bims-ovdlit Biomed News
on Ovarian cancer: early diagnosis, liquid biopsy and therapy
Issue of 2023‒03‒12
three papers selected by
Lara Paracchini
Humanitas Research

  1. Ther Adv Med Oncol. 2023 ;15 17588359231157644
      Poly (ADP-ribose) polymerase inhibitors (PARPis) represent a therapeutic milestone in the management of epithelial ovarian cancer. The concept of 'synthetic lethality' is exploited by PARPi in tumors with defects in DNA repair pathways, particularly homologous recombination deficiency. The use of PARPis has been increasing since its approval as maintenance therapy, particularly in the first-line setting. Therefore, resistance to PARPi is an emerging issue in clinical practice. It brings an urgent need to elucidate and identify the mechanisms of PARPi resistance. Ongoing studies address this challenge and investigate potential therapeutic strategies to prevent, overcome, or re-sensitize tumor cells to PARPi. This review aims to summarize the mechanisms of resistance to PARPi, discuss emerging strategies to treat patients post-PARPi progression, and discuss potential biomarkers of resistance.
    Keywords:  PARP inhibitor; biomarkers; homologous recombination deficiency; ovarian cancer; replication stress
  2. Nat Biotechnol. 2023 Mar 06.
      Recent studies have emphasized the importance of single-cell spatial biology, yet available assays for spatial transcriptomics have limited gene recovery or low spatial resolution. Here we introduce CytoSPACE, an optimization method for mapping individual cells from a single-cell RNA sequencing atlas to spatial expression profiles. Across diverse platforms and tissue types, we show that CytoSPACE outperforms previous methods with respect to noise tolerance and accuracy, enabling tissue cartography at single-cell resolution.
  3. Nat Ecol Evol. 2023 Mar 09.
      Spatial properties of tumour growth have profound implications for cancer progression, therapeutic resistance and metastasis. Yet, how spatial position governs tumour cell division remains difficult to evaluate in clinical tumours. Here, we demonstrate that faster division on the tumour periphery leaves characteristic genetic patterns, which become evident when a phylogenetic tree is reconstructed from spatially sampled cells. Namely, rapidly dividing peripheral lineages branch more extensively and acquire more mutations than slower-dividing centre lineages. We develop a Bayesian state-dependent evolutionary phylodynamic model (SDevo) that quantifies these patterns to infer the differential division rates between peripheral and central cells. We demonstrate that this approach accurately infers spatially varying birth rates of simulated tumours across a range of growth conditions and sampling strategies. We then show that SDevo outperforms state-of-the-art, non-cancer multi-state phylodynamic methods that ignore differential sequence evolution. Finally, we apply SDevo to single-time-point, multi-region sequencing data from clinical hepatocellular carcinomas and find evidence of a three- to six-times-higher division rate on the tumour edge. With the increasing availability of high-resolution, multi-region sequencing, we anticipate that SDevo will be useful in interrogating spatial growth restrictions and could be extended to model non-spatial factors that influence tumour progression.