bims-ovdlit Biomed News
on Ovarian cancer: early diagnosis, liquid biopsy and therapy
Issue of 2023‒06‒25
three papers selected by
Lara Paracchini
Humanitas Research

  1. Lancet Oncol. 2023 Jun 20. pii: S1470-2045(23)00288-7. [Epub ahead of print]
  2. Ann Oncol. 2023 Jun 15. pii: S0923-7534(23)00724-X. [Epub ahead of print]
      BACKGROUND: The isolation of cell-free DNA (cfDNA) from the bloodstream can be used to detect and analyze somatic alterations in circulating tumor DNA (ctDNA) and multiple cfDNA targeted sequencing panels are now commercially available for FDA-approved biomarker indications to guide treatment. More recently, cfDNA fragmentation patterns have emerged as a tool to infer epigenomic and transcriptomic information. However, most of these analyses used whole-genome sequencing, which is insufficient to identify FDA-approved biomarker indications in a cost-effective manner.PATIENTS AND METHODS: We used machine-learning models of fragmentation patterns at the first coding exon in standard targeted cancer gene cfDNA sequencing panels to distinguish between cancer vs. non-cancer patients, as well as the specific tumor type and subtype. We assessed this approach in two independent cohorts: a published cohort from GRAIL (breast, lung, and prostate cancers, non-cancer, N=198) and an institutional cohort from the University of Wisconsin (UW; breast, lung, prostate, bladder cancers, N=320). Each cohort was split 70/30% into training and validation sets.
    RESULTS: In the UW cohort, training cross validated accuracy was 82.1%, and accuracy in the independent validation cohort was 86.6% despite a median ctDNA fraction of only 0.06. In the GRAIL cohort, to assess how this approach performs in very low ctDNA fractions, training and independent validation were split based on ctDNA fraction. Training cross validated accuracy was 80.6%, and accuracy in the independent validation cohort was 76.3%. In the validation cohort where the ctDNA fractions were all <0.05 and as low as 0.0003, the cancer vs. non-cancer AUC was 0.99.
    CONCLUSION: To our knowledge, this is the first study to demonstrate that sequencing from targeted cfDNA panels can be utilized to analyze fragmentation patterns to classify cancer types, dramatically expanding the potential capabilities of existing clinically used panels at minimal additional cost.
    Keywords:  cancer; cell-free DNA; fragmentomics
  3. Nat Methods. 2023 Jun 22.
      Capture array-based spatial transcriptomics methods have been widely used to resolve gene expression in tissues; however, their spatial resolution is limited by the density of the array. Here we present expansion spatial transcriptomics to overcome this limitation by clearing and expanding tissue prior to capturing the entire polyadenylated transcriptome with an enhanced protocol. This approach enables us to achieve higher spatial resolution while retaining high library quality, which we demonstrate using mouse brain samples.