Louis Vermeulen

Peritoneal metastases are a common form of tumor cell dissemination in gastrointestinal malignancies. Peritoneal metastatic disease (PMD) is associated with severe morbidity and resistance to currently employed therapies. Given the distinct route of dissemination compared with distant organ metastases, and the unique microenvironment of the peritoneal cavity, specific tumor cell characteristics are needed for the development of PMD. In this review, we provide an overview of the known histopathological, genomic, and transcriptomic features of PMD. We find that cancers representing the mesenchymal subtype are strongly associated with PMD in various malignancies. Furthermore, we discuss the peritoneal niche in which the metastatic cancer cells reside, including the critical role of the peritoneal immune system. Altogether, we show that PMD should be regarded as a distinct disease entity, that requires tailored treatment strategies.

Intratumor heterogeneity (ITH) is an omnipresent property of cancers and predicts poor survival in most types of cancer. The propensity to metastasize and the regrowth of tumors after therapy are both associated with ITH. Quantification of the level of ITH in a malignancy is hence of great interest, and accurate inference of ITH could guide clinical decision making. However, ITH is an emergent property of billions of cells and requires mathematical modeling for inference from a limited number of measurements. Over the last decade, numerous mathematical and computational models have been introduced to infer ITH from variant‐allele frequencies, copy number variations, or from data of experimental model systems. These quantitative modeling efforts have advanced the understanding of tumor evolution, underlined poor prognosis associated with ITH, and elucidated the importance of functional heterogeneity, that is, cancer stem cells. Yet, a comprehensive overview of the different mathematical models, their underlying assumptions, their limitations, and their strengths is missing. In this Perspective, we highlight the achievements of mathematical modeling and present a framework which allows better understanding of the mathematical models themselves.