway analyses revealed that BM1 is characterized by KRAS/AKT pathway activation, mTOR/4EBP deregulation, and EMT whereas BM2 displays important deregulation of the cell cycle. Proteomics data validated these observations as BM1 is characterized by high phosphorylation levels of AKT and 4EBP1, and BM2 patients display high CDK1 and low cyclin D1 levels. We provide a global assessment of gene expression motifs that differentiate BRAF V600E subtypes from other colorectal cancers. We suggest that BRAF mutant patients should not be considered as having a unique biology and provide an in depth characterization of heterogeneous motifs that may be exploited for drug targeting. Clin Cancer Res; 23(1); 104-15. ©2016 AACR