bims-ovdlit Biomed News
on Ovarian cancer: early diagnosis, liquid biopsy and therapy
Issue of 2021‒07‒04
nine papers selected by
Lara Paracchini
Humanitas Research

  1. J Pers Med. 2021 Jun 28. pii: 612. [Epub ahead of print]11(7):
      DNA double-strand breaks foster tumorigenesis and cell death. Two distinct mechanisms can be activated by the cell for DNA repair: the accurate mechanism of homologous recombination repair or the error-prone non-homologous end joining. Homologous Recombination Deficiency (HRD) is associated with sensitivity towards PARP inhibitors (PARPi) and its determination is used as a biomarker for therapy decision making. Nevertheless, the biology of HRD is rather complex and the application, as well as the benefit of the different HRD biomarker assays, is controversial. Acquiring knowledge of the underlying molecular mechanisms is the main prerequisite for integration of new biomarker tests. This study presents an overview of the major DNA repair mechanisms and defines the concepts of HRR, HRD and BRCAness. Moreover, currently available biomarker assays are described and discussed with respect to their application for routine clinical diagnostics. Since patient stratification for efficient PARP inhibitor therapy requires determination of the BRCA mutation status and genomic instability, both should be established comprehensively. For this purpose, a broad spectrum of distinct assays to determine such combined HRD scores is already available. Nevertheless, all tests require careful validation using clinical samples to meet the criteria for their establishment in clinical testing.
    Keywords:  BRCA; BRCAness; DNA double-strand break; HRD score; PARPi; homologous recombination
  2. JAMA Netw Open. 2021 Jun 01. 4(6): e2114162
      Importance: Tailoring therapeutic regimens to individual patients with ovarian cancer is informed by severity of disease using a variety of clinicopathologic indicators. Although DNA repair variations are increasingly used for therapy selection in ovarian cancer, molecular features are not widely used for general assessment of patient prognosis and disease severity.Objective: To distill a highly dynamic characteristic, signature of copy number variations (CNV), into a risk score that could be easily validated analytically or repurposed for use given existing US Food and Drug Administration (FDA)-approved multigene assays.
    Design, Setting, and Participants: This genetic association study used the Cancer Genome Atlas Ovarian Cancer database to assess for genome-wide survival associations agnostic to gene function. Regions enriched for significant associations were compared to associations from scrambled data. CNV associations were condensed into a risk score, which was internally validated using bootstrapping. The participants were patients with serous ovarian cancer (stages I-IV) diagnosed from 1992 to 2013. Statistical analysis was performed from April to July 2020.
    Main Outcomes and Measures: Overall survival (OS).
    Results: Among 564 patients with serous ovarian cancer, the mean (SD) age was 59.7 (11.5) years; 34 (6%) identified as Black or African American. A total of 13 genome regions, comprising 14 alterations, were identified as significantly risk associated. Composite risk score was independent of total CNV burden, total mutational burden, BRCA status, and open-source genome-wide DNA repair deficiency signatures. Binned terciles yielded high-, standard-, and low-risk groups with respective median OS estimates of 2.9 (95% CI, 2.3-3.2) years, 4.1 (95% CI, 3.7-4.8) years, and 5.7 (95% CI, 4.7-7.4) years, respectively (P < .001). Associated 5-year survival estimates in each tercile were 15% (95% CI, 10%-22%), 36% (95% CI, 29%-46%), and 53% (95% CI, 45%-62%). The risk score had more discriminatory ability to prognosticate OS than age, clinical stage, grade, and race combined, and was strongly additive to significant clinical features (P < .001). Simulated adaptation of FDA-approved assays showed similar performance. Gene ontology analyses of identified regions showed an enrichment for regulatory miRNAs and protein kinase regulators.
    Conclusions and Relevance: This study found that a CNV-based risk score is independent to and stronger than current or near-future ovarian cancer genomic biomarkers to prognosticate OS. CNV regions identified were not strongly associated with canonical ovarian cancer biological pathways, identifying candidates for future mechanistic investigations. External validation of the CNV risk score, especially in concert with more extensive clinical features, could be pursued via existing FDA-approved assays.
  3. Int J Cancer. 2021 Jul 02.
      Ovarian cancer therapy has remained fundamentally unchanged for 50 years, with surgery and chemotherapy still the frontline treatments. Typically asymptomatic until advanced stages, ovarian cancer is known as 'the silent killer'. Consequently, it has one of the worst 5-year survival rates, as low as 30%. The most frequent driver mutations are found in well-defined tumor suppressors, such as p53 and BRCA1/2. In recent years it has become clear that, like the majority of other cancers, many epigenetic regulators are altered in ovarian cancer, including EZH2, SMARCA2/4 and ARID1A. Disruption of epigenetic regulators often leads to loss of transcriptional control, aberrant cell fate trajectories and disruption of senescence, apoptotic and proliferation pathways. These mitotically-inherited epigenetic alterations are particularly promising targets for therapy as they are largely reversible. Consequently, many drugs targeting chromatin modifiers and other epigenetic regulators are at various stages of clinical trials for other cancers. Understanding the mechanisms by which ovarian cancer-specific epigenetic processes are disrupted in patients can allow for informed targeting of epigenetic pathways tailored for each patient. In recent years, there have been groundbreaking new advances in disease modelling through ovarian cancer organoids; these models, alongside single-cell transcriptomic and epigenomic technologies, allow the elucidation of the epigenetic pathways deregulated in ovarian cancer. As a result, ovarian cancer therapy may finally be ready to advance to next-generation treatments. Here, we review the major developments in ovarian cancer, including genetics, model systems and technologies available for their study and the implications of applying epigenetic therapies to ovarian cancer. This article is protected by copyright. All rights reserved.
    Keywords:  Ovarian cancer; chromatin remodeling; disease modelling; epigenetic drugs; precision oncology
  4. Nat Cancer. 2021 Jun;2(6): 598-610
      DNA polymerase theta (POLθ) is synthetic lethal with Homologous Recombination (HR) deficiency and thus a candidate target for HR-deficient cancers. Through high-throughput small molecule screens we identified the antibiotic Novobiocin (NVB) as a specific POLθ inhibitor that selectively kills HR-deficient tumor cells in vitro and in vivo. NVB directly binds to the POLθ ATPase domain, inhibits its ATPase activity, and phenocopies POLθ depletion. NVB kills HR-deficient breast and ovarian tumors in GEMM, xenograft and PDX models. Increased POLθ levels predict NVB sensitivity, and BRCA-deficient tumor cells with acquired resistance to PARP inhibitors (PARPi) are sensitive to NVB in vitro and in vivo. Mechanistically, NVB-mediated cell death in PARPi-resistant cells arises from increased double-strand break end resection, leading to accumulation of single-strand DNA intermediates and non-functional RAD51 foci. Our results demonstrate that NVB may be useful alone or in combination with PARPi in treating HR-deficient tumors, including those with acquired PARPi resistance. (151/150).
    Keywords:  Fanconi Anemia; HRD cancer; Homologous Recombination; MMEJ; Novobiocin; PARP inhibitor resistance; Polymerase theta (POLθ)
  5. J Pers Med. 2021 Jun 11. pii: 546. [Epub ahead of print]11(6):
      The microbial colonization of the lower female reproductive tract has been extensively studied over the past few decades. In contrast, the upper female reproductive tract including the uterine cavity and peritoneum where the ovaries and fallopian tubes reside were traditionally assumed to be sterile under non-pathologic conditions. However, recent studies applying next-generation sequencing of the bacterial 16S ribosomal RNA gene have provided convincing evidence for the existence of an upper female reproductive tract microbiome. While the vaginal microbiome and its importance for reproductive health outcomes has been extensively studied, the microbiome of the upper female reproductive tract and its relevance for gynecologic cancers has been less studied and will be the focus of this article. This targeted review summarizes the pertinent literature on the female reproductive tract microbiome in gynecologic malignancies and its anticipated role in future research and clinical applications in personalized medicine.
    Keywords:  endometrial cancer; female reproductive tract microbiome; gynecologic cancer; ovarian cancer; upper reproductive tract microbiome; uterine microbiome; vaginal microbiome
  6. Front Oncol. 2021 ;11 689829
      PARP inhibitors (PARPi) have shown promising clinical results and have revolutionized the landscape of ovarian cancer management in the last few years. While the core mechanism of action of these drugs has been largely analyzed, the interaction between PARP inhibitors and the microenvironment has been scarcely researched so far. Recent data shows a variety of mechanism through which PARPi might influence the tumor microenvironment and especially the immune system response, that might even partly be the reason behind PARPi efficacy. One of many pathways that are affected is the cGAS-cGAMP-STING; the upregulation of STING (stimulator of interferon genes), produces more Interferon ϒ and pro inflammatory cytokines, thus increasing intratumoral CD4+ and CD8+ T cells. Upregulation of immune checkpoints such as PD1-PDL1 has also been observed. Another interesting mechanism of interaction between PARPi and microenvironment is the ability of PARPi to kill hypoxic cells, as these cells show an intrinsic reduction in the expression and function of the proteins involved in HR. This process has been defined "contextual synthetic lethality". Despite ovarian cancer having always been considered a poor responder to immune therapy, data is now shedding a new light on the matter. First, OC is much more heterogenous than previously thought, therefore it is fundamental to select predictive biomarkers for target therapies. While single agent therapies have not yielded significant results on the long term, influencing the immune system and the tumor microenvironment via the concomitant use of PARPi and other target therapies might be a more successful approach.
    Keywords:  PARP inhibitors; immune checkpoint inhibitors; immune system response; ovarian cancer; tumor microenvironment
  7. Int J Mol Sci. 2021 Jun 18. pii: 6532. [Epub ahead of print]22(12):
      Ovarian cancer response to immunotherapy is limited; however, the evaluation of sensitive/resistant target treatment subpopulations based on stratification by tumor biomarkers may improve the predictiveness of response to immunotherapy. These markers include tumor mutation burden, PD-L1, tumor-infiltrating lymphocytes, homologous recombination deficiency, and neoantigen intratumoral heterogeneity. Future directions in the treatment of ovarian cancer include the utilization of these biomarkers to select ideal candidates. This paper reviews the role of immunotherapy in ovarian cancer as well as novel therapeutics and study designs involving tumor biomarkers that increase the likelihood of success with immunotherapy in ovarian cancer.
    Keywords:  biomarker; immunotherapy; ovarian cancer
  8. Pharmaceuticals (Basel). 2021 Jun 21. pii: 596. [Epub ahead of print]14(6):
      In the era of precision medicine, it is crucial to identify molecular alterations that will guide the therapeutic management of patients. In this context, circulating tumoral DNA (ctDNA) released by the tumor in body fluids, like blood, and carrying its molecular characteristics is becoming a powerful biomarker for non-invasive detection and monitoring of cancer. Major recent technological advances, especially in terms of sequencing, have made possible its analysis, the challenge still being its reliable early detection. Different parameters, from the pre-analytical phase to the choice of sequencing technology and bioinformatic tools can influence the sensitivity of ctDNA detection.
    Keywords:  bioinformatics; cell-free DNA; circulating tumoral DNA; sequencing technologies
  9. Ann Oncol. 2021 Jun 23. pii: S0923-7534(21)02046-9. [Epub ahead of print]
      BACKGROUND: A multi-cancer early detection (MCED) test used to complement existing screening could increase the number of cancers detected through population screening, potentially improving clinical outcomes. The Circulating Cell-free Genome Atlas study (CCGA; NCT02889978) was a prospective, case-controlled, observational study and demonstrated that a blood-based MCED test utilizing cell-free DNA (cfDNA) sequencing in combination with machine learning could detect cancer signals across multiple cancer types and predict cancer signal origin (CSO) with high accuracy. The objective of this third and final CCGA substudy was to validate an MCED test version further refined for use as a screening tool.PATIENTS AND METHODS: This pre-specified substudy included 4077 participants in an independent validation set (cancer: n = 2823; non-cancer: n = 1254, non-cancer status confirmed at year-one follow-up). Specificity, sensitivity, and CSO prediction accuracy were measured.
    RESULTS: Specificity for cancer signal detection was 99.5% [95% confidence interval (CI): 99.0% to 99.8%]. Overall sensitivity for cancer signal detection was 51.5% (49.6% to 53.3%); sensitivity increased with stage [stage I: 16.8% (14.5% to 19.5%), stage II: 40.4% (36.8% to 44.1%), stage III: 77.0% (73.4% to 80.3%), stage IV: 90.1% (87.5% to 92.2%)]. Stage I-III sensitivity was 67.6% (64.4% to 70.6%) in 12 pre-specified cancers that account for approximately two-thirds of annual USA cancer deaths and was 40.7% (38.7% to 42.9%) in all cancers. Cancer signals were detected across >50 cancer types. Overall accuracy of CSO prediction in true positives was 88.7% (87.0% to 90.2%).
    CONCLUSION: In this pre-specified, large-scale, clinical validation substudy, the MCED test demonstrated high specificity and accuracy of CSO prediction and detected cancer signals across a wide diversity of cancers. These results support the feasibility of this blood-based MCED test as a complement to existing single-cancer screening tests.
    Keywords:  cancer; cell-free nucleic acids; liquid biopsy; machine learning; methylation; multi-cancer early detection