Background:Classification of endometrial carcinomas (ECs) by morphologic features is inconsistent, and yields limited prognostic and predictive information. A new system for classification based on the molecular categories identified in The Cancer Genome Atlas is proposed.Methods:Genomic data from the Cancer Genome Atlas (TCGA) support classification of endometrial carcinomas into four prognostically significant subgroups; we used the TCGA data set to develop surrogate assays that could replicate the TCGA classification, but without the need for the labor-intensive and cost-prohibitive genomic methodology. Combinations of the most relevant assays were carried forward and tested on a new independent cohort of 152 endometrial carcinoma cases, and molecular vs clinical risk group stratification was compared.Results:Replication of TCGA survival curves was achieved with statistical significance using multiple different molecular classification models (16 total tested). Internal validation supported carrying forward a classifier based on the following components: mismatch repair protein immunohistochemistry, POLE mutational analysis and p53 immunohistochemistry as a surrogate for ‘copy-number' status. The proposed molecular classifier was associated with clinical outcomes, as was stage, grade, lymph-vascular space invasion, nodal involvement and adjuvant treatment. In multivariable analysis both molecular classification and clinical risk groups were associated with outcomes, but differed greatly in composition of cases within each category, with half of POLE and mismatch repair loss subgroups residing within the clinically defined ‘high-risk' group. Combining the molecular classifier with clinicopathologic features or risk groups provided the highest C-index for discrimination of outcome survival curves.Conclusions:Molecular classification of ECs can be achieved using clinically applicable methods on formalin-fixed paraffin-embedded samples, and provides independent prognostic information beyond established risk factors. This pragmatic molecular classification tool has potential to be used routinely in guiding treatment for individuals with endometrial carcinoma and in stratifying cases in future clinical trials.

Molecular classification of ECs can be achieved using clinically applicable methods and provides independent prognostic information beyond established clinicopathologic risk factors available at diagnosis. Consistent, biologically relevant categorization enables stratification for clinical trials and/or targeted therapy, identification of women who are at increased risk of having Lynch syndrome, and may guide clinical management. Cancer 2017;123:802-13. © 2016 American Cancer Society.

We have developed, confirmed, and now validated a pragmatic molecular classification tool (ProMisE) that provides consistent categorization of tumors and identifies four distinct prognostic molecular subtypes. ProMisE can be applied to diagnostic samples and thus could be used to inform surgical procedure(s) and/or need for adjuvant therapy. Based on the IOM guidelines this classifier is now ready for clinical evaluation through prospective clinical trials.

To identify common alleles associated with different histotypes of epithelial ovarian cancer (EOC), we pooled data from multiple genome-wide genotyping projects totaling 25,509 EOC cases and 40,941 controls. We identified nine new susceptibility loci for different EOC histotypes: six for serous EOC histotypes (3q28, 4q32.3, 8q21.11, 10q24.33, 18q11.2 and 22q12.1), two for mucinous EOC (3q22.3, 9q31.1) and one for endometrioid EOC (5q12.3). We then meta-analysed the results for high-grade serous ovarian cancer with the results from analysis of 31,448 BRCA1 and BRCA2 mutation carriers, including 3,887 mutation carriers with EOC. This identified an additional three loci at 2q13, 8q24.1 and 12q24.31. Integrated analyses of genes and regulatory biofeatures at each locus predicted candidate susceptibility genes, including OBFC1, a novel susceptibility gene for low grade/borderline serous EOC.

High-grade serous ovarian cancer (HGSC) is characterized by poor outcome, often attributed to the emergence of treatment-resistant subclones. We sought to measure the degree of genomic diversity within primary, untreated HGSCs to examine the natural state of tumour evolution prior to therapy. We performed exome sequencing, copy number analysis, targeted amplicon deep sequencing and gene expression profiling on 31 spatially and temporally separated HGSC tumour specimens (six patients), including ovarian masses, distant metastases and fallopian tube lesions. We found widespread intratumoural variation in mutation, copy number and gene expression profiles, with key driver alterations in genes present in only a subset of samples (eg PIK3CA, CTNNB1, NF1). On average, only 51.5% of mutations were present in every sample of a given case (range 10.2–91.4%), with TP53 as the only somatic mutation consistently present in all samples. Complex segmental aneuploidies, such as whole-genome doubling, were present in a subset of samples from the same individual, with divergent copy number changes segregating independently of point mutation acquisition. Reconstruction of evolutionary histories showed one patient with mixed HGSC and endometrioid histology, with common aetiologic origin in the fallopian tube and subsequent selection of different driver mutations in the histologically distinct samples. In this patient, we observed mixed cell populations in the early fallopian tube lesion, indicating that diversity arises at early stages of tumourigenesis. Our results revealed that HGSCs exhibit highly individual evolutionary trajectories and diverse genomic tapestries prior to therapy, exposing an essential biological characteristic to inform future design of personalized therapeutic solutions and investigation of drug-resistance mechanisms.

The evolution of cancer genomes within a single tumor creates mixed cell populations with divergent somatic mutational landscapes. Inference of tumor subpopulations has been disproportionately focused on the assessment of somatic point mutations, whereas computational methods targeting evolutionary dynamics of copy number alterations (CNA) and loss of heterozygosity (LOH) in whole-genome sequencing data remain underdeveloped. We present a novel probabilistic model, TITAN, to infer CNA and LOH events while accounting for mixtures of cell populations, thereby estimating the proportion of cells harboring each event. We evaluate TITAN on idealized mixtures, simulating clonal populations from whole-genome sequences taken from genomically heterogeneous ovarian tumor sites collected from the same patient. In addition, we show in 23 whole genomes of breast tumors that the inference of CNA and LOH using TITAN critically informs population structure and the nature of the evolving cancer genome. Finally, we experimentally validated subclonal predictions using fluorescence in situ hybridization (FISH) and single-cell sequencing from an ovarian cancer patient sample, thereby recapitulating the key modeling assumptions of TITAN.

We performed phylogenetic analysis of high-grade serous ovarian cancers (68 samples from seven patients), identifying constituent clones and quantifying their relative abundances at multiple intraperitoneal sites. Through whole-genome and single-nucleus sequencing, we identified evolutionary features including mutation loss, convergence of the structural genome and temporal activation of mutational processes that patterned clonal progression. We then determined the precise clonal mixtures comprising each tumor sample. The majority of sites were clonally pure or composed of clones from a single phylogenetic clade. However, each patient contained at least one site composed of polyphyletic clones. Five patients exhibited monoclonal and unidirectional seeding from the ovary to intraperitoneal sites, and two patients demonstrated polyclonal spread and reseeding. Our findings indicate that at least two distinct modes of intraperitoneal spread operate in clonal dissemination and highlight the distribution of migratory potential over clonal populations comprising high-grade serous ovarian cancers.

We studied the whole-genome point mutation and structural variation patterns of 133 tumors (59 high-grade serous (HGSC), 35 clear cell (CCOC), 29 endometrioid (ENOC), and 10 adult granulosa cell (GCT)) as a substrate for class discovery in ovarian cancer. Ab initio clustering of integrated point mutation and structural variation signatures identified seven subgroups both between and within histotypes. Prevalence of foldback inversions identified a prognostically significant HGSC group associated with inferior survival. This finding was recapitulated in two independent cohorts (n = 576 cases), transcending BRCA1 and BRCA2 mutation and gene expression features of HGSC. CCOC cancers grouped according to APOBEC deamination (26%) and age-related mutational signatures (40%). ENOCs were divided by cases with microsatellite instability (28%), with a distinct mismatch-repair mutation signature. Taken together, our work establishes the potency of the somatic genome, reflective of diverse DNA repair deficiencies, to stratify ovarian cancers into distinct biological strata within the major histotypes.