bims-ovdlit Biomed News
on Ovarian cancer: early diagnosis, liquid biopsy and therapy
Issue of 2024–06–30
two papers selected by
Lara Paracchini, Humanitas Research
  1. Incidence of Peritoneal Cancer Following Oophorectomy among BRCA1 and BRCA2 Mutation Carriers.
  2. Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis.
  1. J Natl Cancer Inst. 2024 Jun 27. pii: djae151. [Epub ahead of print]
    Incidence of Peritoneal Cancer Following Oophorectomy among BRCA1 and BRCA2 Mutation Carriers.
    Steven A Narod, Jacek Gronwald, Beth Karlan, Pal Moller, Tomasz Huzarski, Nadine Tung, Amber Aeilts, Andrea Eisen, Susan Randall Armel, Christian F Singer, William D Foulkes, Susan L Neuhausen, Olufunmilayo Olopade, Tuya Pal, Robert Fruscio, Kelly Metcalfe, Rebecca Raj, Michelle Jacobson, Ping Sun, Jan Lubinski, Joanne Kotsopoulos.
       BACKGROUND: To estimate the incidence of primary peritoneal cancer following preventive bilateral oophorectomy in women with a BRCA1 or BRCA2 mutation.
    METHODS: A total of 6,310 women with a BRCA1 or BRCA2 mutation who underwent a preventive bilateral oophorectomy were followed for a mean of 7.8 years from oophorectomy. The 20-year cumulative incidence of peritoneal cancer post-oophorectomy was estimated using the Kaplan-Meier method. A left-truncated Cox proportional hazard analysis was used to estimate the hazard ratios (HRs) and 95% confidence intervals (CI) associated with the age at oophorectomy, year of oophorectomy, and family history of ovarian cancer as well as hormonal and reproductive risk factors.
    RESULTS: Fifty-five women developed primary peritoneal cancer (n = 45 in BRCA1, 8 in BRCA2, and 2 in women with a mutation in both genes). Their mean age at oophorectomy was 48.9 years. The annual risk of peritoneal cancer was 0.14% for women with a BRCA1 mutation and was 0.06% for women with a BRCA2 mutation. The 20-year cumulative risk of peritoneal cancer from the date of oophorectomy was 2.7% for BRCA1 carriers and was 0.9% for BRCA2 mutation carriers. There were no peritoneal cancers in BRCA1 carriers who had the operation before age 35 or in BRCA2 carriers who had the operation before age 45.
    CONCLUSIONS: For BRCA1 mutation carriers, the annual risk of peritoneal cancer for 20 years post-oophorectomy is 0.14% per year. The risk is lower for BRCA2 carriers (0.06% per year).
    Keywords:   BRCA1 ; BRCA2 ; oophorectomy; ovarian cancer; peritoneal cancer
    DOI:  https://doi.org/10.1093/jnci/djae151
  2. Sci Rep. 2024 06 26. 14(1): 14797
    Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis.
    Minjung Kim, Juntae Park, Seonghee Oh, Byeong-Ho Jeong, Yuree Byun, Sun Hye Shin, Yunjoo Im, Jong Ho Cho, Eun-Hae Cho.
      Detecting aberrant cell-free DNA (cfDNA) methylation is a promising strategy for lung cancer diagnosis. In this study, our aim is to identify methylation markers to distinguish patients with lung cancer from healthy individuals. Additionally, we sought to develop a deep learning model incorporating cfDNA methylation and fragment size profiles. To achieve this, we utilized methylation data collected from The Cancer Genome Atlas and Gene Expression Omnibus databases. Then we generated methylated DNA immunoprecipitation sequencing and genome-wide Enzymatic Methyl-seq (EM-seq) form lung cancer tissue and plasma. Using these data, we selected 366 methylation markers. A targeted EM-seq panel was designed using the selected markers, and 142 lung cancer and 56 healthy samples were produced with the panel. Additionally, cfDNA samples from healthy individuals and lung cancer patients were diluted to evaluate sensitivity. Its lung cancer detection performance reached an accuracy of 81.5% and an area under the receiver operating characteristic curve of 0.87. In the serial dilution experiment, we achieved tumor fraction detection of 1% at 98% specificity and 0.1% at 80% specificity. In conclusion, we successfully developed and validated a combination of methylation panel and a deep learning model that can distinguish between patients with lung cancer and healthy individuals.
    Keywords:  EM-seq; Lung cancer; MeDIP-seq; Methylation; ctDNA
    DOI:  https://doi.org/10.1038/s41598-024-63411-2
This page is being maintained by Thomas Krichel. It was last updated on 2024‒11‒22 at 4:26.