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ARID1A , encoding a subunit of the SWI/SNF chromatin-remodelling complex, is the most frequently mutated epigenetic regulator across all human cancers. ARID1A and TP53 mutations are typically mutually exclusive. Therapeutic approaches that correlate with this genetic characteristic remain to be explored. Here, we show that HDAC6 activity is essential in ARID1A -mutated ovarian cancers. Inhibition of HDAC6 activity using a clinically applicable small-molecule inhibitor significantly improved the survival of mice bearing ARID1A -mutated tumours. This correlated with the suppression of growth and dissemination of ARID1A -mutated, but not wild-type, tumours. The dependence on HDAC6 activity in ARID1A -mutated cells correlated with a direct transcriptional repression of HDAC6 by ARID1A. HDAC6 inhibition selectively promoted apoptosis of ARID1A -mutated cells. HDAC6 directly deacetylates Lys120 of p53, a pro-apoptotic post-translational modification. Thus, ARID1A mutation inactivates the apoptosis-promoting function of p53 by upregulating HDAC6. Together, these results indicate that pharmacological inhibition of HDAC6 is a therapeutic strategy for ARID1A -mutated cancers. Bitler et al.  show that HDAC6 activity is essential for the survival of ovarian cancer cells carrying loss-of-function ARID1A mutation, thus representing a promising therapeutic target.

ARID1A is a core DNA-binding subunit of the BAF chromatin remodeling complex, and is lost in up to 7% of all cancers. The frequency of ARID1A loss increases in certain cancer types, such as clear cell ovarian carcinoma where ARID1A protein is lost in about 50% of cases. While the impact of ARID1A loss on the function of the BAF chromatin remodeling complexes is likely to drive oncogenic gene expression programs in specific contexts, ARID1A also binds genome stability regulators such as ATR and TOP2. Here we show that ARID1A loss leads to DNA replication stress associated with R-loops and transcription-replication conflicts in human cells. These effects correlate with altered transcription and replication dynamics in ARID1A knockout cells and to reduced TOP2A binding at R-loop sites. Together this work extends mechanisms of replication stress in ARID1A deficient cells with implications for targeting ARID1A deficient cancers.

Ovarian cancer comprises a broad range of histologically and genetically different tumours. In this Opinion article, Karnezis et al . explore the different origins of ovarian cancers and how these contribute to our understanding of genetic and environmental risk to better prevent and treat these tumours. Ovarian cancer is the fifth cause of cancer-related death in women and comprises a histologically and genetically broad range of tumours, including those of epithelial, sex cord-stromal and germ cell origin. Recent evidence indicates that high-grade serous ovarian carcinoma, clear cell carcinoma and endometrioid carcinoma primarily arise from tissues that are not normally present in the ovary. These histogenetic pathways are informing risk-reduction strategies for the prevention of ovarian and ovary-associated cancers and have highlighted the importance of the seemingly unique ovarian microenvironment.

Endometrial cancer (EC) has four molecular subtypes with strong prognostic value and therapeutic implications. The most common subtype (NSMP; No Specific Molecular Profile) is assigned after exclusion of the defining features of the other three molecular subtypes and includes patients with heterogeneous clinical outcomes. In this study, we employ artificial intelligence (AI)-powered histopathology image analysis to differentiate between p53abn and NSMP EC subtypes and consequently identify a sub-group of NSMP EC patients that has markedly inferior progression-free and disease-specific survival (termed ‘p53abn-like NSMP’), in a discovery cohort of 368 patients and two independent validation cohorts of 290 and 614 from other centers. Shallow whole genome sequencing reveals a higher burden of copy number abnormalities in the ‘p53abn-like NSMP’ group compared to NSMP, suggesting that this group is biologically distinct compared to other NSMP ECs. Our work demonstrates the power of AI to detect prognostically different and otherwise unrecognizable subsets of EC where conventional and standard molecular or pathologic criteria fall short, refining image-based tumor classification. This study’s findings are applicable exclusively to females. Endometrial cancer (EC) has four molecular subtypes; of these, the No Specific Molecular Profile (NSMP) subtype encompasses patients with heterogeneous outcomes. Here, the authors use artificial intelligence and histopathology images to differentiate p53abn and NSMP subtypes in EC, and identify one distinct subgroup within NSMP with unfavourable outcome.

Rare cancers, as a collective, account for around a quarter of all cancer diagnoses and deaths. Historically, they have been divided into two groups: cancers defined by their unusual histogenesis (cell of origin or differentiation state)—including chordomas or adult granulosa cell tumours—and histologically defined subtypes of common cancers. Most tumour types in the first group are still clinically and biologically relevant, and have been disproportionately important as sources of insight into cancer biology. By contrast, most of those in the second group have been shown to have neither defining molecular features nor clinical utility. Omics-based analyses have splintered common cancers into a myriad of molecularly, rather than histologically, defined subsets of common cancers, many of which have immediate clinical relevance. Now, almost all rare cancers are either histomolecular entities, which often have pathognomonic mutations, or molecularly defined subsets of more common cancers. The presence of specific genetic variants provides rationale for the testing of targeted drugs in rare cancers. However, in addition to molecular alterations, it is crucial to consider the contributions of both mutation and cell context in the development, biology, and behaviour of these cancers. Patients with rare cancers are disadvantaged because of the challenge of leading clinical trials in this setting due to poor accrual. However, the number of patients with rare cancers will only increase as more molecular subsets of common cancers are identified, necessitating a shift in the focus of clinical trials and research into these cancer types, which, by epidemiological definitions, will become rare tumours.

OVARIAN CARCINOMAS CONSIST OF AT LEAST FIVE DISTINCT DISEASES: high-grade serous, low-grade serous, clear cell, endometrioid, and mucinous. Biomarker and molecular characterization may represent a more biologically relevant basis for grouping and treating this family of tumors, rather than site of origin. Molecular characteristics have become the new standard for clinical pathology, however development of tailored type-specific therapies is hampered by a failure of basic research to recognize that model systems used to study these diseases must also be stratified. Unrelated model systems do offer value for study of biochemical processes but specific cellular context needs to be applied to assess relevant therapeutic strategies. We have focused on the identification of clear cell carcinoma cell line models. A panel of 32 \"ovarian cancer\" cell lines has been classified into histotypes using a combination of mutation profiles, IHC mutation-surrogates, and a validated immunohistochemical model. All cell lines were identity verified using STR analysis. Many described ovarian clear cell lines have characteristic mutations (including ARID1A and PIK3CA) and an overall molecular/immuno-profile typical of primary tumors. Mutations in TP53 were present in the majority of high-grade serous cell lines. Advanced genomic analysis of bona-fide clear cell carcinoma cell lines also support copy number changes in typical biomarkers such at MET and HNF1B and a lack of any recurrent expressed re-arrangements. As with primary ovarian tumors, mutation status of cancer genes like ARID1A and TP53 and a general immuno-profile serve well for establishing histotype of ovarian cancer cell We describe specific biomarkers and molecular features to re-classify generic \"ovarian carcinoma\" cell lines into type specific categories. Our data supports the use of prototype clear cell lines, such as TOV21G and JHOC-5, and questions the use of SKOV3 and A2780 as models of high-grade serous carcinoma.

Sohrab Shah, David Huntsman and colleagues report the genomic analysis of 133 ovarian cancers spanning different subtypes. They identify seven subgroups using point mutation and structural variation signatures and use these genomic features to stratify ovarian cancers both between and within histotypes. 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.

Sohrab Shah, Samuel Aparicio and colleagues analyze whole genomes and single cells from ovarian cancers in the peritoneal cavity to establish patterns of disease spread. They determine the clonal relationships between multiple tumor sites and characterize the migratory potential of genomically diverse clones. 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.

Jeffrey Trent, David Huntsman and colleagues identify the SWI/SNF chromatin-remodeling gene SMARCA4 as commonly mutated in small cell carcinoma of the ovary, hypercalcemic type (SCCOHT). Their results implicate SMARCA4 as a crucial factor in the oncogenesis of SCCOHT, a rare but highly malignant cancer. Small cell carcinoma of the ovary of hypercalcemic type (SCCOHT) is an extremely rare, aggressive cancer affecting children and young women. We identified germline and somatic inactivating mutations in the SWI/SNF chromatin-remodeling gene SMARCA4 in 69% (9/13) of SCCOHT cases in addition to SMARCA4 protein loss in 82% (14/17) of SCCOHT tumors but in only 0.4% (2/485) of other primary ovarian tumors. These data implicate SMARCA4 in SCCOHT oncogenesis.

DICER1 syndrome is a tumor predisposition syndrome that is associated with up to 30 different neoplastic lesions, usually affecting children and adolescents. Here we identify a group of mesenchymal tumors which is highly associated with DICER1 syndrome, and molecularly distinct from other DICER1-associated tumors. This group of DICER1-associated mesenchymal tumors encompasses multiple well-established clinicopathological tumor entities and can be further divided into three clinically meaningful classes designated “low-grade mesenchymal tumor with DICER1 alteration” (LGMT DICER1), “sarcoma with DICER1 alteration” (SARC DICER1), and primary intracranial sarcoma with DICER1 alteration (PIS DICER1). Our study not only provides a combined approach to classify DICER1-associated neoplasms for improved clinical management but also suggests a role for global hypomethylation and other recurrent molecular events in sarcomatous differentiation in mesenchymal tumors with DICER1 alteration. Our results will facilitate future investigations into prognostication and therapeutic approaches for affected patients. DICER1 syndrome is associated with a predisposition to multiple tumor types. Here, the authors identify and characterize 3 molecular subgroups of mesenchymal tumors with DICER1 mutations.