Histomorphological and molecular profiling: friends not foes! Morpho‐molecular analysis reveals agreement between histological and molecular profiling

Abstract

Aims Whereas current cancer diagnosis largely relies on the well‐established organ and tissue typing of tumours, partially complemented by molecular properties, the comprehensive molecular profiling efforts of recent years have stimulated proposals for molecular reclassifications of tumours independently of anatomical origin. Proposals based only on mutational profiles show the least concordance with histotypes, whereas greater concordance is achieved when various genomic and proteomic data are included. Methods and results The most comprehensive molecular reclassification of tumours, by Hoadley et al (Cell, 158, 2014; 929) and Hoadley et al (Cell, 173, 2018; 291), integrated multi‐omics data, and proposes novel molecular tumour classes. To investigate the relationship between the proposed molecular classes and the original histological tumour types, we re‐examined the histomorphology of molecularly reclassified cases. Our results show that the claimed molecular reclassification is associated with and explainable by specific histological subtypes in 70% of the reclassified cases. Conclusion Therefore, in contrast to the proclaimed reclassification and independence of molecular and histological tumour types, our analysis demonstrates that comprehensive molecular profiling, which includes gene expression and methylation as well as proteomic profiling in addition to mutational analyses, is largely consistent with histomorphological tumour properties.

Lobular Breast Cancer: Histomorphology and Different Concepts of a Special Spectrum of Tumors

“…Modern tumor classifications are based on histomorphology enriched with molecular analyses that confirmed the taxonomic network of tumor entities established by histomorphology [1,2]. Clinical "basket trials" with multiple cancer types have shown that histological entities determine the clinical behavior of tumors to a degree not predicted by preclinical models [3].…”

Analysing cerebrospinal fluid with explainable deep learning: From diagnostics to insights

“…Moreover, pathology and medicine, in general, are characterised by “long tail” distributions of diagnoses, that is, only a few disease entities make up the majority of the cases and a plethora of differential diagnoses exist that are very rare. Most current alleged “clinical grade” AI approaches perform well for frequent diseases, but are unable to properly classify rare cases [12]. Furthermore, the clinical value of algorithms is limited by oversimplification and reducing the complexity of the pathologists' tasks by incorporating several layers of information into the decision‐making and diagnostic labelling of samples [13].…”