Genomic alterations in gynecological malignancies: histotype-associated driver mutations, molecular subtyping schemes, and tumorigenic mechanisms

There are numerous histological subtypes (histotypes) of gynecological malignancies, with each histotype considered to largely reflect a feature of the cell of origin, and to be tightly linked with the clinical behavior and biological phenotype of the tumor. The recent advances in massive parallel sequencing technologies have provided a more complete picture of the range of the genomic alterations that can persist within individual tumors, and have highlighted the types and frequencies of driver-gene mutations and molecular subtypes often associated with these histotypes. Several large-scale genomic cohorts, including the Cancer Genome Atlas (TCGA), have been used to characterize the genomic features of a range of gynecological malignancies, including high-grade serous ovarian carcinoma, uterine corpus endometrial carcinoma, uterine cervical carcinoma, and uterine carcinosarcoma. These datasets have also been pivotal in identifying clinically relevant molecular targets and biomarkers, and in the construction of molecular subtyping schemes. In addition, the recent widespread use of clinical sequencing for the more ubiquitous types of gynecological cancer has manifested in a series of large genomic datasets that have allowed the characterization of the genomes, driver mutations, and histotypes of even rare cancer types, with sufficient statistical power. Here, we review the field of gynecological cancer, and seek to describe the genomic features by histotype. We also will demonstrate how these are linked with clinicopathological attributes and highlight the potential tumorigenic mechanisms.

Automated summary

Gynecological malignancies have various histological subtypes, each reflecting the cell of origin and closely linked to clinical behavior and biological phenotype of the tumor. Advances in massive parallel sequencing have provided a comprehensive view of genomic alterations in individual tumors, highlighting driver-gene mutations and molecular subtypes associated with specific histotypes. Large genomic datasets, such as the Cancer Genome Atlas, have been crucial in identifying clinically relevant targets and biomarkers, and in characterizing genomes, driver mutations, and histotypes of even rare cancer types.