bims-tumhet Biomed News
on Tumor Heterogeneity
Issue of 2022‒09‒04
two papers selected by
Sergio Marchini
Humanitas Research

  1. Clin Cancer Res. 2022 Sep 01. pii: CCR-22-0749. [Epub ahead of print]
      BACKGROUND: The identification of patients with homologous recombination deficiency (HRD) beyond BRCA1/2 mutations is an urgent task, as they may benefit from PARP inhibitors. We have previously developed a method to detect mutational signature 3 (Sig3), termed SigMA, associated with HRD from clinical panel sequencing data, that is able to reliably detect HRD from the limited sequencing data derived from gene-focused panel sequencing.METHODS: We apply this method to patients from two independent datasets: (1) high-grade serous ovarian cancer and triple-negative breast cancer (TNBC) from a Phase 1b trial of the PARP inhibitor olaparib in combination with the PI3K inhibitor buparlisib (BKM120) (NCT01623349), and (2) TNBC patients who received neoadjuvant olaparib in the Phase II PETREMAC trial (NCT02624973).
    RESULTS: We find that Sig3 as detected by SigMA is positively associated with improved progression-free survival and objective responses. In addition, comparison of Sig3 detection in panel and exome sequencing data from the same patient samples demonstrated highly concordant results and superior performance in comparison with the genomic instability score.
    CONCLUSION: Our analyses demonstrate that HRD can be detected reliably from panel sequencing data that are obtained as part of routine clinical care, and that this approach can identify patients beyond those with germline BRCA12 mut who might benefit from PARP inhibitors. Prospective clinical utility testing is warranted.
  2. Nat Methods. 2022 Sep 01.
      A central problem in spatial transcriptomics is detecting differentially expressed (DE) genes within cell types across tissue context. Challenges to learning DE include changing cell type composition across space and measurement pixels detecting transcripts from multiple cell types. Here, we introduce a statistical method, cell type-specific inference of differential expression (C-SIDE), that identifies cell type-specific DE in spatial transcriptomics, accounting for localization of other cell types. We model gene expression as an additive mixture across cell types of log-linear cell type-specific expression functions. C-SIDE's framework applies to many contexts: DE due to pathology, anatomical regions, cell-to-cell interactions and cellular microenvironment. Furthermore, C-SIDE enables statistical inference across multiple/replicates. Simulations and validation experiments on Slide-seq, MERFISH and Visium datasets demonstrate that C-SIDE accurately identifies DE with valid uncertainty quantification. Last, we apply C-SIDE to identify plaque-dependent immune activity in Alzheimer's disease and cellular interactions between tumor and immune cells. We distribute C-SIDE within the R package .