bims-tumhet 2024-05-26 papers

Nat Commun. 2024 May 18. 15(1): 4253

Histopathologic image-based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer.

Platinum-based chemotherapy is the cornerstone treatment for female high-grade serous ovarian carcinoma (HGSOC), but choosing an appropriate treatment for patients hinges on their responsiveness to it. Currently, no available biomarkers can promptly predict responses to platinum-based treatment. Therefore, we developed the Pathologic Risk Classifier for HGSOC (PathoRiCH), a histopathologic image-based classifier. PathoRiCH was trained on an in-house cohort (n = 394) and validated on two independent external cohorts (n = 284 and n = 136). The PathoRiCH-predicted favorable and poor response groups show significantly different platinum-free intervals in all three cohorts. Combining PathoRiCH with molecular biomarkers provides an even more powerful tool for the risk stratification of patients. The decisions of PathoRiCH are explained through visualization and a transcriptomic analysis, which bolster the reliability of our model's decisions. PathoRiCH exhibits better predictive performance than current molecular biomarkers. PathoRiCH will provide a solid foundation for developing an innovative tool to transform the current diagnostic pipeline for HGSOC.

DOI: https://doi.org/10.1038/s41467-024-48667-6

J Midlife Health. 2024 Jan-Mar;15(1):15(1): 3-4

Enigma of Ovarian Cancer - Early detection Challenges and Solutions!

Yashodhara Pradeep.

DOI: https://doi.org/10.4103/jmh.jmh_52_24

Cancer Control. 2024 Jan-Dec;31:31 10732748241255548

Ovarian Cancer Diagnosis and Prognosis Based on Cell-Free DNA Methylation.

Yajuan Gao, Nanyang Zhou, Jie Liu.

Background: Ovarian cancer stands as the deadliest malignant tumor within the female reproductive tract. As a result of the absence of effective diagnostic and monitoring markers, 75% of ovarian cancer cases are diagnosed at a late stage, leading to a mere 50% survival rate within five years. The advancement of molecular biology is essential for accurate diagnosis and treatment of ovarian cancer. Methods: A review of several randomized clinical trials, focusing on the ovarian cancer, was undertaken. The advancement of molecular biology and diagnostic methods related to accurate diagnosis and treatment of ovarian cancer were examined. Results: Liquid biopsy is an innovative method of detecting malignant tumors that has gained increasing attention over the past few years. Cell-free DNA assay-based liquid biopsies show potential in delineating tumor status heterogeneity and tracking tumor recurrence. DNA methylation influences a multitude of biological functions and diseases, especially during the initial phases of cancer. The cell-free DNA methylation profiling system has emerged as a sensitive and non-invasive technique for identifying and detecting the biological origins of cancer. It holds promise as a biomarker, enabling early screening, recurrence monitoring, and prognostic evaluation of cancer. Conclusions: This review evaluates recent advancements and challenges associated with cell-free DNA methylation analysis for the diagnosis, prognosis monitoring, and assessment of therapeutic responses in the management of ovarian cancers, aiming to offer guidance for precise diagnosis and treatment of this disease.

Keywords: DNA methylation; cell-free DNA; diagnosis; ovarian cancer; prognosis surveillance; response to therapy

DOI: https://doi.org/10.1177/10732748241255548

Int J Cancer. 2024 May 20.

Fragmentomics features of ovarian cancer.

Xiaopei Chao, Zhentian Kai, Huanwen Wu, Jing Wang, Xiaojing Chen, Haiqi Su, Xiao Shang, Ruijue Lin, Lisha Huang, Hongsheng He, Jinghe Lang, Lei Li.

Ovarian cancer (OC) is a major cause of cancer mortality in women worldwide. Due to the occult onset of OC, its nonspecific clinical symptoms in the early phase, and a lack of effective early diagnostic tools, most OC patients are diagnosed at an advanced stage. In this study, shallow whole-genome sequencing was utilized to characterize fragmentomics features of circulating tumor DNA (ctDNA) in OC patients. By applying a machine learning model, multiclass fragmentomics data achieved a mean area under the curve (AUC) of 0.97 (95% CI 0.962-0.976) for diagnosing OC. OC scores derived from this model strongly correlated with the disease stage. Further comparative analysis of OC scores illustrated that the fragmentomics-based technology provided additional clinical benefits over the traditional serum biomarkers cancer antigen 125 (CA125) and the Risk of Ovarian Malignancy Algorithm (ROMA) index. In conclusion, fragmentomics features in ctDNA are potential biomarkers for the accurate diagnosis of OC.

Keywords: differentiating; fragmentomics; machine learning; ovarian cancer; screening

DOI: https://doi.org/10.1002/ijc.34981

Int J Mol Sci. 2024 May 13. pii: 5282. [Epub ahead of print]25(10):

Special Issue "Ovarian Cancer: Advances on Pathophysiology and Therapies".

Giovanni Tossetta, Annalisa Inversetti.

Ovarian cancer is a gynecologic cancer with a high mortality rate, and its incidence has increased significantly over the past 50 years [...].

DOI: https://doi.org/10.3390/ijms25105282