bims-ovdlit Biomed News
on Ovarian cancer: early diagnosis, liquid biopsy and therapy
Issue of 2024–01–28
four papers selected by
Lara Paracchini, Humanitas Research
  1. FinaleMe: Predicting DNA methylation by the fragmentation patterns of plasma cell-free DNA.
  2. Machine learning to detect the SINEs of cancer.
  3. Plasma Cell-Free Tumor Methylome as a Biomarker in Solid Tumors: Biology and Applications.
  4. Deploying blood-based cancer screening.
  1. bioRxiv. 2024 Jan 02. pii: 2024.01.02.573710. [Epub ahead of print]
    FinaleMe: Predicting DNA methylation by the fragmentation patterns of plasma cell-free DNA.
    Yaping Liu, Sarah C Reed, Christopher Lo, Atish D Choudhury, Heather A Parsons, Daniel G Stover, Gavin Ha, Gregory Gydush, Justin Rhoades, Denisse Rotem, Samuel Freeman, David Katz, Ravi Bandaru, Haizi Zheng, Hailu Fu, Viktor A Adalsteinsson, Manolis Kellis.
      Analysis of DNA methylation in cell-free DNA (cfDNA) reveals clinically relevant biomarkers but requires specialized protocols and sufficient input material that limits its applicability. Millions of cfDNA samples have been profiled by genomic sequencing. To maximize the gene regulation information from the existing dataset, we developed FinaleMe, a non-homogeneous Hidden Markov Model (HMM), to predict DNA methylation of cfDNA and, therefore, tissues-of-origin directly from plasma whole-genome sequencing (WGS). We validated the performance with 80 pairs of deep and shallow-coverage WGS and whole-genome bisulfite sequencing (WGBS) data.
    DOI:  https://doi.org/10.1101/2024.01.02.573710
  2. Sci Transl Med. 2024 Jan 24. 16(731): eadi3883
    Machine learning to detect the SINEs of cancer.
    Christopher Douville, Kamel Lahouel, Albert Kuo, Haley Grant, Bracha Erlanger Avigdor, Samuel D Curtis, Mahmoud Summers, Joshua D Cohen, Yuxuan Wang, Austin Mattox, Jonathan Dudley, Lisa Dobbyn, Maria Popoli, Janine Ptak, Nadine Nehme, Natalie Silliman, Cherie Blair, Katharine Romans, Christopher Thoburn, Jennifer Gizzi, Robert E Schoen, Jeanne Tie, Peter Gibbs, Lan T Ho-Pham, Bich N H Tran, Thach S Tran, Tuan V Nguyen, Michael Goggins, Christopher L Wolfgang, Tian-Li Wang, Ie-Ming Shih, Anne Marie Lennon, Ralph H Hruban, Chetan Bettegowda, Kenneth W Kinzler, Nickolas Papadopoulos, Bert Vogelstein, Cristian Tomasetti.
      We previously described an approach called RealSeqS to evaluate aneuploidy in plasma cell-free DNA through the amplification of ~350,000 repeated elements with a single primer. We hypothesized that an unbiased evaluation of the large amount of sequencing data obtained with RealSeqS might reveal other differences between plasma samples from patients with and without cancer. This hypothesis was tested through the development of a machine learning approach called Alu Profile Learning Using Sequencing (A-PLUS) and its application to 7615 samples from 5178 individuals, 2073 with solid cancer and the remainder without cancer. Samples from patients with cancer and controls were prespecified into four cohorts used for model training, analyte integration, and threshold determination, validation, and reproducibility. A-PLUS alone provided a sensitivity of 40.5% across 11 different cancer types in the validation cohort, at a specificity of 98.5%. Combining A-PLUS with aneuploidy and eight common protein biomarkers detected 51% of the cancers at 98.9% specificity. We found that part of the power of A-PLUS could be ascribed to a single feature-the global reduction of AluS subfamily elements in the circulating DNA of patients with solid cancer. We confirmed this reduction through the analysis of another independent dataset obtained with a different approach (whole-genome sequencing). The evaluation of Alu elements may therefore have the potential to enhance the performance of several methods designed for the earlier detection of cancer.
    DOI:  https://doi.org/10.1126/scitranslmed.adi3883
  3. Curr Oncol. 2024 Jan 13. 31(1): 482-500
    Plasma Cell-Free Tumor Methylome as a Biomarker in Solid Tumors: Biology and Applications.
    Danielle Benedict Sacdalan, Sami Ul Haq, Benjamin H Lok.
      DNA methylation is a fundamental mechanism of epigenetic control in cells and its dysregulation is strongly implicated in cancer development. Cancers possess an extensively hypomethylated genome with focal regions of hypermethylation at CPG islands. Due to the highly conserved nature of cancer-specific methylation, its detection in cell-free DNA in plasma using liquid biopsies constitutes an area of interest in biomarker research. The advent of next-generation sequencing and newer computational technologies have allowed for the development of diagnostic and prognostic biomarkers that utilize methylation profiling to diagnose disease and stratify risk. Methylome-based predictive biomarkers can determine the response to anti-cancer therapy. An additional emerging application of these biomarkers is in minimal residual disease monitoring. Several key challenges need to be addressed before cfDNA-based methylation biomarkers become fully integrated into practice. The first relates to the biology and stability of cfDNA. The second concerns the clinical validity and generalizability of methylation-based assays, many of which are cancer type-specific. The third involves their practicability, which is a stumbling block for translating technologies from bench to clinic. Future work on developing pan-cancer assays with their respective validities confirmed using well-designed, prospective clinical trials is crucial in pushing for the greater use of these tools in oncology.
    Keywords:  5-methylcytosine; DNA methylation; biomarkers; cell-free DNA; liquid biopsy
    DOI:  https://doi.org/10.3390/curroncol31010033
  4. Science. 2024 Jan 26. 383(6681): 368-370
    Deploying blood-based cancer screening.
    Douglas S Micalizzi, Lecia V Sequist, Daniel A Haber.
      AI-based risk assessment may enable personalized blood-based multicancer screening.
    DOI:  https://doi.org/10.1126/science.adk1213
This page is being maintained by Thomas Krichel. It was last updated on 2024‒11‒22 at 4:26.