bims-ovdlit Biomed News
on Ovarian cancer: early diagnosis, liquid biopsy and therapy
Issue of 2022‒11‒20
seven papers selected by
Lara Paracchini
Humanitas Research

  1. Cancer Cell. 2022 Nov 15. pii: S1535-6108(22)00513-X. [Epub ahead of print]
      In the Circulating Cell-free Genome Atlas (NCT02889978) substudy 1, we evaluate several approaches for a circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED) test by defining clinical limit of detection (LOD) based on circulating tumor allele fraction (cTAF), enabling performance comparisons. Among 10 machine-learning classifiers trained on the same samples and independently validated, when evaluated at 98% specificity, those using whole-genome (WG) methylation, single nucleotide variants with paired white blood cell background removal, and combined scores from classifiers evaluated in this study show the highest cancer signal detection sensitivities. Compared with clinical stage and tumor type, cTAF is a more significant predictor of classifier performance and may more closely reflect tumor biology. Clinical LODs mirror relative sensitivities for all approaches. The WG methylation feature best predicts cancer signal origin. WG methylation is the most promising technology for MCED and informs development of a targeted methylation MCED test.
    Keywords:  CCGA; MCED; cancer screening; cfDNA; multi-cancer early detection; single nucleotide variants; somatic copy number alterations; whole-genome bisulfite sequencing; whole-genome methylation
  2. Int J Gynecol Cancer. 2022 Nov 16. pii: ijgc-2022-003911. [Epub ahead of print]
      OBJECTIVE: Ovarian cancer is known for its poor prognosis, which is mainly due to the lack of early symptoms and adequate screening options. In this study we evaluated whether mutational analysis in cervicovaginal and endometrial samples could assist in the detection of ovarian cancer.METHODS: In this prospective multicenter study, we included patients surgically treated for either (suspicion of) ovarian cancer or for a benign gynecological condition (control group). A cervicovaginal self-sample, a Papanicolaou (Pap) smear, a pipelle endometrial biopsy, and the surgical specimen were analyzed for (potentially) pathogenic variants in eight genes (ARID1A, CTNNB1, KRAS, MTOR, PIK3CA, POLE, PTEN, and TP53) using single-molecule molecular inversion probes. Sensitivity and specificity were calculated to assess diagnostic accuracy.
    RESULTS: Based on surgical histology, our dataset comprised 29 patients with ovarian cancer and 32 controls. In 83% of the patients with ovarian cancer, somatic (potentially) pathogenic variants could be detected in the final surgical specimen, of which 71% included at least a TP53 variant. In 52% of the ovarian cancer patients, such variants could be detected in either the self-sample, Pap smear, or pipelle. The Pap smear yielded the highest diagnostic accuracy with 26% sensitivity (95% CI 10% to 48%). Overall diagnostic accuracy was low and was not improved when including TP53 variants only.
    CONCLUSIONS: Mutational analysis in cervicovaginal and endometrial samples has limited accuracy in the detection of ovarian cancer. Future research with cytologic samples analyzed on methylation status or the vaginal microbiome may be relevant.
    Keywords:  Ovarian Cancer; Pathology
  3. J Biomed Sci. 2022 Nov 14. 29(1): 96
      In the past decade, single-cell technologies have revealed the heterogeneity of the tumor-immune microenvironment at the genomic, transcriptomic, and proteomic levels and have furthered our understanding of the mechanisms of tumor development. Single-cell technologies have also been used to identify potential biomarkers. However, spatial information about the tumor-immune microenvironment such as cell locations and cell-cell interactomes is lost in these approaches. Recently, spatial multi-omics technologies have been used to study transcriptomes, proteomes, and metabolomes of tumor-immune microenvironments in several types of cancer, and the data obtained from these methods has been combined with immunohistochemistry and multiparameter analysis to yield markers of cancer progression. Here, we review numerous cutting-edge spatial 'omics techniques, their application to study of the tumor-immune microenvironment, and remaining technical challenges.
    Keywords:  Heterogeneity; Multi-omics; Spatial; Tumor-immune microenvironment (TIME)
  4. Front Immunol. 2022 ;13 996721
      Interpreting the mechanisms and principles that govern gene activity and how these genes work according to -their cellular distribution in organisms has profound implications for cancer research. The latest technological advancements, such as imaging-based approaches and next-generation single-cell sequencing technologies, have established a platform for spatial transcriptomics to systematically quantify the expression of all or most genes in the entire tumor microenvironment and explore an array of disease milieus, particularly in tumors. Spatial profiling technologies permit the study of transcriptional activity at the spatial or single-cell level. This multidimensional classification of the transcriptomic and proteomic signatures of tumors, especially the associated immune and stromal cells, facilitates evaluation of tumor heterogeneity, details of the evolutionary trajectory of each tumor, and multifaceted interactions between each tumor cell and its microenvironment. Therefore, spatial profiling technologies may provide abundant and high-resolution information required for the description of clinical-related features in immuno-oncology. From this perspective, the present review will highlight the importance of spatial transcriptomic and spatial proteomics analysis along with the joint use of other sequencing technologies and their implications in cancers and immune-oncology. In the near future, advances in spatial profiling technologies will undoubtedly expand our understanding of tumor biology and highlight possible precision therapeutic targets for cancer patients.
    Keywords:  immuno-oncology; proteome; spatial profiling technologies; transcriptome; tumor heterogeneity
  5. Int J Radiat Oncol Biol Phys. 2022 Dec 01. pii: S0360-3016(22)00269-3. [Epub ahead of print]114(5): 833-835
  6. Nature. 2022 Nov 16.
      The tumour-associated microbiota is an intrinsic component of the tumour microenvironment across human cancer types1,2. Intratumoral host-microbiota studies have so far largely relied on bulk tissue analysis1-3, which obscures the spatial distribution and localized effect of the microbiota within tumours. Here, by applying in situ spatial-profiling technologies4 and single-cell RNA sequencing5 to oral squamous cell carcinoma and colorectal cancer, we reveal spatial, cellular and molecular host-microbe interactions. We adapted 10x Visium spatial transcriptomics to determine the identity and in situ location of intratumoral microbial communities within patient tissues. Using GeoMx digital spatial profiling6, we show that bacterial communities populate microniches that are less vascularized, highly immuno‑suppressive and associated with malignant cells with lower levels of Ki-67 as compared to bacteria-negative tumour regions. We developed a single-cell RNA-sequencing method that we name INVADEseq (invasion-adhesion-directed expression sequencing) and, by applying this to patient tumours, identify cell-associated bacteria and the host cells with which they interact, as well as uncovering alterations in transcriptional pathways that are involved in inflammation, metastasis, cell dormancy and DNA repair. Through functional studies, we show that cancer cells that are infected with bacteria invade their surrounding environment as single cells and recruit myeloid cells to bacterial regions. Collectively, our data reveal that the distribution of the microbiota within a tumour is not random; instead, it is highly organized in microniches with immune and epithelial cell functions that promote cancer progression.
  7. Front Public Health. 2022 ;10 1030304
    Keywords:  HPV screening; VIA; cervical cancer; cervical cytology screening test; deep learning; machine learning